AlastairThomson.com

The arrival of the internet brought many advantages. Instant global communications. The ability to work anywhere. Finding books your local bookstore was unlikely to stock.

But the internet brought its fair share of problems too – all manner of cybercrimes which didn’t exist before, and making it fast, simple, and cheap to spread extremist views, for example.

On the whole, the internet is probably a net positive to humanity, but (net-net) only benefits humanity by a fraction of its true potential after you factor in all the negatives. If perfection was represented by 100%, I’d say the negatives accounted for about 80%, meaning humanity was just 20% better off – still a positive outcome overall, but bringing nearly as many disadvantages as advantages.

However there is one feature of the internet which drives me particularly crazy and that’s the need to gamify everything – allocating points, scores, badges, unlocking achievement levels, and all manner of other nonsense.

The business world suffers from a similar problem. A focus on numbers that are easy to track, without a moment’s thought about the real objective.

For example, my real objective in having a Kindle is to learn new things by reading books. However Amazon sends me all manner of regular communications to tell me I’m a “gold level reader”, or whatever standard I’ve reached this week or this month.

I understand why. Amazon has no idea if I’ve understood a single word of what I’ve read, but it’s really easy for them to track when I switch my Kindle on, and how many pages I’ve clicked “next” on.

Many digital businesses think that having scores, or leaderboards, or unlocking new levels keeps people addicted to their product as they want to win more points or get to an even higher level.

Frankly, nothing wants me to engage with something less than a software company sending me seven emails a day telling me I’m just 10 minutes away from unlocking the next level.

And the reason for that is it’s really clear, whenever anyone does this, that they only care about themselves, they have no interest in you as a consumer whatsoever.

Who is likely to benefit from you scoring a few more points on a game? It’s unlikely to be you. But it’s great news for the person selling the game because now they have more “loyal players” than they had before. And that probably bumps you into a different email sequence where they try to sell you even more things than they did when you were a mere silver-level player.

Frankly, if playing that game, or reading another few pages on my Kindle, was the most important thing in my life at that moment, I’d do it anyway. I wouldn’t need a prompt.

The fact that I haven’t means that I have more important things to do, but every tech company behaving like a whiny 3-year-old demanding I play a game with them right this minute or they’ll keep moaning and crying until I do, is profoundly irritating.

Unlike children, of which you’re likely to have a limited number around you at any one time, basically every tech company you have ever interacted with becomes a snivelling, whiny 3-year-old who never shuts up about wanting your attention.

None of us needs any more whiny, needy tech companies in our lives.

It misses the point

Even assuming all this needy whining works, at some level, it misses the point.

“Forcing” loyalty by an array of points, levels, or rewards means that loyalty only lasts for as long as there’s another reward someone wants to win. There is a diminishing returns curve at some point beyond which you’ve offered everything that can reasonably be offered and your attempts to force people into loyalty just tails off.

Or people just get bored of Game A and switch to Game B instead.

In a world where switching costs are essentially zero, as long as they can put up with the needy, whiny emails they’ll get for the rest of their life from the producers of Game A, there is very little downside for people to switch to something different.

Although this often comes as a surprise to the makers of Game A, because they confused a surface level of compliance and an appearance of loyalty with the real thing.

Don’t get me wrong, actual loyalty is a very valuable thing.

But even though it’s easy to track how many pages I read on my Kindle, it doesn’t mean I necessarily feel particularly loyal to Amazon in general or my Kindle in particular. There are other ways I can read books – and, back in the day, at least Waterstones didn’t come round to my house 17 times a week to tell me I’d nearly achieved Gold Level Reader status, so I should read a few more pages tonight rather than whatever I had been planning to do instead.

If anything, I read more books before I got my Kindle than I have since. As someone who travels a lot for work, I just find it easier to slip a Kindle into my pocket than an actual book.

While I’m sure Amazon mistake that for loyalty, that would be a mistake.

The reason I read a lot of books is because I have always been highly intrinsically motivated to read more as a way to learn more. I’m not a “Kindle addict” or even an “Amazon addict”, although I’m sure Amazon thinks I’m both of those things. I’m a book addict and/or a learning addict, which is entirely independent of the device I read my books on or the people who sell me physical books.

And intrinsic motivation is a vastly more valuable thing than the appearance of surface-level, points-based “loyalty”. (In quotes, because it’s nothing of the sort.)

The problem with all points-based systems…and yes, this is a metaphor for your work KPIs too…is that, if it works at all (which it often doesn’t), you are incentivising collecting points, you aren’t incentivising the behaviours you want to see more of.

The truth is, no matter how well you think you’ve designed your systems, it’s more or less impossible to design any system so well that you trigger intrinsic motivation instead of the significantly less valuable surface-level, points-based behaviours. Although admittedly the latter are easier to spot.

The Achilles’ Heel for many organisations is mistaking one for the other. You might be surprised how often that turns out badly.

I learned this the hard way

When I was younger, I had more hair, and I was less cynical. I also used to believe you could use points and targets to incentivise behaviours to a much greater extent than I do now.

Until one day, while I was running a large call centre business.

We were really good at what we did, in a market where customer service standards were generally poor, and I was proud of the business and the people in it.

However, like all large-scale call centre operations, we had an array of targets which represented a “good performance” for our call centre agents, and a regular tracking system to make sure performance was maintained.

One of the most important targets was our average call length. All our costings were based on calls lasting an average of three minutes – a fairly typical call duration at the time for the type of work we did.

And we had one superstar agent who kept his calls tight and was always top of the leaderboard for this KPI. He was so good, we were considering him for promotion to supervisor.

Until we found out why he topped this particular leaderboard.

Part of our systems included regular call auditing, where a couple of supervisors would listen to the recordings of a random selection of calls from agents from other teams and score them against a matrix of factors – how well the agent led the call, whether they displayed good listening skills, how effectively they diagnosed the problem, and so on.

Now, this agent was generally a very good operative, and a nice human being in person. So he tended to score well against those metrics too, making promotion even more likely.

But one of our more experienced supervisors noted something unusual, and went digging.

Her digging got this superstar agent fired.

Ticking the boxes but missing the point

Our superstar agent, let’s call him Kevin, was extremely good at all the things on the matrix. He built rapport, controlled the call well, diagnosed the problem effectively every time.

His recorded calls were often used to show other agents how to do those things.

In a business which prided itself on delivering high levels of customer service, many of the things Kevin did set the standards for others to aspire to.

From the outside, he looked like an exemplary call centre agent, because he not only ticked all the boxes on the performance matrix, he set many of the standards by which other agents were measured.

His call duration stats were the best in the business. And that was on top of all the other things he did so well while handling the calls themselves.

You can see why he was being considered for promotion.

At least, until one of our more experienced supervisors (let’s call her Jill) spotted something.

As part of the normal quality monitoring process, one day Jill was listening to a random selection of call recordings from other teams, which happened to have two calls from Kevin in her sample.

The first call went well, exactly as she expected from someone with Kevin’s reputation.

At least up until about the three minute mark, when the call suddenly cut off mid-sentence while Kevin was talking.

Now, that happened sometimes. It wasn’t unusual for someone’s mobile phone signal to drop out, for example.

But Jill got suspicious when she heard exactly the same thing happen in Kevin’s second call from the same batch.

While calls did drop out occasionally, the odds of it happening twice in the same batch of quality checks were low, especially at the same point in the call, at around the three minute mark.

So Jill dug further and asked for a wider selection of Kevin’s calls.

While most of the calls were fine – and well up to the standard we had come to expect from him when scored against the performance matrix – every call he took which was not getting close to a resolution by the three minute mark mysteriously cut off while Kevin was talking.

With a bit more digging through our systems, it became apparent that what Kevin was doing was preserving his call duration stats by making sure his calls never went much belong three minutes. He just pulled the plug on the calls which were dragging on, to preserve his call duration stats.

Extrinsic vs intrinsic

To be candid, this state of affairs was my fault, ultimately. I had signed off the performance matrix and the review processes. I had thought we had scientifically designed a near-perfect scoring mechanism, entirely appropriate for a business which set so much store by the quality of its customer service.

We put a lot of effort into finding empathetic people who could establish an easy rapport with customers on the phone, and who would move heaven and earth to solve customer problems.

Most of our people were intrinsically motivated to care about our customers and solve their problems, because that’s the sort of people they were.

Even if that meant their call durations occasionally spiked up to six or seven minutes. Because they cared more about the customer than their call stats, if the occasional call took a bit longer they saw the problem through to the end to make sure the customer was happy.

It turned out we’d misjudged Kevin because he was so good at the things on the performance matrix. He was mainly extrinsically motived – that is, he did what he did for the bonuses and the promotion opportunities, not because he cared about our customers.

We mistook a superficial set of statistics as representing the underlying reality of the situation, when, in fact, all those KPIs didn’t accurately indicate the outcomes we wanted at all – an unquestionably first-class approach to customer service.

Putting the phone down on clients with hard-to-solve problems was the absolute antithesis of the business we wanted to be.

So Kevin was fired on the spot.

Not for missing his KPIs – which he hadn’t. He had met or exceeded all of them.

But for not living out the values we really wanted, which was an unswerving dedication to solving customer problems.

We had taken a set of KPIs as a proxy for reality. Kevin taught me that was a mistake.

League tables

While I’m not suggesting for a moment you can run a business without any KPIs, as business leaders it’s important to remember that someone can tick all their KPI boxes and still not be delivering on the business’s mission.

A bit like whether I’m a Kindle Gold Reader this week or not.

If I’m prepared to press “next page” often enough, I can power through enough reading material to achieve any level of Kindle accolade I like.

Somewhere at Amazon somebody gets to tick a box and claim a KPI prize of their own because (they think) they’ve manipulated me into spending more time on my Kindle. In reality, its time I’d have gladly spent anyway, and if I had something more important to do, I wouldn’t have picked up my Kindle in the first place.

So all the gamification was ultimately pointless.

As it is for anyone who isn’t either eight years old or a simpleton. I can forgive 8 year-olds wanting another gold star from their teacher. And simpletons can’t help themselves, so who am I to judge.

But for everyone else, while you can track some KPIs for sure, what you’re really looking for is people who live and breathe your company’s mission.

The question is, given a choice, do your people choose to tick the surface-level KPI boxes or do they take decisions consistent with your mission as a business.

Perhaps paradoxically, one of the main ways to identify what sort of person you’re looking at is an occasional willingness to blow their KPIs out of the water, like the call centre agents who would stay with a call for six or seven minutes instead of putting the phone down after three.

If you’re serious about your mission, occasional KPI “mishaps” should be celebrated and encouraged because, in those fleeting moments, you get to see what someone’s intrinsic motivation really is. The decision point where someone makes a choice to pursue the greater good, in terms of your company’s long-term interests, instead of ticking the box on their KPI sheets.

Yet, in most organisations, doing that would get you fired, because all most organisations do is track the numbers and try to manipulate performance solely through extrinsic motivation – the bonuses, the accolades, the promotions.

Mistaking one for the other, just because all the KPIs are hit, is the biggest disconnect between most organisations and their missions. And, ironically, the biggest reason why customers are less loyal than they were before.

It turns out you can’t gamify your way to success after all.

You only get there by focusing on what really matters, and delivering on that.

And the likelihood of that being a KPI – even a KPI that appears to be measuring the right behaviours, like the NPS (Net Promoter Score) – is pretty much zero.

I’ve always been grateful to Kevin (and even more grateful to Jill) for teaching me a valuable lesson – you can fake your KPIs, but you can’t fake reality.

And of the two, reality matters more if you’re serious about building a business.

The world is full of mysteries.

Why don’t more people find my jokes funny?

Why doesn’t Paul Simon have a Nobel Prize for Literature, like Bob Dylan?

Why can’t Alfa Romeo make slightly more reliable cars so I can post-rationalise buying one?

But one of the biggest mysteries for me is why, given its almost non-existent cost, more businesses don’t leverage the benefits of even pretty basic customer service.

The tragedy is that the bar for customer service is so low, you need to do very little to appear to be twice as good as everyone else.

It’s staggeringly simple, possibly the easiest and lowest-cost way to set yourself apart from the competition. Done well, great customer service will generate a stream of referrals – an astonishingly high proportion of which will turn into customers at pretty much zero cost, if you do it right.

Nowadays, people think “huge expense” when they think “customer service” but that’s only because enough people have been selling non-value adding CRM systems for long enough that people have learned by osmosis to think that looking after customers is expensive and doesn’t work very well.

By and large, that’s because they’ve been doing it wrong.

CRM systems can be helpful in large-scale customer interactions, but many businesses aren’t in that market. It’s amazing what you can do without one – or with a tiny amount of extra effort.

The coffee shop

There’s a coffee shop I visit semi-regularly…perhaps between two and four times a week.

There are about half a dozen core staff in there, who rotate on shift patterns to provide all-day cover seven days a week. And over the 18 months or so I’ve been visiting this coffee shop semi-regularly, the staff behind the counter have come to recognise me as a regular and know my order without me having to ask.

“Flat white, as usual?” the person on the till asks, with a friendly smile, when I get to the front of the line.

After I’ve confirmed that and paid, I have a quick chat with whoever is making the coffees that day about something relatively inconsequential (in the run-up to Christmas, the effort they all put into their Christmas jumper game was a particularly welcome source of material for our 6.30am chats).

All three of us wish one another a good day when the coffee’s ready, and I go on my way.

I’ve never timed these interactions, but this takes a minute or two at most from start to finish.

These people don’t know my name, what I do for a living, or even how funny my jokes are. Yet one of the reasons I keep coming back is because I feel welcome at this coffee shop just because they have taken fractions of a second, in their heads, to realise I come in regularly and always order the same thing.

The cost of doing this is precisely £0 – I’m not taking up any more time than they would be spending anyway taking my order or making my coffee, so this interaction is essentially free to the coffee shop chain. However, I now go there in preference to anywhere else because I’m made to feel welcome in that coffee shop.

Now, this coffee shop chain is not the one which irritatingly asks for your name for them to write on your cup in order to “make you feel more welcome”. The customer service in that chain is universally terrible, in my experience, so this is definitely a non-value-adding activity for them. It means taking an order takes longer, and impact of this process on how that chain makes their customers feel is somewhere between indifference and active loathing. (I’m in the latter camp.)

So, this other coffee shop chain who tries so hard to hit a KPI ends up doing worse than the people who don’t know my name.

Weird, huh?

Except it isn’t, if you remember the role of customer service is in how you make a customer feel, not how many boxes on your internal KPI scorecard you get to tick.

Either way, in my regular coffee shop – also part of a large, nationwide chain – they’ve locked in my regular visits (and, I’m sure, lots of other regular visitors too) by something as simple as recognising another human being and remembering the drink they order every time they come in.

Cost: £0.

Benefits: whatever they charge for 2-4 coffees a week, more or less for ever.

RoI: Off the scale.

(Side note: one of the major problems with customer service is that some people think they’re managing an internal process. Great customer service is how you help customers feel about your business. When you think it’s about managing internal processes, you end up spending £millions on IT solutions that don’t work very well, making customer service appear expensive and low value-adding. Working on how you help customers feel better about your business is almost free and the upsides are substantial, leading to the best RoI of just about anything you can do in your business.)

The hotel

If I’m staying over in London overnight for work, I generally stay in the same hotel. While I’m not there as often as I visit my local coffee shop, I’m there often enough that I recognise the staff on the check-in desk.

They have absolutely no idea who I am, though, and always ask “have you stayed with us before?” to save them launching into a detailed description of the restaurant opening hours and how to get in after the front door is locked at 11pm each night, which they keep for first-time visitors.

Sometimes, I’m asked that question twice in the same week by the same person.

Yet here’s the irony. The reservation system they use to book me in and hand across my room key knows I’ve stayed there before.

I know for sure it does, because the order confirmation I get when I book always says “thanks for choosing us again”. So their reservation system 100% knows I’m a returning guest.

Despite having this valuable information – much more information than they have in the coffee shop, for example – nobody in this hotel chain has thought of flagging the screen reception staff use to book guests in with the information that they have stayed at this hotel before.

It’s a tiny amount of code, not very difficult to do, and easy to get a screen pop when a name or reservation number is entered into the check-in system by the front-desk staff.

Yet, this hotel chain hasn’t put even the tiniest amount of effort into making returning guests feel welcome. And that pervades the attitudes of their staff who, despite being pleasant enough as individuals, don’t say to themselves “hang on, this is someone I see regularly”.

Actually, that’s not quite true. There is one person who does recognise me – I see him about one visit in 10 to this place, so not super-often.

But the first time he checked me in, he asked if I’d had a good day, in that formulaic way front-desk people sometimes do. He was genuinely shocked when I said I had, and then asked him how his day had gone. “Nobody ever asks us that” he responded, and thanked me for asking.

Now, every time he checks me in at this hotel, he greets me as a returning guest without having to input my reservation number and we have a quick chat of the sort I have with the people in the coffee shop, before he sends me off to my room feeling that I’ve been treated as fellow member of the human race, and not someone who was a glitch in the otherwise smooth running of the check-in desk.

Great customer service is free

There is a classic business book, “Quality Is Free” by Philip B. Crosby which I read back in the 1990s. He was something of a godfather of the Total Quality Management movement, for those of us old enough to remember that.

Anyway, Crosby’s fundamental thesis was that doing quality work isn’t a cost. At worst, it breaks even but, more often than not, improving quality adds to your bottom line when thoughtless cost-cutting and corner-cutting would (contrary to popular belief) damage it.

All my experience of business is that this is absolutely true. Getting something right up-front generally costs about 10% of what it costs to put something right after it’s gone horribly wrong.

So it is with customer service.

Great customer service can be literally free to deliver (eg in the coffee shop) or it can cost fractions of pennies per transaction (eg writing the code for a screen-pop for returning customers on the check-in system).

However the RoI is generally significant. If all you can do is get one extra cup of coffee a week from someone, against a base cost of £zero, that’s a phenomenal RoI. Do that often enough and your coffee shop will be vastly more profitable than anyone else’s, no matter how hard they try gimmicks like asking you for your name.

Break the link in your mind with customer service requiring £million IT solutions.

In huge organisations that might be an essential part of how you run your business. But in most organisations, you can do easy, simple things for either no cost at all, or for fractions of a penny per transaction.

Factor in even a tiny blip in your revenues – just one extra cup of coffee a week, for example – and spending time thinking about how you can make your customers feel more positive towards your business will be one of the highest RoI activities you are ever likely to carry out.

That’s not a bad outcome for something as simple as learning how to greet your customers with a warm “hello again!”

It’s the first week after the Christmas break for most people. And if you’re like most people (OK, just me?) you probably overindulged a little over the break. The trousers or skirt you did up first thing on Monday morning, for the first time in a fortnight, were probably a little tighter than you remember them being just before Christmas, weren’t they?

That’s why there are so many ads for weight loss products at this time of year. Fad diets. Gym memberships. Healthy eating plans.

Many of these things come with bold, dramatic promises – “Lose 10 pounds in 10 days” or “Abs of steel in just 30 days” or “Get thin by eating only grapefruit for 28 days”.

Despite the fact that most sentient beings know in their heart of hearts those promises are, to put it kindly, over-ambitious for most people, a lot of products like those sell by the boatload in January.

Of course, by the end of January, they haven’t worked.

So we go quiet for a while, before the approaching summer holidays remind us we need to get “beach body ready” and the whole cycle of scammy ads and over-ambitious promises, followed by plunging disappointment, starts again.

Now, I can confidently say I’ve never been beach body ready at any point my entire life. Unless you like visiting the beaches where migrating walruses like to sunbathe, in which case my body is definitely ready for that beach. I’ll be the pale-looking Scottish one.

However, fad diets aren’t just restricted to your post-Christmas feelings of guilt at eating too much over the holidays. In a lot of organisations, fad diets damage your bottom line in a similar way, ending in the same inevitable disappointment as all those unfulfilled new year’s resolutions to shed the extra Christmas pounds.

So, if you’re serious about your bottom line, here are my tips for healthy cost control to get your bottom line health moving in the right direction for the new year.

Avoid the fads

As with fad diets, cost-cutting fads will take you down the same inevitable path of over-hyped promises and crushing disappointment.

The basic rule is if something sounds too easy to be true, it almost certainly is.

Over the years there have been plenty of faddish cost-control diets. The current one is AI, which claims to magically solve all your problems and reduce your cost base dramatically.

I can’t say this strongly enough – with very rare exceptions outside activities like managing datacentres or writing low-end code – it is a complete lie to say that AI will boost your bottom line performance.

The problem is that, by the time you notice, all your customers are likely to have taken their business somewhere else due to the difficulties in dealing with your business and the truly appalling customer service AI seems to be set up to deliver.

AI will inevitably fail (at least as a mass-market product – again, some specialist areas might well see some benefit), but don’t be disheartened. There will be another fad diet along soon…I’m not sure what it might be yet, but there will be one, I can guarantee you that.

Grifters always need something to grift, so when AI fails, they’ll just pick up the next grift as if nothing happened.

Staying away from fads means you’ll minimise the chances of doing anything stupid and, in the medium-to-long term, just not doing stupid things on a consistent basis means you’re almost guaranteed to be successful. (To paraphrase Warren Buffett’s long-time partner, Charlie Munger.)

Avoid short-term thinking

Any idiot can cut costs…and thereby appear to flatter the bottom line…in the short term. And, every year, that’s precisely what a significant number of idiots do.

I once worked for an organisation where the executive team patted themselves on the back for one of their number slashing marketing expenses to hit a profit target one year. The following year they couldn’t figure out why they had no customers.

You can achieve much the same results if you halt all the maintenance expenditure in your business. But don’t be surprised if your machine break-downs the following year cost you 10x more in engineering costs and lost production than it would have cost to get the scheduled maintenance carried out on your machines when it was originally supposed to happen.

Similarly, you can fire experienced staff and replace them with trainees to bank a massive “instant” cost saving.

Roll the clock forward a few months and I can guarantee you’ll be dealing with a mountain of problems you never knew existed because your old, more expensive, team just dealt with these things and stopped them from ever getting to your desk or cheesing off a customer.

To be fair to the trainees, they don’t know what they’re doing, so chaos ensues.

It’s not their fault (specifically, it’s yours) but chaos is never the sign of an organisation running at its most cost-effective.

Avoid more KPIs

A common reaction to cost pressures is to start tracking all sorts of data points in the hope that somehow, the mysteries of the universe (or at least your business) will be revealed.

While there is definitely an art and science to setting and tracking KPIs, it’s really unlikely that the lack of a KPI is the biggest problem affecting your bottom line.

It’s much more likely that there’s something more fundamental at play. Maybe your price point is wrong. Or you’re selling to the wrong target market. Or your product isn’t competitive with other offerings in your sector.

No internal measurement tool is going to tell you any of that.

But it’s possible – common, even – to compound this problem in a quite spectacular way.

Businesses often set up a new team just to track and measuring of things. That team needs all sorts of fancy software to do their job. Most of your management team is now tied up in meetings about reporting and KPIs instead of managing their part of your business. The cost is phenomenal, but the business thinks more measurement will solve its problems, so they go ahead anyway.

In those settings, I’ve only very rarely seen performance improve. Even then, it was only because the performance previously was so horrifically bad that almost any tightening up of processes would have delivered a positive RoI.

I mean, they still missed the point because the thing they did which made a positive difference might have been 27th on the list of things that would have made the business better, and they completely missed the 26 more powerful bottom line-enhancing strategies above it, but still…they had a positive impact on the business.

So what should you do?

If those are some of the things you shouldn’t do, what should you do?

Well, good cost management is (conceptually, at least) relatively simple. Here are three strategies I always recommend:

1-Manage for the medium term

Every cost cutting decision you take should be considered in a medium-term context, say the next 3-5 years.

If you cut your marketing costs to zero today, that’s not likely to have a positive outcome on sales in a 3-5 year time-horizon. So don’t do that.

Most of the very worse cost-cutting decisions can be avoided just by having a timeline which extends beyond this month or this quarter.

2-Consider the consequences

Have some idea of the impact of, say, a 5% reduction in your customer base because customers don’t like your new, cheaper formulation, or the fact they can’t talk to a human being on the phone any more.

While you might think of your cost base in the same silos you report in your P&L, your customers think of the total experience your company delivers. So there’s a non-zero chance that substituting a cheaper ingredient into your production process will cheese off at least some of your customers.

By and large, you shouldn’t do that. You’d be surprised how warmly some organisations congratulate the person who saved £50k in their department but overlook the fact that their actions cost the business £1million in sales when customers took their business elsewhere.

If the consequences of, say, 5% of your customers taking their business elsewhere would dwarf the benefits you bank from any cost saving idea, by and large you shouldn’t take the cost saving.

It’s tempting to think customers won’t notice, but in the end they all will…and some of them will be bothered enough about it to buy from one of your competitors instead.

Most businesses would lose money if just 5-10% of their customers stopped buying. So not considering the potential damage caused by cheesing off a relatively small proportion of your customer base isn’t wise.

3-Think cumulatively

In many organisations, cost saving strategies are set as “everyone has to save 10% in their department”.

It’s often forgotten that, from a customer perspective, those savings represent a cumulative downgrading in your products and services.

If you save 10% on the ingredients, 10% on the packaging, 10% on the logistics, 10% on the staff time to make the products, 10% on your overheads, and so on, you can easily end up with a customer’s experience of dealing with your business being only half as good as it was before.

So, unless you reduce your prices to reflect that (which would rather defeat the object), you have just made your business less attractive for customers to deal with. There are very few situations in which that’s a smart move, no matter how clever you think you’ve been in getting every department to save 10% from their budgets.

Stay away from fad diets for your bottom line

As for diets, the sad truth is that there is no magic way you can dramatically reduce your cost base overnight with zero pain and unlimited upside.

The only sustainable way to lose weight is to cut out junk food, eat healthily, and exercise regularly.

By the same token, the only sustainable way to build your bottom line is to develop reliable systems, resource them properly, and continuously improve the ways you look after your customers.

I’ll grant you, that’s a lot less glamorous than riding a white steed into your company’s car park with a big flag trailing behind boasting of a new magic solution to the cost pressures facing your company.

But it’s also the only sustainable way.

And a year from now, you’ll be vastly better off than your competitors who spent £000s on “magic instant solutions” in the meantime, only to end up, like every January’s crash dieters, pretty much where they started. Only poorer and more disillusioned.

At the time of writing (who knows, the whole grift-fest might have blown up by the time this article is published) huge investment plans are being announced, seemingly every day, for some datacentre or other to support the “AI boom”. (I put that in quotes because I don’t believe it, but that’s not really the point of this article.)

Apparently, the reason for those massive investment plans is that the big AI companies need “more compute” – that is, ever-increasing amounts of data, chips, and data centres – in order to make their products work.

If only they can buy enough “compute”, claim the AI companies, all their problems will be solved, and we’ll inevitably reach The Singularity in the next six months.

To use a word a good deal more polite than the one I normally use to describe the AI industry, this is hokum.

Now, to be fair, in the early days of the AI industry, it wasn’t. Or perhaps, more accurately, I didn’t know enough about it yet to form that view.

That’s because, despite a 30 year career as an accountant, I have remained relatively optimistic and upbeat about what humans can achieve. I was as excited as anyone else when those “magic black box” AI technologies came out of the woodwork in the early 2020s, because I like to try new things.

It’s always easy to say “no”, of course. But during my career, I’ve generally found that, while this sounds like the safe option, it’s usually anything but. Saying “no” to everything guarantees that you stay pretty much where you are while the rest of your industry passes you by. You’re on the road to irrelevance if you carry that stance to extremes.

Now, if your business is selling antique-style scarves, say, which are hand-dyed and hand-stitched using the original methods and machinery from the 1870s, you probably should say no to every new idea about how you can make your production more efficient – in that sort of business the tradition – the inefficiency, if you like – is the main reason people can sell scarves which functionally serve the same purpose for £2,000 on a boulevard in Paris and 99p in a branch of Primark.

That extra £1,999.01 is, in part, to compensate you for the relative inefficiency of your traditional methods because if buyers didn’t do that, their only option would be a 99p scarf from Primark.

But if you’re reading this, it’s unlikely you’re selling £2,000 scarves from a Parisian boutique, so we can put that option aside.

Saying yes…with conditions

So, to anyone who isn’t in the Parisian scarf-selling sector, saying “no” all the time is rarely a good idea, even if you are an accountant.

Because if your business isn’t innovating, it’s highly likely one of your competitors is. And that means, at some point in the not-too-distant future, your business will be left behind. Ultimately you might not have a business at all.

For that reason, in the early days of a new idea, you should ideally by trying to say yes, if you can, to get it to the point where you’ve got enough of a proof of concept to make a final decision with a much better chance of making the right call.

Now, that doesn’t mean every manifestly stupid idea should get the green light. But it is to say, if the idea isn’t completely barking, you should probably try to let it run for a bit if you can.

In general, a little bit of early stage proof of concept work is relatively inexpensive and, you never know, it might turn out to be the most brilliant idea your sector has ever seen. Taking a chance here and there is unlikely to materially hurt your bottom line, and it might just be the foundation for an exciting new chapter for your business.

There is another important reason to take this approach where you can.

Assuming you employ competent, hard-working people, the idea probably isn’t completely mad. By green-lighting a little bit of exploration, you’re showing them respect and appreciation, which is rarely a bad thing.

But if you kill the idea on the spot, your team will start dreaming about their idea when they should be thinking about your business and your clients, and there will always be an “if only we green-lit that project in 1997, imagine what sort of a business we could be today” vibe about the projects which were summarily canned.

Your best option is to do a bit of exploration (with not a lot of money, and not a lot of time, until you know there’s something worth taking forward).

However your green light should come with some conditions. That way, you can either kill the idea if the conditions aren’t met, or give yourself the confidence that there could be something in the idea which you want to explore further.

Impact

One of those conditions should depend on the potential impact of the idea on your business.

It’s a matter of personal taste, but if someone proposes an idea which might not transform the world, but which is fine as far as it goes and doesn’t cost much to implement, my default setting is just to tell people to get on with it. There’s no need for formality, reporting, and meeting conditions if you’re moving the stationery cupboard from one side of the Reception desk to the other – just get on with it.

For more serious projects, though, business impact is critical, otherwise you risk key members of your team frittering away their time and energy on projects which are inconsequential in the context of your business as a whole.

Here I’m probably looking for a minimum 5% impact on the top line or a 2-3% impact on the bottom line, for a first cut.

Anything less than that is unlikely to generate an RoI on the time, effort and money which will need to go into the project.

So the first port of call is a “back of an envelope” calculation of the potential impact.

Again, if it doesn’t have that 5% impact, but is inexpensive, you might as well do it anyway. It won’t cause any harm and it means the person whose idea you green-lit is more likely to come forward with another idea in the future – and that could be the one which turns out to be transformational.

But if you’ve got a 5%-er, the next stage is to do a proof of concept.

As a general rule, if something doesn’t work small-scale, it won’t work large-scale.

Large-scale is where you drive your efficiencies and make a return on your investment. But there’s nothing magic about scale – if something doesn’t work when it’s small scale, it won’t work when it’s large scale either.

The step ladder

Going from an idea to a full-launched product is like a step ladder. You have to take the next step before you can take the one after that, for example.

And if you think about it, there are multiple steps to any project which is going to have a significant impact on your business as you move from the “prove it in the lab” stage right through to having a finished product on supermarket shelves.

For example, in the early days, you might use only the staff members working on the project to direct how the project goes. At the later stages, you will almost certainly want to involve a survey group, or a taste panel, made up of ordinary members of the public to double check that the idea your team had stands a chance of making it in the real world.

Whatever those stages might be for your business, once you’ve green-lit an idea, there needs to be an understanding that the project is only green-lit for each stage in turn. Giving the OK up front doesn’t mean one of your team has the authority to build a new factory in Outer Mongolia – it might just be OK’ing a train ticket to visit the Mongolian embassy in London to start researching potential sites.

If you take this approach, however, you will find that some projects – no matter how promising they seem up front – gradually run out of steam. Generally that’s because a good idea in a lab is not necessarily a good idea, or at least a do-able idea, in the real world.

When a project starts to run out of steam, that means the momentum has turned downwards and, usually, the law of diminishing returns has started to kick in.

If that happens at Stage 2 of a six-stage process, I can guarantee you that this project will never be successful in the real world. The worst thing you can do is persist with an idea that clearly isn’t delivering results. The kindest thing to do for everyone involved is to congratulate them warmly on the good idea they had, reflect that the real-world data isn’t coming in as people had initially hoped, and wind the project up completely.

Neither you nor your staff should be upset about an idea not working.

Just having an idea good enough to be green-lit in the first place means you’ve got someone there with some real potential for the future. You should be nurturing them, not demoting them, when an idea is canned at Stage 2 of 6 – probably, they’re the people who will come up with an even better idea next time.

Not more, but different

However, what I usually find is that an idea which is stuck at Stage 2 won’t get any better if you throw more resources of the same type that were deployed up to that stage.

Ultimately, there’s an economies of scale question to be resolved, of course – will an idea which works in a lab also work on a fully-automated assembly line in a vehicle manufacturing plant, for example.

But the way you get round a problem about things not working in a lab is rarely “more lab”.

Almost always there was some fundamental misunderstanding about the process, some conceptual blockage, some principle which doesn’t work as well in practice as it does in theory at the heart of the problem.

“More lab” doesn’t address any of those issues. It just piles up costs on a project you are eventually going to have to can anyway. That’s just not smart.

Think of it like being a championship dancer.

The difference between being an OK dancer and a champion dancer isn’t knowing the steps any better. You needed to know them already to get up to “OK” level.

Going from “OK” to “champion” means working on your musicality, the grace and elegance with which you use your body to tell a story, the way you draw influences from the greats of dance history to delight other championship dancers even though most of the general public won’t even spot the little bits of homage you’re paying to one of the greats.

None of those things are “doing steps harder”. They are entirely different activities.

And that, in a nutshell, is the issue I have with AI, and the industry’s claims that, if only they had more computing power they could solve all their platforms’ problems.

Ever since I experimented with AI early doors, I’ve found it underwhelming (issues of intellectual property theft and morality aside).

And as investment has poured into the sector, the results have not become any more whelming (is that a word?) than they were before.

Going from Ver 3 to Ver 4 has become a yawn-fest – and, if anything, performance is getting worse from one model to the next, which is hardly a positive sign.

More importantly, if “more compute” was the answer to AI, we’d have solved all of AI’s conceptual problems well before now, after spending the first few hundred billion dollars, and we’d just be in scaling-up mode, which I accept would take significant capital investment.

But normally, you’d only put in that investment to scale up a project which was already working in principle.

You don’t pour investment into an idea that’s run out of steam in the hope that scaling up will fix a conceptually broken model.

That’s never going to work. The only people who like it when that happens are the bankruptcy lawyers because they are the people who stand to gain the most, ultimately, from a multi-billion dollar investment into technology which fundamentally doesn’t work.

Throwing ever-increasing amounts of the same resources as you’ve deployed up to now in the hope that this will fix any product development problem is at least unwise… and possibly insane. You need to do something completely different to reach excellence.

Perhaps start by thinking less like a tech exec and more like a dancer. At least dancers understand that excellence is unlikely to be reached by doing more of the same, and more likely to be reached by integrating ideas you haven’t work on up till now into the equation.

Alternatively, you could just decide it’ll never work and can the project before it bankrupts your business.

But, whatever you do, don’t keep doing what you’re doing.

As Albert Einstein said, the definition of insanity is doing the same thing over and over again, while expecting different results.

I wonder if tech people have ever heard of Albert Einstein? All the available evidence suggests they haven’t.

The first time I heard the expression “the map is not the territory” was from my old pal Joseph Pritchard.

At first, like many things Joseph said over the years, it didn’t make much sense to me. But in the years since, I’ve kept coming back to this expression as summarising the root of a lot of the problems inside organisations.

The example Joseph used to help reinforce the point was the classic London Underground map, drawn by Harry Beck, which is rightly seen as an iconic piece of design work.

However, if you know London at all, you’ll know it’s not an exact representation of the city. Far from it – some of the stations appear much closer together on the London Underground map than they are if you walked or drove between the two – others are much further apart.

Does that mean the map is useless, given its many inaccuracies?

Not at all. For a start, the fact that the London Underground map has lasted in much the same form since the 1930s suggests that generations of Londoners and tourists have had a lasting benefit from it. Despite the fact it’s widely known to not be an exact representation of the city of London on the ground.

But also, in communications, there is a trade-off to be made between total accuracy and ease of transmitting the information.

When I explain double-entry bookkeeping to someone who’s never done it before, I use a Sherlock Holmes story to explain how it works. I don’t start with “well, back in 1494, an Italian monk called Luca Pacioli wrote a book called Summa de Arithmetica, in which he set out the principles of debits and credits we use today.”

Is there some information that accounting nerds might find fascinating between 1494 and today that I skip over when explaining double-entry bookkeeping through my Sherlock Holmes story? There certainly are.

But I’m not usually wanting to give someone a Mastermind quiz on “The History of Double-Entry Bookkeeping: 1494 to the Present Day”. I just want them to understand how it works so they can do their job properly.

I’m very conscious that the map is not the territory when it comes to my explanation of double-entry bookkeeping – instead, I deliver a “map” of how to navigate it which helps people understand how double-entry bookkeeping works in a simple-to-understand and, I hope, memorable way.

Ditto Harry Beck with his London Underground map.

He wanted people to find their way around London easily. He wasn’t nearly so bothered about explaining that Charing Cross and Embankment stations are much closer to one another at street level than the Tube map would make you think.

The problem isn’t the map

The problem with this sort of communication isn’t the map.

It’s mistaking the map for the territory, and thinking that one is an accurate representation of the other.

Imagine, for example, someone who gets a contract to replace all the paving stones on London’s pavements, but they have to arrange deliveries of the required number of paving stones to each Tube station, and the stones delivered to each station have to reach to the next station on the line, where the next delivery batch awaits.

If all they were given to plan with was a London Underground map, this would be a complete fiasco. At some stations 10x the number of stones required would turn up. At others, not nearly enough.

So, of course, no sane person bidding for this imaginary contract would prepare a quote based on the Tube map. They would get out on the streets and measure all the distances for themselves, before calculating the number of paving stones required to stretch from one station to the next, and arranging their paving stone deliveries accordingly.

But many organisations get confused and imagine the map really is the territory.

That’s not entirely the fault of people running those organisations. For people who need to learn something about a specific discipline or activity, we have to break the process down into small chunks and build up gently from there. Especially early on, we will generally skip over a few of the trickier details because, at that point, the person we’re teaching don’t know enough about the discipline to go into depth to understand fully.

That’s the way most of us are taught at school and university, whatever subjects we took.

The problem here, though, is that not only are you just given a map when you are being taught about any field of endeavour, and not only does that convey very little understanding of the real-world nuances you’ll encounter. But also, the map you’re given is only partial.

Imagine, for example, having to navigate from Plaistow, on the District Line, across London to Perivale on the Metropolitan Line, but only being given a map of the Northern Line to accomplish your mission with.

Now the map of the Northern Line might be factually accurate as far as it goes (with the accompanying “map not being the territory” caveats, of course). It’s just completely useless for your objective of travelling from Plaistow to Perivale on the London Underground network.

Some areas are harder than others

Confusing a map with the territory is where many organisations go wrong.

That’s especially true for people in those organisations with a background in tech, engineering and…ahem…accounting.

Those professions seem to find it very easy to confuse the map with the territory, largely because they all work mostly on surface logic. People with those backgrounds sometimes struggle to see features which are beyond the realms of pure logic and/or which are not visible on the surface, even when those features represent a major driver of the problem.

Which isn’t entirely the fault of those professionals, because, in most academic realms, but those three in particular, you get to progress in your career by going deeper into the nuts and bolts of a subject.

By the time of my final professional exams, for example, the work I was doing was still double-entry bookkeeping at heart, but it was a transformatively more sophisticated version of what I’d done in my very first double-entry bookkeeping class a few years earlier.

Unfortunately, most real-world problems don’t present themselves like that.

They generally can’t be solved by going deeper. They can only be solved by going wider.

If you work in tech, engineering, or accounting – or, heaven forbid, you’re a politician, who are particularly poor at this – you’ll find that hard to cope with.

Relatively few people from those professions are comfortable moving into an illogical world where deeper motivations drive people’s behaviours than whatever they can see on the surface.

In the interests of full disclosure, I wouldn’t claim this is something I do perfectly myself. So I’m not being disrespectful to the professions mentioned above (although I’m very happy to be disrespectful to politicians).

I’m just illustrating how hard it is to solve problems when all you’ve got is an entirely logical, surface-level view of the world, and a map which will be of very little help getting you from Plaistow to Perivale.

An example

Let’s make this real with an example.

At the moment, politicians are making a lot of fuss about the cost of the NHS. And, in the unholiest of unholy alliances, politicians seem to think that “more technology to drive efficiencies” is best the way to bring the costs down.

In general, that’s baloney. Although not surprising from two groups as intellectually challenged as tech folk and politicians. One group is super smart in the computer lab, but super dumb in the real world. The other group isn’t even smart in a computer lab.

But, when the problem is defined by politicians as “how do we run the NHS but spend less money on it?”, it’s easy to see why people as dumb as the average politician thinks that tech might have the answer. And it’s equally understandable why tech people are more than happy to sell tech solutions which claim to do that (although they rarely do, in practice).

Thinking more broadly, instead of more deeply, you get an entirely different perspective.

Because of cuts elsewhere in sectors like education and social care, the NHS has become the public service of last resort for many.

Which makes no sense, because inside a busy A+E department is just about the most expensive place to try to solve many of the problems which only present themselves there because people have nowhere else to go.

Compounding the problem, if you introduce more activity into an already insanely busy, high-pressure environment, you’re very unlikely to make that more efficient.

More likely everything becomes dramatically less efficient and, because people are waiting longer and longer for treatment, and that treatment can only be superficial because another boatload of ambulances just turned up with 20 more people who need urgent treatment, outcomes are poorer as well.

Which, in turn, means many those same people will be back in a few weeks’ time because whatever problem they had last time either hasn’t been fully dealt with, or they’ve found themselves in exactly the same situation again. For example, an old lady who had a fall this week and ended up in A+E, in the absence of any other intervention, is pretty likely to have another fall in a few weeks’ time and end up in A+E again.

In that environment, unless you’re a tech industry grifter with a product to sell, or a politician being paid off by a tech donor, defining the problem you’re trying to solve as existing entirely within a busy A+E department will likely deliver little or no real world improvement – although it will have the usually not-coincidental side benefits of making lots of money for the tech company and the politicians concerned.

Going broader, not deeper

Of course, sometimes problems get solved by going deeper, not broader. Especially when the problem is an “in domain” problem.

If I prepare a report which doesn’t add up to the number I thought it should, I’m going to check that the Excel formula is correct, and that the range of cells the formula is working with are correctly specified.

As is, for example, a piece of code not delivering visitors to the intended page destination when they click a link on a website.

The solution to those problems is not “out there”. It’s “in here”, within the same domain where the problem manifests itself.

But that’s true of relatively few problems nowadays. The biggest problems of our time will not be solved by “in domain” expertise.

It’s one reason I think AI will never work. The people running AI companies think the reason their software is largely nonsense is because of a shortage of deeper, in-domain expertise. I can pretty much guarantee it isn’t – not least because of the fact that, after spending several years and billions of dollars in development funding, it that statement was true, we would already have perfect AI everywhere. Yet it’s still mostly garbage.

There’s another element to the problem of reducing the cost of the NHS that tech folk, politicians, and a fair few accountants, I’m sorry to say, don’t fully appreciate.

And that’s because the ultimate low-cost solution might involve spending more money somewhere else.

Those groups find that really hard to understand, but as any good CFO knows, the secret to truly effective organisational cost reduction is understanding that what matters in the end is the balance of costs across the organisation, not the spend in any specific department.

For example, if I can spend 2x what I spend now in Department A, but save 10x that amount across Departments B, C and D as a consequence, the lowest operating costs for the business as a whole are reached by doubling Department A’s budget.

Remarkably few organisations will make that choice in practice, I’ve found…to my eternal mystification. But what we’ve done here is move from thinking deeper to thinking broader and, like most of the problems in the world today, that’s where you’ll find almost all of the solutions.

I say that with confidence because, given the amount of time, money and resources spent over the last several decades trying to solve problems by going deeper, we’ve got more problems in the world than ever. If “deeper” was the solution, most of the world’s problems would have been solved by now.

And they haven’t. So we need to try something different, and go broader instead.

Where to start

For the NHS, there are two woefully neglected areas of our public services which directly lead to bulging A+E departments, and packed hospital beds.

Firstly, fix the education system so that people have hope for the future and a decent job to go to. There’s been a criminal lack of investment in education for as long as I can remember – especially in our Further Education Colleges which could be a tremendous source of economic growth if anyone in government ever cared enough about them to invest properly in the skills that lead to jobs, which in turn lead to vibrant, healthy communities.

A tiny fraction of the multi-million pound budget required to build and equip a brand new hospital, were it to be invested in FE and the education sector more generally, would return many times the economic and social benefits of a new hospital to patch people up after they have already become unhealthy and despondent.

Secondly, invest in social care. The NHS spends a large proportion of its budget dealing with the problems of frailty in old age, one way or another. Again, tiny factions of the budget for a new hospital spent on social care instead would minimise the number of times some of the most frequent client groups (not just the elderly) turn up in A+E for help because they have nowhere else to go.

While those two actions on their own won’t solve every problem in the NHS, they’ll make a pretty good start. And, despite spending more in some areas (eg schools, colleges, and social care) the savings from not having to build more and more hospitals will lead to a lower cost of public services overall.

To solve problems like this, we need to remember that the map isn’t the territory.

Once we define “the map” as “how do we reduce the cost of an emergency operation in A+E?”, we stop looking for solutions outside the operating theatre in a hospital. Even though that’s where the biggest solutions are likely to be found.

For tech folk and politicians, the map is the territory. They are incapable of thinking any other way.

So, when it comes to any areas where an unholy alliance of tech folk and politicians are to be found, you should give their solutions about as much credence as you would to a guy who gives you a map of the Northern Line to help you find your way from Plaistow to Perivale.

Unless you want to go down the tubes.

What you think you want ain’t necessarily what you need.

In a previous role, I used to help companies turn around their bottom line financial performance.

Nearly always, at the start of the assignment, the client would say something like “I know we’ve got a good business here. We just need to make more sales, then everything will be fine.”

And, nearly always, that wasn’t actually their problem.

That approach kinda makes sense as far as a P&L goes – mathematically if you increase your revenue line and keep all your other costs the same, your bottom line increases.

But, for a really good CFO, your P&L is only part of the picture. And not even a very big part of the picture at that.

For one simple reason. By the time something lands on your P&L, it’s already happened. You can’t do anything about it. P&Ls just report on history.

And while that’s valuable in many ways, if your P&L isn’t where you want it to be in bottom line terms, there’s very little point spending more time analysing your P&L. That’s not where your problem is.

Your problem lies “upstream” of your P&L.

Swimming upstream

There were some financial analytics I used to do first, just to get the lay of the land, but generally my first port of call was the company’s gross margins.

Because if you want to grow your bottom line, you’re extremely unlikely to be able to do that if your gross margins aren’t high enough.

So, quite quickly, I’d generally move the conversation from a pressing need to do something about the bottom line, through a conversation about needing to “sell more”, to the real issue which was that the gross margin wasn’t high enough to cover the company overheads and leave a profit.

Now, there are two important caveats here.

Firstly, you need to make sure the gross margin is calculated correctly – which in an amazing number of businesses it isn’t. That’s a topic for another day, but if the gross margin which shows up in your P&L isn’t a true gross margin, all your decisions from that point onwards will be sub-optimal at best, and will quite likely make things worse.

Secondly, what I’m talking about here is your gross margin in cash terms, not in percentage terms.

There is absolutely no certainty that a business with 80% gross margins (assuming they calculated them correctly, of course) is a more profitable business in bottom line terms than one with 20% gross margins.

Sure 80% margins make it sound like you’re a business mastermind, but if your 80% gross margins translate into a gross profit of £1million and your overheads are £2million, you’re not going to solve your bottom line problem any time soon, even if you get that 80% margin up to 90% or 95%. The maths just doesn’t work.

In the interests of balance, the other common option at this point is to take an axe to that £2million in overheads.

I’m not for a moment saying that’s never the right answer – occasionally, when a company is in deep financial distress, there’s no other option to steady the ship fast enough – but taking an axe to overheads before you’ve fully understood what’s going on at gross margin level will often take you in the wrong direction too.

Especially since the process of really getting to grips with how your gross margin works, when done well, only takes a couple of weeks. We’re not talking months or years here.

Unless the liquidator was in the car park, I used to counsel clients to hold their nerve for a couple of weeks to make sure they weren’t going to make decisions they might come to regret, or decisions which would have long term cost implications much greater than any short-term benefit.

Going incremental

The most important question to know the answer to when analysing gross margin is what the frictional gross margin looks like.

That is, for every additional unit sold, what are the incremental costs and revenues of making that sale.

Usually, an incremental sale is highly profitable.

In most companies, overheads remain pretty much the same when you produce one extra unit of product (at least up to a threshold where you need to build a new factory or take another floor in an office building to accommodate all the extra people you’re hiring).

So the incremental costs tend to be low.

When I worked in the printing industry, our incremental costs were the extra sheets of paper we printed onto, plus a small amount of ink.

We were already paying for the printers, the machinery, the warehousing, and all the other costs of production. They didn’t change when we printed one more unit.

I find it’s not uncommon for businesses which might make a 5-10% bottom-line profit to have 70-90% margins on each incremental unit produced.

That being the case, the company’s perceived problem of a lack of bottom-line profits…which was translated into a “we need more sales” mantra…and became a “cash terms gross margin” issue…often morphs into a “how effectively we use productive capacity” problem. Often that’s the real root cause of a company’s bottom line problems.

And by productive capacity here, I’m not just talking about factories with machinery – I’m also talking about the fact that a lawyer, say, only has so many hours in a day to dispense advice they can charge clients for.

Are you charging enough?

Once you understand how that all works, your next thought should generally be “am I charging enough?”

Next to the issue of managing productive capacity well, this is the second most common issue I used to come across on a regular basis,

I’ve been lucky enough to work for some great businesspeople who were smart, knowledgeable, and hardworking, but who, for whatever reason, found it hard to charge an amount which reflected the value their business really brought to its clients and customers.

There is usually no faster way to solve a gross margin problem than to put your prices up. All your other costs remain “as is” – you’re just getting 10%, 20% or more for each unit you currently produce, exactly the way you produce it now, and with no costs of transformation or disruption or training to fund either.

Now, there is an important point to bear in mind before you try this.

You have to understand the value your business brings to its customers, first and foremost.

And, ideally, you have to over-deliver on that value.

For example, if you charge £100 to deliver £100 of value, some people will pay that. But you’ll never get them to pay you £120 unless all your competitors go out of business.

More commonly, I found, companies were charging £100 to deliver £200, £500 or £1,000 of value.

In a situation like that, for a likeable, trustworthy, dependable supplier, not many people are going to refuse to pay £120 instead of £100.

Now there is an art to demonstrating the value you bring in such a way as to get that extra £20, and you might need to do a deal along the way to phase in your increased pricing, but it’s generally do-able, when presented in the right way, if you deliver enough value to your customers.

It’s not sales, it’s margin

I fully accept that, having started this article poo-poohing the idea that you might need more sales, it now looks like we’re now back to where we started, and agreeing that putting more sales through the revenue line of your business would solve your problems.

Except we’re not really putting more sales here, even though the revenue line on your P&L is where that extra £20 will show up.

What we’re really doing is delivering more gross margin.

Your original £100 sale, with a marginal cost of £80, say, has now turned into a sale of £120 with a marginal cost still at £80.

In a business that was losing £10 on the bottom line against a revenue line of £100, you’re now making a profit of £10 on a revenue line of £120.

I don’t know many people who wouldn’t consider that a victory.

And, bear in mind, they haven’t touched anything inside their business yet. All they’ve done is deliver more gross margin.

Which is the important thing here, let’s not forget.

On the other hand, if you were losing £10 on a revenue line of £100, and decided to offer customers a 20% discount in the hope that would increase your sales, the most likely outcome is that you would end up losing £30 against a revenue line of £80.

That, most definitely, is not a victory. And illustrates why just increasing sales isn’t the solution unless you deliver an increased gross margin, in cash terms, by doing do.

All your costs are paid in £s, remember, not %s. So how much cash gross margin you create is the question, not what percentage number do you get to boast about to your mates.

Don’t neglect overheads

Although we haven’t spoken about it much in this article, don’t neglect your overheads. If there’s cost there to be removed, then by all means remove it.

But until you know where you’re going with your pricing strategy and your gross margin strategy, unless there’s anything outrageous in there, you should probably hold off significant cuts.

There is one very good reason for this.

As a sweeping generalisation, organisations which expect higher prices need to deliver higher levels of service. Everything needs to be slicker, better managed, more problem-free, and so on.

That means you can’t just implement some ridiculous AI chatbot (none of which are remotely helpful) to provide customer service. In general, to sustain a higher price you need to over-index on the level of service you provide.

While that might sound counterintuitive, remember that top hotels employ someone decked out in a top hat and a frock coat to open your car door.

That isn’t because you don’t know how to open a car door yourself. It’s an extra touch that makes guests think “Wow, the service around here is really good – that’s well worth another £100 a night”. (All credit to the great Rory Sutherland‘s “doorman fallacy” here, one of the smartest observations on pricing I’ve ever read.)

This is why you have to work out what you’re doing with your gross margins before you take a look at your overheads. Otherwise you might take decisions you come to regret.

Take away the doorman, in this example, and ultimately you haven’t saved a tiny salary in the context of a luxury hotel’s cost base. You’ve lost £100, or more, per room per night for eternity.

Given a choice, the smart thing to do is to keep the doorman employed and just accept that your overheads will be a tiny bit higher than they would be without him – but also acknowledge that your revenues are 100s of times higher than they would be without him.

(If you find that a difficult business decision to make, you might have much bigger problems than just the state of your bottom line…)

It’s a trade-off

Every business decision is a trade-off. Rarely does “painting by numbers” work. Certainly not in anything other than the short-term.

While I applied a consistent framework to get to the heart of what the issues were in my clients’ businesses, I can assure you there isn’t a one-size-fits-all solution here. Every solution for every client was different because the context of each client was different.

Anyone who thinks finance is just a matter of running up a few scenarios in a spreadsheet doesn’t understand what lies at the heart of good financial decision-making.

What always lies at the heart of a good financial decision is always a trade-off.

More cost today in return for less cost tomorrow?

Higher sales revenues, but with a higher fixed cost base to cover as a consequence?

Highly variable cost base, through using consultants and freelancers, but with the downside that they can all down tools tomorrow and go and work for your number one competitor, knackering your business in the process?

None of these decisions are objectively always right or always wrong. You have to understand the context to make the right decision.

And the context you need to understand more than anything else is how your gross margin really works, and what levers you can deploy to bring in more cash gross margin than you do today.

To find the best solution for your business, you have to dig more deeply than just demanding “more sales”.

Sales to the wrong sort of customer, or sales which don’t carry enough cash gross margin to cover your overheads, or sales that are made to hit a sales target without any thought of achieving a profit target, will take your business backwards, not forwards.

Those sorts of sales reduce your bottom line. They won’t increase it. No matter what some people will tell you.

The answer isn’t always “more sales”. But it is always “more gross margin”.

To work out how to do that, though, you need to dig deeper than most organisations do when they’re up against it and are frantically trying to cobble together some strategies to repair a big hole in their bottom line performance.

The good news is, having helped dozens of people fix a range of bottom-line problems, I’ve found that digging deeper is the only way they solve their bottom line problems for good.

It’s always worth the effort.

Many people think numbers are “objective” and therefore represent some pure form of decision-making. Nothing could be further from the truth.

For now, I’ll skip over the question of whether objective decision-making should be regarded as the best kind of decision-making, as I have some questions about that. But even if it is, equating numbers with objectivity is just baloney.

There’s nothing less objective than a number – a number on its own means nothing without the full context.

Here’s a quick example.

Let’s imagine we did a survey of 30 people in a room and asked how many of them had heard of Einstein’s famous E=mc² formula.

And let’s imagine none of them had.

That’s a real indictment on the UK education system, isn’t it? Possibly the world’s most famous equation ever, and nobody out of 30 people had even heard of it, never mind knew what it meant!

Shocking, right?

Well it is. Until I give you the context that this imaginary experiment was done with a group of 30 under-5s at a nursery school.

A number without context means absolutely nothing.

Politicians are the best at this

Just before we get into the meat of this article, it’s worth saying that I’m not taking a political stance here. This is just about how to interpret numbers – I’m not supporting one interpretation or the other, just pointing out how people use numbers to mislead others.

If you know me at all, you’ll know my views that, irrespective of party, I regard all politicians as sitting on a scale that varies between disdain and outright contempt. So I’m not supporting any party’s political views here.

However, exactly the same tricks are deployed by people in business every day of the week -generally less expertly than the average politician, but the themes are broadly the same so, by the end of this article, hopefully you’ll have some pointers about what to look out for.

It’s no exaggeration to say that being able to spot flaws in the numbers that are presented to me has been at the core of the career success I’ve had. and it comes from something I learned at university, where I studied Law.

A professor in a second year law class told us that, in every legal dispute, there were always at least two sides – sometimes more – and, whichever party we represented, it was our job to uncover all the different sides to the argument, and examine whether they were persuasive or not, in light of the available evidence.

It was not our duty just to parrot our client’s case as they relayed it to us, he said – at least not unless we wanted to lose a lot of cases, because our clients would generally only have one view of the matter. Their own. They were blind to all the other ways of looking at the situation

Seeing the other side of the argument was drummed into us then and it’s a skill that has carried me through my career.

So, whenever someone sticks a set of numbers in front of me, they are essentially making a case like a lawyer would in court, wanting me to agree with their view of the situation. Which is why I always ask myself “what might the other side of the story be here?”

If you get nothing else from this article, whenever anyone presents you with a set of numbers, ask yourself that question. Don’t be railroaded into agreeing with whatever the person presenting those numbers wants to do.

Otherwise you might end up unintentionally raging about why a group of 4-year-olds doesn’t know all about E=mc².

Comparison is the thief of joy (and also thinking)

US President Theodore Roosevelt supposedly come up with the expression “comparison is the thief of joy”.

Whether he did or not, isn’t the point (online sources differ), because all you need to know here is that comparison is often the basis for someone using numbers to trick you.

What they are trying to do is short-circuit your thinking processes by presenting something outrageous, or controversial (or sometimes, seemingly uncontroversial, like a generally-accepted truth) to give some superficial rationale for you taking the action they want you to take.

Comparisons are a good way to do this.

Stimulating your emotional thought process with the aid of a set of “objective numbers”, then presenting a simple solution on a place, (ie the action the person presenting those numbers wanted you to take all along) is a surprisingly effective way for the unscrupulous to get what they want.

When I worked in higher education, league tables were a great way of doing this. (League tables, by the way, are just about the worst way to manage anything, which is why politicians like them – it gives a superficial illusion of oversight, while actually lets politicians abdicate their responsibilities to make the country better.)

The pitch usually went something like this (deliberately choosing a ridiculous example to protect the guilty): “Institution A is 10 places higher in the league table than us. They recently painted all the walls in their student canteens purple. So we should repaint our canteens purple and we’ll climb up the league tables”.

I won’t distress you with the number of times a group of otherwise intelligent people were moved to action on the back of a pitch like that.

So every time someone starts their pitch with a comparison, be very, very suspicious. The odds are high they are trying to trick you into agreeing with a position they have already decided beforehand.

Despite appearing to consult with you, the last thing they want is for you to ask any awkward questions.

Which is precisely why you should.

Three recent examples

As it happens, I’ve seen three great examples of this phenomenon recently, which attracted quite a degree of press coverage and online attention.

The key thing to remember here is that all the numbers presented are factually accurate, insofar as they go. They are the truth, they are just not the whole truth.

I find it’s rare, albeit not unheard of, for people to completely make up a set of numbers. Although it happens.

More often, they don’t understand the context for those numbers well enough, which is why so many proposals don’t stand up to detailed scrutiny.

Or alternatively, they understand the context perfectly, but it suits their case to ignore the wider context, so they just cherry-pick the numbers which best support the case they wanted to make all along.

I’m not sure whether incompetence or malevolence is a more attractive trait, but I have very little time for either of them. Which might explain my dim views of politicians…

Anyway, here are three stories which have consumed countless column inches over the past few weeks to illustrate how the process works, and what to look out for:

1-Pensions

If you live in the UK, you’ll be aware that we have something called the “triple lock” on old age pensions. Introduced by a Conservative-led government in 2011, the intention is to ensure dignity in retirement by increasing state pensions each year by the highest of the rate of CPI inflation, the rate of earnings growth, or 2.5%.

And lately, you can’t help but notice the number of commentators who say that the 4.8% increase pensioners will be getting in 2026 is unaffordable. It is, for example, “ruinously expensive” according to one Daily Telegraph commentator.

Yet the UK still has one of the least generous state pensions in the developed world – which should give us all pause for thought about how bad things must have been before 15 years or so of triple-lock.

A report for the House of Commons (linked below) highlights that 20-odd, developed nations have a lower percentage of pensions living in poverty than the UK. The same report also highlights that the UK spends a much smaller percentage of GDP on pensions than 27 other advanced nations.

I’m not a pensions expert, so I don’t know the right answer. But I do know that stoking outrage about increases in state pension is a great way to dodge the question about how badly-off the average UK pensioner actually is.

If the triple-lock was an attempt to do something about that, albeit is very slowly and over 15 years or more, then surely trying to alleviate the number of pensioners in poverty is the bit we should focus on, not the annual rate of pension increase announced for 2026.

(Full disclosure: I have major issues with how “poverty” is defined in this government statistic, but that’s not to detract from the point made above – it’s the comparison designed to stoke outrage I’m encouraging you to be alert for.)

2-Taxes paid by the rich

Some right-wing commentators are claiming that the top 0.1% of taxpayers pay more tax than the bottom 50% of taxpayers, generally as a justification for not taxing the highly-paid any more than they already are.

Now, I don’t have access to all the information I would ideally like to unpick this, but I’m prepared to believe that the numbers quoted above are mathematically correct. It’s the conclusion we need to challenge.

There are some listings around, such as The Times’ Tax List, which takes the methodologically questionable basis of adding things like the corporation tax paid by their companies into an estimate of how much total tax the super-rich pay.

I say “methodologically questionable” because if those super-rich left the country, but kept their companies operating, they would pay just as much corporation tax as they do now.

But looking at HMRC’s own stats on income tax specifically, we get a slightly different picture.

Firstly, their stats on taxpayers show that the income cut-off point for the bottom 1% of taxpayers is an annual salary of around £12,900.

Now, given that the annual personal allowance (the amount us Brits get tax-free before having to pay tax) is currently £12,570, the average person in the bottom 1% of taxpayers will be paying tiny amounts of tax over the course of the year.

The top 1%, on the other hand, which starts at a salary of £219,000 but clearly moves up into the stratosphere from there, will clearly pay vastly more tax. Assuming they don’t pay into a pension for the sake of simplicity, someone on a salary of £219,000 will pay £79,082 a year, according to MoneySavingExpert’s online calculator.

Someone in the bottom 1% would, on the same basis, pay about £64 a year, assuming they all earned £12,900.

Put another way, it takes about 1,235 people in the bottom 1% to pay the same amount of income tax as one person earning £219,000 a year.

When you understand that context, it’s much less of a surprise that a small group of very wealthy people at the top of the income scale pay so much tax that, collectively, they pay more tax than a large number of relatively poor people at the bottom end of the income scale.

Frankly I’d be more surprised if that wasn’t the case, given that the tax take starts at £64 a year in the bottom 1% and shoots up to almost £80k in the top 1%.

Now, that’s an entirely separate argument from the question about whether we are over- or under-taxing the super-rich (or, more accurately, the people paying income tax, which isn’t quite the same thing).

Someone on a gross salary of £219,000 a year is paying about 36% of their income in tax at MoneySavingExpert’s estimate of £79,082.

You might think that’s too low, too high, or about right. I’m not expressing a view here.

And you might think that someone earning £12,900 and paying £64 in tax (about 0.5% of their income) is being taxed too low, too high, or about right.

My problem with the argument as it’s usually stated is that the fact that wealthy taxpayers pay a lot more than poorer taxpayers is exactly what I’d expect to see.

It’s not necessarily an argument for or against increasing tax on high earners – although the people writing opinion pieces would like to you uncritically accept their view that “people at the top of the income spectrum are already doing more than their fair share”, based on the way they present the data.

That said, according to OECD comparisons, the UK is generally at the lower end of tax rates across major OECD economies for someone on average earnings.

My purpose here is not to suggest one solution or another. Just to say that the “right” rate of tax is a lot more complicated than tweets and soundbites from interested parties would have you believe.

3-AI adoption

There’s only one group of people I trust less than politicians, and that’s people selling AI solutions.

Recently this chart has been doing the rounds, showing the speed of AI adoption relative to the speed of internet adoption.

This is often used to promote the idea that AI is hugely successful and we should integrate it into our everyday lives at every opportunity.

Of course, this is precisely what AI folk would like you to do, as it’s in their direct financial interests. It’s no great surprise that OpenAI, a leading AI company, is quoted as a source for this chart. It’s hard to imagine a group of people with a greater interest in getting you to think AI adoption is inevitable.

But in this case, while it might not be the thief of joy, comparison is at least misleading.

Think back. In the first couple of years of the internet it was just three people scattered around the world. Internet adoption in the 1990s was slow because you couldn’t do anything with it – you needed to buy a computer, hook up a modem, find a telecoms provider who, if you were lucky, could install an ISDN line for you.

There were no games, no emails, and no social media. Of course adoption was slow.

By the time AI came along, you could download an app to your phone in 30 seconds, so of course more people “adopted” it.

And AI started being baked into all sorts of products (earning their makers my enduring hatred, as a rule), so of course more people “adopted it”. They didn’t have a choice.

Thanks, if that’s the right word, to AI, ordinary people, with no training, can do things more or less out-of-the-box with AI, like make videos of their dead cat from an old photograph, whereas in the early days of the internet you needed a degree in computer science to send an email.

The fact that most of the things you can do with AI are pretty pointless, and generally offer little-to-no real-world RoI for businesses is a separate issue.

But the speed of AI adoption is only possible because the internet was invented before AI was. If the internet hadn’t been invented first, AI would still be something three people did in a lab somewhere. It’s “rapid growth” is no more of a surprise than the fact that Angry Birds or Wordle went from being nowhere to being everywhere in a short period of time.

The speed of AI adoption is not collateral in the arguments from AI folk that their technology represents the future.

But that’s what the people running AI companies would like you to believe. So they are more than happy to try their best to mislead us by promoting charts and graphs like the one above.

I’m sure the numbers are true, as far as they go. But think about the context for longer than a second or two and you’ll realise AI companies are trying to manipulate you by making an unfair comparison.

Check out the other side

It’s easy to get swept along by someone with a bit of enthusiasm, backed up by what appears to be “objective numbers”.

Every time someone turns up with a heady blend of enthusiasm and mathematics, your initial realisation should probably be that someone is trying to bounce you into making a decision you’ll probably come to regret, but which is very much in their own self-interest.

So, stop a moment, and take the advice of my old law professor.

For everyone presenting a case, there’s always at least one other side to it.

Take a few moments to think those options through before saying “yes”.

Every decision you make will be better for checking out the “other side” of a proposal, even if you end up taking the action proposed to you with that blend of enthusiasm and maths, because you’ll have weighed up the options.

And, every now and again, this process will either save you from disaster or open up a bigger opportunity that had been missed by the person making the proposal in the first place.

For those reasons and more, always check out the other side. You never know what’s there until you take a look.

There are always at least two sides to everything.

When it comes to managing the bottom line, very few people are the real deal. Mostly it’s people talking about slashing costs or “cutting out the nice to have’s”.

With rare exceptions, those people are idiots. They’re not the real deal – they just talk tough and hope they don’t get caught out.

A few of the more delusional ones actually think they know what they’re doing, even though every time they unleash another bout of their strategic genius on a business, things get worse instead of better.

Some people never learn.

In the interests of full disclosure, I’ve seen people from just about every profession do this. People who have learned to talk the talk without ever learning how to walk the walk.

But talk the talk well enough – whilst making sure to move to another company before the somewhat awkward chickens come home to roost, of course – and this can be the foundation for a lucrative career for the hard of thinking and/or people who are too cynical to care.

Some days, I wish I was enough of an idiot and/or enough of a sociopath to join them. It certainly sounds like a much easier life than working on your craft for long enough to get good at it.

At least the vast majority of the time – nobody who’s any good has a perfect batting average. Occasionally, the best laid plans of mice and men don’t quite work out the way you hoped. And sometimes life has to teach you a lesson to keep you humble. That happens.

But anyone with an “infallible recipe” or a “standardised process” who is trying to manage your bottom line for you almost certainly doesn’t understand what they’re doing well enough to be allowed out without a responsible adult present.

Tech people and engineers are particularly susceptible to this sort of thinking, I’ve found, because they are used to working in controlled environments where they can manage all the variables.

Not good ones, obviously – really good tech people and engineers can get beyond that very restricted thinking. But an amazing number of third-rate ones think they know how to manage a bottom line with their box of magic tricks. And yet they get it completely wrong, because they don’t really understand what they’re doing.

Nobody is immune

Before tech people and engineers get too defensive, this is not something only they do – I’ve just found it’s more common there than in most other places.

Accountants suffer from this too. I’ll happily acknowledge that very few people can destroy an otherwise successful business more comprehensively than an accountant who doesn’t know what they’re doing running rampant through the business, cutting costs wherever they find them.

But today, I’m not talking about those professions – albeit I think an accountant or two might well have been involved in the story below somewhere along the way.

Today I’m talking about marketers.

Normally, I think marketers get an unfairly hard time. They perform one of the few roles in most businesses where they need to reach out to the outside world, with no guardrails or safety net, and try to do something new to have a positive impact with customers of the business.

Whilst that doesn’t always work, of course, at the point they press “send”, nobody knows for sure one way or the other, no matter how much testing has been done with sample groups in advance.

It could be a great success. It could be a giant flop. Nobody knows for sure until the campaign runs.

But somehow, marketers have to develop a constant stream of new ideas, and put themselves out there every day, hoping they get more clicks and likes than they did the day before, or that their brand-scoring metrics at the end of the month will show an uptick based on whatever campaign they launched at the start of the month.

That’s hard. Really hard. So, I usually have a big dose of respect for the people who even try. It’s a tough gig.

The big picture

One thing marketers are almost invariably good at is keeping the focus on the big picture. The mission. The key attributes. The ICP. The brand values.

But occasionally, marketers lose the plot too.

Sometimes, they’re just having a momentary episode of some sort.

Other times, they start behaving like those engineers and tech people and end up making a whole host of short-sighted decisions which end up damaging the bottom line.

In some organisations, this is quite the badge of honour internally. At least until they get found out.

It can be a big career boost if the CFO gets wind of the fact that someone in the marketing department is taking an axe to the cost base and (so they think) managing the bottom line more purposefully than it was managed before.

In those environments, frankly the CFO is every bit as much to blame as the marketer. It’s a sure sign that, not only doesn’t the marketer understand their business, but their CFO doesn’t understand it either.

That’s unlikely to lead to a positive outcome.

The holidays are coming

This dynamic has played out recently in the Coca-Cola commercial for Christmas 2025.

There has been a lot of online commentary about this ad, with the word “backlash” making a regular appearance in articles written about it, because Coca-Cola has decided to make this year’s Christmas ad using AI.

My longstanding position as an AI sceptic notwithstanding, it’s a great illustration of how some unholy alliance of tech people, marketers, and accountants can spend $millions making everything worse, while completely missing the point in the process.

Of course, the tech folk and marketing people involved are talking up what hard work this all was, and what creative geniuses they are.

Which is nonsense, of course.

While it would be unfair to call the ad terrible – there are plenty of other, equally bad ads around – the finished product is neither creative nor genius.

But it took 100 people to refine 70,000 video clips, according to the hagiographic PR put out by those involved, in a vain attempt to make us think this was a good piece of work. The truth is, it’s an extremely average digital re-hashing of themes which had already been developed in past generations of Christmas Coca-Cola commercials over several decades.

I’m sure there were some tricky technical issues to overcome in the process, but whatever they were, when it comes to managing Coke’s bottom line, this commercial achieved just about the worst possible outcomes.

Seeing the wood for the trees

You see, creating an ad isn’t like negotiating a cheaper price for printer paper, where all the paper you might buy has identical technical characteristics, and all you’re doing is trying to buy it for the smallest amount of money possible.

When you’re buying printer paper, that’s a perfectly decent strategy for managing your bottom line.

Running an ad, however, is different.

For a start, none of your customers care where you buy your printer paper from. You can buy it from Mars, for all they care.

With an ad, though, whilst you might want to push the envelope and all that, the first thing you have to make sure of is that you don’t completely cheese off the customers you already have now.

In that context, given the number of times the word “backlash” appears in reports and commentaries about Coke’s Christmas ad this year, I’d say this campaign fell at the first hurdle.

If Coca-Cola is your favourite soft drink, you’re probably not going to stop buying it tomorrow. Equally if almost any brand irritates you enough, for long enough, you’ll switch.

For example, as a Windows user for 30-odd years, I’m now in the position where Microsoft are cheesing me off so much with the AI nonsense they’re packing into all their products, that I’m seriously thinking of switching my allegiance to a MacBook.

Rule number one of advertising: don’t cheese off all your customers.

Outcomes, not inputs

The second rule of advertising is that what matters are the results you get, not the inputs that go into the process.

If I could sell $100million of products on the back of a $2million commercial, but you wanted me to cut my commercial budget to $1million, on the back of which my ad only shifts $50million of product, who’s the winner here?

Not the customers, not the company, not the brand.

And, in the long run, not even the CFO or the investors either. Provided they keep their eyes on the prize.

See, there are very few circumstances, with the possible exception of impending bankruptcy restricting access to cash, when even the CFO and investors wouldn’t prefer to generate $98million in net revenue, after ad costs, than $49million.

The cost of the ad here is secondary to the additional revenue it brings into the business.

And if one of the outcomes from your ad is that a significant number of your customers hate what you’ve done to your once-iconic Christmas commercials so much, to the point where some of them are organising consumer boycotts of all the brands you own, that’s not a good outcome.

No matter how much you’ve saved in production costs.

The Real Thing

You’ve also got to keep the brand in mind at all times.

For a business like Coca-Cola, their brand is incredibly valuable to them. After all, the cost of mixing water with some sugary syrup is pennies a bottle.

At the end of Q3 2025, Coca-Cola had a market capitalisation of $285billion, despite only having a balance sheet a third that size.

While there are some technical reasons for that difference I won’t go into here, the fact that Coca-Cola is worth 3x as much on the stock market as the value of its assets is based on – in the widest possible sense – the value of its brand.

Remember, there are no subscription revenues here. Nobody is locked into buying Coke for the next 5 years. There’s no recurring revenue. No certainty as to the income stream.

Every time someone wanders into a shop, they decide whether to buy Coke or some other brand of fizzy, sugary water.

And ironically, for a lot of people of a certain age, one of Coke’s more memorable tag lines over the years is “The Real Thing”. Coke put a lot of time, effort, and money into convincing consumers that some other bland, average, sweet, fizzy water that looked a bit like Coca-Cola was a third-rate fake of “the real thing” – ie Coca-Cola.

Against the backdrop of using “The Real Thing” to promote and protect your brand, using AI to create your ads – and being publicly self-congratulatory about it, to make things worse – essentially says that “the real thing” doesn’t matter any more.

If any sort of fake garbage someone throws together in an AI studio can represent the real thing in the world of Coca-Cola’s advertising, so can the bootleg Coke I brew up in my bathtub and sell by the case-load off the back of a van in the dodgier areas of town.

That’s every bit as real as the advertising Coca-Cola puts out.

I might be in a small minority here, but if I was Coke’s CFO or CEO, the last thing I’d be wanting to do with my valuable intellectual property and brand positioning in my customers’ eyes as “the real thing” would be to encourage anyone else to think that large-scale fakery was OK.

As a way to damage my own brand in real time, there are not many better ways of doing that, as the fake soft-drink syrup taking the enamel off the inside of my bathtub at the moment can corroborate.

And it’s all so pointless

Industrial-scale stupidity like this is bad enough.

What makes it worse is that this was all so pointless.

Nobody buys Coca-Cola because they use AI in the commercials. That’s not going to bring in a single new customer, and risks cheesing off a significant number of existing customers.

The whole point about Coca-Cola’s holiday commercials is that they speak to a tradition. They’re supposed to bring back warm childhood memories. They’re not trying to sell you the latest version of a video game. All this tech-enabled advertising nonsense is an irrelevance.

Actually, more than an irrelevance. A pointless irrelevance.

Had I been Coke’s CFO or CEO (open to offers, folks) I’ve got a really easy way to cut costs, flatter the bottom line, and not risk cheesing off a significant number of my customers, while still evoking every bit of the warm childhood traditional feel that so many people look forward to in the Coca-Cola ads.

I just run “a classic ad from the archives” each year.

Sure I still have the same media costs. A 30-second commercial costs the same whatever imagery I put on there – even if it’s imagery from three or four years ago.

But my production costs each year are pretty much zero (and, possibly, actually zero).

It wouldn’t even take a huge amount of work to spin all this as speaking to the traditions of yesteryear, which are never far below the surface at Christmastime.

So, I’d get all the benefits of reminding people of my brand, I’d keep my position front-and-centre as one of the western world’s major Christmas holiday traditions, and I don’t have my customers organising boycotts of all the brands I own.

At the same time as I’m doing all that, instead of spending $millions on a new campaign (whether AI generated or not), all my savings from re-running “one from the archives” flow straight down to the bottom line.

And not only does nobody care that I’m running “one from the archives”, people actually look forward to it, if I do this the right way, and I enhance my brand at a minimal cost.

The bottom line lesson

In the words of the great Peter Drucker, there is nothing so pointless as doing something efficiently that doesn’t need doing at all.

To run this season’s AI-generated ad, Coca-Cola might well have thought they were pushing the envelope and developing some cutting-edge brand collateral, while boosting their bottom line.

Sadly, they have probably achieved none of that. And they managed to cheese off a significant number of their customers in the process.

Not what I’d call a victory – either for that business, or for common sense more generally.

This happens to be a particularly pertinent example, but the purpose of this article is to make this point.

When you’re managing your bottom line well, you’re not just managing your cost base.

You have to manage a much more complex series of variables, including the impact on your customers, whether you increase or reduce your average customer lifespan, whether your decisions will adversely impact your productive capacity, how motived your staff will be, and a range of other, often subjective and hard-to-pin-down, variables.

But the alternative is that you spend $millions to end up with your customers organising boycotts of your products.

However smart you think you’re being, that’s particularly dumb.

If you employ anyone in your finance, technology or marketing teams who think otherwise, I’d recommend firing them now.

No matter how good they are at talking the talk on a technical level, if you let those folks walk the walk, they’ll walk all over everything good about your business, and you’ll end up regretting it.

I promise. That’s the real thing.

A long time, when the Industrial Revolution was getting under way, just having some new invention was reason enough for people with enough cash in the bank to try it out.

“So you’ve invented a steam-powered horseless carriage, have you? I must try one of those out!” was probably something members of the landed gentry said back in the late 1800s.

Of course, some of them were dead against giving up their actual horses, as those had been a mainstay of the lives of lords and ladies of the manor for hundreds of years up till that point.

But a few of the younger, or crazier, members of the landed gentry with more money than sense were prepared to give it a try. And out of that, the automobile industry was born.

For the first couple of decades, automobiles were for the rich. They were hand-built from the ground up, and required the sort of skills that were hard to come by in the middle of the Industrial Revolution.

Then Henry Ford came along and invented mass production.

Now cars were affordable for just about everyone – including the people who worked in Henry Ford’s own factories. They were many more vehicles on the streets and, over the next few decades, automobiles became ubiquitous.

Now we’ve got more of them than we know what to do with, and many cities are clogged up pretty much 24/7 with motor vehicles of one sort or another.

What started out as a niche pastime for the idle rich became something that almost everyone owns.

The early years

In the early years, automobile manufacturers focused on refining their vehicles, and making them work better.

We went from steam-powered monstrosities through to the internal combustion engine fairly rapidly and, by the time the first Model Ts were rolling off Henry Ford’s assembly lines, the internal combustion-powered motor vehicle existed in pretty much the same format we use it today.

Sure, it wasn’t nearly as aerodynamic as a modern vehicle, nor could you plug in your phone to fire up Google Maps, but no adult today could look at a Model T Ford and be confused about whether or not it was a car…albeit a prehistoric one.

But the key thing is that a large part of the development of the automotive industry took place when there were less than 10,000 vehicles on the roads in the United States.

While there were, of course, engineering and technical challenges in the process, it’s a lot easier tinkering with one car at a time in a specialist workshop full of skilled mechanics than it is to try to fix Model T Fords when they’re coming off the assembly line at the rate of one every couple of minutes.

You just can’t fix things fast enough to put right a fundamentally broken production line process at the end of the process, just before despatch to your customers. You have to fix everything before you crank up the production line, not after.

But once you’ve got a template that works, you can roll out thousands of near-identical models of a Ford Model T without too much difficulty.

Smart people know you fix the system first, then crank up the volume. Not the other way round.

Features first, benefits second

Another feature of the early years of the automotive sector was that, in the beginning, just having a “horseless carriage” to sell was exciting enough – even if it wasn’t terribly practical and broke down a lot.

There was an infinitesimally small number of incredibly wealthy people who could afford one and they acquired significant personal pleasure and, I’m sure, status amongst their peers, just by owning a horseless carriage.

Even if it required coal to be shovelled into a boiler on a regular basis, and had a top speed of only 4mph.

The point here wasn’t that it was a more practical proposition than a horse. Because, in the early days, it wasn’t.

Rather, the attraction was that horseless carriages were new and novel. The sort of things only rich, sophisticated people who fancied themselves at the cutting edge of science and technology would consider buying.

When hand-built motor vehicles cost 5x an average annual salary, only the super-wealthy could afford one.

So, in the early days, just putting a sign outside your workshop saying “Horseless Carriages For Sale” would bring in the customers.

99.999% of the people passing your workshop knew they couldn’t afford one.

And the tiny number of very wealthy people in your neck of the woods would go to the only place around where horseless carriages were sold, if they wanted to buy one.

So selling wasn’t all that hard.

And, at a time when the motor vehicle industry was predominantly comprised of locally-based artisan workshops, realistically very few people were going to make the trip from, say, Los Angeles to Cincinnati to check out the new model of motor vehicle being built up there. Not least because there the car that could get you there hadn’t been invented yet, and the interstate highway network hadn’t been built.

So, in those days, cars were sold mostly on their features. “Look! We have a car for sale. And it’s got a front windscreen and a spare wheel.” (Or a roof for the passengers. Or a handbrake. Or something else that sounded cool in the early years of the 20th century.)

All of those were novelties to a group of people who had previously ridden horses everywhere, because there was no other way to get around.

When the novelty value is high, and you’re appealing only to a tiny group of very wealthy people, this features-led approach will work perfectly well.

It’s largely how luxury goods are still sold today.

Hermes scarves, Louis Vuitton handbags, and Dolce and Gabbana dresses are sold on their features (high-grade silk from artisan Cambodian silk worm farms located high in the mountains to ensure the very smoothest weave, for example – which I’ve totally made up, by the way).

And possessing one of those scarves, handbags, or dresses is enough to cement your status as one of the cognoscenti.

You can imagine Patsy and Eddie gushing about the mountain-top Cambodian silkworm farms on Absolutely Fabulous.

All they want is something to gush about that sets them apart from the hoi-polloi.

After that, they’re not too bothered.

And the product doesn’t even need to be particularly functional. I’ve no idea what the insulating qualities of a Hermes scarf are, because I’ve never owned one.

But I do know that the people who buy a Hermes scarf are not buying them because they’re planning to climb Everest and they’ve heard what great insulation your neck gets from a Hermes scarf.

People buy Hermes scarves because they are Hermes scarves and very few people can afford one – the Cambodian silk farm story, or whatever they say about their products, is mostly incidental but it helps to sell luxury goods, so who am I to complain?

Going mass market

For as long as your target market is rich people with time on their hands and money in the bank, you can get away with doing very little actual selling. The features alone are usually enough.

People come to you.

And, on the flimsiest of precepts – which may, or may not, include stories about mountain-top silk farms – they’ll give you $1000s to buy a product not that different in a functional sense from something you can buy for $10 at a mass-market outlet.

When you go mass market, however, features alone aren’t enough to sell.

You need to major on the benefits. Both benefits compared to other people who sell similar products or services to the ones you sell. And, ideally, benefits compared to doing nothing at all.

Nowadays, this is not usually done too overtly, although back in the early days of mail order retail, for example, the benefits were usually rammed down your throat whether you wanted them or not.

Let’s take toothpaste as our example.

In my local supermarket there is regular toothpaste. Extra-whitening toothpaste. Gum health toothpaste. Fresh breath toothpaste. Stain removal toothpaste. Toothpaste where the effects last more than 24 hours. Toothpaste 8 out of 10 dentists recommend. And dozens of other claims of one sort or another.

Now, I suspect 95% or more of any toothpaste is much the same as any other toothpaste, but there might be small percentages of active ingredients which are different between, for example, tooth-whitening brands and gum health brands.

My point here is that, for a mass market product, you have to sell the benefits, because finding toothpaste on a supermarket shelf is not difficult. My local supermarket has probably 20 brands or more vying for each shopper’s attention.

But depending on which solution I wanted – better gum health or whiter teeth, say – I now have only two or three possible options and I’ll pick one based on whether I want the cheapest, the most long-lasting effects, the freshest breath or whatever other criteria I might consider important.

Where it goes wrong

Where things often go wrong is when a mass market product (or one with aspirations to become a mass market product) is sold on features, not benefits.

Not many people would wander down the aisle of my local Sainsbury’s and respond positively to a big sign saying “Look! A tube of toothpaste!”

We all know what a toothpaste is and what it does, at least conceptually. What we need is information about why we should by Brand A or Brand B based on what criteria we have for selecting a toothpaste, be it gum health or whiter teeth.

Trying to sell a mass-market product, which requires strong benefits, as if it was a luxury good, when you can just witter on for a while about features like mountain-top silkworm farms for a while, is generally a mistake.

A great place to see that at the moment is the pitches for AI products.

I mean, it’s great that you can use AI turn an old photo of the cat you had as a teenager into an animated video of Snuggles walking around your back garden today. I’m sure that’s an impressive piece of technical work, much like inventing the internal combustion engine or pneumatic tyres was for the motor industry.

And if you’re selling to exclusively wealthy people with time on their hands and plenty of cash, knock yourself out. That’s probably enough. All they want is the exclusivity of being able to do something the hoi-polloi can’t do.

When you’re trying to sell a product to the entire planet, though, that doesn’t work any more.

Firstly, not that many people would want to create a video of a dead cat anyway. And very few of those who do are going to spend $1000s to see Snuggles walk around their garden again.

So, if you want your mass-market product to be commercially successful, you need some benefits.

And frankly, however interesting and clever the underlying technology is, there are virtually no benefits to reanimating Snuggles to the vast majority of the population.

All things being equal, therefore, they won’t.

Sure, people might mess around with it for a while, for fun, if it’s free or very cheap.

But the modern consumer has more than enough things to waste their time on already. Beyond the initial endorphin boost of seeing Snuggles again for the first time, how many videos of their dead cat does any sane person want to make?

Not many.

So, without a meaningful benefit, people stop using your AI video service and move on to a different, more beneficial (to their way of thinking – not necessarily yours) way of wasting their time.

I saw a pitch for an “AI-powered” accounting system recently, which very excitedly told me all of its features. The thing the tech folk who developed it didn’t seem to have understood was that their whizzy new technology didn’t give me anything I couldn’t already get from my £20 per month Xero subscription.

There was no discernible benefit to me at all, with the exception of being able to boast that I was one of the cool kids using AI.

You could sell horseless carriages in the late 1800s like that, when your audience was a very niche market comprising exclusively extremely wealthy people, on novelty value alone.

You can’t sell an aspiring mass-market accounting product in 2025 with a feature (“Look! AI!”) which, in the use-case of every accountant in the land, delivers no significant tangible benefits beyond what we can already get from Xero for £20pm.

It got worse

When I gently pushed back against this less-than-compelling pitch, it got worse.

I was told that it would allow me to run 100s of reports which would help me “stay on top of my business”. Doh! What do they think Xero does now?

And also that it would allow me to manage risk better, even though none of the features they claimed to deliver would allow anyone to manage risk better, AI-powered or not.

This exchange just demonstrated how little the people selling this product knew about accounting software. Which, if I’m honest, did not increase the chances of me wanting to buy what they had to sell – if a software company doesn’t understand their own target market, and hadn’t done even a rudimentary competitor analysis, they don’t deserve to make any sales.

Outside accounting, this is also a characteristic many of the “It’s X…but with added AI!” pitches I see at the moment.

AI is generally delivering little or no tangible benefit over and above what existing software products are doing perfectly well already.

So AI companies are piling in more and more features in the hope that enough of the planet will go “hey, that’s cool” and shell out $000s a year to use that product or service.

But ultimately the AI companies are wasting their time. Particularly in business, where nobody is going to invest in anything without a cast-iron business case. (The B2C market is slightly different, albeit significantly less lucrative.)

And for a cast-iron business case you need benefits, not features. Yet, there are remarkably few of those around.

Which, I suspect, is why AI companies are struggling to generate a viable revenue stream.

The truth is that, beyond an initial “hey, that’s cool” reaction, like the cat videos, nobody cares.

And certainly nobody cares enough to shell out the enterprise-level $millions a year those businesses need to stay afloat when their seed capital runs out.

Frankly, I’m suspicious

This is all so obvious that I can only form one view of the AI industry.

And it’s this – even they are acknowledging, internally if not externally, that their products offer little or no real-world benefits.

And I can be pretty sure of that because if there were any benefits, they’d be shouting them from the rooftops by now and we’d all be handing over $000s a year for the privilege of using their services.

It’s such an obvious route to monetisation at a level beyond every venture capitalist’s dreams that the fact they’re not doing it can only mean there aren’t any real-world benefits to the technology at all. Which means, when the investor cash runs out, they don’t have a viable business.

Why would any sane person take the much harder route of finding hundreds of new and novel features in the hope that at least one of them captures the public’s imagination, if a really simple route akin to “buy this toothpaste for whiter teeth” was available without breaching all the “truth in advertising” legislation.

Actually, I’m being slightly unfair there. The only benefit AI companies talk about regularly is “we can do what you do now, just a bit cheaper”.

Frankly, that’s just about the laziest benefit there is.

And it’s a particularly stupid “benefit” because, however cheap Company A does something, I can guarantee there’s a Company B out there somewhere who can do it a little bit cheaper than Company A does.

Before you know where you are, everyone is playing off all those companies off against one another and they’re all supplying incremental customers at a price below their marginal cost of production.

I say that with confidence because that’s what happens in every sector which can only deliver a price-based benefit. The industry competes itself down to margins of pretty much zero…which isn’t the world’s most long-lasting strategy for running a successful business. Especially if you want a business worth $billions.

And that’s even assuming AI companies deliver tangible cost savings at all, which I haven’t seen yet.

There’s been a recent MIT report saying that 95% of AI implementations haven’t delivered the business benefits originally claimed, for example.

And IBM have reportedly had to hire back most of the people they let go on the basis that AI had made their jobs redundant, because AI could not, in fact, replace those human beings on a like-for-like basis. If a massive tech company could be hoodwinked like that by the false promises of AI technology, there’s probably no hope for the rest of us.

So, my working assumption – which I’m happy to revise in the face of evidence to the contrary – is that AI delivers no significant business benefits. If it did, we’d be hearing about them every day of the week and we’d all be happy to pay AI companies $000s a year just for the privilege of using their products.

Sell the benefits, not the features

If, fundamentally, your product doesn’t work, and you’re about as ethically-challenged as the average AI company, by all means sell the features of your product, because that’s probably all you’ve got.

It’s like structuring your entire sales process around a “Look! A Squirrel!” strategy.

But if you deliver a real-world benefit you’ll scale faster and become more profitable if you sell those benefits.

(Unless the “benefit” is “we do X, but cheaper” in which case you and your competition will drive your margins down to almost nothing because that’s what happens with commodity products.)

Selling the benefits is much easier because who wouldn’t want a tangible bottom-line boost to their business, provided they believe that the product you’re selling will do exactly that on a sustainable basis.

As you deliver more and more products, and more and more people appreciate the tangible benefits your business brings, word gets round and it becomes easier and easier to make the next sale…and the next…and the next…

If AI companies actually delivered real bottom line benefits, we’d be hearing all about it.

And we’re not.

Which is why I don’t think they deliver any real-world benefit at all.

But, when thinking about real-world benefits, you have to do something that tech folk are very poor at (in fairness, so are many engineers and accountants, in my experience) which is to look at what you think a benefit is through your customer’s eyes to make sure it isn’t just a feature really.

This requires you to realise that you don’t know everything about the world…and to accept that there is a real world outside your lab/workshop/finance department which might not conform to the neat and tidy rules you can implement inside the closed system that is your lab/workshop/finance department.

That’s where people – especially in tech and those other professions – go wrong.

They think just because they’ve come up with some cool idea that it’s automatically a benefit. But usually it isn’t. At best it’s a feature. Often, it’s an irrelevance.

For a business, being able to animate videos of dead cats, for example, is a feature, not a benefit. Or more likely an irrelevance, in a business setting.

So let’s look at a few real world examples to make a point – mostly non-tech based, just for balance, because other industries get this wrong too.

Some benefit examples (or not)

When you look at a problem through your customers’ eyes, you get a very different perspective from “look, I developed this cool thing in a lab”.

Here are three quick examples of something that might sound like a benefit to the people trying to shift products, but which, in fact, aren’t really benefits at all. If I was being kind, I’d say they were features. Some aren’t even that.

1-Cartonboard

Many years ago I worked in the printing industry. We made printed folding cartons (the brightly coloured boxes you see in the supermarket with breakfast cereal inside them, or tea bags, or a frozen dinner).

One of our suppliers wanted us to buy their super-duper carton board to make those products, which was well-engineered and super-smooth. It would, they claimed, allow us to run our presses faster and give us a better finished product with less wastage.

We tried it on a job alongside our standard carton board and the claims the manufacturer made were true. We could run our presses faster and we did have a better finished product with less wastage.

The problem was the price they wanted for their carton board meant that the best economic decision for our business was to run our presses a little slower and tolerate a slightly higher wastage in production.

The well-engineered, super-smooth product was a feature. It delivered no bottom line benefit to the business, so we never used it again.

2-Cars

The motor trade generates lots of examples of not taking a customer view of your selling messages.

For example, for some time I’ve been targeted by online ads telling me I should buy a new hybrid car because it delivers just over 50mpg.

That sounds like a benefit from the manufacturer’s perspective, I’m sure.

Except the car I have now does 41/42mpg. So they want me to spend £40-50k buying a new car “because it’s more fuel efficient” for about another 8 or 9pmg.

Factor in that, at the moment, I’m driving less than 3,000 miles a year and there is no economic benefit whatsoever to me spending £40-50k to get another 8 or 9mpg.

Part of me would like a swanky new car. But whatever else is going on here, that 50mpg car delivers no bottom line benefits to me.

Whereas the same pitch targeted at a high-mileage driver might well be seen as a good reason to spend upwards of £40k on a new car.

What might be a benefit to them isn’t a benefit to me. Not every customer is the same. Not every “benefit” is seen as a benefit to every customer.

The art of sales is knowing which is which.

3-Customer service

Many organisations parrot a line you’ll have heard before.

You find it on packaging sometimes, and also in the recorded message you get when you call a call centre, shortly after the recorded message saying, for the 8,000th day in a row, that the company is experiencing “unusually high call volumes” and “you might need to wait longer than usual to be served”.

Once they’ve softened you up with that, the next recorded message says something like: “for a faster service, please go to our website where we can serve you 24/7”.

While I would concede that my experience of most organisations’ call centres is variable (some are excellent, some are terrible), my experience of customer service delivered via a website is uniformly terrible.

First, we have the hunt for our customer number on the invoice that we don’t have any more, or we need to remember the email and password we used to log into the darned website originally, which can take some time.

Next, we get the dreaded automated chatbot which spends 10 minutes not understanding our problem, before directing us to the same FAQ pages we’d already checked out before calling the call centre.

Then we might get a human chat agent.

Finally, we might get to speak with a human.

And finally, finally, finally…if we’re really, really lucky…we might get our problem solved.

24/7 customer service sounds like a benefit, but it isn’t.

For two reasons.

Firstly, it’s invariably terrible.

Secondly this “benefit” has turned a 3 minute phone call into an hour-long extravaganza (a particular thanks to Norton anti-virus products here, who kindly gave me the inspiration for this article a couple of weeks ago).

From a customer point of view, making me spend 57 minutes more than I have to for the privilege of doing something simple is not…by even the most fevered stretch of imagination…a benefit to me.

It’s the exact opposite. It’s a perfect reason to shop somewhere else, quite frankly.

While 24/7 customer service sounds like a benefit, if it doesn’t actually solve your customer’s problem in a timely manner, it probably isn’t even a feature. It’s more like an irritation or a turn-off, if anything.

The moral of our tale

So, there are three morals to today’s tale.

Firstly, it is so much quicker and easier to sell when you focus on the benefits you offer, rather than the features of your product.

If you only sell the features, your market is limited to people who think the same as you on a topic – that videos of your dead cat brought back to life are cool, for example.

That’s likely to be 5% or less of the total potential market for your products, so this is a very short-sighted approach.

Secondly, don’t get confused between what a feature is and what a benefit is. Not everything you put into your product is a benefit to anyone, however cool you think it is.

Finally, a benefit is only a benefit if customers see it as such, otherwise it’s just a feature and isn’t going to help you make a sale – that £40k-plus car that was going to get me another 8mpg for example. Or 24/7 customer service that doesn’t actually serve any of your customers in a timely manner.

Fundamentally, if you’re not leading with a strong list of benefits, my working assumption is that there aren’t any benefits in buying from your business compared to buying from one of your competitors, so I’m likely to shop around and just buy the cheapest because you haven’t given me any good reasons to prefer your business over your competitors.

While there will always be some people why buy the most expensive product (the Hermes scarf) or buy something because it’s cool (animated videos of dead cats brought back to life) or buy on the back of some grifter’s sales spiel about changing the world (most AI products), if you have to depend on those techniques to make a sale, your business is not built on solid foundations.

So you might want to think again.

And if you can’t think of any genuine benefits from your customers’ perspective, you might want to think about doing something else before the cash runs out, because you don’t have a viable business.

Imagine you want to understand a subject you know currently nothing about.

You start off in complete ignorance. Knowing nothing whatsoever.

Then you pick up a book, and read a chapter or two. Or do a search for the topic online and read a blog post or two.

Or, heaven forbid, read one of those tiresome AI summaries which are popping up all over the place at the moment.

Congratulations. You have just had the highest RoI learning experience on that topic you’ll ever have.

You’ve gone from knowing zero, to knowing a tiny amount – a fraction of 1% of all the available knowledge in the world on that subject.

Starting from a base of zero, that’s an exponential return. So enjoy it – you’ll never get as high a percentage return on anything more you learn about that subject.

That’s because of the Law of Diminishing Returns.

To get your head around that concept, imagine someone else who has read 100 published books on a topic.

How much more are they going to learn about that topic from reading their 101st book?

Well, possibly not absolutely zero, although the chances are high that the 101st book is just a rehash, to a greater or lesser extent, of things already said in the other 100.

A little bit of maths

Let’s take this example a little further and imagine that the first person went from knowing zero to knowing 0.1% of the available information on that topic.

And the second person went from knowing 99% of the available information on that same topic and they now know 99.1% of the available information on it.

So they’ve both increased their knowledge base by 0.1%, haven’t they?

In absolute terms, that’s true.

But in relative terms, the second person has increased their knowledge by slightly over 0.1%, whereas the first person increased their knowledge by infinity % (don’t come at me maths geeks), which is a lot more than 0.1%.

Let’s make this a little clearer. And the maths a little easier. And get the maths geeks off my back.

Imagine they both increase their knowledge by a further 0.1% by reading another book.

Person A has gone from 0.1% to 0.2% – that’s a 100% improvement on their base of 0.1%. A doubling of their knowledge on a topic.

Person B has gone from 99.1% to 99.2% – that’s an improvement of a tiny fraction over 0.1%.

So there’s a curve somewhere between knowing 0.1% about a topic and knowing 99.1% about it that diminishes steadily the more you know – each incremental bit of effort you put into learning more pays back less and less the more you learn. In relative growth terms, at least.

What do you need to know?

Why is this important in business?

Well, unless you want to waste endless amounts of time and resources, you need to work out what you need to know to make a decision. And then make a decision.

In other words (well, actually in Michael Jackson’s words) you’ve got to stop when you get enough.

Any other outcome is seriously detrimental to your bottom line.

Yet, the mindset in most businesses is that you can never have too much information.

That’s dangerous. Really dangerous. To your bottom line.

I’ve seen organisations invest in six figure software products because it “gave them more information” and “allowed them to produce more detailed reports”.

I’ve seen other organisations invest in data analysts, at huge expense, to analyse all manner of data.

And nowadays, organisations often implement some sort of AI solution to “manage all their data and reporting needs”.

With rare exceptions, all of those are terrible decisions. That sort of thinking will destroy your bottom line faster than almost anything else you might be tempted to do – and that includes telling your biggest customer to get lost.

So what’s my point?

Here’s my point.

Up to a certain point, more data and more analytics produces more bottom-line enhancing insight than it costs. A bit like Person A above – they rapidly get better because they start out knowing so little.

Beyond that point, every further investment in people and/or technology brings more cost to the organisation than the thin layer of extra benefits would justify.

That’s like the second person above. It’s almost certainly worth precisely zero to your business to go from having 99.1% of the available information to knowing 99.2% of it.

Except in the unlikely event that extra 0.1% unlocks some transformational knowledge that you were previously completely unaware of – and the odds of that are worse than the odds of you winning a million on the lottery – any spend which gets you from the 99.1% level to the 99.2% level is almost certainly stealing money away from your bottom line, not adding to it.

Of course, no software salesperson is ever going to admit that. If they even recognise the phenomenon.

In the world of data and analytics sales, any outcome that gives you less than 100% of all the available information means there’s some potentially untapped opportunity for tech folk to sell their software.

I don’t particularly blame tech folk for that. It’s the way their brains are wired. And they’ve got software to sell. So good luck to them.

That doesn’t mean you have to swallow their sales pitch, though.

But it starts in your head

You see, it’s never been easier for data and analytics software companies to sell their software.

And that’s because most people have bought into the mindset that “you can never have enough data”. This is the insidious message that all sorts of people drip feed to you every day – the giant consulting firms, the tech folk with software to sell, the people convincing you to hire another data analyst “to understand your metrics better”…and thousands of other people.

Believing the message that the combined PR efforts of thousands of interested parties with a product to sell has tricked vast swathes of people into believing that anything less than 100% knowledge of all the data in the world affecting a particular topic is a dereliction of your duty as a business owner.

However, that’s completely untrue.

Rather, as a business owner, your mindset should be that every time you spend a pound, you should expect to get back more than a pound.

If you do that consistently, you’ll build a great business.

Now, sometimes you’ll invest a pound today and not get more than that pound back for a year or more – for example, if you buy a new machine for your factory. But the principle remains that you’re looking to spend money where you are most likely to get a multiple of your original investment back over a sensible period of time.

And also that, all things being equal, you should rank the RoI on everything you might potentially do so that you implement the highest RoI activities first and only get to the lower RoI activities after all the better uses of your money have been taken care of.

To give a tangible example, there is almost certainly a higher RoI on investing in a couple of days of induction training with a new member of staff so they can learn all about your business than it is to invest in a new data tracking system to take your understanding of the data from your company’s petty cash system from 99.1% of all the available data to 99.2%.

Taking your new member of staff from knowing zero about your business to knowing even just 10% or 20% of it is a phenomenal RoI for the cost of only a couple of days’ salary, in terms of how they can get up to speed faster, do their job more efficiently, and serve your customers better.

If you learn anything useful at all after getting that extra 0.1% of data out of your petty cash system, I’d be astonished.

Think incrementally

To counteract the mindset that tech companies’ PR systems have been borrowing deep inside your head for years, you have to think incrementally.

What I mean by that is thinking about what extra return, over and above what you have now, you get from making a new investment, and comparing that to the additional cost (both up-front and ongoing).

The people selling tech solutions will try very hard to convince you to buy their products, so it’s in their interest to muddy the waters around all this, in order to make it seem like a no-brainer to buy their product.

In particular, there are three aspects to look out for which often creep into tech pitches…often subtly, so they can be hard to spot:

1-Project RoI

A common tech pitch asks you to give them £X and get £Y back again.

Even if that’s true (and it usually isn’t, by the way), that’s not really the question you should ask yourself.

The question you need to ask is whether, out of the dozens or hundreds of potential things you could invest in for your business, that’s the number one candidate for investment in terms of incremental bottom line performance.

If it’s going to make more of a difference than anything else (which is unlikely), of course you should invest in that product.

More likely it’ll make a marginal-at-best impact, in which case you should invest your time and money on whatever your number one RoI project is instead of messing around the margins with comparative irrelevancies.

Saying “no” to pointless or marginal-at-best projects is one of the most powerful things you can do for your bottom line. Instead, focus your energy on projects that will make a transformational difference to your business – unless you work in tech, it’s extremely unlikely that’s going to be a tech product.

2-You’ll find something

I’ve been on the receiving end of a version of this pitch twice recently.

It goes something like this: “Look, currently you only track 40 datapoints in your business and we’ve identified another 1200 potential data points you currently know nothing about. You’re bound to find something in there that will make a huge difference to your business – you’re only scratching the surface at the moment.”

This is a slightly more disingenuous pitch than the one above, where there was at least a theoretical project RoI, even if it wasn’t the biggest potential RoI in your business. But it preys on the several decades of “you can never have enough data” messaging that’s lodged deep inside your brain.

I can save you a lot of time here.

If you’re already tracking 40 datapoints and managing them well, you’re probably at 99.1% of all the information you need to know about your business. And the extra 1200 datapoints will get that up to 99.2%.

Armed with that knowledge, how much time, money, and effort are you willing to spend on this proposed tech project?

Think very carefully if your answer is anything other than “zero”.

3-What’ll you do different?

Information alone is pretty much worthless.

The worth comes from what you would do differently, if you had the information, that you’re unable to do now.

And if you wouldn’t do anything different to what you’re already doing, more information is worthless. So spending even a penny on it is seriously detrimental to your bottom line. There is no upside to getting this information because it won’t make any difference to what you’re already doing.

A couple of years back, I had a client who wanted to invest in a tech solution to track all sorts of KPIs for the entire sales team where there were a couple of underperformers who, they weren’t generating enough leads to hit their sales targets.

So I asked, given that we already know what the problem is (lack of leads), what would we do differently on their lead generation if we knew that, for example, we could organise their in-person client visits more efficiently by better grouping together of in-person sales calls?

Of course, the answer was “nothing”. However interesting the software might have been if we had a “you don’t use your time efficiently when you’re out on sales calls” problem, knowing that information would not help us to increase their lead flow in the slightest.

Thankfully, that idea was shelved and we spent the time helping the salespeople generate more leads instead, which was the actual problem we were grappling with.

So be clear in your own mind that any proposed solution isn’t just a nice idea in and of itself. For it to have a bottom-line benefit to your business, you have to do something different to whatever you’re doing now to solve a current problem in your business.

If the proposed investment either isn’t going to do that (ie it solves an entirely different problem), or it might do something to help with your current problem, but there are other, better, higher RoI ways of solving that problem, then don’t invest.

You SHOULD stop when you get enough

Your ambition shouldn’t be a dispassionate “nothing less than 100% of all the data is acceptable” approach, no matter how much time and effort tech folk put into convincing you how important that is.

Rather, set out to find the difference between having enough data to set a course of action – up to and including the point at which the information benefits of more data or analysis are outweighed by the costs of producing them.

And when you find that point, stop spending money. Anything you spend from that point onwards has a negative impact on your bottom line.

Michael Jackson almost got it right.

He sang “don’t stop ’til you get enough”.

The bit he left out is that “but when you’ve got enough, for the sake of all that’s holy, stop!”