AlastairThomson.com

I know Behavioural Science is a bit of a buzzword at the moment (can you have a buzzword made out of two words…? I’m not clear on that, but that isn’t my point, so we’ll press on…).

The concept behind Behavioural Science, to horribly over-simplify a complex topic, is that understanding how humans behave in the real world can help businesses understand their needs better and thereby get them to do something the business would like them to do, whether as customers or employees.

In marketing and advertising, people like Rory Sutherland and Richard Shotton showcase how the way we talk about products and services can make it more or less likely that someone is going to buy the products a business is selling. The differences are often subtle, but with out-sized returns if you make the right choice.

My pal Christian Hunt has done a tremendous job implementing the concepts behind behavioural science into the seemingly unrelated field of compliance – but it turns out that skills of a marketer or advertiser are very similar to the skills an organisation needs to get people to follow the rules, especially in areas with significant regulatory oversight, like in financial services, for example.

Yet behavioural science is not a concept I hear people talking about in accounting all that often.

To be fair, that’s partly because accountants have a (generally well-deserved) reputation of not engaging with any airy-fairy hippy nonsense that you can’t capture on a spreadsheet.

But I think that’s a mistake because understanding human behaviour is a shortcut to much better, bottom-line-boosting business decisions.

And if your CFO isn’t wanting to make a lot more of those, frankly I don’t know what else they think they ought to be concerned about.

Traditional financial analysis

Somehow, there’s an expectation – both of accountants by non-accountants, and of accountants themselves – that to make any important decision you need a 90 day-long project to extract mountains of data which someone is going to produce enough charts and tables from to explain every possible outcome and variation.

Let’s be up-front about this.

That’s always an expensive process. However well-intentioned it might be, tying up senior people in weeks or months of meetings, analysing spreadsheets, charts and graphs until the numbers give up their secrets is horrifyingly expensive.

Sometimes people tell me that doesn’t matter because these people are paid a salary “so it doesn’t cost any more to get this work done”.

When people tell me that, I know they’re either not an accountant or, if they are an accountant, they’re a particularly third-rate one.

Internal projects are a huge time suck…and therefore cost fortunes, although generally nobody joins the dots to work that out.

And they often lead to very equivocal conclusions – “well maybe this might work, but equally it might not”.

Frankly, you don’t need half a dozen very well-paid people spending 90 days together to work that out. I could probably have come to that conclusion myself with 10 minutes thinking time and a calculator.

If you didn’t do the analysis project at all, and fired half your finance department because they were no longer needed, you’d make a guaranteed bottom line return on Day 1. And your business would probably be no worse off because the project’s conclusion was only ever going to be “meh” anyway.

So what do you do?

Now, at some point in the process, you do need to do some thorough analysis. I’m not suggesting for a moment that you commit $ millions to an idea someone had in a fever dream without checking it out thoroughly first.

However, my suggestion is that you don’t knee-jerk your way into an analytics project at the start.

I kick things off with two other tools long before any serious analysis takes place.

The first of these, I call “a rough cut”.

That really is me spending 10 minutes with a calculator and a bit of paper (I wasn’t joking about that).

Doing rough cuts is not the point of this article – we might do that another time. But, briefly, I’m trying to work out “what’s the prize?” in this stage. Put another way, is this project likely to be worth the time and effort required to explore it further?

You’d be amazed at the number of projects which have floated across my desk over the years which can’t even meet that hurdle rate.

In my home life, I once had a salesperson try to convince me to switch my utility provider because they could save me something like £2.73 a year compared to what I was paying now. There is almost no amount of work I’m prepared to put into something that costs less than a cup of coffee in Costa, and spending an hour on a phone with the utility company’s salesperson didn’t sound like the best use of time to me.

At work, I’ve often had people try to flog some system or other which requires me to spend £1million up-front in return for a £50k-£100k annual cost saving, after factoring in the cost of the system.

But pause for a moment and cost in the disruption, training, and general inefficiencies in implementing any new system…together with the fact that you can be pretty sure that anyone flogging a system to do anything will, at most, deliver half the benefits the salesperson claims…and that project is a breakeven project at best. Certainly not one I’m going to commit hundreds of hours of highly-paid people’s time to analyse in excruciating detail.

There just isn’t enough upside in it to make it viable.

Behavioural accounting

To be fair, this is a term I’ve probably made up. (If it is, I hereby claim the copyright. If it isn’t, the expression is the property of its current owners.)

But what this is about is looking at what people actually do, and extrapolating a decision from there.

Now, it has to be what people actually do. That’s really important.

It’s not what they say – whether that’s people answering a survey or a business claiming to have thousands of happy customers.

It has to be what they do.

And you’ve got to be especially careful when the proposal in front of you suggests a course of action which is not consistent with what people actually do. The cost of behavioural change on an organisation-wide level is prohibitively expensive and while I wouldn’t say there is never a business case for it, the number of times you are likely to get a positive RoI on an investment of that kind is no more than once or twice in the course of your entire career.

Looking at what people actually do is a great guide to the decisions you should take – and, often more importantly, the decisions you should avoid.

It’s been said the eyes are the windows to the soul.

Well, the decisions people make are the windows to their soul too.

Perhaps a couple of examples would help…

1-Taxis

If I was in the market for a relatively inexpensive, good value, reliable, easy and cheap to repair sort of car, I could conduct an extensive piece of research into every model offered by every manufacturer on the market, compile huge cross-model comparison spreadsheets, and go on dozens of test drives.

Or…I could just look at what taxi drivers choose.

If there is one group of people who are optimising for exactly the attributes I’m looking for in a car, it’s taxi drivers. (Not London-type black cabs, but the normal cars which regional taxi operators tend to use.)

Everywhere I go, taxi drivers overwhelmingly choose diesel Skoda Octavias for the job. And if they don’t drive one of those, they almost certainly drive a Toyota Corolla Estate Hybrid. While there is the occasional other model, 80% or 90% of the taxis I see are one or other of those models.

So I don’t need to conduct extensive research over the course of several months to choose a good value, reliable car. I just buy one of the two models that the overwhelming majority of taxi drivers actually drive on a day-to-day basis.

Equally, if I ran a taxi fleet and someone pitched me on buying 100 of some other brand for my fleet, I’d be very suspicious because if it was such a good idea, I’d see a vastly higher number of Brand X on the roads working as taxis than I do.

Decision made. I’m buying 100 Skoda Octavias, probably.

2-Inexplicable inconsistencies

The time I’m most sceptical of a proposed course of action is where the people who are proposing it are not acting consistently with the opportunity they are pitching.

A great example of that at the moment is people selling AI solutions.

People claim that AI will save businesses $ billions. But if that’s true why are all the AI companies so focused on helping you make animated videos of your dead cat?

Frankly, the real world business case for videos of people’s dead cats is pretty much zero. Sure, people might enjoy playing around with that, and posting their videos on social media. But there is not a billion-dollar market in consumers ponying up thousands of dollars a year to make videos of Muffin, their much-beloved, sadly-departed cat from when they were a teenager, brought back to life.

Pretty much no-one is going to be handing over more than pennies a month for that.

On the other hand, AI companies claim to have technology solutions which will drastically reduce companies’ operating costs through automating business processes.

That market genuinely is worth billions of dollars a year.

Yet, in recent weeks, MIT have published a study suggesting that businesses see no real world benefits from AI in 95% of the projects they analysed. And there are also some stats suggesting that most organisations have not reduced headcount even when they have implemented AI solution. That’s because a large number of humans are required to check that AI is doing the job properly…because, by and large, it doesn’t.

Now, I am quite convinced that there are some excellent real world applications for AI – running datacentres, perhaps, or automating low-end computer programming. But those are vanishingly small areas of operation for a typical business.

So, my question is, if huge tech companies who claim to have a magic solution to problems worth $ billions to businesses around the world, actually spend most of their own time and money perfecting videos of dead cats, why would any rational seller of tech solutions do that?

And the answer is that AI doesn’t work all that well outside a computer lab. Maybe it will one day, but right now tech companies are turning their back on a business market worth billions of dollars to service a consumer market worth pennies on the dollar.

That’s only a rational decision for tech companies if AI solutions don’t actually work as well for businesses as the people pushing AI solutions claim.

Otherwise I have an inexplicable inconsistency between what tech companies say and what they do.

Faced with an inconsistency like that (in my new, made-up discipline of behavioural accounting) I look at what tech companies actually do, and pay very little attention to what they say.

At least for the moment, AI is an easy “no”. If they people pushing the solutions demonstrate by their behaviour that they are more concerned about perfecting cat videos than automating credit control processes (or whatever other business activity), you can be pretty sure that their business solutions don’t work, or they’d be pursuing a market worth $ billions over a market worth pennies.

3-“Playing what’s not there”

Celebrated jazz pioneer Miles Davis once said the secret to a great performance was not in playing “what’s there” (ie the notes on a page) but playing “what’s not there” (ie how you play the notes).

Another of my behavioural accounting techniques is to look for what’s not there…but should be.

This draws a little from my early career as an auditor – if a company claims to have banked £10 million in sales this year we didn’t see £10 million or so being deposited in the company bank account, we were taught to immediately become highly suspicious of everything that organisation told us, and to make sure we confirmed every piece of company data with independent sources in case the organisation, or its officers, were lying to us.

To apply this in practice, what you do is ask yourself “what would need to be true for X to be true?”

In auditing, you quickly discovered that people tend not to pay you until you submit an invoice, for example. And also, they don’t pay you if they don’t think you’ve done any work for them requiring payment.

So if one company make a payment to another company – evidenced by a payment flowing into your client’s bank account – you can be pretty sure a genuine piece of work was carried out, and duly invoiced. (You do need to check that the second company doesn’t somehow funnel the cash back to the first company again, directly or indirectly, but I’m trying to keep this example as simple as possible.)

When I worked in the printing industry, we didn’t have a product without buying paper or carton board to print on. So if sales were high, but purchases or paper were low, on the face of it (adjusting for any stockholding) either the sales number is wrong, or the company hasn’t recognised enough cost for the paper it must have bought to make the products it sells.

Because buying paper was a necessary precursor for making a sale, so you would expect those two numbers to move more or less in lock-step.

In the part of the printing industry I worked in, you could even prove the sales made to each client if you wanted because we used a lot of special colours of ink (think M&S green, Sainsbury’s orange, or Cadbury’s purple).

If we claimed to be making lots of sales to M&S but were not buying much M&S green, on the face of it, our claimed sales to M&S are unlikely to be accurate. Buying the right shade of ink was a necessary precursor to making products that M&S were going to buy, because they were printed in M&S’s house colours.

Behaviour first, numbers second

Where a lot of decisions go wrong is that there’s a (generally well-intentioned) drive inside organisations to launch a huge analysis project of some sort when faced with a big decision.

Let me be clear, there will times you do need to do a full analysis. But that’s going to happen less than 100% of the time you’re faced with a decision. Significantly less.

What you should do first is triage the problem or opportunity.

First, a quick rough cut to make sure the project is worth doing at all – that there’s enough of a potential upside to make all the analysis, delivery, and ongoing operation worthwhile. If it’s not looking attractive at that level, no amount of data analysis is going to make that into a good project – generally things only look worse when you dig into the detail, so if it doesn’t look good at a “headline level” it’s never going to look good “down in the weeds”.

Then check the behavioural accounting. Look at what people do and ask yourself whether the decision you’re being asked to make is consistent with what you see people doing.

If the answer is “no”, then the quicker you shelve that idea, the faster you’ll stop wasting money on it. Your chances of making that work at all are 100-1 against – and likely to take vastly longer than you think and cost a lot more in the process.

The best decision you can make for your bottom line is to bail out early and do something more productive with your time.

As Charlie Munger (Warren Buffett’s long-term business partner) said: “It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.”

Make sure you’re the right side of the line on that, and your business bottom line will thank you.

There is a big problem with being efficient.

It’s a particularly big problem amongst tech folk, accountants, and engineers (but there are plenty of people in other professions who fall for this too). And it comes about from not thinking deeply enough about issues.

Often “being efficient” just means skating over the surface of an issue, doing the very minimum required to chalk up a “victory”, and moving on to the next thing selected for a completely superficial make-over.

That’s why people who try to “maximise efficiencies” often end up delivering outcomes which cost the company more than it needed to…and often more than the option of doing nothing at all.

Ultimately, this all becomes completely pointless.

The notes

A great example of this phenomenon at the moment are those AI note-takers people take to meetings with them.

Recently, I was in a meeting with three other people, each of whom had a notetaking app “joining in the meeting”. I had a notebook and a pen.

While we were talking, those apps were busying away, transcribing every word everyone said (sometimes hilariously, but that’s not my point here). And at the end of the meeting I received 3 sets of AI note-taker transcriptions together with a list of action points from each.

Now, first of all, those action points were all slightly different. This is a problem in itself, potentially, but also not my main issue with this process. (Although I should point out that having 4 people all running off to do slightly different versions of the same action point is a recipe for chaos, not a recipe for efficiency.)

But my main issue here is with the process.

In the name of efficiency, we all now had 3 sets of verbatim notes to read if we wanted to make sure we had been correctly quoted – these notes ran to about 10 pages of A4 from each app.

So that’s 30 pages of A4.

Google tells me that would take about half an hour to read. Times 4 people, in this instance. So 2 hours overall.

The people in that meeting were all well-paid professionals, and while I don’t know the day-rate of everyone involved, let’s imagine that’s several hundred pounds-worth of time, collectively.

That’s the cost of auditing the output of a notetaking app for a single meeting to make sure it’s not taking you (or anyone else) out of context.

“Ah, but I only check my own!”

Sometimes people tell me that’s not true because they only check their own notes and don’t read everyone else’s.

Firstly, even if that’s true, it’s still costing you 10 minutes of your own time. If you have 8-10 meetings a day, that’s about an hour and a half of your time every day. On the assumption you’re being paid a salary, that time isn’t free. It has a cost.

Secondly, that’s unlikely to be of much help if it comes to a fight. If someone else’s notetaking app recorded that you said “X”, and you didn’t challenge it, the person you’re arguing with has a pretty decent argument that you did, in fact say “X”.

So if you read all the notes, it’s costing you and your company £100s.

And if you don’t read all the notes, at some point you’re going to get sued so hard you might end up wishing you had read all the notes.

Whether you acknowledge it or not, there’s cost and risk aplenty here.

However, from what I’ve seen in practice, a large number of people don’t read the notes at all. They just rely on the action points generated by the notetaking app.

That’s great for as long as you can get away with it, without being sued. But if anyone ever takes legal action against your business, you can be absolutely certain that the notes you never read will be used by the “other side” in evidence, and you might end up looking like a complete prat who didn’t do their homework properly when the original notes were circulated.

Win, lose, or draw the legal action, getting a reputation as someone who doesn’t do their homework diligently is not going to be good for your career prospects with your current employer.

But it’s worse than that

However, it’s even worse than that.

Let’s say you don’t read any of the notes, perhaps even including your own, because you are “being efficient”.

That means the whole industrial infrastructure of the AI note-taking ecosystem is a complete waste of everyone’s time and money. All those millions of gigawatts of electricity. All those super-powered microchips. All that time and effort in software development.

All to produce a product nobody even glances at.

Because people are too busy “being efficient”.

This is precisely the situation Peter Drucker had in mind when he said: “There is nothing so useless as doing efficiently that which should not be done at all.”

Nobody in their right mind would employ a staff member to take verbatim notes of every meeting you attend.

So using tech to “do it more efficiently” is not an advance for society. On average, it’s a net dis-benefit. It makes us all collectively poorer when we do things that take our time, energy, and money, but which don’t move us forward in the slightest.

“But the tech is really clever!”

I’m prepared to bet the tech in a notetaking app is very clever.

But a technology which is very clever, yet ultimately produces no discernible benefit for your business or society at large, is still worthless.

There are plenty of very clever people stuck in dead-end jobs because they were incapable of producing something of value.

Cleverness, of itself, is not something which has economic value – it’s the application of cleverness which, potentially, can create economic value.

There is, however, no economic value in producing sets of verbatim notes nobody reads.

That’s almost a textbook description of a completely pointless activity.

Yet it’s a completely pointless activity that a remarkable number of people are paying $200-500 a year to use.

Here, it’s very clear what’s happening.

The notetaking apps are skimming value out of your business to put into their own pockets. They are considerably richer. Every other business is considerably poorer, to an equal and opposite amount of money.

Across society as a whole, precisely zero value has been added. Money has just been moved out of your pocket into someone else’s.

Another way

I don’t use a notetaking app. I use a pen and notebook.

And I don’t take verbatim notes. I only record action points.

I tend to type them up (I’m a fast typist so it doesn’t take long). But I used to know a guy who would take a picture of his handwritten action points with his phone and send the photo to everyone who was at the meeting so they knew what they had to do before the next one.

I also do something else which AI doesn’t do, can’t do, and never will do.

As we go along, I make a habit of repeating the action point I’ve written down to get everyone’s agreement to that being an accurate reflection of our discussion.

So there are, hopefully, no post-meeting misunderstandings about what people agreed to do.

Now, you might say, that’s what the action point summary of the notetaking app records too.

But firstly, nobody has agreed those action points because nobody knew what they are until the action point summary was circulated. So there is some process, however, short of reading them, assessing them against your own memory of the conversation and/or your own verbatim notetaking app.

So that takes time. And that costs money.

My approach isn’t free. I get paid a salary and I try to always add value.

But 5 minutes to type up some action points I’ve agreed in advance with all the participants doesn’t cost much. And there’s no downstream “that’s not quite what I said” or “I thought you meant this” arguments – so there’s a saving there too.

Factor in the monthly costs of your notetaking app of choice over and above this and the economic return for your business from this whole process is, at best, flat and, more likely, negative.

All from doing something efficiently that needn’t be done at all.

Technology can be efficient – but isn’t necessarily efficient

Amongst the simpler folk in society – people like politicians and tech people – there’s a belief that technology necessarily makes everything more efficient.

I can’t say this strongly enough. People who think those two statements follow one another as unerringly as day follows night are idiots.

Tech can make things more efficient, but it can also make them a lot worse than they were before.

Tasks that used to be as simple, and low-cost, as picking up the phone and speaking for two minutes now take ten times as long by the time I’ve located my account number to log into something, verified my two factor authentication, given the 8th and 14th letter from my security phrase, and entered the PIN I last used six months ago and can’t remember off the top of my head (necessitating a password reset process that takes several minutes more).

That’s not efficient – that’s a process that converted a 2 minute phone call for 2 people (ie 4 minutes in total) into a 10-20 minute process that’s all on me as the customer because the business I’m trying to do something with has decided to “be efficient” and make everything worse for all their customers in the process.

The only thing that “more efficient” process has done is make me determined to find another business who sells what yours does and buy from them instead.

Set against that, tech can lead to improvements.

Moving to computerised bookkeeping was a definite tech improvement, as was the introduction of Word instead of typewriters.

In industry, CNC machines have been a boon, and when I worked in the manufacturing sector, we had equipment which performed activities using automated processes which had previously required human intervention. This saved us time and money, and made the process safer from a health and safety point of view too.

So technology can be transformative.

It just ain’t necessarily so.

It can also be a completely pointless, value-destroying activities like notetaking apps, which might make someone in Silicon Valley wealthy, but all they’ve done is take money out of your pocket and put it into theirs without delivering any benefit.

(Or at least without delivering any meaningful benefit – while it might be clever tech, producing verbatim notes that nobody reads has precisely zero economic benefit to anyone beyond the software developer.)

When we put an automated process into the factory, we had a business case that said “this activity costs us £X per unit now – installing this new machinery to automate part of the process will cost half of £X per unit instead”.

I’m prepared to bet that nobody using a notetaking app had someone sat next to them taking verbatim notes before. And people have got by pretty OK without that since the dawn of the industrial revolution, until now, seemingly.

If we could operate perfectly well before without notetaking apps, introducing a way to produce verbatim records of conversations (even assuming the apps record them correctly) is by definition not adding any value because there is no corresponding saving.

And if it’s taking people’s time to read verbatim notes they never had to read before because they didn’t exist, that’s a reduction in bottom line performance, not an increase due to efficiency.

Granted, it sounds more efficient if you don’t think about it for long – that’s the surface-level thinking tech people are good at.

But dig deeper to really understand what’s going on, and digital notetaking apps drain resources from a business. They don’t add value to the bottom line.

That’s true of most tech nowadays. The times it delivers true bottom line value are few and far between once you understand what’s really going on.

If you’re serious about your bottom line, ask yourself not just about the tech, but about the business processes you’ll need that go around whatever tech solution you choose.

Add up the cost of the subscriptions, the cost of operating all the new business processes, and the impact on your customer experience. Then decide if the new tech tool you want to introduce really adds value.

When you do that rigorously, you’ll find remarkably few tech “innovations” are worth investing in for your business.

They are, at best, often just a distraction from the job in hand. At worst, they destroy value.

Make sure you know which one it is before you sign the order form. After all, there’s nothing so useless as doing something efficiently which need not be done at all.

The other day, I briefly stopped by a raging debate on Twitter about the Laffer Curve and “trickle-down economics” and got frustrated for the umpteenth time about how readily people mix those concepts up.

So much so, that I suspect many of the people in the debate don’t get the point on purpose because it would put a hole below the waterline in some of their predetermined conclusions.

Before I get into the meat of this article, though, let me say two quick things.

Firstly, I’m an accountant, not an economist. Professional economists may feel I’ve over-simplified some of these issues, which I probably have – both in the interests of space and also in the interests of not having too many people fall asleep while they are reading this article.

Secondly, I’m not taking a political stance here, for or against any side in the debate. I’m just trying to explain the issues the best a humble accountant can. Admittedly, partly in the hope that I don’t end up arguing with you on Twitter about it at some point in the future.

But in popular culture, if not amongst professional economists, nearly everybody gets this wrong and thinks the Laffer Curve and trickle-down economics are the same thing. So I’m trying to untangle the nonsense you’ve probably been fed over the years, in the interests of both your sanity and my Twitter feed.

The Laffer Curve

Famously drawn on the back of a restaurant napkin by economist Arthur Laffer, the Laffer Curve is usually drawn something like the image at the top of this article – essentially a bell curve.

Sometimes people talk about the napkin-drawing episode in derogatory terms: “it’s just nonsense some bloke drew up on the back of a napkin”.

But it’s a big mistake to think that.

An early boss of mine (different times…) used to say “if you can’t explain something on the back of a fag packet, you don’t understand it well enough”. (For non-Brits, that’s a packet of cigarettes, back when people routinely smoked in the workplace. Although, ironically, not my old boss, who was a confirmed non-smoker.)

For me, almost the purest form of explanation is something that can be reduced to a single image. It’s one reason I’m such a big fan of newspaper cartoonists who sum up the biggest stories of the day in a single image that forces you to think more deeply about the headlines. The Dilbert comic strips and the Alex cartoon in the Telegraph are also great examples of this style of impactful storytelling with just three or four images and a handful of words.

There’s a big difference between “simple”, ie reduced to its essential elements for impactful communication – like a newspaper cartoon – and “simplistic”, ie delivered at the level of a 5-year-old by someone who doesn’t know what they’re talking about – like when a professional politician of any party speaks, for example.

So, the Laffer Curve is simple. It’s not simplistic.

And if you think about it for more than 2 seconds, and you’re even a tiny bit smarter than the average politician (to be fair, that’s not hard – I own pencils for which that statement is true), the message of the Laffer Curve is inarguable.

What’s more, it’s inarguable because pretty much every person in the world applies the principles which underpin the Laffer Curve whether they choose to recognise that or not.

Its principles are simple. If taxes were set at 0% across the board, Arthur Laffer illustrated that a government would collect no tax revenues.

And if the tax rate was 100%, government tax revenues would also be pretty much £0, because nobody would have any incentive to work, so the government wouldn’t collect any tax.

In between the 0% and 100% tax rates, however, there is a point at which a government will maximise its tax revenues. Below that point, they are “leaving money on the table”. Above that point, there is an increasing disincentive to work, so people choose not to, leading to a reduced tax take.

It’s the real world

Many people would have you believe this is some sort of right-wing free market messaging, but the concept behind the Laffer Curve reflects the real world almost perfectly.

If you have ever employed a tradesperson who insisted in cash for the job, they were applying the Laffer Curve. By the time they had declared the income and been taxed on it, they would rather have been sat at home watching the telly than fixing your blocked sink.

At the moment in the UK, there is a significant cluster of business reporting revenues just under £90,000pa because if they go over that level they have to start accounting for VAT and pass on an extra 20% tax hike to their customers, which (so those businesses believe) would reduce their income. (I accept this might be more a regulatory compliance cost factor, rather than the tax itself, however it illustrates the principle, so I’m leaving it in.)

And if you are lucky enough to be in a salaried role in the UK which pays £100,000 a year, the last thing you want is a pay rise, because between £100k and £125k a year, the government currently takes 60% of your salary in extra income tax. (Which reduces to 45% once you get to £125k – see my comment above about how smart politicians are compared to pencils.)

So, people who would otherwise earn, say, £110k a year often lock away that extra £10k in a pension fund they can’t touch until retirement, instead of taking the extra pay and spending some of it, after taxes, in the real economy and thereby boosting economic growth.

While people might disagree on what the precise tax rate at which government tax revenues are maximised might be, very few people think that suddenly paying 60% tax on your income at £100,001-plus would encourage people to work harder and earn more money, given how little of it ends up in their pockets.

Love it or hate it, the Laffer Curve reflects a real dynamic in ordinary people’s lives every day of the week. It may be politically inconvenient to some, but we all make decisions based on the principles underpinning the Laffer Curve.

The “back of a napkin” thing is simple. But it isn’t simplistic. It’s an insightful summing up of an essential truth.

What it isn’t, though, is an endorsement of “trickle-down economics”, as some people like to claim – either in ignorance or as a deliberate attempt to mislead.

The Laffer Curve is purely a way to talk about how to maximise the government tax take, and puts forward the proposition that there is an optimum tax rate at which government tax revenues are maximised. Taxing at rates either below or above that point leads to a reduction in tax revenues.

You might like the Laffer Curve, or not like it, but like the famous Winston Churchill quote about the truth, in the end, there it is.

Everyone in the whole world – including you – applies the principles behind the Laffer Curve every time the question of how much tax they pay comes under consideration.

Precisely where the optimum tax-maximising point is, is another matter and it’s not something Arthur Laffer built into his curve.

It’s likely to be different between countries, and even between different groups in the same country. But overall, if you aggregate all the decisions in the country, the Laffer Curve reflects the reality of human decision-making.

The trickle-down

I don’t especially like the expression “trickle-down economics” because it always suggests someone with a bladder problem to me. However, I’ll use it here as it’s a commonly-used term.

The principle behind trickle-down economics is also simple…albeit possibly simplistic.

The principle here is that if you cut tax for high earners, they will go and spend more money in the economy at large which, in turn, will create more jobs – and, in time, higher incomes – for everyone else too.

A bit like the “just a scribble on the back of a napkin” critique of the Laffer Curve, “trickle-down economics” is a deliberately slightly derogatory term for what might more properly be called supply-side economics.

Popularised by the influential Chicago School of economists – and much loved by political leaders on both sides of the Atlantic in the 1980s – supply-side economics suggests that low taxes and reductions in government regulations will encourage companies to invest, thereby boosting economic growth and bringing prosperity to all.

Now, at some level, this isn’t the craziest concept in the world either.

Some of the applications of it have, arguably, been crazy, but the concept is sound.

To give a bit of a real world example (and I accept this isn’t strictly an issue of trickle-down economics) it’s been quite fashionable of late to let businesses pay lower taxes “to stimulate investment” by giving generous allowances against their corporation tax for capital investments businesses make.

The theory is that the effective reduction in tax (that’s where the trickle-down but comes in) will motivate business owners to spend more – and specifically to spend more on capital equipment which will help grow the economy and provide jobs.

That’s a reasonable enough theory. But it’s just theory. In the real world something a bit different is going on.

A good recent example is when small business owners in the UK were able to fully expense capital equipment purchases, up to a limit, in the year in which they bought their equipment. At least some small businesses bought more items of capital equipment because they got an immediate 100% allowance against their tax bill for doing so.

So far so good, right? Working exactly as planned.

Where the principle goes a bit iffy is that, for a while, some of the capital investment was in things like a new electric BMW for the company’s owner. Now, I have zero objection to company owners buying themselves BMWs, or anything else for that matter, but the principles of supply-side economics fall away somewhat when a UK business owners buys a new BMW.

In that scenario, if there is any economic benefit to businesses at all (and that’s pretty marginal if you’re buying a car, however cool it might make you feel), it is flowing mostly to the good folk at Bayerische Motoren Werke in Munich, together with the people across Germany who make up their supply chain.

While a tiny amount of economic benefit will stick with the UK-based dealer who sold you that new beemer, the dealer element of the total bill you get stuck with for a new electric BMW is a tiny proportion of the whole.

Not, by the way, that I’m advocating protectionism. Far from it.

I’m just illustrating that not all capital spend you get allowed as a write-off against your UK tax will necessarily deliver an economic benefit to the UK economy. The same dynamic is true if, for example, you buy a new assembly-line robot from Japan, or a bunch of microchips for your datacentre from Taiwan.

That’s why, under the principles of trickle-down (or supply-side) economics, you can have high levels of tax-allowable capital investment, whilst still having a moribund UK economy which isn’t seeing the benefit of that tax allowance in jobs or incomes.

While the loss of tax revenues is borne by UK taxpayers, the benefits of economic growth from those investments flow mostly to the citizens of Munich, Tokyo or Taipei. So substantial capital investment in the UK economy can be significantly less beneficial to UK citizens as a whole than a simplistic view of trickle-down economics might suggest.

There is some benefit to the UK, of course. Those new machines are presumably making a UK business more productive to some extent as well. But the nature of capital investment is that you accrue the benefits over a long period of time – perhaps 5 or 10 years or more.

So all the up-front tax loss might take 10 years or more to come back again – and factor in the time-value of money (which I’m not going to do here) it’s probably more like 15 years before the UK taxpayer is back to the point they started, following a decision to use reductions in tax, through stimulating capital investment, in the hope of growing the economy.

Where it goes wrong

Where this goes wrong (especially on Twitter… 😉) is when people run those two concepts together and rail against both the Laffer Curve and trickle-down economics in the same breath.

They are two entirely different things, based on different principles, measured in different ways.

Now, it is true that, broadly, if you’re a believer in supply-side/trickle-down economics, you are also likely to believe that the Laffer Curve is true. In fact, people who promote trickle-down economics often use the Laffer Curve to illustrate how reducing taxes will actually lead to an increase in government tax revenues, which, they believe, bolsters their case for tax cuts.

There is an important pre-supposition here, of course, even assuming trickle-down economics works. And that is this assumes the tax rate is currently above the “maximum amount of tax collected” point.

I’m not expressing a view here on whether it is or not. And what might be true in some countries might not be true in others.

I’m merely pointing out that there is a potential flaw in thinking that a tax-cutting, supply-side economics agenda will necessarily result in increased tax payments flowing into the government’s coffers. If the “optimum collection point” was at a 40% tax rate, say, and proponents of supply-side economics pushed through a reduction to 35%, then the government’s tax take, according to the principles of the Laffer Curve which they used to justify the tax cuts in the first place, would be less, not more.

And, as I said at the outset, I’m an accountant, not an economist. There are, I am sure, a wide range of economic arguments for and against supply-side/trickle-down economics that I’m not qualified to express a view on.

But the point I want to make here is that the Laffer Curve is a pithy reflection of an economic phenomenon which accurately reflects real world behaviour by individual taxpayers. I say that without judgement as to what their behaviour should be – merely that those judgements are the judgements pretty much everybody in the whole world makes as they draw on the essence of human nature.

You might not like that answer, but it’s the truth.

Supply-side/trickle-down economics is conceptually fair enough, but has some problems when it comes face-to-face with the real world. I don’t have the technical expertise to say whether trickle-down economics works or not. I just know that, on its own terms, and applying the Laffer Curve which most people promoting the trickle-down economics agenda use as part of their justification, there are scenarios in which it might not be true that tax cuts automatically increase government tax income and/or boost the economy as a whole, along with increasing employment opportunities.

But whether you love supply-side/trickle-down economics or hate the concept to your very core, please don’t – on Twitter or anywhere else – make the mistake of thinking that the Laffer Curve and trickle-down economics are the same thing.

They are not.

What this means for business

This newsletter is supposed to be about business, not economics, so what’s the business insight here, you might ask?

Well, there are a few:

• Simple isn’t the same as simplistic. It really is true that if you can’t explain something on the back of a fag packet (or a napkin) you don’t understand it well enough. You don’t always need that 400-page report to make a decision. Sometimes the back of a napkin is plenty.
• Watch the concepts you lump together – people will often try to get you to link A and B because B is what they really want, but A makes the argument for B more plausible, even if they are not really connected at all. The Laffer Curve can be used to make trickle-down economics seem more palatable in the same way that convincing your boss that because people matter in your business, you ought to have 17 people in the HR department. In both those cases, one statement can be true without the other.
• Reality beats theory every time. The Laffer Curve reflects reality. Supply-side economics is a theory which may or may not be true in every scenario. To butcher one of my favourite business quotes, from Jeff Bezos: “when reality and theory throw up different results, usually the reality is right”.
• You don’t need to like it – you may not like the implications of the Laffer Curve shows, but the real world doesn’t care whether you like it or not. It just is what it is. Try not to let your emotions and beliefs get in the way of big decisions. You can argue against reality all you like, but in the end you can’t out-run it. At best, you can hold it off for a little while, but you’ll never escape it.
• “Directionally right” is usually good enough. For most business decisions, the nth degree of precision doesn’t matter – and the cost of working it out to six decimal places, even where you can, is generally prohibitive anyway. A common criticism of the Laffer Curve is that Arthur Laffer never said what the perfect tax-maximising % was, but that’s to miss the point. The lack of a precise % doesn’t invalidate the concept – and it’s almost certainly different in different countries anyway. In business, when a decision is directionally right, just make the decision and fine-tune is as you go. For example, there’s no need to wait three years for the £million study from a fancy firm of consultants to tell you that the reason all your customers are unhappy is because you don’t employ enough call centre agents to answer the volume of calls you receive on a daily basis. Just employ more people in your call centre, and when all your calls get answered in a reasonable amount of time, consider fine-tuning that a little.

And please, please, please – if we ever cross paths, on Twitter or elsewhere – please don’t confuse the Laffer Curve and supply-side/trickle-down economics. They are two entirely different concepts and one can be true without the other necessarily being true.

OK. “Saviour of the universe” might be putting it a bit strongly.

But it can certainly pull you out of some sticky holes. And, most importantly, give you extra room for manoeuvre when you need it.

What am I talking about? Well Flash Reporting, of course.

Flash Reporting

In case you’re not familiar with the term, flash reporting is the technique whereby a “first look” of the monthly financial results is available within 24/48 hours of the books closing at the end of the month.

In a lot of organisations, it takes 2-3 weeks to produce the previous month’s accounts – and I’ve seen some where “last month’s accounts” were not available until almost the end of the following month, nearly 4 weeks after the month they relate to had ended.

Especially in this day and age, that really isn’t good enough.

Ask yourself: if there was a big problem with your business finances, would you rather know about it in 24/48 hours, or are you happy waiting the best part of a month before you even find out there’s a problem, much less start to do anything about it?

Well, unless you’re insane, the only credible answer to that question is 24/48 hours, isn’t it?

But there’s a problem.

For very good reasons, a lot of checking and double-checking goes into preparing a full set of monthly accounts. After all, this is the sort of thing you want to be as close to 100% accurate as possible.

But checking and double-checking takes time. And before you think that AI can do that, it can’t. “Checking and double-checking” doesn’t just mean doing the maths – accounting systems already do the maths perfectly well without the aid of AI.

Rather, checking and double-checking is more of an investigative process where your CFO makes sure that all the elements of the story “add up” and there are no inconsistencies which might need further investigation.

This often means comparing performance in several different areas of the business and unpicking what might have happened, so the CFO can satisfy themselves the accounts are an accurate picture of that month’s performance.

For example, a record month in sales, but the quietest month on record in the factory is, on the face of it, inconsistent. And that should trouble your CFO.

Although, on investigation, it might just be that you shipped more product than usual from stock this month, or that a large part of the sales were some sort of “pass through” charge where you bill for a third party’s product or service which forms part of the “complete package” you sell to clients, even though it takes no time in your factory to make.

However no self-respecting CFO would close that month’s accounts until they were happy that they could explain why those inconsistencies had arisen and were very comfortable that the accounts would stand up to scrutiny by the board, the auditors, and the investors.

Time isn’t on your side

At some level, of course, everyone running a business understands the need for the checking and double-checking. But, in the past, the idea that financial results were not available for several weeks after the month-end meant the Finance Department sometimes acquired a reputation for being “unhelpful” or “not responsive enough to commercial pressures”.

It’s one of those “lost in translation” scenarios. For an accountant, accuracy is, like cleanliness, next to godliness. And it’s pursued with the same zeal you can expect from someone pursuing a holy mission.

In its own world, there’s nothing wrong with prizing accuracy above all else. It’s wise. Commendable, even.

But outside the Finance Department it’s probably the least welcome trait of a top-notch Finance Team.

So, in the last dozen years or so, a fashion for “flash reporting” has crept in so that the wider business can get a quick fix on the financial results for last month and get on with the current month, even while the Finance Department continues with its usual round of checking and double-checking prior to the final “official” accounts being released.

The quick fix

There is a trade-off, though. A quick fix on the monthly results will never be as accurate as a final set of accounts. The trade-off is that you get some slightly less accurate information almost instantly, instead of having to wait 3 weeks for the 100% accurate stuff.

When flash reporting first came in, most accounts teams thought this was crazy. Why wouldn’t you prefer more accurate information over less accurate information?

Well that’s because they tended to miss the wider business benefits of having 95% accurate information three weeks earlier – information that helped the rest of the business outside the Finance Department stay light on their toes and respond in the best possible manner to news, both good and bad, more or less as it happened.

Outside the Finance Department, speed matters more than accuracy (as long as the flash report isn’t out by much from the final reported accounts). That’s because the rest of the business tends to manage itself directionally, not accurately.

The sales team needs to know if it’s a busy month or a quiet month, for example.

If sales were 65% of target, the sales management isn’t going to do anything much different to what they’d do if sales were 60% of target of 70% of target.

That +/- 5% is irrelevant to the course of action the sales management team will be taking, even though their Finance Department will be obsessed with determining that the definitive number is 63.5% of target.

By getting a flash result 3 weeks before the definitive 63.5% number becomes available, the business can take action 3 weeks sooner, and almost certainly get the results 3 weeks earlier as well.

I’m a fan

I’m a big fan of flash reporting because it helps the business take action faster, and that’s almost always a better idea than taking action later.

But for flash reporting to work, it needs to be pretty accurate or you risk the business working on entirely the wrong problem for 3 weeks, and then flip-flopping back into fixing the right problem after that month’s accounts have been finalised. That is not a recipe for a well-run business.

To do flash reporting well, you need really good systems in place.

Not so much accounting systems, although you need those too.

But systems so you know what’s really going on in the business, because that’s what enables you to do at least an element of the double-checking you would normally do after the month-end close in time for the flash report.

And by systems here, I don’t necessarily mean IT-based systems, although that might be part of it.

But you need to be talking with the sales team regularly – not just the sales managers – to get some idea of the deal flow, long before it hits the formal reporting systems.

You need to walk through the factory regularly and assess how busy is it. Are machines cranked up to full speed or idle? Has the same four pallets of half-finished goods been in the same spot for the last three days, implying there’s a bottleneck in production, perhaps? In the loading bay a hive of activity with trucks going in and out more or less constantly, or is it like a wasteland in there most days?

That’s the information you need to do a reliable flash report, to within a 5-10% accuracy. And while systems can tell you some of the information, fundamentally you need a feel for business activity you’ll never get from a spreadsheet or a report from your MIS.

Your “feel” is doing the sense-checking job for the flash report that represents the equivalent of the “checking and double-checking” does for the formal monthly accounts. While those four pallets of half-finished goods might not, in themselves, make a difference to your flash report, noticing them means your eyes will be peeled for other signs of bottlenecks in production which might mean the factory is running inefficiently this month, with possible consequences in terms of sales volumes.

Of course, you can just prepare a flash report mechanically from the numbers available to you, but they tend to be wildly inaccurate and not that helpful for business decision-making.

We have seen a version of this recently in both UK and US government statistics, where their equivalent of a business’s “flash results” have been subject to huge revisions a few weeks or months later.

That’s because government statistics are, perhaps inevitably, prepared purely from numbers on an MIS report or a spreadsheet.

That’s “objective”, because it’s factual. But I would argue that being factually accurate about a set of inevitably inaccurate numbers is not really a positive move in financial reporting.

And that’s true at a company level as well as at a national level.

Even though, as a business, you might be prepared to trade a little bit of accuracy for a whole truckload of immediacy when it comes to financial reporting, that doesn’t mean that a wildly inaccurate “early peek” at the month’s results has any benefit to your business.

Fast, but inaccurate reporting is no better – and probably worse, on balance – than slow, but accurate reporting.

Even if the report is “objective”, because it was prepared from the numbers which were available at the time.

Done well, flash reporting can be the saviour of your universe.

Done badly, it can propel you towards a financial black hole faster than you can say “gravitational pull”.

If you’re reading this article, odds are you’re trying to grow your business and put more money on your bottom line.

And if you are trying to grow your bottom line, there’s one very important concept to get your head around: being purely rational at all times is not the best way to build your bottom line, despite what lots of people tell you.

There’s a place for rationality, of course. But if “rationality” appears above the title in the movie of your business, odds are you’re missing huge opportunities. It should appear somewhere alongside the list of this week’s guest stars in a long-running TV show.

All the very best ideas in any business are irrational, at least at first. Generally the role of rationality in business is to smother those ideas at birth just because overly-rational people don’t understand how the world’s greatest opportunities are created.

The other day, I was having a chat with a pal about the role of branding and how good branding built businesses better, and faster, than just about anything else.

That lead us onto a conversation about the role of intangible assets and the complexity surrounding brand valuation, which you’ll be relieved to hear I’m going to save for another day.

But it did lead to us having a chat about intangible assets.

Intangible assets

Intangible assets are not the most fascinating subject in the world for non-accountants (but really fascinating for accountants…).

Basically, “intangible assets” is an expression which covers the assets in a business you can’t touch.

Your office building, the machinery in your factory, the stock in your warehouse, the trucks you use to deliver to your customers…you can touch all of those. They are tangible assets.

Patents, trademarks, goodwill and so on are intangible assets. While you can’t touch them – apart, perhaps, from the bit of paper which certifies that you own the assets concerned – these are some of the most important assets in many businesses.

To illustrate, imagine you run a pharmaceutical business. Of course you need a factory, machinery, and trucks – tangible assets – to run your business. But without the original patent for whatever the medicined you developed, most pharmaceutical businesses wouldn’t be worth much.

However, when a pharmaceutical company starts the development work on a new treatment, the decision to press “go” is not rational.

Most new drug developments fail to make it through clinical trials for any one of hundreds of reasons. According to the National Institutes of Health, the success rate for drug development is just 10-15%, a number that has been broadly steady for many years.

Of course, in the early days of drug development, pharmaceutical companies aren’t spending billions of dollars.

Usually they start with an idea which might have some promise, a small team, and a small budget. As each part of the development process is completed successfully, a little bit more investment goes in, the team gets a little bit bigger, and the bet gets a little bit bigger.

But, however well-informed it might be, every penny spent in the development phase is a bet. Sure, an experienced, well-resourced pharmaceutical company might be able to tilt the odds in its favour a little by drawing on the combined expertise and experience of their staff.

It’s still a bet though.

The nearest equivalent is perhaps an expert card counter at a Las Vegas blackjack table. For them, no matter what they do, the odds are skewed in favour of the house, just as a new pharmaceutical product getting the OK for development is more likely not to work than to make it through clinical trials.

At the time the decision to go ahead with the development is made, there’s at least an 85% chance of it not being successful.

When you look at it in those terms, it’s not rational to develop anything with those odds of success. Yet businesses worth trillions of dollars have been created as a result of taking decisions which could not be considered rational at the time they were made.

Every one of those trillion dollar businesses got to be worth that much by navigating a series of leaps into the unknown, each with a high chance of failure, even though it’s not rational to bet against an expected 85% failure rate.

Balance sheets

Balance sheets are rational places too. They are a collection of assets and liabilities, all verified by the auditors as being valued correctly under the appropriate accounting standards.

Broadly speaking (and I’m wildly over-simplifying here) physical assets are valued at the lower of cost or net realisable value. Put another way, what you paid to buy it, or what it’s estimated to be worth now.

For example, a 10 year-old machine probably isn’t worth what you paid for it. With 10 years’ wear and tear, even excluding the possibility that a change in technology made the machine obsolete in the meantime, most machinery is worth pennies on the dollar against the original purchase price.

Because balance sheets are completely rational – the auditors can trace back every item on the balance sheet to proof of its original cost or evidence of a liability to a third party – you’d expect them to control how a business is run.

Yet, as non-accountants are often surprised to learn, almost no decisions about running a business are made based on its perfectly rational balance sheet.

A balance sheet serves some purposes for technical calculations like Return on Capital Employed (ROCE) which get accountants and investors excited, but for most day-to-day purposes a balance sheet is not a huge factor in company decision-making, despite it being incredibly rational.

That’s especially true when it comes to valuing a business, where scarcely any attention is given to the balance sheet even though, in theory, the value of the company’s assets, minus its liabilities, is what most non-accountants think a business ought to be worth.

To take an extreme case, Apple Inc had a balance sheet worth $330 billion at the end of June 2025, but at the same date it had a market capitalisation (what the total of all its shares were worth on the stock market) of $3.5 trillion.

Put another way, only 10% of Apple’s valuation was “rational” in the sense that it was represented by assets, less liabilities, on its balance sheet which the auditors could check back to source documentation.

The rest was “irrational” in the sense that it’s based on people’s guesses about what a unit of Apple stock might be worth.

There is a slightly technical answer to that difference which I won’t bore you with here, but the non-technical answer is that irrational thinking about Apple’s prospects was worth 90% or so of its stock market valuation at the end of June 2025, and only 10% of the valuation is rational, based on the value of assets and liabilities on its balance sheet.

Where do you focus?

That being the case, how much of your time would you spend on the “irrational” things in a business like Apple, and how much would you spend on the rational sort of things you find on a balance sheet?

Well, rationally (sorry, I couldn’t resist) you’d split your time 90/10 in favour of irrational things, wouldn’t you? After all, that’s where 90% of the value of the business is.

Of course, most businesses aren’t like Apple. I deliberately chose an extreme case. But on the US stock market, most businesses have just 25-30% of their valuation based on their tangible assts, and 70-75% is based on intangible (or irrational, you might say) asset valuations – assets like the value of the brand, their future product development cycle, and so on.

I don’t know what the split is for your business, but it might be worth taking a look.

Odds are somewhere between 50% and 75% of the value of your business, if not more, is based on outsiders’ view of the value of things they can’t see, feel, and touch (because if they could, those assets would appear on your balance sheet for an entirely rational valuation under accounting standards).

The question for you, though, is do you spend your time in proportion to the elements of your business which generate the most value?

If you ran a property company, which are largely based on the value of their physical assets, you would spend most of your time checking the properties are in good repair, the tenants are paying their rent on time, nearby planned developments are not going to impinge on the valuation of your properties, and so on.

It makes sense for people who run physical assets businesses, like property companies, to spend most of their time working with their physical assets because that’s where by far the majority of the company’s valuation comes from.

But if, say, 75% of your company’s valuation was based on “assets” like brand value, future product development pipeline, or distribution networks, how should your time be split?

Well, 75/25 in favour of things you can’t see, feel, or touch, right?

Do the maths. What’s your split?

And how does that compare to your diary?

Are you spending your time where the value is, or are you spending all your time on things that only account for 25% of your business valuation? (If it’s the latter, don’t worry. That’s what a lot of people do. Just make a start on redressing the balance right away and you’ll be fine.)

Branding

All of this really comes home to roost in areas like branding, which I’m using here in its very broadest sense to avoid having dozens of marketing strategy purists come after me.

Many people – in particular, accountants, engineers, and software developers who largely operate in “rational mode” – poo-poo the idea of brands having a value and are very reluctant to invest in building or maintaining a brand even though, done well, that might account for 75% or more of the value of the business.

While I’m not suggesting that the 90% of Apple’s stock market valuation which isn’t based on its balance sheet is all about Apple’s skill in branding their company and their products, equally I’m sure we can all agree it isn’t 0% either.

Apple is one of the world’s most powerful brands. It has a non-zero impact on what the business is worth.

But even if the brand was worth just 10% of the total valuation of Apple, which feels very conservative, that’s still an asset worth $330 billion.

I don’t know how much time, money, and resources you would spend to protect and build an asset worth $330 billion, but the answer probably isn’t “zero”.

On a smaller scale, getting a male model to remove his jeans and stick them in a washing machine was worth an 800% growth in sales of Levi’s in the mid-1980s. Even though that was a completely irrational idea which had almost nothing to do with jeans – Levi’s even degraded their signature patch on the back so you couldn’t tell, at the point Nick Kamen was throwing his jeans in the washing machine, that he was wearing Levi’s.

How much time would you spend developing assets which could generate an 800% increase in sales?

Well, rationally, not zero. Even though the activities you are working on when it comes to laundrettes and male models are entirely irrational.

And on an even smaller scale, very small businesses who do this well can make an impact far beyond anything they could achieve if they only ever took rational decisions.

My favourite current example of this is the wonderful Michelle J Raymond (not forgetting the equally wonderful Liliane Abboud) who have structured an entire chunk…pun intended…of content on LinkedIn around Australia’s national biscuit – the Tim Tam – with their Friday Tim Tam Tips.

Think about what Michelle and Lil have done here. They have taken a product they didn’t even invent (the Tim Tam) and structured an entire fun-packed narrative around the idea of being Australian and teaching people how to build their business on social media.

I don’t know what a packet of Tim Tams cost (personally I’m much more focused on the cost of Tunnock’s Caramel Wafers…ideally the plain chocolate ones). But just a few dollars in cost brings a boatload of fun, laughs, and learning which gets spread on social media and builds Michelle and Lil’s business.

The role of Tim Tams in building a business though? Completely irrational.

And yet, in this case, somehow perfect.

The moral of this story?

Often it’s the irrational factors which create the most value in a business, not the rational ones.

If you spend all your time on factual, rational, tangible things, your bottom line is probably suffering.

50% or more of the value of your business is likely to be based on the value outsiders place on the intangible, non-obvious, “irrational” aspects of your business.

Invest your time, money, and resources accordingly.

One of the joys of social media is that strangers will stop by your profile every now and again to tell you that you’re completely wrong about something. Often something that you’re an expert in and they are just over-confident amateurs.

The area that happens most for me is when I talk about whether there is a true RoI (Return on Investment) for a particular course of action.

Bear in mind that I’m a qualified accountant, with many years assessing investment proposals across a wide range of sectors. So it’s always a delight when someone working in tech who has just asked ChatGPT what a business case is decides to stop by to tell me I don’t know what I’m talking about.

That’s partly because tech folk suffer from a common ailment (not known only to tech folk, to be fair) of deliberately “failing to understand something when their salary depends on them not understanding it”.

Tech folks’ share award vesting depends on nobody ever thinking that might not be the case. So you can forgive them for finding it easier not to ask themselves the hard questions. Their future wealth depends on nobody, themselves included, asking those.

It’s also partly because tech folk think that knowing how to populate formula for calculating something is the same as understanding what the formula means.

To be fair, that is true when writing code, which is why this is a frequent blind spot for tech folk.

You only work with surface-level thinking when you write code – you need to explain to your computer, one logical step and a time, exactly what you want it to do next. The computer can’t think for itself – a programmer has to tell it what the sequence of events, and decision points, are.

So, in the tech world, understanding the formula (or programming language, if you like) is the same as understanding how it all works, because tech has no depth to it.

Sure, some people carry out the task a little more elegantly than others, but fundamentally the only way your printer is going to print a sheet of A4 paper is if pretty much the same instructions, word for word, flow from your computer to your printer. Your computer has to communicate in the rigid, pre-determined language structures your printer understands, or no printing is going to happen any time soon.

In the interests of full disclosure, though, this is not just a disease tech folk suffer from. I’d be lying if I said I haven’t come across quite a few accountants over the years who made most or all the same mistakes that tech folk commonly do. And other professions are not immune to it either.

So even if you’re not a tech person, there’s something in this article for you.

Stay tuned, and you’ll discover what a real business case RoI looks like – believe me, this is a lot more complicated than knowing the right formula in Excel.

There are seven areas where people drastically misunderstand what a real RoI looks like. So let’s dive in:

1-It’s not the numbers

The most common mistake too many people make is to take numbers at face value.

I’ve worked with numbers for a long time and I’ve got to say a staggering amount of the numbers people throw into a business case are wrong.

Not mathematically wrong, usually. Most people can get Excel to add up a column of numbers at least semi-competently.

But wrong in the sense that the numbers are a biased view of the problem (consciously or unconsciously), or that they address only a partial view of the data, or that they sound pertinent to the case being put forward but in reality are only tangentially relevant.

My most recent interaction with an over-confident tech person was them claiming that a particular app (which they may or may not have had a hand in writing or specifying) was generating “high levels of satisfaction”.

When I checked the App Store, this app had a 3.8 rating – which isn’t terrible, but isn’t stellar either.

But this tech person missed the point: people didn’t want to use the app at all. They wanted a different solution. So they were dissatisfied. But if the app was the only way to get the service they needed, then, their experience was that the app itself wasn’t terrible.

I don’t know about you, but that’s not a set-up I’d use the expression “high levels of satisfaction” to describe. But this tech person had missed the bigger point that people fundamentally weren’t happy about their experience.

Pretty much every number presented as part of a business case for anything is either less than the full story, or capable of one or more possible interpretations which are different from the rationale being put forward.

If you think a business case is just about finding some numbers and slotting them into a model, you’ve missed the point. You need a much deeper understanding of reality to truly understand whether a proposed course of action is likely to have a positive RoI.

2-Task not system

As I often say, one of the biggest lies in business is the old mantra that to solve any problem, you break it into its component parts and solve each component separately.

This mindset tricks a lot of tech folk into thinking they are making a positive impact on the bottom line when they are doing the exact opposite.

Imagine your business wants to dramatically grow its revenue line. There are many, many components to make a sale in most businesses, but every sales process starts with finding a lead.

Now, if it’s my job to generate leads, I guarantee you I can generate cheaper leads very easily.

Over the last couple of decades I’d have switched from off-line to on-line. I’d have switched from SEO to social media. I’d have switched from text to video. I’d have switched from long-form video to short-form video. I’d have switched from Facebook to TikTok. I’d have integrated AI into whatever I was doing. And so on.

Each one of those – at first, anyway – was cheaper than whatever most businesses were doing before. (In time, they also all trend back to being about as expensive as whatever solution they replaced, but that’s not my point here.)

But, as each wave of new tech is introduced, and as costs reduce dramatically (at first, anyway) that sounds like a positive RoI to most tech folk.

Except your business objective is to grow the revenue line. Lead gen that costs half as much but converts half as well is no better than whatever you were doing before. And even reaching that conclusion requires you to ignore switching costs and training costs.

Focusing on a narrow task can make it look like a business is generating positive bottom line results at the same time as the business is actually going backwards at the “whole system” level.

It’s rare I come across a tech person who is able to do that – especially when the definition of “whole system” encompasses things that can’t be done with tech alone.

3-Visible vs invisible

Tech only works in a visible dimension – what you see is what you get.

If a few lines of code say “send a message to the printer to let it know I’m about to send a document to print and it needs to wake itself up”, then that’s exactly what happens. Every. Single. Time.

That code will never do anything else because it’s incapable of doing anything else. That’s an entirely visible process.

Where humans are involved – which sooner or later they will be, until tech folk work out a way to sell only to other spambots – not only is an exclusive focus on the visible elements less than the full picture, it’s often positively harmful.

For example, I know very few humans who are enthralled by the constant diet of AI spam which pollutes most social media platforms these days. Yet there seems to be no end to it.

The visible dimension might still be getting attended to – maybe your AI tools are auto-posting to Instagram for you. And maybe you’re even still getting clicks and impressions (although it’s no coincidence that those measures are down across most platforms as AI spam overwhelms our social feeds).

Here, the visible dimension of “still posting 3x a day” doesn’t help in the slightest when it comes to assessing how your social media followers feel about your business. The clicks and likes are circumstantial at best…and nowadays are probably mostly automated anyway, so they mean little or nothing.

When we focus only on the visible elements of whatever we’re doing, and forget about the seven-eighths of the invisible elements below the waterline, odds are we’re going drastically off-course and won’t notice until it’s too late because all the visible elements seem OK.

The invisible is a valuable leading indicator of the visible which you ignore at your peril – yet most tech folk never let it enter their thinking.

4-Logical not illogical

Tech is entirely logical. That’s how it works. Logic plus a lot of 1’s and 0’s.

The problem is that only perhaps as much as 5% of everyday life is exclusively logical. So the thinking processes deployed in tech are only valuable 5% of the time.

The tech view of the world is that to sell a pair of jeans you run lots of online ads, get people to click on them, join a sales funnel of some sort, go on at some length about features and benefits, and eventually X% of the people you shovelled into the sales funnel will buy some jeans.

Back in 1985, top London ad agency BBH got Nick Kamen to take off his Levi’s and throw them into a washing machine in a laundrette while Marvin Gaye’s “I Heard It Through The Grapevine” played over the top.

This is a classic, much-admired, award-winning ad.

But not a single element of that was logical. Not the actor. Not the laundrette. Not the song.

None of them had anything to do with jeans in general or Levi’s in particular.

The brand wasn’t mentioned in a voiceover. Only in the last couple of seconds of the ad do you discover what the ad is for. There are no long lists of features and benefits. No sales funnels. No tech.

Yet sales of Levi’s went up 800% in the aftermath of that TV ad running.

I can pretty much guarantee that nothing you do by way of AI-generated Facebook ads is going to grow your sales 800%.

That’s because we live in a predominantly illogical world. Logic only gets you so far. But, in the world of tech, logic is all there is.

5-Tech view not user view

Tech folk get very excited about the tech, but often forget about the user.

The problem with that is, ultimately, the success of your business is determined by your customers buying from you, not by how clever your tech is.

For example, I’ve yet to come across a chatbot which wasn’t complete garbage. So the minute I see one of those popping up on a website, I know the business concerned doesn’t really care about me as a customer.

If it did, either I wouldn’t have had the problem in the first place, or there would be an easier way of sorting it out than going through a series of inane questions which I had to rephrase 5 or 6 times because I didn’t use the precise language the chatbot was programmed to understand when describing my problem.

Almost every piece of tech I interact with has this problem in spades. It’s one of the main reasons I’m deeply sceptical that tech can provide a positive RoI solution to many more of the world’s problems.

However I have previously written about my experiences of trying to book my car in for a service before which I won’t repeat here.

All you need to know is that poorly thought-through tech is just about the most unhelpful way of getting simple things done that you can imagine.

6-Real world vs laboratory environment

A lot of tech works fine in theory.

In a lab, where they can control all the variables, feed a model a pre-determined case study to demonstrate how smart the tech is, and the responses are selected from a pre-determined, pre-optimised list, tech folk can make almost anything look slick.

Trouble is, the real world looks nothing like that.

In my days running a 1,000 seater call centre, while of course there were some well-worn topics people used to call up about, scarcely a day went by when a caller didn’t raise something completely random that we hadn’t heard before.

Real life humans are vastly more unpredictable than tech folk think they are. Even when we thought we had made everything simple and obvious, to a fair proportion of the people who called us up 24/7/365, they weren’t.

And that’s where the tech falls down.

Doing something clever in a lab proves nothing about how it’s going to work in the real world, with a whole boatload of extra randomness thrown in.

The thing is, in our call centre we already know how to respond to things that happen regularly. When someone called up to change their address on our records, pretty much everyone in the call centre could do that competently with little or no tech.

These calls were simple and easy and dispatched in a minute or two by a human for next to nothing, without requiring us to invest in a £10m data centre for our online AI chatbot and integrated website first.

And tech was pretty much useless for anything outside the mundane because the developers had no idea what the real world looked like for us and our customers.

While humans are really good at responding to new and unusual challenges on the fly, tech just drops into a doom loop of serving up essentially the same FAQ write-up which didn’t fit the customer’s needs 10 minute ago when it served them the same article either, but still refuses to connect the customer to a human.

7-Cost to customer, not cost to firm, is what matters

Whenever I hear a sales pitch about how much money a piece of tech will save, I silently roll my eyes. (And sometimes, not so silently.)

Of course, I don’t want to run my business any more expensively than I have to. But that’s not the dimension that really matters here…except to the tech folk who have hung their whole sales pitch on that.

What tech folk (and quite a few accountants) fail to understand is that the dimension we are really trying to manage here is the RoI to the customer of dealing with our business.

Not the cost to the firm.

Those are two entirely different – and often completely unrelated – concepts.

Put yourself in a customer’s position for a moment.

Let’s say I used to be able to ring your call centre and get my address changed in the course of a 2-minute phone call. That was a typical call duration for us in my old call centre.

But now, to save the firm money, the business fires everyone in the call centre and invests half of the savings in a new tech stack to automate all their customer interactions. That’s an impressive RoI, right…?

Wrong.

Now I’ve got to go to your website and navigate some crappy chatbot for a few minutes to find out what to do.

Then I need to enter my customer account number – which I can’t remember off-hand, so I need to search through my emails to find that information. Thankfully “in the interests of security” you only ever show a line of asterisks and the last four digits of my account number in the emails you send me several times a week.

So now I need to go back in my emails for three or four years until I find the one that has my full account number on it from when I originally set up my account.

Five or ten minutes later, I’m back at the login screen with the required information.

Except now I need my password. A password I input when I set up the account four years ago and have never used since.

So I click the “reset password” button and go back to my emails to pick up the magic link – only valid for 15 minutes – which lets me reset my password.

Now I need to guess which rules your tech person has randomly decided I need to follow in setting passwords – capitals, or not? A number, perhaps? Maybe some special characters – but obviously not all the special characters as tech folk like to give us a challenge. And, often, not a password I’ve used before on your website.

So some hacking around to find a combination acceptable to the glitzy tech website takes another couple of minutes.

Next, I’m allowed to go back to the login screen again with the details I’ve now written down on a Post-It note next to my PC. Even though your website told me I’m not allowed to write those details down “because of GDPR” or something.

Ten minutes later, after hunting through a range of menus and options, I’ve finally been able to update my new address in your system

Except now I need to go back into the email system I just signed out of to click the acknowledgement that it really was me who changed my address on your system.

Which takes me to the screen which forces me to set up two-factor authentication in order to be allowed to purchase the notebooks I buy from your business once or twice a year.

The net effect of all this from a customer perspective is that this business has converted what was a 2-minute job, for both me and the business, into a 10, 15, 20 minute job entirely for me where I am required, for free, to give up my valuable time to do something your business should be doing for me if it wants to retain my custom.

Here, whatever the tech people said the RoI to the business might have been on a spreadsheet, I’ve gone from being a loyal customer of many years’ standing to someone who is unlikely ever to buy from you again, thanks to an interaction with your tech.

Ultimately, your customers make buying decisions by factoring in the RoI on their time too. Any business which becomes too demanding on their customers’ time, with too little upside for their customers in the process, just encourages their existing customer base to buy elsewhere instead.

The RoI you really need to work on with tech projects is the RoI to your customer, not the RoI for your firm’s operating costs.

Making your business simple and easy to deal with is a sales growth superpower – because many businesses in many sectors are neither of those things.

It becomes easy to make a sale when your service is both first-rate and reliable, and doesn’t require me to take 20 minutes of my time, without remuneration, to navigate my way around the third-rate tech that some tech evangelist has convinced you will deliver a great RoI for your business.

It’s not just tech folk

As I said earlier, these problems are not exclusively caused by tech folk – I find engineers and accountants also particularly susceptible to most of them.

But a mindset of “all tech is good” or “tech is always a guaranteed money-saver” is almost certainly taking your business in the wrong direction, in the company of grifters who make big promises but don’t deliver.

Not that they don’t deliver to the narrow tech spec which you signed off. They nearly always do that.

But because they don’t deliver where it really matters – with your customers.

Because the RoI on tech projects tend not to consider the points listed above on anything other than a superficial level, it’s becoming rare to see genuinely value-adding tech in a business setting.

I’m not suggesting all tech is bad – computerised accounting was a definite improvement (albeit not without its downsides), as is email as a form of communication. The banking app I use is genuinely excellent. And Google Pay/Apple Pay have made paying for things in shops much easier.

But increasingly Big Tech is running up against the problem that only 5% of the world’s problems can be solved exclusively with logic under laboratory conditions. Those things already have a range of perfectly serviceable tech solutions in place – like Xero and Sage for small business accounting, for example.

Everything else in the world is varying degrees of not logical and/or not operating under laboratory conditions.

While ignoring non-logical and non-laboratory factors can help you show a positive RoI on a spreadsheet relatively easily, the problem is that business case doesn’t reflect reality – either for your business or your customers.

Price in the non-logical and non-laboratory factors, and increasingly I suspect tech solutions deliver a negative real-world RoI.

If you want to avoid doing the same, make sure you check off the seven points above next time you consider making an investment into new tech.

Sometimes the best solution is not to invest in new tech at all. But to do something else instead.

Today I’m going to tell you about one of the highest cost ways to run your business.

When I explain this phenomenon, you’ll recognise it immediately. Yet most organisations refuse to recognise it, much less factor it in to their business planning and operations.

Which is a shame – because if you don’t pick up the Road Runner Effect early enough, the damage to your bottom line is going to be considerable.

And, often, impossible to recover from.

You’ll recognise the Road Runner Effect in most of our public services – anything where politicians meddle is at a particular risk of experiencing the Road Runner Effect (ironically meaning that everything is more expensive to run than it needs to be, and produces fewer positive outcomes).

You’ll recognise it in the working practices of a range of major corporations.

You’ll certainly recognise it amongst the tech industry, but nowhere is immune.

What is the Road Runner Effect?

So, what is the Road Runner Effect?

Well, it’s based on a regular scene in the old Road Runner cartoons. Remember them – the little Road Runner bird and Wile E. Coyote…?

Wile E. Coyote was always trying to bring about the untimely demise of the little bird, with the aid of a range of not terribly well-designed products from the Acme Corporation.

As part of their rushing around chasing one another routine, there was often a scene where Wile E. Coyote ran over the edge of a cliff, above a precipitous drop.

For a moment, his feet would keep running and he would be suspended in mid-air, defying the laws of gravity – all-too-briefly, from Wile E. Coyote’s perspective.

Then he would realise there was nothing beneath his feet and a look of inevitability came over the coyote’s face, before he plunged earthwards at a fair rate of knots.

A couple of seconds later, a puff of smoke rose from deep down inside the canyon where Wile E. Coyote made contact with terra firma once more – usually face first – with an impact that would have finished off mere mortals. Thankfully it had very little lasting effect on the cartoon coyote, who would pop up in mint condition a few seconds later, hatching another dastardly plot to finish off the Road Runner.

The Road Runner Effect is the name I give for the period between Wile E. Coyote’s feet losing contact with the earth, but before he realises the laws of gravity are about to take over and plunge him earthwards with considerable force.

His legs are still pumping away at top speed. His feet are cycling through at pace, imaginary step after imaginary step, as he pursues that pesky Road Runner. He has not yet realised that forward motion has ceased and rapid downward motion is now inevitable.

What this means for your business

When the Road Runner Effect kicks in, your business runs at its lowest efficiency and highest cost. So it’s important for your bottom line to pick this up early, and ideally not to let it happen at all.

Ironically, this is also likely to be the point where you think your business is running at peak efficiency.

It’s that element which makes the Road Runner Effect tricky for the uninitiated to spot.

You see, everyone will convince themselves they’re being super-efficient, and taking the best possible care of the bottom line, when the exact opposite is taking place.

That’s why, as a business leader, you need to be ahead of the game on this and provide the challenge to your team, who will mostly have convinced themselves that they are doing everything they possibly could to protect and enhance the bottom line.

Here’s an example from when I worked in the printing industry for two of the best bosses in succession I’ve ever worked for.

Printing is a tough business, with high capital costs and low margins. So being cost effective was really important.

We operated huge pieces of machinery, which each required a minimum of 3-4 people just to make it work – one on the back-end loading the paper, one in the middle topping up the ink and ensuring the machinery was running correctly, one on the front end to control the lithographic process so that the work produced was absolutely on-spec for our clients.

It was genuinely physically impossible to run one of these machines with fewer than three people, given that they stretched the entire length of our factory. Even Usain Bolt couldn’t have sprinted from one end to the other fast enough to do all three jobs competently.

Given the need to manage our costs, in the past there had been a ruling that we had to tot up the minimum people resources required for each of our machines, and that became our staffing budget.

So far, so good, you might think. Seems entirely sensible. Sounds like someone is taking a very strong decision to protect the bottom line.

Except…

What did we do when someone called in sick, or went on holiday for a couple of weeks?

Being a printer is a highly skilled job. It’s not like finding a temp to sit on your reception desk and greet visitors with a cup of coffee. Skilled printers are not sat around at home on the off-chance that someone might need them to cover a holiday in a printing factory. They’re all in full-time jobs.

So we had two choices – mothball the machine for a fortnight, at an enormous opportunity cost.

Or, get the other printers for that machine on our normal three-shift operation to switch to two 12-hour shifts instead so the machine could keep running through someone’s holidays. The only catch being that we had to pay the printers doing the 12-hour shifts at overtime rates for the 4 hours they did on top of their normal shifts.

Despite the edict from Head Office, my boss told me to hire another couple of printers who were designated as the “cover printers”. They would help out in the factory when nobody was away, to make sure we ran at maximum efficiency. But every time someone was off sick or on holiday, they switched into providing cover for the missing printer.

The kicker is, though, they provided cover at standard rates, because they were already employed to work those times in the business, not at overtime rates.

Overall, it cost us a lot less to employ two extra people than continually pay out at overtime rates when cover was required.

A mindset shift

Seeing the Road Runner Effect requires a mindset shift.

And it nearly always requires a more strategic view of the problem.

For as long as people thought that not employing any more printers than the minimum required to run our machines was a good decision for the bottom line, that’s what we did, even though overtime costs were considerable.

When my boss took a different view, and looked at the real bottom line implications of that head office decision, he took a very different decision which dramatically lowered our costs.

All the effort that went into (supposedly) keeping costs low (but requiring lots of overtime), instead of just keeping cost low by not paying out as much money as we did before, is an example of the Road Runner Effect.

Head Office had run off the edge of a cliff and hadn’t realised it. Their little legs were still pumping away…their little feet were cycling round and round…for a while they didn’t realise they were no longer making progress. Everyone was too busy congratulating one another on their wise decision to reduce staff numbers and enhance the bottom line.

It was only when the inevitable forces of gravity took hold that people started to notice the bottom line was getting worse, not better. And nobody could figure out why, given all the effort they had put into reducing staff numbers.

Nobody except my boss, that is.

You’ll see the Road Runner Effect everywhere now.

It’s a way to think about organisations which persist with a mental model of how things are supposed to work which has lost touch with the reality of how things actually work in practice.

The point at which their legs are still pumping away, and their feet are still cycling through, but all forward motion has stopped – that’s when the Road Runner effect has kicked in.

The impact is often compounded

The Road Runner Effect, as I’ve described it above, is bad enough.

But what happens in most organisations is that a compounding effect kicks in which reinforces the pointlessness of the original decision and makes the organisation even more expensive to operate.

So, although this didn’t happen in the printing business I worked for, I did in another business which decided that the solution to the bottom line not shifting in the way their original decision had predicted, was to hire a team of data analysts and beef up the management infrastructure to make sure everyone was working hard.

The only reason why the bottom line could still be underperforming, those geniuses thought, was that their people were lazy.

This had a number of interesting side effects – which is why, when the Road Runner Effect kicks in, this is generally by far the most expensive way to run your business.

For this second business there were two pretty rapid consequences from their decisions to hire more analysts and beef up the management infrastructure.

Firstly, staff motivation, which hadn’t been great up till that point, collapsed through the floor.

It turns out that calling all your people lazy wasn’t the great motivator the people at head office had imagined it was. Who’d have thought, right?

Output per person declined because they reverted to doing the contractual minimum they were required to do, rather than going above and beyond as they had been previously. (The problem in this business was not a lack of hard work, but chaotic business systems and processes which hadn’t been invested in for decades, and were no longer a good fit for how customers wanted to interact with this business.)

In addition, the salary bill went up to cover the costs of the analysts and the managers.

The one-two punch to the bottom line as a result of simultaneously reducing output and increasing costs was not the genius move those head office folk imagined it was. This business required a significant emergency refinancing and the senior leadership team were all fired.

But this is the Road Runner Effect in action – just running harder once the edge of the cliff is behind you won’t make the slightest difference.

You need to think differently about the problem – and, generally, more strategically – if you want the best result for your bottom line.

You need to be prepared to challenge the fundamental assumptions which, at some level, usually make some sort of logical sense, so lots of people get suckered into thinking they made the right decision.

You need to look for counter-intuitive solutions, like hiring more printers to reduce the cost of employing printers. By any standards that’s a non-obvious solution, and one everyone else had missed.

Decisions like that are the sign of a bottom-line genius – someone who gets their head out of the lazy, seemingly-logical decisions everyone else has unconsciously absorbed into their thinking as if they were the words from a sacred text.

That’s how to avoid the Road Runner Effect.

Avoid that, you’ll open up more bottom line growth than you ever thought possible.

Beep-beep…!

One of the best ways to set your business apart is by looking after the small details well.

Most organisations don’t.

They think details are unimportant when set against a sea of grand strategy, so they leave it to whoever is looking after things today to make their own decisions as they go along.

Or some clown questions whether the details “add value”.

Done well, details always add value. Although I’d also concede that “focusing on the details” and “doing the details well” are worlds apart as concepts.

When I worked with large public service organisations, there were armies of people focused on the details.

Most of those details were irrelevant. Some were actually harmful to the stated mission of the organisation. But the detail monsters ran with it, and managed those details more tightly than most people hold a 2-year-old’s hand when navigating a busy high street.

So a focus on the details, for the sake of the details, is usually counterproductive, and detracts from your bottom line rather than adding to it.

But a focus on the right details…for the right reasons…can transform an otherwise me-too experience into something exceptional.

Christina Aguilera

My favourite example of this is late-90s singing sensation Christina Aguilera.

In the late 1990s I watched a lot of MTV (back in the days when they actually showed music videos, rather than the terrible reality shows they show today) and Christine Aguilera’s video for “Genie in a Bottle” played several times a day.

As it should have done. After all, “Genie in a Bottle” topped the US singles charts for five weeks and was the second best-selling single of 1999. (Regular viewers of late-90s MTV won’t be surprised to learn that “…Baby One More Time” by Britney Spears was the best-selling US single of 1999.)

However my favourite example of managing details well isn’t from late-90s Christina Aguilera. Rather, it’s from her mid-2000s reincarnation as an old-time soul singer, with heavy influences from people like Eartha Kitt and Etta James.

In that phase of her career, Christina Aguilera recorded my favourite song of hers, “Ain’t No Other Man” (her slightly earlier cover of “Lady Marmalade” with Lil’ Kim, Mya, and Pink is a very close second, though).

Why is “Ain’t No Other Man” my favourite Christina Aguilera song?

Details, my dear Watson. Details.

Scratching an itch

Christina Aguilera’s vocal performance on “Ain’t No Other Man” is amazing. She really lets rip, and there’s a power in her voice that wasn’t there for “Genie in a Bottle” a few years earlier.

But that’s not the best bit of that song for me – impressive though her vocals are. Christina Aguilera won the Best Female Pop Vocal Performance Award at the Grammys for it, so you know her vocal performance is about as good as it gets.

The attention to detail on “Ain’t No Other Man” is exceptional though.

On the video (linked below) Christina Aguilera plays a Golden Age of Hollywood screen siren character. And in a really nice bit of detail, the video is shot in black-and-white, not in colour, to give it a Golden Age of Hollywood vibe.

The clothes she’s wearing seem like the sort of clothes a screen goddess from the 1940s might well wear. More nice detail.

I don’t have the technical recording studio language to tell you what’s happening in technical terms, but her vocal doesn’t sound “clean and crisp”, which is the style in a 21st century recording. The vocal sounds like it was recorded back in the 40s or 50s on an old-fashioned microphone.

That piece of sonic detail enhances the original vocal performance and helps tell the story of the song. The interesting thing about this creative choice is that, by some standards, this makes the vocal “worse” by today’s standards (ie less crisp and clear). Yet making the vocal “worse”, it actually makes the vocal better, and more in keeping with the character Christina Aguilera plays in the video.

We also have an old-style horn section on the song, again sounding like it was recorded through old-style microphones. (And a great horn section it is too!)

All of that is fabulous attention to detail, but we haven’t even got to my favourite bit yet.

What’s absolutely glorious about this song is that, in the background, they put in scratches like the ones you’d hear if you played a vinyl 78 on an old gramophone.

The net effect is that “Ain’t No Other Man” really does sound like a recording from 50 years ago – it’s an amazing level of attention to detail considering that by the mid-2000s recording technology had advanced so much all of this might be considered unnecessary, or at least old-fashioned.

Depending on how good your hearing is, you can just about make it out on a car stereo. But put some decent headphones on, and all of those wonderful scratches – which again, by some standards, make the record objectively “worse” but, in reality, make it 1000 times better – are really easy to pick out.

But that’s not all

What makes “Ain’t No Other Man” extra-glorious is that it isn’t just a pastiche of an old track from the 1950s.

All of that wonderful, old-time-sounding detail is there. Yet there are also beats and samples in there too, bringing an old-style song right up to date.

It would have been easy to stop at just providing a decent representation of an old-style song. And if the writers, artist, and producers had stopped there, it would still have been a pretty good record.

However, attention to detail came to the fore again when the creative team involved asked themselves how they could create a homage to old-style Hollywood and, at the same time, produce a contemporary piece of work that would stand up to comparison with everything else on the charts in the mid-2000s.

They had to balance the creation of something so unusual that it would stand in a category all if its own, without turning it into something your grandma might listen to.

From the very first note, listeners had to know exactly what song it was. The writers, artist, and producers made sure there wasn’t the slightest chance of their song being mistaken for anything else on the radio.

So, against a base of a 1950s Golden Age of Hollywood-themed sound, the producer (DJ Premier) brought the track right up to date with some of the latest musical styles.

And guess what? That required attention to detail.

Too much of an early 2000s influence and it would make a mockery of the original 1950s-themed concept. Not enough, and “Ain’t No Other Man” turns into a record your grandma would listen to.

That took some real judgement and creative skills to pull off.

However, it was worth the effort.

“Ain’t No Other Man” won a Grammy and sold over 2 million copies, so the writers, artist, and producers did something right.

Attention to detail

The main thing they did right was attention to detail.

However, they didn’t obsess over trivialities – which is what many detail-focused people tend to do.

Every single little detail made the song more true to its mission, even though it took more effort than just pumping out something generic and hoping that Christina Aguilera’s star power would turn it into a hit.

Ironically, some of the details arguably made the finished product better by making it worse.

Like adding scratches which weren’t there. And making the record sound like it had been recorded on out-of-date microphones when modern recording technology could capture cleaner, clearer vocals.

And, although I don’t imagine it cost a huge amount, it is clearly more expensive to add old 78-style scratches into a three-minute pop song than not to bother. But they did it anyway, and made the finished product better in the process.

All of this is, of course, a metaphor – unless, by some miracle this ends up on Christina Aguilera’s desk, in which case I hope she sees it as a heartfelt tribute to one of my favourite songs.

If you want your business to be a great success, that’s a bit like a recording artist winning a Grammy.

It’s not easy to do. You often have to think counter-intuitively, and not be afraid to go against what everyone else is doing.

As a wise old boss of mine used to say, “you don’t stand out from the crowd by being the same as everyone else”.

Sometimes organisations think that means you have to invent whole new product lines, at huge expense, with major capital investment consequences.

More often, in my experience, you can stand out from the crowd just by learning how to pay attention to detail.

Not detail for the sake of it, like some box-ticking bureaucrat. That’s not going to help you achieve anything.

But attention to detail where it enhances the product or service you provide, and gives you a way to set your business apart from every other business in your sector.

Almost always, that requires no new products, no £multi-million capital investment programme, no need to bet the company on your idea working in the markets you serve.

All it takes is the right sort of attention to detail. Including introducing those details which some might consider makes your product worse, by some objective or popular standard, while actually being the nugget that sets you apart from your competition.

Which, after all, is what every business should be trying to do.

There’s a quote attributed to Albert Einstein, but since I read it on the internet, the chances are high he never said it at all.

It goes something like this: “You can’t solve a problem with the same level of thinking that created it.”

Whether or not Albert Einstein ever said those words…or anything close to them…this is a pretty good rule to keep in mind.

Although it’s a rule I see organisations breaking a lot. Generally at great cost to their bottom line.

While trying to fix a problem with their bottom line, the thinking processes often deployed in organisations make the problem worse not better because they don’t elevate their thinking to the level it needs to be at in order to make the best decisions for their bottom line.

The three levels

In terms of corporate decision-making, I believe there are three levels of thinking:

Data: this is just about getting the numbers.

Information: this is collating numbers into a report of some sort, perhaps with charts and graphs.

Understanding: this is being able to interpret the story the numbers tell well enough to make good decisions for your bottom line.

Mostly, organisations are stuck at either the data or the information level.

That’s unfortunate.

Data is a commodity nowadays. It’s cheap, easy to find, and there’s no shortage of it.

All sorts of software businesses will line up to sell you a $multi-million system to track all sorts of data you don’t track now, often using slogans like “what doesn’t get measured can’t get managed”.

The decision to bring in a solution like that is almost always bad news for your bottom line, because lack of data is unlikely to be your problem. Or, to the extent that it’s a problem at all, it’s a minor by-product of some other, bigger issue which is, as yet, unrecognised.

You almost certainly already have too much data already. Adding more of it just keeps your thinking at the same level at which your problem was created.

If Albert Einstein dropped into one of your meeting rooms, he would probably be concerned that he’d gone to a lot of trouble to make up fake quotes from himself on the internet, but people were ignoring them completely.

In the spirit of full disclosure, sometimes more data is exactly what you need.

Not so long ago I helped a client who had traditionally operated on a “make to order” model switch into a “make for stock in larger batches” model and they definitely needed more data about their operations because their information systems were set up to give them the information they needed for their old business model.

Without more data, they would have been “flying blind” in their new business model, which would have been reckless, to say the least. So they took the very sensible decision to invest in more data.

But those situations are rare. Unless you’re making a dramatic change to your business model, you probably don’t need more information, no matter what a shiny-suited software salesperson tells you.

Information

Once you’ve got the data, the next level is information.

That’s about collating the data in a way that can be presented to other people through graphs, charts, tables, interactive dashboards, and whatever else you can think of.

Information has some value. It can be helpful, on occasion, to illustrate a point visually, for example.

But information is not a differentiator any more, in a way it might have been 20 years ago.

Fire up Excel, PowerBI, or ChatGPT, and dump a load of data into it. You’ll be able to produce charts, graphs and interactive dashboards with very little difficulty.

But so can everyone else – although there is no shortage of people who try to convince you to pay them a six figure annual subscription to access their data analysis platform so you can download your interactive dashboards.

And information alone doesn’t come with any guarantees.

I’ve seen some catastrophically stupid decisions made in organisations – and pretty much every time there was lots of data, and no shortage of information. In fact, the information was proudly front and centre of those organisations’ announcements about what strategy they were about to pursue.

The only problem was the information didn’t mean what they thought it did.

Without understanding the issues fully, the data and information those organisations held was somewhere on a continuum between worthless and self-destructive.

Understanding

That’s why understanding is the highest level of thinking – and incredibly rare.

Knowing what numbers really mean, and why, is about the rarest skill I’ve seen in business.

Too much business decision-making is at a surface level where there’s insufficient challenge and insufficient critical thought. That’s because the only thinking that takes place is at the level of data or information. By far the most important piece of the jigsaw is missing.

Think about it – if “everything you need to make a decision” represents 100% of a pie chart, and we’ve already agreed that data alone is a pretty much worthless commodity, and information is scarcely more valuable, then the missing element – understanding – must represent 90-95% of that circle. 90-95% of “everything you need to make a decision”.

However, inside most organisations, 90-95% of the time, effort, and expense goes into data and information. At most 5-10% goes into understanding – and, not-infrequently, no time, effort, or expense at all goes into that category. People are just painting by numbers from data they don’t understand, and information that operates on a surface level only.

Which is odd. Because, for your bottom line, understanding is where all the value is.

Understanding gives you the comfort that you’re making the right decisions for the right reasons, not just making a knee-jerk reaction to an interactive dashboard or a fancy graph.

Why understanding is so rare

One of the reasons understanding is so rare is that it’s hard to scale and monetise.

Tech folk would rather persuade you that data and information is all you need to make decisions, because they can make those scalable pretty easily, and have no qualms about sticking a fancy front-end on an Excel workbook and charging you six figures a year to access it.

And, as Albert Einstein may have said, the bigger problem is that, however well presented it might be, more data and more information keeps you at the same level of thinking as the problem.

As a consequence, most software produced nowadays – focusing, as it does, on data and/or information, because those can easily be monetised – is of very little bottom line benefit to organisations.

You don’t solve a problem with software any more. You just have a more expensive way of tracking the problems you already have.

Which doesn’t seem like a great bottom line decision to me.

For understanding, you need people. Despite what the scam artists and evangelists claim, AI is not going to replicate human understanding – at least not in the next few hundred years.

Although, just as a side note, you’ll note those tech folk always talk about data and information in their pitches. And they claim that, with the right information systems, computers will unerringly make the right decisions 100% of the time.

Well, maybe when pigs learn to fly they will, but not before then.

When good people really understand what they’re doing, you can always tell.

At the moment, I work with a team of amazingly talented economists who all use data and information to help them weigh up what’s happening in the world’s major economies, but they all bring a level of understanding that transcends just the data and information.

You can’t get understanding without data and information – otherwise that’s just guesswork – but data and information alone are not enough.

Understanding is hard to pin down, admittedly. But you know it when you see it – there’s nuance, an appreciation of complexity, an ability to flow seamlessly through several dozen potential interpretations, before weighing up carefully which is the most likely.

Really good people can do this “on the fly”. People who think data and information is all there is, can’t.

When I worked in the printing industry many years ago, we had a printer who had the nickname Billy the Whizz (older Brits will get the reference) because of his ability to make adjustments to a printing press running at 8-10,000 impressions an hour on the fly with zero wastage.

Less talented printers would power down their machine, make the adjustments, fire it up again, and be on their way. However they would lose 20-30 minutes of productive time, and probably waste the best part of a pallet of paper in the process.

Because Billy understood what was going on at a deeper level, he could operate in an entirely different way to people who, to be fair, were doing a competent-enough professional job, but operating only at a data/information level.

Billy’s skills meant higher average speeds on our machines, less downtime, and less wastage.

It’s no coincidence that when a particularly tricky job was scheduled for production, all the sales reps would try to call in favours with our production planner to get their job scheduled for when Billy was in charge of the press.

That’s the example I carry round in my head of where understanding, as a higher-level power, really pays off.

When people talk to me about numbers, I’m always looking out for the tells as to whether they are operating at an “understanding” level or just a “data/information” level.

When someone talks to me about numbers the way Billy the Whizz used to run a printing press, I know they really understand what they’re talking about. They’re not just going through the motions or recycling something ChatGPT told them.

When you’re making important decisions for your bottom line, those are the people you need on your side.

There are very few of them about. But when you find one, like the economists I work with, the decisions you make as a consequence are several orders of magnitude better than they would be otherwise.

Sure, they use data and information as part of the process. But what you get out the other end is not just repackaged data and information…it’s qualitatively something very different.

And that’s what Albert Einstein was talking about.

Now you have thinking on a different level than the thinking that created the problem in the first place.

And you’ll make better decisions for your bottom line as a consequence.

Sometimes the world of business can be a hard and unforgiving place. When the pressure is on for results…the bank or investors are breathing down your neck…and you’ve got more problems than there are baubles on the Trafalgar Square Christmas Tree…it’s tempting to think that immediate, drastic action is required.

And maybe it is.

But how do you know you’re taking the right action to protect, and ideally build, your bottom line?

A good place to start is getting really clear where you are. And that’s a lot rarer than you might think.

Someone will look at the monthly KPI report and say something like “we need to boost our margins” or “the rate of staff turnover is too high” or “our NPS score needs fixing”.

The minute you hear anyone say that I’ll give you odds of 10-to-1 that this organisation is about to release a series of well-intended, but fundamentally ill-considered and ultimately self-destructive, actions.

Why is that?

Well, it’s because they’re starting from inside a spreadsheet. They’re not starting with reality.

Jeff and Winston

One of my very favourite business quotes is from Amazon founder Jeff Bezos: “where the anecdote and the data disagree, it’s usually the anecdote that’s right”.

And one of my favourite Winston Churchill quotes is: “The truth is incontrovertible. Malice may attack it, ignorance may deride it, but in the end, there it is.”

Where those two concepts overlap is where businesses looking to make a change often get it badly wrong, resulting in higher costs, not lower costs, and lower profits, not higher profits.

The minute someone in a management meeting says “the problem is X” you’re on a path which is unlikely to lead to a positive outcome. That’s because the “usually right” anecdote is being prioritised over the data on a spreadsheet somewhere.

In those meetings – and believe me, I’ve sat through plenty of them over the years – there is an unspoken assumption that there are no extraneous factors which need to be taken into consideration. Everything you need to know is on the spreadsheet.

This leads you down a very narrow, and ultimately sub-optimal, path.

Let’s imagine someone says the problem that needs fixing is the NPS. Customer satisfaction is falling and the NPS is dropping to concerning levels.

Instantly, people’s brains move into “fixing the NPS” mode.

“Can we ask fewer questions so people don’t get bored and mark us down?”

“Can we reword the questions to make a positive answer more likely?”

“Can we call customers before we send the questionnaire so we can pretend to check they were happy with our service, but really use it as an excuse to remind them ‘if there really wasn’t anything we could have done better, please remember to mark us as a 10 on the satisfaction questionnaire that will be in your email inbox in the next couple of days'” (The dealer where I get my car serviced does this – I’m not making it up.)

What, typically, you end up with is a series of hacks to fix the NPS (or whatever measure you decided the problem was) irrespective of the reality going on around you.

Numbers

This might seem like an odd thing for an accountant to say, but numbers are not reality.

Numbers are an abstraction of reality.

Numbers are “the map, but not the territory” of running an organisation of any size.

And the trouble with numbers is they’re easy to fiddle.

The dealer who services my car is no doubt doing what they do because they worked out the cost of a 60-second phone call is less than the benefits they get from having an NPS of 8 instead of 7 (or however their numbers work out).

But what’s really going on here?

Has it changed my experience in any way? Has it improved the service I received? Is my car magically more reliable after that call than before it?

Of course not. None of those things are true. The 60-second phone call is a crude attempt to fiddle their NPS results, pure and simple.

There are two problems with this:

Firstly, I now know I’m dealing with an organisation which is more concerned with fiddling their NPS than with actually looking after me and my car when it goes in for a service. I immediately suspect that this is unlikely to be the only way in which this dealer prioritises their own interests over mine.

That’s unlikely to encourage me to stay with them for longer and spend more money with them (the theory behind NPS in the first place).

But the bigger problem is that fixing the NPS is not the same as fixing the problem, even if that might look like the same thing on a spreadsheet.

The one-eighth of the problem above the waterline is the NPS result.

The seven-eighths of the problem below the waterline is how the garage carried out the work on my car, how welcoming the service staff are, how difficult it is to book a service appointment at a time that’s convenient for me, and literally dozens of other considerations.

Fiddling the numbers while Rome burns

I get it that all this looks like activity. The boss sees the NPS ticking up. The spreadsheets show a line moving up and to the right.

Everyone is happy.

Everyone except the customer that is.

The inconvenient truth is that until you genuinely engage with customers and solve their issues, no amount of fiddling the numbers is going to help.

Getting your NPS up from 6 to 7 is unlikely to staunch the outflow of customers. Especially if that improvement has been more about fiddling around on spreadsheets than solving real-world customer problems.

Even if you get a short-term bump to the numbers, eventually you run out of road to improve the NPS just by fiddling the numbers. Your incremental RoI on fiddling declines dramatically after the first round of tricks to nudge the numbers on the spreadsheet in the right direction.

That’s when things get really dangerous.

Because typically what organisations with this mindset tend to do is introduce all manner of ideas that they think ought to get customers excited. Even though customers aren’t excited about those things at all, and still haven’t had their original problem fixed.

They are just as unhappy as they were before, albeit with a superficially better result on the NPS.

This sort of thinking has brought us wifi-enabled washing machines, AI-powered toothbrushes, and tech-enabled bins.

All things nobody asked for, needed, or wanted. But all probably unveiled by organisations with NPS results under pressure who thought their customers would be as excited about their wifi-enabled washing machine as the person at Head Office whose idea it was is.

Bear in mind this is the industry which displays a “12 minutes left” indicator on the front of their washing machines to represent an ever-changing timescale which can mean the wash cycle actually ending anywhere between the next 3 minutes and an hour-and-a-half later.

Think about it: the people who can’t tell the time in their washing machine accurately think the thing we need most if to add wifi to our washing machines.

I assure you, a million times more people would be excited about that clock giving an accurate count-down time than they ever would be by the thrill of checking how their spin cycle is going from the queue at Starbucks.

Work in the real world

If you’re serious about improving your NPS (or anything else in your business, for that matter) you need to work in the real world. Not the abstracted world of board reports and KPI dashboards.

But, using the NPS just by way of example, check first of all if that’s the problem you’re really trying to solve.

I’d venture to suggest it isn’t.

The problem that most businesses are really trying to solve is probably the fact that their customer churn is much higher than the level of churn their business model is predicated on.

Most businesses don’t want to improve their NPS at all. What they want is plug the black hole on their bottom line that’s sucking all the cash out their business because too many customers buy once or twice and never come back again.

That’s the reality they are trying to do something about.

But someone in the boardroom who had heard of the NPS just proposed that as a solution and people put all their efforts into managing the abstraction of the NPS instead of fixing the reasons why customers buy once and don’t come back.

The fastest, cheapest, and easiest way to solve that problem is to ignore the NPS completely and go and talk to a dozen or so customers who bought once and never came back again.

Bribe them if you have to – give them £20 in cash or a gift voucher of their choice.

But talk to your actual customers.

Ask them what the biggest single reason why they don’t buy from you any more is. Anything that three or more of those customers say is the problem is your biggest issue. Whatever that is, go and fix it.

And fix it for good – don’t botch a solution or patch something together and hope nobody notices. They all will. And then they’ll trust you even less than they do now, so you’ll just make your job of getting repeat purchases and boosting customer lifetime value even harder.

Once you’ve done that, find a different group of a dozen or so former customers and ask them the same question.

Do the same thing if three or more people in the second group give the same reason.

Then keep going.

After a while you’ll find that nothing will be mentioned more than once or twice by any given group of a dozen customers. That’s when you need to bump up your sample size, to probably 20 or 25, and do the same thing. Fix anything that group says three or more times.

After a little bit longer, you’ll be back to getting issues in 1s and 2s again from each group. That’s when you bump up your selection to 50 customers and, again, fix anything mentioned three times or more.

Do that diligently and when each group of 50 customers is only mentioning issues in 1s and 2s, you’ve probably solved 95%+ of all the problems your customers experience.

Then by all means go back and fix the 1s and 2s you’ve collected during this cycle. That’ll get you up to 99% of all the problems in your business.

Now check your NPS again. You’ll find that, despite doing absolutely no work at all on the NPS, your score will have shot up.

That’s the power of working with reality rather than fiddling the numbers on a spreadsheet.

You’ll make lasting improvements faster, cheaper, and more effectively when you start with reality.

Know when to stop

Once you’ve done all that, and had a few days to pat yourself on the back for the increase in NPS scores, there’s one really important thing you need to do.

Touch nothing.

Don’t be tempted to come back and fiddle with this recipe. Don’t switch suppliers of a key component. Don’t bring in a new production line. Don’t make your product wifi-enabled for no very reason, even if that’s what all the cool kids are doing.

For goodness’ sake, leave it alone.

Yes, you should continue to monitor the situation. You should continue to ask your customers what you can do better. If you hear the same issue mentioned more than occasionally, do something about it.

But in the same way as a Michelin-starred chef is not going to throw all sorts of random ingredients into their signature dish just to see what happens, you should not be messing with the successful recipe for running your business that your customers have just helped you establish.

Now and again, you might experiment with a new dish. Perhaps offer it for free to a couple of longstanding clients to see what they think about it. Put a little bite of it on a tasting menu to get some feedback. Maybe cook it for a chef friend and see what they think.

All of that is perfectly sensible. I’m not suggesting that you let your business atrophy and the cobwebs start to form.

But I am suggesting that, in the absence of a clearly better approach, backed by a lot of enthusiastic customer feedback, don’t mess with things that work perfectly well.

No Michelin-starred chef decides on a whim to throw a Pot Noodle into their signature bechamel sauce one night and serve it as a main course.

Yet, in a lot of organisations, that’s pretty much what they do with what had been a successful recipe up to that point. They feel the need to wifi-enable their washing machines.

Then they wonder why their NPS results are in the toilet again and the business is leaking cash like there’s no tomorrow.

Get a successful recipe, then leave it alone.

Not only will this make your customers happy, it will save you the cost of an army of NPS “experts” who will happily charge you $1,000 an hour to show you how to fiddle your NPS results to enable you to pretend you don’t have a customer satisfaction issue, even though the bottom line of your P&L will be suggesting the exact opposite.

To build your bottom line, don’t start with the numbers. Start with reality.

Fix your customers’ reality, and your numbers will look after themselves.