Imagine you want to understand a subject you know currently nothing about.
You start off in complete ignorance. Knowing nothing whatsoever.
Then you pick up a book, and read a chapter or two. Or do a search for the topic online and read a blog post or two.
Or, heaven forbid, read one of those tiresome AI summaries which are popping up all over the place at the moment.
Congratulations. You have just had the highest RoI learning experience on that topic you’ll ever have.
You’ve gone from knowing zero, to knowing a tiny amount – a fraction of 1% of all the available knowledge in the world on that subject.
Starting from a base of zero, that’s an exponential return. So enjoy it – you’ll never get as high a percentage return on anything more you learn about that subject.
That’s because of the Law of Diminishing Returns.
To get your head around that concept, imagine someone else who has read 100 published books on a topic.
How much more are they going to learn about that topic from reading their 101st book?
Well, possibly not absolutely zero, although the chances are high that the 101st book is just a rehash, to a greater or lesser extent, of things already said in the other 100.
A little bit of maths
Let’s take this example a little further and imagine that the first person went from knowing zero to knowing 0.1% of the available information on that topic.
And the second person went from knowing 99% of the available information on that same topic and they now know 99.1% of the available information on it.
So they’ve both increased their knowledge base by 0.1%, haven’t they?
In absolute terms, that’s true.
But in relative terms, the second person has increased their knowledge by slightly over 0.1%, whereas the first person increased their knowledge by infinity % (don’t come at me maths geeks), which is a lot more than 0.1%.
Let’s make this a little clearer. And the maths a little easier. And get the maths geeks off my back.
Imagine they both increase their knowledge by a further 0.1% by reading another book.
Person A has gone from 0.1% to 0.2% – that’s a 100% improvement on their base of 0.1%. A doubling of their knowledge on a topic.
Person B has gone from 99.1% to 99.2% – that’s an improvement of a tiny fraction over 0.1%.
So there’s a curve somewhere between knowing 0.1% about a topic and knowing 99.1% about it that diminishes steadily the more you know – each incremental bit of effort you put into learning more pays back less and less the more you learn. In relative growth terms, at least.
What do you need to know?
Why is this important in business?
Well, unless you want to waste endless amounts of time and resources, you need to work out what you need to know to make a decision. And then make a decision.
In other words (well, actually in Michael Jackson’s words) you’ve got to stop when you get enough.
Any other outcome is seriously detrimental to your bottom line.
Yet, the mindset in most businesses is that you can never have too much information.
That’s dangerous. Really dangerous. To your bottom line.
I’ve seen organisations invest in six figure software products because it “gave them more information” and “allowed them to produce more detailed reports”.
I’ve seen other organisations invest in data analysts, at huge expense, to analyse all manner of data.
And nowadays, organisations often implement some sort of AI solution to “manage all their data and reporting needs”.
With rare exceptions, all of those are terrible decisions. That sort of thinking will destroy your bottom line faster than almost anything else you might be tempted to do – and that includes telling your biggest customer to get lost.
So what’s my point?
Here’s my point.
Up to a certain point, more data and more analytics produces more bottom-line enhancing insight than it costs. A bit like Person A above – they rapidly get better because they start out knowing so little.
Beyond that point, every further investment in people and/or technology brings more cost to the organisation than the thin layer of extra benefits would justify.
That’s like the second person above. It’s almost certainly worth precisely zero to your business to go from having 99.1% of the available information to knowing 99.2% of it.
Except in the unlikely event that extra 0.1% unlocks some transformational knowledge that you were previously completely unaware of – and the odds of that are worse than the odds of you winning a million on the lottery – any spend which gets you from the 99.1% level to the 99.2% level is almost certainly stealing money away from your bottom line, not adding to it.
Of course, no software salesperson is ever going to admit that. If they even recognise the phenomenon.
In the world of data and analytics sales, any outcome that gives you less than 100% of all the available information means there’s some potentially untapped opportunity for tech folk to sell their software.
I don’t particularly blame tech folk for that. It’s the way their brains are wired. And they’ve got software to sell. So good luck to them.
That doesn’t mean you have to swallow their sales pitch, though.
But it starts in your head
You see, it’s never been easier for data and analytics software companies to sell their software.
And that’s because most people have bought into the mindset that “you can never have enough data”. This is the insidious message that all sorts of people drip feed to you every day – the giant consulting firms, the tech folk with software to sell, the people convincing you to hire another data analyst “to understand your metrics better”…and thousands of other people.
Believing the message that the combined PR efforts of thousands of interested parties with a product to sell has tricked vast swathes of people into believing that anything less than 100% knowledge of all the data in the world affecting a particular topic is a dereliction of your duty as a business owner.
However, that’s completely untrue.
Rather, as a business owner, your mindset should be that every time you spend a pound, you should expect to get back more than a pound.
If you do that consistently, you’ll build a great business.
Now, sometimes you’ll invest a pound today and not get more than that pound back for a year or more – for example, if you buy a new machine for your factory. But the principle remains that you’re looking to spend money where you are most likely to get a multiple of your original investment back over a sensible period of time.
And also that, all things being equal, you should rank the RoI on everything you might potentially do so that you implement the highest RoI activities first and only get to the lower RoI activities after all the better uses of your money have been taken care of.
To give a tangible example, there is almost certainly a higher RoI on investing in a couple of days of induction training with a new member of staff so they can learn all about your business than it is to invest in a new data tracking system to take your understanding of the data from your company’s petty cash system from 99.1% of all the available data to 99.2%.
Taking your new member of staff from knowing zero about your business to knowing even just 10% or 20% of it is a phenomenal RoI for the cost of only a couple of days’ salary, in terms of how they can get up to speed faster, do their job more efficiently, and serve your customers better.
If you learn anything useful at all after getting that extra 0.1% of data out of your petty cash system, I’d be astonished.
Think incrementally
To counteract the mindset that tech companies’ PR systems have been borrowing deep inside your head for years, you have to think incrementally.
What I mean by that is thinking about what extra return, over and above what you have now, you get from making a new investment, and comparing that to the additional cost (both up-front and ongoing).
The people selling tech solutions will try very hard to convince you to buy their products, so it’s in their interest to muddy the waters around all this, in order to make it seem like a no-brainer to buy their product.
In particular, there are three aspects to look out for which often creep into tech pitches…often subtly, so they can be hard to spot:
1-Project RoI
A common tech pitch asks you to give them £X and get £Y back again.
Even if that’s true (and it usually isn’t, by the way), that’s not really the question you should ask yourself.
The question you need to ask is whether, out of the dozens or hundreds of potential things you could invest in for your business, that’s the number one candidate for investment in terms of incremental bottom line performance.
If it’s going to make more of a difference than anything else (which is unlikely), of course you should invest in that product.
More likely it’ll make a marginal-at-best impact, in which case you should invest your time and money on whatever your number one RoI project is instead of messing around the margins with comparative irrelevancies.
Saying “no” to pointless or marginal-at-best projects is one of the most powerful things you can do for your bottom line. Instead, focus your energy on projects that will make a transformational difference to your business – unless you work in tech, it’s extremely unlikely that’s going to be a tech product.
2-You’ll find something
I’ve been on the receiving end of a version of this pitch twice recently.
It goes something like this: “Look, currently you only track 40 datapoints in your business and we’ve identified another 1200 potential data points you currently know nothing about. You’re bound to find something in there that will make a huge difference to your business – you’re only scratching the surface at the moment.”
This is a slightly more disingenuous pitch than the one above, where there was at least a theoretical project RoI, even if it wasn’t the biggest potential RoI in your business. But it preys on the several decades of “you can never have enough data” messaging that’s lodged deep inside your brain.
I can save you a lot of time here.
If you’re already tracking 40 datapoints and managing them well, you’re probably at 99.1% of all the information you need to know about your business. And the extra 1200 datapoints will get that up to 99.2%.
Armed with that knowledge, how much time, money, and effort are you willing to spend on this proposed tech project?
Think very carefully if your answer is anything other than “zero”.
3-What’ll you do different?
Information alone is pretty much worthless.
The worth comes from what you would do differently, if you had the information, that you’re unable to do now.
And if you wouldn’t do anything different to what you’re already doing, more information is worthless. So spending even a penny on it is seriously detrimental to your bottom line. There is no upside to getting this information because it won’t make any difference to what you’re already doing.
A couple of years back, I had a client who wanted to invest in a tech solution to track all sorts of KPIs for the entire sales team where there were a couple of underperformers who, they weren’t generating enough leads to hit their sales targets.
So I asked, given that we already know what the problem is (lack of leads), what would we do differently on their lead generation if we knew that, for example, we could organise their in-person client visits more efficiently by better grouping together of in-person sales calls?
Of course, the answer was “nothing”. However interesting the software might have been if we had a “you don’t use your time efficiently when you’re out on sales calls” problem, knowing that information would not help us to increase their lead flow in the slightest.
Thankfully, that idea was shelved and we spent the time helping the salespeople generate more leads instead, which was the actual problem we were grappling with.
So be clear in your own mind that any proposed solution isn’t just a nice idea in and of itself. For it to have a bottom-line benefit to your business, you have to do something different to whatever you’re doing now to solve a current problem in your business.
If the proposed investment either isn’t going to do that (ie it solves an entirely different problem), or it might do something to help with your current problem, but there are other, better, higher RoI ways of solving that problem, then don’t invest.
You SHOULD stop when you get enough
Your ambition shouldn’t be a dispassionate “nothing less than 100% of all the data is acceptable” approach, no matter how much time and effort tech folk put into convincing you how important that is.
Rather, set out to find the difference between having enough data to set a course of action – up to and including the point at which the information benefits of more data or analysis are outweighed by the costs of producing them.
And when you find that point, stop spending money. Anything you spend from that point onwards has a negative impact on your bottom line.
Michael Jackson almost got it right.
He sang “don’t stop ’til you get enough”.
The bit he left out is that “but when you’ve got enough, for the sake of all that’s holy, stop!”