Oct 8, 2024 | Analytics, Innovation, Strategy
Have you ever been in a situation where you just ‘know’ a course of action is right?
No data, no detailed scenario planning, you just know.
I have.
Where does that confidence come from, and is it justified?
Have you distinguished between genuine intuition, based on experience and knowledge, and the overconfidence that can arise from a lack of awareness of one’s limitations?”
In my experience which includes choices that have been both very good, and very poor, there are two qualitative drivers of those good choices.
Significant domain experience.
This experience does not come from being around for a while, it comes from taking action many times, and learning from the outcomes, resetting, and trying again.
For example: a seasoned chess grandmaster can often intuitively anticipate the best move without consciously calculating every possible outcome, drawing on years of experience and pattern recognition.”
Learning from analogy.
When you see a course of action succeed in other domains that have some similarity to your own, you can infer that the success may be repeatable in yours.
For example: The introduction of disc brakes in cars came from their development for use in stopping aeroplanes when landing.
In a world increasingly dominated by data, it’s crucial to remember that while numbers provide valuable insights, they should not be blindly trusted. True wisdom often lies in the delicate balance between data-driven analysis and the intuition honed through experience and learning from mistakes.
Chess is a game where a grand master has a store of intuition gathered and sorted by years of practice that is leveraged instinctively when playing.
Sep 18, 2024 | Analytics, Governance
Economic Value Added, EVA, is another of those annoying acronyms accountants tend to use to confuse simple marketers. Therefore, it is a term marketers must understand if they are to hold their own in the boardroom.
EVA is a calculation used to measure the net cash flow from an asset, after taking into account the cost of the capital necessary to acquire that asset. It is often a part of a business case made to support a major investment or M&A proposition.
There are a couple of calculations that need to be made, all from the standard company accounts.
- The net cash flow is obvious, what comes in versus what goes out, as a result of deploying the asset.
- The cost of capital will be some combination of the cost of equity and the cost of necessary borrowings.
When the net cash flow is greater than the cost of capital, the asset is generating value. When it is less, it is destroying value.
The formula is simple: EVA = Net cash after tax – capital invested X the weighted cost of that capital.
The shortcomings of an EVA calculation are twofold:
- It is based on the past. The cost of capital yesterday is unlikely to be the same tomorrow. Interest rates bounce around, and the mix of debt and equity while not as volatile does change with circumstances.
- Increasingly business transactions are being done on the basis of intangibles. Costing the replacement value of intangibles, is a practise lacking discipline, consistency, and financial rigor.
Building a business case for an investment always requires deep consideration of the cash flow results of that investment. By definition, that requires a forecast of the future be done as the driver of that cash flow.
It is always easier to take the past and extrapolate, than to spend the time and energy building a strategic case for an investment. A strategic case requires that the relative costs and benefits of differing choices be articulated, in an environment of information scarcity. A much more demanding task than constructing a future that is the same as the past, and hoping that this time, it will be.
Header illustration by AI, in a few minutes.
Aug 14, 2024 | Analytics, Marketing, Sales
There is lots of talk, often sales-hype from digital urgers, about Lifetime Customer Value. When applied correctly, it is a vital measure, but when you look closely, it often means lifetime customer revenue.
Revenue is of little commercial value in the absence of margin, so the discussion can be completely misleading.
Understanding the margin generated by customer segments, or in some cases, individual customers is an immensely valuable metric that enables you to focus resources where there is the most benefit to the enterprise. You can make informed tactical choices with a great level of confidence based on the margin delivered.
Customer margin is also an enormously useful metric elsewhere.
Sales people are often rewarded on revenue, which can be gamed. Margin over time is much harder to game, and a far better measure of the effectiveness of a salesperson in delivering value to the enterprise while serving customers.
Similarly, calculating the cost of acquisition of a customer gains traction when measured against margin rather than revenue.
One of my clients businesses relies on referrals as a source of business. Increasingly they are moving towards margin on converted referrals as the single metric that best measures the impact of their marketing and product delivery efforts.
You cannot generate margin in the absence of revenue, but you are easily able to generate revenue without margin. Not a good idea!!
As an aside, also beware of the difference between margin and mark-up. They are similarly often used to mislead the unwary.
Jun 11, 2024 | Analytics, Governance
Goodhart’s law is a much quoted adage that states: When a measure becomes a target, it ceases to be a good measure’.
When we see numbers cited as evidence, we tend to instinctively give them more credibility than they may deserve. Without an examination of the source and scope of the numbers, just believing them on face value can lead to very bad choices.
Charles Goodhart is a prominent British monetary economist. His public profile started as a footnote to a 1975 article and read: ‘any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes’. It took on its now well-known form, above, when restated by an anthropologist seeking to link the idea to a much wider context than economics.
In almost every business I visit, I see examples of Goodhart’s law. The most common is the use of EBIT as a measure of success. The reality however, is that it is simply a financial measure of the outcome on the myriads of smaller actions and decisions that have been taken in the deployment of resources.
Two of the most public failures of measurement should make us all wary of using quantitative targets as a measure of performance.
The 2010 Volkswagen software scam. VW installed software in a range of cars with their turbocharged direct injection diesel engines that ensured that when being tested, the cars made the emissions standard of the US EPA.
The Vietnam War body count. The US defence department sought to justify the billions of dollars and thousands of lives expended in the defence of a corrupt government in South Vietnam. The measure became the body count of VC fighters and North Vietnamese regulars, which led to wholesale slaughter. These imaginary and bloody numbers were used for years as the basis for increasing commitment. It finally became obvious that they did not in any way reflect the ability or willingness of much of the population to fight what they saw as aggression by the US.
There are thousands of examples. If you looked, you would see them every day, especially when politicians open their mouths and quote numbers.
Header Photo: Charles Goodhart. Professor Emeritus at the London School of economics.
PS. When you look at the header, Dr. Goodhart is looking directly into your eyes. You will tend to believe anything he tells you.
Apr 10, 2024 | Analytics, Communication
It seems that ‘the truth’ is a malleable concept.
We are overwhelmed by opinion masquerading as fact, economic and social models designed to deliver a predetermined outcome, managed correlation equated to causation, and market research that asks the wrong questions of the wrong people.
What is truth to one person is nonsense to another.
We should be able to see ‘the truth’ about what has passed, there is data that should distinguish fact from fiction. However, we still fail to discern the truth from amongst the data available for analysis.
Who is winning the war in the Ukraine?
Depends on who you ask, and both sides have data that shows conclusively that they are winning.
Remember Vietnam? I do.
The Americans had an overwhelming advantage in material, technology, and logistics. How could a little country with few resources and no technology of their own, face and win against the mightiest war machine the world has ever seen?
Impossible but it happened.
Until the Tet offensive commenced in January 1968, there was no doubt in anyone’s mind, apart from the North Vietnamese, that it was only a matter of time until the might of the Americans became overwhelming.
The Americans had data that proved to them they were winning, despite the secret conclusions contained in the Pentagon Papers. It was not until the spring offensive in 1974 that it was obvious to all that the American ‘Facts’ that were being analysed were irrelevant, and the conclusions drawn were terminally wrong.
The clear answer to the question in the header is: ‘only when you analyse the right data.’
Header credit: Hugh McLeod at Gapingvoid.com
Mar 15, 2024 | Analytics, Marketing
Every market research proposal must answer a duo of critical questions before it proceeds, if it is to be of any value.
What is it for, and how will it be used?
Market research is done for all sorts of reasons. Many commissioned projects have little to do with the examination of the critical factors in driving success.
They just provide a convenient crutch.
Several projects commissioned and paid for from marketing budgets I controlled would come in under the ‘what the F&&k’ category. However, in my defence they were usually quant studies designed to generate the numbers necessary to pass the accountants various thresholds. This enabled me to progress projects that qualitatively and ‘in my guts’ were winners. That is the way they usually turned out!
In the absence of clearly understanding how the research results were to be used, how they would add strategic, operational, or technical value, why should you bother?
There is a further tier of understanding that is required: Are you looking to define an objective outcome, or are you seeking understanding and insight?
In the case of the outcome required being quantitative, simple yes/no, black/white answers to a question are sufficient.
When you are looking for insight, there may be a few numbers, way below a level of statistical significance, but they can be reassuring. However, the value lies in discovering the connections, implications, options, and potentially hard to anticipate consequences.
Research is a critical step in successful marketing programs. However, in the absence of a very clear and compelling answer to the ‘What is it for’ question, it should not proceed.
The header illustration is the only AI used in this post.
