Sep 28, 2026 | Branding, Marketing
A workshop with idle machines needs a different management and marketing tactic from one that cannot reliably deliver the work it has already sold.
Yet the pro forma solution from many ‘digital marketing gurus’ would be to ‘generate more leads.’ For one, it may be sensible advice, for the other, totally wrong.
The same business can move from the first situation to the second in the blink of an eye. The advice that helped fill the order book can then make the delivery problem worse.
Good marketing advice carries small print, ‘be sceptical, trust your instincts, and understand all the numbers’. The ‘numbers’ always includes the elusive, hard to measure factors that are often the game-changers.
We make grand assumptions constantly. An impressive case study becomes a conference presentation, then a rule that everyone should follow. Somewhere along the way, we lose sight of the buying cycle, the available resources, and the particular customer problem the business set out to solve.
Consider the instruction to concentrate on retaining customers because keeping one costs less than finding a new one. Usually this is very good advice.
For a subscription business losing customers through poor service, understanding why customers are not renewing would usually be better. Spending more on acquisition while customers leave in frustration just gives the service department a larger queue of complaints that leads to further erosion.
However, a roofing contractor cannot persuade a satisfied homeowner to need another roof next year. The relationship still matters for referrals and reputation, but growth requires access to other people who need a roof. A retention campaign that ignores the purchase cycle solves a problem the business does not have.
Even where customers buy regularly, the economics deserve scrutiny.
A discount to someone who would have bought at the normal price reduces margin without changing behaviour. Retention only creates additional value when your action changes an outcome worth changing. The constant promotional pressure exerted by supermarkets on suppliers has served only to erode supplier margins and destroy brands.
Then there is the instruction to put more money into the marketing activity that delivers the best measurable return. Sensible enough, provided you understand what the measurement tells you, and it is a useful addition to your understanding of customer behaviour.
A customer may hear about you from a colleague, read your material, and later search for your business by name. The final enquiry tells you where they entered the sales process. It does not, by itself, explain what persuaded them to look for you.
Move all your money towards the activity that collects the enquiries, and you risk starving the activities that created the interest. You can improve the reported efficiency of your marketing while weakening its ability to generate future demand.
That does not give anyone permission to hide poor performance behind the words “brand building.” It means you need evidence that suits the decision, and a measurement period that respects the buying cycle.
The common mistake in these examples is accepting the prescription before diagnosing the problem.
More leads assume you can profitably serve more customers. Retention assumes you can influence another purchase or prevent a worthwhile customer from leaving. Increasing a marketing budget assumes the next dollar will create enough additional value to justify its cost.
Write down the assumption before you approve the activity, and then ask what evidence would show you that it no longer holds.
“It depends” offers no help unless you can explain what it depends on, and how that changes the decision.
Nor should we use context as an excuse to discard experience whenever a fashionable idea comes along. Understanding the customer, keeping promises, and earning an adequate margin remain essential to success. The tactics we use to achieve them needs more frequent examination.
The practical question is what constrains profitable growth here, now. A shortage of suitable buyers requires a different response from poor conversion, inadequate capacity, or a service failure.
Before accepting the next piece of marketing advice, ask: “What would have to be true for this to work in my business?”
If the adviser cannot answer that, a larger font on the presentation will not help.
The character of Don Draper in ‘Madmen’ depicted in the header probably said it best.
Aug 19, 2026 | AI, Marketing
Cost reduction remains the default KPI for most manufacturing organisations. In a world where nearly every product category faces relentless commoditisation, aggressive new competitors popping up unexpectedly, and razor-thin margins, squeezing out cost feels like the only sensible survival strategy.
The alternative strategy is challenging, as it requires looking at a fundamental paradox in modern industry.
As automation has accelerated, machines have taken over repetitive tasks with unmatched reliability. Yet human labour hasn’t vanished, it is progressively relocating.
Value behaves much like the conservation of energy in Einsteins equations. It is not destroyed, it merely changes form under pressure and moves elsewhere.
If we apply this idea to manufacturing, the primary mandate of leadership is no longer just cutting costs. It is becoming the larger challenge of expanding the capabilities of employees and other stakeholders.
Value creation is migrating.
Over the last 50 years, machines mastered physical repetition, which reduces costs by replacing people, and maintaining consistency of throughput and standards. It also moves the point of value creation towards work machines cannot reliably perform: judgement, creativity, problem solving, and leadership.
As AI accelerates, we are confronted by the question of where those displaced by technology will go, what will they be doing, and what are the social and economic consequences of the changes.
In past technological shifts, manual labour to steam, then steam to electricity, horses to cars and trucks, fingers, and vacuum tubes to digital, the changes evolved over sufficient time for adjustments to evolve. There was pain associated with the changes, but it was limited albeit unevenly spread. However, each change occurred over a progressively shortening time frame. The AI driven tectonic change is happening in real time, before our eyes, so adjustment time will be very limited. Cost cutting may preserve short term margin, but it does not give customers a reason to choose you.
The scale and short-term horizon points directly toward the need to enable and enhance uniquely human skills: high-level problem solving, empathy, strategic collaboration, adaptability, and social intelligence. These are cognitive areas automation cannot easily replicate.
Despite dystopian sci-fi tropes, AI and advanced automation are simply tools waiting to be directed. Leaders who recognise where human value is moving, and actively reskill their workforce to meet it have the opportunity to redefine the ways they create customer value in their markets.
Those who remain fixated solely on cost-cutting will quickly find themselves commoditised into oblivion.
Aug 11, 2026 | AI, Marketing
In 1984, Dr. Robert Cialdini published Influence: The Psychology of Persuasion, outlining the six core drivers of human compliance:
- Reciprocity,
- Commitment & Consistency,
- Social Proof,
- Authority,
- Liking,
- Scarcity
Decades later, psychologist Daniel Kahneman expanded our understanding of human decision-making in his ground breaking book Thinking, Fast and Slow. By experimentation, with research partner Amos Tversky, he demonstrated how our fast, intuitive “System 1” brains routinely rely on these shortcuts to reach a compelling conclusion. The research on which the book explains in laymans terms earned him the Nobel prize in economics.
Both books issued clear warnings: these principles could be leveraged just as easily by bad actors as by those whose objective is positive.
Cialdini wrote for an analogue world of salesman, direct mail, and print advertising. He could not have foreseen the impact of the internet, social media, and generative AI.
In the analogue era, persuasion took time and human effort. Today, machine learning algorithms execute Cialdini’s drivers at immediate, micro-targeted scale.
The tiny safety mechanism that was present pre digital, of the opportunity to reflect on a choice has been removed by the speed of digital. Any chance rational analysis had of imposing itself on an immediate emotional reaction has been dramatically reduced.
Social proof is manufactured via engagement metrics; authority is mimicked by synthetic media; scarcity is simulated through algorithmic urgency, and consistency has built its own echo chamber.
The result is systemic fragmentation and hardening of an opinion forged by these forces. Once a person takes a position, persuading them to change becomes progressively harder when they perceive that the effort to change is an attack. Our brains evolved to be a safety mechanism, so attack hardens resolve to defend. This has generated the divisions we see around us every day.
Households, public discourse, political systems, and social licenses are splitting into opposing, and polarised positions.
The forces Cialdini identified have not changed, they are the result of millions of years of evolution. What has changed is that AI now automates persuasion on both sides of the divide, compounding the fracture every second.
Aug 7, 2026 | Analytics, Marketing
Twelve years ago, I argued that your website was your digital home. You could run around naked if you wished. Your home, your rules.
Social platforms offered rented accommodation. The landlord made the rules and could change them without asking you. However, it was not clear to me then that it would become an existential distinction.
The traffic coming from these sites was never ‘yours’. It was borrowed from the platforms and subject to their rules, algorithms, and monetisation strategies. The landlord has struck and imposed a pile of new requirements and limitations on your rented digital channel access.
The emergence of AI has been the pivot point.
Search is no longer an appropriate term. It implies looking in several places, considering options, enabling you to make informed choices. The overviews now being delivered give what the platform regards as the default best answer, above any alternative potential source.
Most of us take that answer for granted and look no further, most of the time. Google, and increasingly all platforms, are providing disincentives to leave their ecosystem, while promising incentives by way of algorithmic distribution to stay.
As a result, traffic to websites via digital platforms has crashed.
In the case of the modest StrategyAudit blog, the traffic referred by Google and LinkedIn, the major channels I used has eroded by around 80% over the last 3 years, accelerating noticeably in late 2024 when Google rolled out overviews to Australia.
LinkedIn has progressively rewarded content that keeps users inside LinkedIn and restricted the reach of posts that send them elsewhere. This week saw another turn of the screw. The garden wall had grown higher again. I received a note from Microsoft telling me that Publisher will be removed from the Microsoft 365 subscription from October 1. The opening sentence of the email is ‘Microsoft is committed to improving your Microsoft 365 subscription’
How removing something I have paid for and is of value to me is an improvement in the absence of a price reduction is beyond my simple understanding. It does however make the point that the landlord can do whatever they like and you have no redress. They very kindly gave me list of instructions about how best to preserve publisher files so I could use them again. This is a bit like the landlord putting on a sad face and giving you a list of alternative rented locations as they throw your furniture out onto the street.
We gave the digital landlords our attention, data, and content. In return, they gave us apparently free access. They have not broken that bargain so much as rewritten it, without negotiation.
For those like me who use the web as a creative outlet with no expectation of a financial return, the only impact such changes have is on my ego. So few people are reading and responding to my brain-farts. However, for someone who had built a business on rented digital space, it is a rapidly advancing disaster, as the foundations of their business model are being rapidly removed.
The only antidote is to build your own digital house and fill it with furniture valuable to those with whom you wish to interact, so they choose to visit. The platforms must become the paths that lead to your home, not the real estate upon which your home is built.
P.S. The link in the first sentence of this post sends me to ‘LinkedIn gaol’. You will be lucky to find this post on any platform. Subscription, which is absolutely free and will not solicit anything, is the only way to ensure you see future posts.
Aug 4, 2026 | Analytics, Marketing
Calculating probabilities of an outcome is not the same as making a prediction.
What is the probability that a potential customer will buy your product rather than an alternative?
What is the probability that they will choose one SKU rather than another from your range?
Will your new advertising tagline remain in their memory long enough to influence a purchase?
Will a price increase reduce volume by more than the additional margin generated?
These are probability questions, but that does not make them the same statistical problem. They are not predictions.
Marketers have always tried to put numbers around questions like these, because the accountants and engineers who generally run the place distrust anything they cannot squeeze into a spreadsheet. A number, particularly one that looks like a detailed calculation, attracts far less scrutiny than an honest admission of uncertainty.
Probability models cannot remove uncertainty. They can, however, show us what normal buying behaviour looks like, and provide a benchmark against which we can test our assumptions.
Two drivers in any repeat purchase market
In established FMCG and other repeat-purchase markets, two forces largely shape buying behaviour.
The first is how often someone buys from the category.
Take the yoghurt market as an example. In every case, the behaviour and volume of buying will vary from heavy users to occasional buyers, from brand and sub category agnostic price buyers to brand advocates, and every point between.
Statisticians describe the pattern created by these different buying rates with the eye watering name of Negative Binomial Distribution, usually shortened to NBD.
NBD does not tell us that an individual will buy yoghurt next Thursday. It describes how purchase frequency spreads across the whole population: a few heavy buyers, many light buyers and a group who buy nothing during the measurement period.
The second force is brand choice.
Most buyers choose from a brand and variety repertoire. One brand may dominate their purchases, but price, availability, flavour, pantry stock and the occasionally volatile demands of the household influence each decision.
This is the Dirichlet model which describes how buyers divide their purchases across that repertoire. It is a weighted average of buyer behaviour across the market for each individual possible choice and combination of choices.
Think of each buyer rolling a set of weighted dice. Every brand appears on the dice, but some brands occupy more faces than others. The result remains uncertain, but it does not remain completely random.
Combine category purchase frequency with brand-choice probabilities and you can build a picture of the market. Statisticians would call it an NBD–Dirichlet market model.
From untidy households to stable market models
Continuing the yoghurt example.
The market contains multiple brands and many SKUs covering plain, fruit, Greek, low-fat, full-fat, lactose-free and some emerging and specialty products that represent a purchase choice in the wider yoghurt market.
Each household behaves differently. One buys frequently and moves between several brands. Another buys occasionally and nearly always chooses the same product. A third selects whichever brand carries the discount sticker.
Individual purchases look erratic. Add thousands of them together over time, and recognisable patterns emerge.
Large brands usually win because more people buy them, not because their customers display dramatically greater loyalty.
Smaller brands suffer a form of ‘double jeopardy’. They attract fewer buyers, and those buyers tend to purchase them slightly less often.
Buyers also share their purchases across competing brands. Your customers do not belong to you. They belong to the category and sometimes, when it suits them, buy your product.
Having operated in many FMCG categories over the years, those observations have held true in every case.
A model is not a crystal ball
The NBD–Dirichlet model works best in relatively stable markets where customers make repeat purchases and treat the competing brands as reasonable substitutes.
Defining the boundaries of the market is therefore crucial.
A parent may not see a child’s yoghurt pouch and a tub of plain Greek yoghurt as alternatives. Combining them in one model will produce statistical anomalies. The analysis should separate meaningful subcategories where buyer behaviour shows clear partitions.
The model cannot tell you whether a new tagline will lodge in buyers’ memories. That requires creative testing and evidence of memory and behavioural effects.
It cannot calculate the price elasticity, the contribution on margins of price changes, and likely competitor responses.
It cannot reliably forecast a genuinely new category for which no pattern yet exists. That task remains in the hands of the creative marketer, an increasingly valuable person in this age of ‘AI everything’
Importantly, the model cannot explain why every change occurred. A promotion, stockout, new distribution agreement, competitor withdrawal or advertising campaign may shift the observed probabilities. The model provides the baseline that helps us recognise when something unusual has happened.
Marketers should use probability models to challenge assumptions, establish realistic benchmarks and identify deviations and outliers worth investigating.
They should not be used as a crutch for decision making, as they cannot tell you why change has occurred.
Note: the combination of NBD and the Dirichlet models to reflect behaviour in a market comes from the Ehrenberg-Bass institute for marketing science.
Jul 24, 2026 | AI, Marketing
There was a time when advertising followed intent.
People searched for information, and businesses met them there with relevant answers. A second key attribute of the ‘old’ system was that advertising created mental availability. This resulted in a brand presence and positioning being already established when someone ventured towards a market. In other words, ‘Brand building’, a set of disciplines too often ignored by those injected with digital as a panacea to all marketing challenges. The exchange was simple: attention for information, need for solution.
That model has been inverted.
Accuracy and relevance are up for auction. Today, marketers rely on platforms like Google and Facebook to push ads towards loosely defined behavioural categories.
Whether the recipient is a genuine prospect or a bot farm is often beside the point. The system optimises for delivery, not relevance. In some cases, it seems that simply being human is no longer the qualifying criterion.
What used to be information-led discovery has become auction-driven visibility.
“Information” is now whatever wins the bid inside an opaque algorithm. The bidder willing to pay enough, while satisfying the platform’s quality formula gets the privileged slot, whether or not they offer the best answer.
It begins to resemble a protection racket worthy of the Corleone family more than a marketplace.
If you do not pay, you do not appear. Visibility is no longer earned through relevance or quality, but purchased through participation.
If we reverse the model, and are prepared to pay for clarity and priority for the needs of the information seeker rather than the advertiser, the logic shifts quickly towards paid access.
AI search sits awkwardly in a new middle ground we have yet to adequately define. It promises a clean, single ‘best’ answer without the clutter of ads. However, the definition of what constitutes a ‘best’ answer is unclear.
What it delivers is not truth., it is probability. A compressed synthesis of what has been said before, shaped into what is most likely to satisfy the query.
That works well for established facts. Ask for a public figure’s birth year, and you will probably get an accurate answer.
Move into ambiguity or historical uncertainty, however, and the cracks appear. The system does not “know” in any meaningful sense. It predicts.
Its goal is coherence, not correctness.
This creates a subtle but important risk: the illusion of authority without the guarantee of accuracy.
Meanwhile, alternatives are available, and more are emerging, where the user once again, becomes the customer.
Smaller search platforms are experimenting with models that shift incentives away from attention and towards trust. Some rely on subscriptions. Others allow limited, user-controlled advertising or blend in affiliate models.
What unites them is a simple premise: you are the customer, not the product.
Today, their market share is marginal, but the underlying value proposition is strong.
As digital environments grow noisier and more manipulative, the ability to control what reaches you becomes a premium feature.
That control will not be free.
But as with any system where incentives shape outcomes, which is most of them, paying for alignment rather than being monetised through distraction may prove to be a far better deal most of the time.
