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 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 30, 2026 | Analytics, Management
Assembling and leveraging the complex mix of information and capabilities required to ‘win’ is a challenge similar to climbing a slippery ladder.
Most rely on data, spreadsheets, and extrapolations, blended in with optimism.
At the bottom of this ladder is data. Raw, unfiltered, and almost useless on its own. Just numbers, words, events. Noise without context. Like a kids jigsaw puzzle upended in a pile on the floor.
The ‘ladder’ has five steps.
Data. The pile of numbers, littered around files, outside sources of information, and peoples brains, without context relevant to the questions being posed.
Information. You start sorting. Cleaning. Labelling. Structure begins to emerge. It’s janitorial, unglamorous, tedious, and essential. This is where many people mistake motion for progress.
Knowledge. Connecting dots delivers knowledge. You see cause and effect, frequency, variation, and importantly the outliers. You can answer questions like “What’s happening?” and “How often?” Knowledge is a map, but it is still two-dimensional.
Analysis. This requires you test the knowledge, isolate variables, run comparisons, identify the outcome drivers, and develop ‘what if’ models. You don’t just have answers; you know how strong the answers are, and where they might fall apart. It gives you that added dimension.
Insight. Here’s where the real game begins. Insight is not just about seeing patterns, it’s about seeing meaning. It’s the inflection point, where a thousand observations converge into a single, clarifying idea.
But insight alone is not enough.
Running parallel to this clean, rational ladder is a tangled, intuitive, and very slippery pole: wisdom. Hard to see, impossible to teach, and absolutely critical to success.
Wisdom doesn’t follow steps. It grows from pattern recognition forged from lived experience. Mistakes made, ‘hunches’ that turned out to be right, and often spectacularly wrong, opportunities misread or missed completely. It takes time, perseverance, an ability to learn, curiosity, and determination.
It’s what lets a seasoned operator say, “That’s not going to work,” without needing a single chart, and be right. It’s instinct layered over time, a mental shortcut engine built on scars, sweat, and sleepless nights. It’s experience distilled into reflex.
The irony is that the higher you climb the corporate hierarchy, the more you need that slippery pole of wisdom.
If you only trust the data ladder, you might build the wrong solution brilliantly. If you only trust the pole, you risk hubris.
But when you climb with both, methodical analysis on one hand, and wisdom on the other, you are better able to understand problems, see the wider context, and spot points of leverage.
That’s the difference between a strategist and everyone else.
