1. Introduction: Foundations of Traditional STP and the Imperative for Behavioural Evolution
The Segmentation, Targeting, and Positioning (STP) framework has been a bedrock of marketing strategy since Philip Kotler popularised it in his 1967 book Marketing Management. It offers a clear, logical method for businesses to understand and approach their markets. Segmentation is the first step, where a large, heterogeneous market is broken down into smaller, more homogeneous groups based on shared characteristics. These can include demographics, such as age, gender, income, or education level; geographics, like urban or rural locations and climate influences; psychographics, encompassing lifestyles, values, personality traits, and social class; or behaviours, including product usage rates, brand loyalty, benefits sought, or occasion-based purchases. Targeting comes next, involving the evaluation of these segments for their attractiveness. Factors like market size, growth potential, profitability, competitive landscape, and the firm’s ability to serve them are considered. Businesses might opt for concentrated targeting, pouring resources into one promising segment, or differentiated targeting, customizing efforts for several. Positioning rounds out the process by developing a distinct image or value proposition in the target customers’ minds, often through messaging that emphasises unique attributes or benefits to stand out from competitors.
This traditional model operates under the assumption of a rational consumer who makes decisions by carefully weighing options and information to maximise personal utility. It has demonstrated practical value, guiding strategies in various industries—for example, a car manufacturer segmenting by family size to target minivans at households with children, or a smartphone company targeting tech-savvy young adults with innovative features. The framework’s enduring appeal lies in its ability to impose order on market complexity, enabling efficient resource allocation and focused, effective campaigns that drive sales and loyalty.
However, the rationality at STP’s core reveals cracks when examined against how people truly behave. Human decisions are not always logical; they are frequently influenced by emotions, cognitive biases, and mental shortcuts that lead to predictable but irrational choices. A key critique is the reliance on aggregated data to form ‘average’ consumer profiles, which can create illusions of uniformity. Take the United States Air Force’s 1950s initiative to design cockpits for an ‘average’ pilot based on mean measurements— it failed because no individual pilot matched the average across all dimensions, illustrating how averages ignore the ‘jagged’ variability in human traits and lead to misfits in design and strategy.
Rory Sutherland’s concept of psycho-logic offers a direct counterpoint, reframing human behaviour as driven by intuition, context, and emotional logic rather than pure calculation. As Vice Chairman at Ogilvy, Sutherland draws from behavioural science to advocate for perceptual alchemy—creating value through subtle shifts in how things are experienced, without altering the underlying reality. For instance, instead of spending billions to make Eurostar trains faster, adding simple luxuries like champagne can enhance perceived satisfaction and value.
This essay argues that incorporating Sutherland’s psycho-logic into STP evolves it into a gold-standard framework, one with superior utility for addressing real human irrationality in marketing practices. By bridging the gaps in the original model with behavioural insights, it promises more adaptive, effective approaches. The discussion will explore psycho-logic’s theoretical foundations, the reframed STP components, practical case studies, and an evaluation of its coherence and potential as a benchmark.
2. Theoretical Foundations: Building Psycho-Logic from Seminal Behavioural Insights
Sutherland’s psycho-logic does not emerge in isolation but rests on a rich foundation of behavioural science that exposes the flaws in assuming people act rationally. Daniel Kahneman and Amos Tversky’s prospect theory, developed in the late 1970s, reveals how individuals weigh losses more heavily than equivalent gains—a bias that explains why a price drop feels less exciting than avoiding a surcharge. This asymmetry drives everyday choices, like opting for insurance to avert potential regret rather than calculating pure odds. Kahneman’s later distinction between System 1 (fast, intuitive thinking prone to errors) and System 2 (slow, analytical reasoning) underscores that most decisions happen automatically, influenced by heuristics rather than deliberation. Dan Ariely extends this with predictable irrationality, showing through experiments how context skews judgements; for example, people rate the same beer higher when told it’s from a premium brewery, highlighting expectation’s power over objective taste. Richard Thaler’s nudges build on these, demonstrating small environmental tweaks—like placing healthier snacks at eye level—can guide behaviour without restricting freedom, pointing to future applications in digital interfaces where algorithms subtly shape user paths.
Marketers have long applied these insights practically. Edward Bernays, in the 1920s, manipulated subconscious desires to make smoking fashionable for women by linking it to empowerment, proving perception can redefine products. Byron Sharp’s work on mental availability emphasises that brands grow by being top-of-mind through fame and emotional cues, not just functional superiority. Sutherland synthesises these at Ogilvy, where campaigns exploit biases like social proof—seeing others endorse a product—to build trust. His psycho-logic thus fuses science with practice, anticipating a marketing landscape where AI amplifies perceptual tweaks for personalised, intuitive appeals.
This foundation sets the stage for reframing STP’s components, where psycho-logic injects behavioural depth into segmentation, targeting, and positioning.
2.1 Evolved Segmentation: From Static to Dynamic Psycho-Logic Clusters
Traditional segmentation in STP relies on static bases that assume people can be neatly grouped by surface traits. Demographics like age or income, geographics such as location, and even psychographics tied to lifestyles often paint a picture of uniformity within categories. This approach creates the illusion of predictable clusters, but it falters because human traits do not align smoothly. People are jagged in their attributes—one person’s spending habits might defy their income bracket due to personal history or mood. The United States Air Force’s cockpit redesign in the 1950s exposes this flaw starkly: engineers averaged pilots’ measurements to build a universal fit, only to discover no pilot matched the average on all fronts, leading to discomfort and inefficiency. In marketing, this averagarianism results in strategies that miss the mark, like assuming all millennials crave the same tech gadgets when their actual choices stem from deeper, irrational drivers.
Psycho-logic demands a shift to dynamic clusters grounded in behaviours and occasions, where biases and contexts reveal true decision patterns. Behavioural segmentation groups people by psychological traits, such as loss aversion, where the fear of missing out trumps potential gains. A bank might identify customers who cling to familiar accounts despite better options, influenced by status quo bias, rather than lumping them by age. Occasion-based segmentation captures situational triggers, like a snack brand targeting late-night impulses when self-control dips, drawing on ego depletion where tiredness leads to unplanned buys. These methods align with prospect theory’s insight that losses loom larger than gains, or predictable irrationality’s proof that context alters value—like rating a meal higher in a cosy setting. Everyday evidence abounds: shoppers grab limited-stock items not for need, but scarcity’s pull, showing how occasions flip disdain to desire.
Implementing this involves tools like digital analytics for real-time data, tracking app interactions to spot patterns without invasive surveys. In the future, AI could refine these clusters further, predicting shifts from emerging behaviours. This evolved segmentation feeds naturally into targeting, where psychological fit takes precedence over sheer numbers.
2.2 Evolved Targeting: Prioritizing Psychological Resonance
Traditional targeting in STP hinges on quantitative metrics that promise efficiency but often deliver mediocrity. Market size, growth rates, profitability, and accessibility guide choices, leading firms to chase large segments with broad appeals. This creates the false comfort of numbers—assuming bigger means better—yet it ignores how human decisions defy averages. A segment might look promising on paper, but if it lumps diverse motivations together, efforts scatter and fail. For instance, targeting all urban professionals by income overlooks that one subgroup avoids risks due to past losses, while another seeks thrills through novelty. The illusion here is stark: rational calculus assumes uniform responses, but people satisfice with emotional shortcuts, not optimise with spreadsheets. This mismatch wastes resources on generic strategies that resonate with few, as seen when brands flood markets with undifferentiated ads, yielding low engagement.
Psycho-logic reframes targeting around psychological resonance, where segments are chosen for their alignment with biases and emotional hooks. Evaluation criteria shift to how well a group’s traits—such as reciprocity or social proof—match the offering’s perceptual strengths. Sutherland stresses outliers over masses; anomalies often hold untapped potential. In the Eurostar case, targeting experience-seekers with simple upgrades like staff interactions or onboard luxuries created disproportionate value, far beyond speeding up trains for an average commuter. Everyday parallels abound: a fitness app targets habit-breakers during New Year’s resolutions, leveraging temporal biases for peak motivation, rather than blanket demographics. This approach draws on prospect theory’s loss-gain imbalances, selecting groups where small reframes amplify perceived benefits.
Experimental methods underpin this, with A/B testing validating resonance through real behaviours, not assumptions. Ethical lines emerge clearly—avoid manipulation that exploits vulnerabilities, focusing instead on nudges that enhance autonomy. Looking ahead, AI-driven insights could automate resonance detection, spotting subtle patterns in data streams. This targeting flows seamlessly into positioning, where perceptual tweaks turn resonance into lasting differentiation.
2.3 Evolved Positioning: The Art of Perceptual Alchemy
Traditional positioning in STP focuses on highlighting product features, benefits, or direct comparisons to competitors, aiming to carve out a logical niche in the consumer’s mind. This assumes people evaluate options through reasoned analysis, prioritising specs like speed or durability. Yet, this creates a narrow battlefield where brands compete on incremental improvements, often leading to commoditisation. The core illusion is that objective facts drive choices, but perception overrides reality—people buy feelings, not things. A watch’s precision matters less than its status signal; without emotional pull, even superior features fade into indifference. Psycho-logic dismantles this by insisting value emerges from how something is framed, not what it is, drawing on biases where intuition trumps logic.
Positioning evolves into perceptual alchemy, transforming mundane offerings through subtle, irrational cues that tap emotional and sensory layers. Techniques include reframing to exploit asymmetries—presenting a product as a loss avoided rather than a gain achieved—or adding signals like scarcity to heighten desire. Everyday examples illustrate this: a hotel places a chocolate on the pillow not for taste, but to signal care, turning a standard room into a thoughtful retreat. Sensory tweaks amplify this; adding weight to a lightweight vacuum creates a illusion of power, aligning with intuitive associations of heft equalling strength. Red Bull positions its drink as an extreme mindset booster, not caffeine, leveraging status biases to command premiums. De Beers turned common diamonds into eternal love symbols by crafting scarcity myths, proving narrative shapes worth over rarity.
Measurement shifts from sales figures to emotional indicators, like satisfaction surveys or engagement rates, allowing iterative refinement. Test perceptual changes through split groups, adjusting based on real responses. In the future, AI could personalise these alchemies at scale, predicting individual biases for tailored signals. This positioning leads directly to practical demonstrations, where case studies reveal its tangible utility.
3. Case Studies: Demonstrating Utility in Practice
Uber’s ride-sharing service exemplifies psycho-logic in consumer goods, where segmentation and targeting focus on behavioural anxieties rather than demographics. Riders are clustered by occasion—peak stress times like rush hour—revealing a group prone to wait-time frustration, rooted in loss aversion where delays feel like stolen minutes. Targeting this outlier, Uber positions its app with real-time tracking maps, not faster cars. This perceptual tweak reduces perceived uncertainty; a dot moving on screen signals progress, turning impatience into reassurance. Facts show engagement rises—users check apps more, boosting loyalty without infrastructure costs. Like adding mirrors by elevators to shorten felt waits, this alchemy proves utility: retention improves, proving small biases yield big gains. Future apps might use AI to predict personal triggers, extending this to seamless experiences.
Netflix applies evolved STP in digital marketing through behavioural segmentation for content recommendations. It groups viewers by viewing habits—binge-watchers versus casual browsers—ignoring age or location for patterns like genre loyalty or drop-off points, influenced by status quo bias. Targeting completion-driven users, Netflix positions shows with ‘next episode’ auto-plays and personalised thumbnails, framing choices as effortless continuations. This exploits reciprocity; suggestions feel tailored, not pushed. Evidence from user data reveals higher retention—subscribers stay longer, cutting churn. Unlike broad ads, this creates emotional hooks, like discovering a hidden gem mid-browse. Speculatively, as streaming fragments, such psycho-logic could dominate, with algorithms refining occasions for hyper-personal nudges. These cases highlight the framework’s real-world edge, setting up its broader evaluation.
4. Evaluation: Coherence, Gold-Standard Potential, and Argument for Utility
This evolved STP framework holds together through its seamless integration of psycho-logic with established behavioural principles, filling the voids in the original model. Coherence shines in how it addresses rationality’s blind spots—traditional STP assumes deliberate choices, but psycho-logic embeds System 1 intuitions, like prospect theory’s loss framing, directly into processes. Segmentation’s dynamic clusters align with predictable irrationality, where contexts trump traits; targeting’s resonance echoes nudges that guide without force; positioning’s alchemy mirrors social proof’s everyday sway, as when friends’ endorsements tip decisions. These elements form a unified path, each step building on the last, without contradictions. Facts from daily life confirm this: people skip logical comparisons for gut feels, as in choosing a branded coffee for its vibe over a cheaper brew. The framework’s logic withstands scrutiny, adapting behavioural science to marketing without distortion.
Its gold-standard potential emerges in perception-driven fields like consumer goods, where it outperforms rational approaches by leveraging biases for loyalty and premiums—Uber’s tweaks cut churn, Netflix’s retain viewers. Limitations exist: in regulated sectors like finance or healthcare, logic must dominate to meet compliance, and over-reliance on psycho-logic risks ethical slips if tweaks border on deception. Yet, in an AI era, it gains edge by using data to spot biases at scale, speculatively enabling hyper-personal strategies that anticipate shifts. This utility argues for adoption—rational models falter in irrational worlds, but this evolution delivers measurable wins.
Broader implications point to marketing as creative psychology, fostering inclusive, effective practices that eclipse outdated efficiency.
This evaluation underscores the framework’s promise, leading to reflections on its future role.
5. Conclusion: The Future of STP in a Psycho-Logical World
This reframed STP integrates psycho-logic to bridge traditional rationality with human biases, evolving segmentation into dynamic clusters, targeting toward resonance, and positioning as perceptual alchemy. Key arguments show it counters averagarianism’s illusions, as in jagged consumer traits, and delivers utility through cases like Uber’s anxiety-reducing maps and Netflix’s habit-driven recommendations, yielding higher engagement without overhauls.
Adopt this in education and practice to craft adaptive strategies; marketers should experiment with behavioural tweaks for real gains. Future research could test its scalability via empirical studies, exploring AI’s role in automating biases for personalised marketing, ensuring it becomes a benchmark in an irrational world.
References
Ariely, D. (2008). Predictably Irrational: The Hidden Forces That Shape Our Decisions. HarperCollins. https://www.harpercollins.com/products/predictably-irrational-dan-ariely This book provides foundational insights into contextual irrationality and expectation biases, informing discussions of perceptual tweaks in positioning and case studies like Netflix recommendations.
Cialdini, R. B. (1984). Influence: The Psychology of Persuasion. Harper Business. https://www.harpercollins.com/products/influence-robert-b-cialdini Seminal work on principles like scarcity and social proof, underpinning psycho-logic techniques in segmentation, targeting, and perceptual alchemy.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. https://us.macmillan.com/books/9780374533557/thinkingfastandslow Explains System 1 and System 2 thinking, central to critiquing rational STP assumptions and emphasising intuitive biases throughout the evolved framework.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292. https://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Behavioral_Decision_Theory/Kahneman_Tversky_1979_Prospect_theory.pdf Introduces loss aversion and framing effects, key to behavioural segmentation, targeting resonance, and perceptual reframing.
Kotler, P. (1967). Marketing Management: Analysis, Planning, and Control. Prentice-Hall. https://en.wikipedia.org/wiki/Philip_Kotler (references the 1967 first edition) The original source of the traditional STP framework, contrasted with the behavioural evolution in the introduction and throughout.
Sharp, B. (2010). How Brands Grow: What Marketers Don’t Know. Oxford University Press. https://www.amazon.com/How-Brands-Grow-What-Marketers/dp/0195573560 Contributes ideas on mental availability, supporting psycho-logic’s focus on emotional cues over functional superiority.
Sutherland, R. (2019). Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life. William Morrow. https://www.harpercollins.com/products/alchemy-rory-sutherland Core text synthesising psycho-logic, with examples like the Eurostar champagne upgrade and critiques of averagarianism, permeating the entire reframing.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press (later Penguin editions). https://www.penguinrandomhouse.com/books/690485/nudge-by-richard-h-thaler-and-cass-r-sunstein Advocates choice architecture and nudges, influencing ethical considerations in targeting and perceptual interventions.