Aqumena Review

Ashoke Agarrwal

September 3, 2026 12:52pm

Ashoke Agarrwal

Abstract: Consumer psychographics seeks to explain why people with similar demographic profiles desire different products, brands and experiences. From the intuitive assessments of bazaar shopkeepers to post-war motivation research and VALS segmentation, marketers have long used attitudes, values and lifestyles to shape strategy. Digital platforms amassed unprecedented behavioural knowledge, yet performance marketing often reduced insight to clicks and conversions. Conversational AI marks a deeper shift: assistants may understand consumers intimately, recommend products and eventually compete in consumer markets themselves. The resulting advantage could transform marketing into a continuous commercial relationship. The challenge is ensuring that relevance does not become concealed persuasion, psychological surveillance or self-serving recommendation by AI conglomerates.

Long before consumer psychographics acquired a scientific name, it was practised in the bazaar.

An experienced shopkeeper could rapidly size up the person entering the shop: conservative or adventurous, confident or anxious, status-conscious or value- conscious, decisive or persuadable. The merchandise displayed, the benefits emphasised, the price quoted and even the manner of address could change accordingly.

The shopkeeper did not possess data dashboards or segmentation algorithms. What he possessed was accumulated human insight.

Consumer psychographics is the attempt to systematise that insight. It is the science—and also the art—of classifying consumers according to their attitudes, values, aspirations, lifestyles and behavioural tendencies, and using these insights to shape product, brand, communication and marketing strategies.

Demographics tell us who consumers are in externally observable terms: age, income, gender, education, location and family structure. Psychographics attempt to explain why people who look identical on a demographic spreadsheet can want entirely different things.

Two 35-year-old, upper-middle-income consumers living in Mumbai may have the same purchasing power but very different motivations. One may seek reassurance, social acceptance and trusted brands. The other may prize experimentation, individuality and being ahead of the curve. Marketing to them in the same way merely because their demographics match is an invitation to mediocrity.

The systematic study of such motivations gathered momentum during the great post- Second World War expansion of consumer markets. Researchers began borrowing from psychology, sociology and anthropology to look beneath observable purchasing behaviour. Ernest Dichter, one of the pioneers of “motivation research”, brought psychoanalytic ideas and depth-interviewing techniques into the world of brands. His work helped demonstrate that people did not simply buy the functional attributes of cars, soaps or packaged foods. They also bought emotional reassurance, identity, fantasy and social meaning.

Some researchers explored adapting tools originating in clinical psychology. The Minnesota Multiphasic Personality Inventory, or MMPI, is sometimes mentioned in this history, although it is important to distinguish it from consumer segmentation systems: the MMPI was designed for psychological assessment, not marketing. Projective techniques, personality inventories and attitudinal studies nevertheless influenced the emerging discipline of consumer research.

The most famous marketer-oriented framework was VALS—Values and Lifestyles—developed in 1978 by social scientist and consumer futurist Arnold Mitchell and his colleagues at SRI International. Its later form organised American consumers into groups such as Innovators, Thinkers, Believers, Achievers, Strivers, Experiencers, Makers and Survivors, using primary motivation and access to resources as its principal dimensions.

Like every segmentation model, VALS simplified reality. Its categories were shaped by American culture and could not be transplanted mechanically to every country. But its great contribution was to give marketers a common language for discussing the inner orientation of consumers.

In the age of mass media, it was difficult to deliver a different television commercial to each psychographic group. Yet psychographics could still influence almost every element of the marketing mix: product formulation, design, packaging, pricing, distribution, brand positioning, choice of celebrity, tone of voice and creative strategy.

International brands provide useful illustrations, even where there is no public evidence that they formally commissioned VALS. Early Apple advertising spoke to Innovators and Experiencers who wanted to challenge convention. American Express built much of its imagery around the recognition and accomplishment sought by Achievers. Harley-Davidson combined the self-reliance of Makers with the rebellion of Experiencers. Patagonia increasingly appeals to consumers for whom International brands provide useful illustrations, even where there is no public evidence that they formally commissioned VALS. Early Apple advertising spoke to Innovators and Experiencers who wanted to challenge convention. American Express built much of its imagery around the recognition and accomplishment sought by Achievers. Harley-Davidson combined the self-reliance of Makers with the rebellion of Experiencers. Patagonia increasingly appeals to consumers for whom

A similar uncertainty hangs over another pillar of India’s media measurement infrastructure—BARC. As television, connected TV, streaming and online video steadily converge, audience measurement has become simultaneously more important and more complicated. Governance debates, commercial tensions and regulatory scrutiny have left the industry’s principal television currency navigating an uncertain future precisely when advertisers require a more integrated understanding of video consumption rather than a fragmented one.

I witnessed the value of this discipline during my strategic planning years.

We positioned Santoor to appeal broadly to the Believer mindset: consumers who were relatively conservative, rooted in tradition and comfortable with familiar, culturally resonant ingredients and local brands. Lux, the dominant beauty soap of the time, drew more strongly upon the Striver world—glamour, aspiration, stylish brands and the emulation of people with greater wealth or celebrity.

Santoor’s sandal-and-turmeric formulation, packaging, positioning and advertising formed a coherent whole. Three decades later, Wipro described Santoor as India’s largest soap brand by revenue in 2025—a claim that should be understood alongside differing market-share measures, but an extraordinary achievement for the former challenger nevertheless.

In cooking oils, the category’s communication was overwhelmingly conservative: care for the family, inherited wisdom and the dutiful homemaker. With Sundrop, a sunflower-oil brand, we addressed a more modern and aspirational consumer. The advertising was energetic, youthful and visibly different from category convention. Psychographics did not merely help select an audience. It helped create the product’s entire world.

Then came digital marketing, apparently the perfect medium for the psychographic age.

Social platforms and search engines could observe interests, browsing patterns, communities, content consumption, searches, purchases and responses to advertising. In theory, this made it possible to move beyond broad segments towards micro-segments—and eventually towards the individual.

Meta and Alphabet have unquestionably developed extraordinary algorithmic models of human interests and behaviour. Their commercial success depends substantially on predicting what will hold attention and what might prompt action. Google offers advertisers affinity, in-market, life-event and custom audience segments. Meta provides interest-based targeting, custom audiences, lookalikes and increasingly automated audience expansion.

These are psychographic ingredients, but they do not amount to a transparent, marketer-owned psychographic system comparable to VALS. The platforms generally do not hand advertisers an intelligible model explaining the consumer’s underlying worldview. Instead, the advertiser supplies an objective, creative material and data signals; the machine finds people likely to respond.

That distinction matters.

Digital marketing promised greater consumer understanding, but the seduction of measurable performance often narrowed the marketer’s field of vision. Click-through rates, cost per lead, conversion ratios and return on advertising spend became the dominant language. Instead of asking, “What does this brand mean in the consumer’s life?”, organisations increasingly asked, “Which execution generated the cheapest click?”

The result was paradoxical: more consumer data, but sometimes less consumer understanding.

Performance systems are very good at exploiting existing demand and identifying correlations. They are less naturally suited to deciding what a brand should stand for over ten years, which cultural tension it should resolve or which aspiration it should make its own. They optimise the response; they do not automatically supply the strategy.

Conversational AI now takes us across another threshold.

A social platform may infer that I am interested in travel because I watched several travel videos. A conversational assistant may know that I dislike crowded destinations, worry about my ageing parents, prefer history to nightlife, enjoy vegetarian food, have a particular budget and feel guilty about taking time away from work. This is not merely an interest signal. It is an unfolding account of my priorities, anxieties, compromises and intent.

The commercial question is no longer theoretical. OpenAI began testing advertising in ChatGPT in 2026. Its system can consider the context and intent of the current conversation and, where personalisation is enabled, selected signals from past chats and memory. OpenAI says advertisers do not receive users’ conversations, memories or personal details. Anthropic has chosen a strikingly different position, declaring that Claude will remain advertising-free because advertising inside an AI conversation would be incompatible with its conception of a trusted space for thought.

We are therefore seeing two possible models: the AI assistant as an intimate commercial intermediary, and the AI assistant as a paid, advertising-free space for thought.

But there is a third possibility—one that could make the debate about advertising almost secondary.

Many boosters of the coming AI age believe that the leading AI companies will not remain merely providers of models, assistants or computing infrastructure. They could vertically integrate into super-conglomerates with interests in financial services, healthcare, education, entertainment, travel, retail and other important consumer markets.

Google has already demonstrated how a technology company can extend across search, advertising, video, mobile operating systems, cloud computing, maps, payments and devices. It is not inconceivable that OpenAI, Anthropic or future AI leaders could follow a still more ambitious path, either by entering consumer categories themselves or through alliances, acquisitions and AI-powered marketplaces.

Imagine the competitive advantage such a company might possess. It could combine advanced research, software-development capability, computing resources and automation with an exceptionally detailed understanding of consumers. It might know not only what people search for or purchase, but how they describe their aspirations, relationships, anxieties, financial constraints and unresolved decisions.

Whether companies would be permitted—or trusted—to use conversational information in this manner is another question. Consent, privacy regulation, data separation and competition law would all matter. Nevertheless, even aggregated and privacy-protected insights drawn from hundreds of millions of interactions could reveal emerging needs and consumer tensions much earlier than conventional market research.

An AI company entering insurance, travel or education might therefore begin with a substantial insight advantage over established players. It could identify the product consumers need, design it rapidly, personalise the experience and improve it continuously. The AI company would no longer merely help advertisers find consumers. It could become the competitor those advertisers must confront.

Even a company that eschews conventional advertising might develop an entirely new form of marketing through its continuing relationships with users. The assistant might suggest a service, incorporate it into a plan, answer objections, customise the offer and facilitate the transaction—all within an apparently helpful conversation. There need be no banner, commercial break, celebrity endorsement or recognisable sales pitch.

Would that constitute advertising? Perhaps not according to today’s definitions. It might be closer to a relationship with a trusted adviser who is simultaneously the recommender, salesperson, marketplace and, potentially, owner of the product being recommended.

This is where the distinction between relevance and influence becomes critical. If an assistant recommends the best product for the user, it is performing a service. If it subtly favours a product owned by its parent company, commercial partner or highest bidder, it is marketing—whether or not the interaction carries an advertisement label.

Traditional advertising regulation is built around identifiable commercial messages. AI may require us to regulate commercial intent: disclosure of economic relationships, separation between advice and ownership, auditable recommendation systems and a clear obligation to act in the user’s interest.

Used responsibly, AI-generated psychographic insight could help marketers create genuinely better products and more relevant communication. It might finally combine the shopkeeper’s sensitivity to the individual with the scale of mass marketing.

Used irresponsibly, it could become psychological surveillance: persuasion calibrated not only to our desires but to our private fears and moments of vulnerability. Used by an AI conglomerate selling its own products, it could confer a competitive advantage that few independent brands could match.

The central issue, therefore, is no longer simply whether AI can understand consumers. It almost certainly can—and with increasing subtlety.

The questions are who owns that understanding, which businesses are allowed to benefit from it, whether the assistant serves the consumer or its corporate parent, what consent has been granted, and where helpful recommendation ends and concealed persuasion begins.

Consumer psychographics began with the observant shopkeeper looking across the counter. In the age of AI, the counter may disappear. The new shopkeeper could know what we bought yesterday, what we confessed at midnight—and manufacture the product it recommends tomorrow.

That will make psychographics vastly more powerful. It must also make marketing, corporate governance and competition policy vastly more responsible.