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Psychographics could fundamentally reshape how we collect, analyze, and apply data in digital marketing and beyond. Marketers and analysts can use it to gain deeper insights into their target markets’ psychological and emotional motivations, and provide more relevant messaging to those audiences.

However, critics say it could pose a great risk to information privacy. Some experts fear the applications of psychographics could lead to a dystopian future — and that it may already be too late to reverse course.

In this explainer, we break down what psychographics is, the ways in which psychographic data is gathered, how that data is typically applied across industries from retail to real estate, and more.

What is psychographics?

Psychographics is the study of consumers based on their activities, interests, and opinions (AIOs).

It goes beyond classifying people based on general demographic data, such as age, gender, or race. Psychographics seeks to understand the cognitive factors that drive consumer behaviors. This includes emotional responses and motivations; moral, ethical, and political values; and inherent attitudes, biases, and prejudices.

Gathering and analyzing this data allows marketers, advertisers, and researchers to create detailed “psychographic profiles” of audience segments, which are then used to create relevant messaging for those segments.

This is valuable to marketers and advertisers because, even within clearly defined demographic groups, there is often significant variance between individuals. Just because two people are roughly the same age and earn similar annual incomes doesn’t mean they share similar political views or personal values, for example.

As a result, advertising strategies such as direct mail, televised ads, and billboards are very blunt approaches. They can reach vast audiences, but the messaging may be irrelevant to huge swathes of those audiences.

In contrast, a psychographic profile contains information around a person’s interests, hobbies, emotional triggers, and lifestyle choices, among other data. This could provide insight into why someone might buy a specific product, support a given cause, vote a certain way, and much more.

For example, political consulting firm Cambridge Analytica created a psychographic profile that placed people in a particular market segment according to the presence or absence of five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism (popularly known as the OCEAN model of personality).

Using this information and more, brands can customize messages and tones accordingly.

Often, marketers will combine both demographic and psychographic data for a more holistic view of a consumer. In doing so, they can provide more tailored messaging and increase their chances of impact.

For example, imagine trying to market a vegan protein bar. You could run a Facebook ad targeted at athletes and fitness enthusiasts, and maybe find some success. But by getting more granular, you could market to a segment of vegans who feel strongly about the mistreatment of animals, or to health-conscious people who feel guilty when they eat sugary energy bars.

In an economy based almost exclusively on clicks, whether on products in online marketplaces or on published content, the time lag between an ad and a purchase or conversion drops to a matter of seconds — and makes every lever count.

Psychographic marketing, which plays on subconscious personality characteristics, is perfectly suited to help advertisers capitalize on impulsive decision-making. According to a 2009 experiment, psychographically-informed behavioral targeting increases click rates by 670%.

A later study, one of the first to test the effectiveness of targeting advertising, showed that because of the “propensity effect” of psychographic marketing to generate clicks, such advertising strategies outperform traditional advertising by a factor of 2 to 1.

But psychographic marketing is only as effective as the data underpinning it. How then, do companies acquire this information?

How psychographic information is collected

A public presentation to the Concordia Annual Summit in September 2016 by Cambridge Analytica’s then-CEO Alexander Nix gave the world one of its first glimpses into the psychographic techniques used by the company.

Discussing his company’s work on the Ted Cruz presidential campaign in the US, Nix acknowledged his company had acquired “four to five thousand data points on every American citizen.” But he gave no details on how the company had acquired it.

There are several different ways to gather and analyze psychographic data. Some methods include the use of:

• Traditional focus groups/interviews
• Set-top box viewing data
• Surveys/questionnaires/quizzes
• Psycholinguistic dictionaries
• Website analytics (e.g. Google analytics)
• Browsing Data
• Social media (i.e. likes, clicks, tweets, posts, etc.)
• Third-party analytics

With each data source, researchers can gain insight into consumer preferences either directly or indirectly. And while the data collection methods may be time consuming, the trove of information gathered can be game-changing.

For example, the main product Cambridge Analytica sold to its political clients like Cruz and Trump was a cutting-edge advertising strategy. To design and deliver these ads, however, it needed data. Lots of data. And where better to find it than on Facebook, a social network consisting of 2 billion users across the globe.

In 2015, Cambridge Analytica approached Dr. Alexander Kogan, a psychological researcher at Cambridge University’s Psychometrics Centre, to develop a Facebook app called “This Is Your Digital Life.” The app took the form of an online personality quiz.

It is now known that Cambridge Analytica harvested user data from Facebook in large part through “This is Your Digital Life,” which violated Facebook’s terms of service by sharing user data with the firm.

Despite all the attention this sensational incident received, it is hardly the only example of data acquisition with a psychographics flavor. There are plenty of legitimate ways to gather psychographic data.

IBM DEVELOPS A PSYCHOGRAPHICS TOOL

Since 2012, IBM has been compiling Linguistic Inquiry and Word Count (LIWC), a psycholinguistic dictionary that uses Twitter as its dataset.

Using this dataset to train its AI engine, Watson, IBM has been able to program an ever more refined set of “algorithms” to sort and retrieve psychographic information from emails, blog posts, text messages, search histories, online purchases, online reviews and comments, and, of course, social likes and shares.

Businesses can leverage these insights to drive more targeted marketing campaigns, acquire new customers, personalize consumer connections, and more.

Text-based datasets like these are particularly useful: “psycholexical studies” have shown that personality traits often show up in people’s word choices. To use one of Nix’s examples, because words like “apparently” and “actually” are indicative of a high degree of neuroticism, people whose harvested data frequently featured words like this would be marked as high “N” in their psychographic profile. Other linguistic markers could be mapped to the other Big 5 personality traits (OCEAN).

Another example is a 2018 study led by Simon Fraser University’s Sarah Lord Ferguson, in which researchers used LIWC to analyze the writing of 100 different wine blogs according to each one’s “analytical thinking, clout, authenticity, and emotional tone.” The team used that data to categorize each blog into 1 of 4 specific groups — “Analysts, Agnostics, Authentic Pessimists, and Confident Optimists” — that marketers could segment and target in specific ways.

Companies & industries leveraging psychographics

A number of startups are working on providing psychographic insights to enhance audience outreach.

It’s important to note that, even though the potential for misuse exists, this can be done without improperly gathering data on individuals. And often, data collection is “anonymized,” meaning the underlying data powering the targeting is scrubbed of individual identifiers.

Many psychographic profiles, as we’ll see in industry examples below, are relatively harmless complements to traditional targeting methods.

Television and video advertising company Videology, for example, has developed a platform that incorporates psychographic segmentation to drive greater advertising results.

CaliberMind, which raised $3.2M in a second tranche of a seed round in 2017, builds psychographic profiles using machine learning and human language analysis. The company assesses a person’s language using natural language processing (NLP) in order to understand what buyers are talking about.

It’s important to note that, even though the potential for misuse exists, this can be done without improperly gathering data on individuals. And often, data collection is “anonymized,” meaning the underlying data powering the targeting is scrubbed of individual identifiers.

Many psychographic profiles, as we’ll see in industry examples below, are relatively harmless complements to traditional targeting methods.

Television and video advertising company Videology, for example, has developed a platform that incorporates psychographic segmentation to drive greater advertising results.

CaliberMind, which raised $3.2M in a second tranche of a seed round in 2017, builds psychographic profiles using machine learning and human language analysis. The company assesses a person’s language using natural language processing (NLP) in order to understand what buyers are talking about.

It’s important to note that, even though the potential for misuse exists, this can be done without improperly gathering data on individuals. And often, data collection is “anonymized,” meaning the underlying data powering the targeting is scrubbed of individual identifiers.

Many psychographic profiles, as we’ll see in industry examples below, are relatively harmless complements to traditional targeting methods.

Television and video advertising company Videology, for example, has developed a platform that incorporates psychographic segmentation to drive greater advertising results.

CaliberMind, which raised $3.2M in a second tranche of a seed round in 2017, builds psychographic profiles using machine learning and human language analysis. The company assesses a person’s language using natural language processing (NLP) in order to understand what buyers are talking about.