Have you ever approved a website cookie to monitor your online behavior, or downloaded a mobile application and allowed it to access personal data on your phone? Most likely you have. But haven’t you ever wondered what kind of information your digital trail leaves behind and how this data is being used?
This article will reveal the digital influencer’s new secret weapon–coined ‘Psychographics‘–and how easily accessible online data is being used to psychologically influence your behavior in both the online and offline space.
The term Psychographics refers to a quantitative method to describe and segment consumers on the basis of psychological attributes such as behavioral preferences, personality, beliefs, opinions, interest, attitudes, values, and to some extent habits and lifestyles.
An individual’s psychographic profile can be inferred, to an accurate level, from the data one leaves behind when surfing online, engaging on a social media site, making an online purchase or using a mobile application.
Using linear regression techniques, algorithms can predict psychological traits and states of individual users and thus tailor messages, web designs, background colors, and products on the fly to match individual needs.
Google knows you better than you know yourself
Search engines, online platforms, and mobile applications collect a tremendous amount of data about their users. Not only do these platforms collect information about the devices and applications you use, but also a lot of (real-time) personal information about you (e.g. voice and visual data, biometrics, personal characteristics) and the data stored on your mobile phone (e.g. phone numbers, call history, private messages, social media likes).
They also monitor your behavior online, carefully tracking the time you spend on sites, the locations you have visited, the reviews you leave behind, and even the payments you make.
The online collection of demographic data (e.g. gender, age, income, nationality) and behavioral data (e.g. online purchasing behavior, clicks, likes), together with psychographic profiling, provide these platforms with highly detailed insights about their users and razor-sharp abilities to accurately predict (predictive analytics) and influence behavior online (targeted actions). Digital platforms now not only know ‘who’ you are and ‘what’ you do, but with psychographics can also explain ‘why’ you do it.
Benefits of using Psychographics to improve performance
Reading the above, you now understand that psychographic information can be used to create predictive tools, optimize online services, and effectively target consumers. The following will provide examples of how psychographics are currently being used in real-life applications.
Predicting (future) product choice
Choice prediction makes use of complex algorithms to analyze consumer data to predict life situations, psychological traits, and product matches. Using predictive analytics, new products are presented to people matching predefined customer profiles based on historical data.
A great example of a company making effective use of product choice prediction is U.S. retail company Target.
Using demographic and psychographic data, search queries and historical product purchase patterns of existing consumers, Target is able to predict current life situations of existing customers.
Based on the above-mentioned data, one specific life situation Target can predict is whether a female customer is pregnant (and more precisely, in which stage of the pregnancy cycle she is in), allowing the company to target specific products to the precise people at the right time.
Political campaign effectiveness
Psychographic data analysis can also be used to influence attitudes and beliefs, potentially swaying voting decisions. Using segmentation data based on demographic, geographic and psychographic data, highly differentiated target groups can be identified (e.g. location, ethnicity, beliefs, opinions, current sentiments, biggest fears), which are then contacted using tailored messages to improve communication effectiveness.
Moreover, standardized media campaigns and communication messages can also be automatically adjusted to match the belief systems, motivations and fears of its target audience, allowing for different variations of the same communication (e.g. email campaign, news article) to reach a single household fitting the exact beliefs and motivations of each family member.
It is believed that the company Cambridge Analytica supported the 2015 Brexit campaign in the UK and the 2016 U.S. presidential elections by providing behavioral insights based on psychographic data to increase political campaign effectiveness.
Personality-based advertising campaigns
Research by Sandra Matz and colleagues (2016) from the Psychometrics Centre at Cambridge University suggest in their article ‘Money Buys Happiness if Spending Fits our Personality’ that creating advertising campaigns (product and design) which match consumers’ most dominant personality traits, can increase online conversion rates of campaigns.
Moreover, according to the article, purchasing products which fit one’s personality has been found to increase happiness. A great example of such a personality-based advertising campaign is that of Hilton Honors. Hilton developed a mobile app for Facebook called Holiday MatchMaker, which personalizes messages to its users based on personality types and lifestyle preferences inferred from social media likes or mini personality quizzes.
Media reported that the Hilton app improved higher click-through rates and online sharing of targeted posts compared to conventional online marketing campaigns.
In the movie ‘Minority Report’ people are arrested for future crimes which haven’t taken place yet. Sounds pretty Sci-Fi, doesn’t it? Well, guess what, recent advancements in predictive analytics have made it more probable that the future arrest of ‘Howard Marks’ could happen very soon.
Police and intelligence forces around the world have started to use artificial intelligence to predict the likelihood of crime offenders recommitting a crime. Using various data points (e.g. national historical data of criminal offenders, demographic data, psychographics and crime severity), algorithms are used to aid jail sentencing and parole decision-making. In the spring of 2017, Eric L. Loomis was sentenced to six years in jail, based in part on a predictive analytical crime management system called Compas, created by U.S. company Northpointe.
Equally interesting to mention is an online platform to identify crime-based danger zones in a city. Using predictive analytics, rio.crimeradar.org predicts potential crime hotspots in the city of Rio de Janeiro (one of the world’s most dangerous cities). It is probable that in the near future national security organizations will use more advanced algorithms to enable improved prescriptive crime-preventing decisions.
An increasing number of insurance companies and healthcare providers are using psychographics and artificial intelligence to improve diagnostics, advance precision treatment therapies, predict/promote positive health-behaviors and predict/minimize the occurrence of potential health conditions.
In the UK, the NHS uses Google’s DeepMind program to help healthcare professionals improve early disease detection and improve treatment decisions using big data and predictive analytics. Insurance companies, on the other hand, use predictive analytics to better align insurance premiums with specific risk profiles. Confused.com is an example of an online insurance company which uses psychographic data to predict the likelihood of a first-time car insurance taker to have an accident.
One of the algorithm’s indicators of risk is personality, which is used to calculate the premium to be paid for a first-time car insurance. For example, it is considered that a highly “conscientious” first-time driver is less likely to have a car accident than other personality types.
One last note…
Interested in finding out what kind of psychographic information social media sites are collecting about you? Check out www.dataselfie.it to track the type of data Facebook is collecting about you, based on your online behavior and your social connections. Learn how Facebook’s machine learning algorithms are using this information to predict your personality to adapt content and advertising to fit your persona!
Want to learn more?
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Ali Fenwick is Professor of Organizational Behavior and Innovation. At HULT, he teaches Leading for Innovation, Organizational Behavior, Behavioral Economics, Management Psychology and Leadership Development. Ali’s research focuses on the behavioral foundations of organizations and management and explores how psychological interventions can be applied within the (digital) workplace to increase employee well-being and organizational performance. Ali is also the Founder and CEO of LEAD TCM&L™, a global behavioral science advisory firm developing nudges and psychological interventions for Business, Education, Government, and NGOs. Ali is a Harvard Business Review Contributor, TEDx and Keynote Speaker, Conference Chair, Author, and Strategic Advisor.