How ML Enhances Psychographic Customer Profiling
8 min read
In today’s age of digitization, you can think that understanding your customers would be optional. Rather, it is quite imperative for modern times.Companies no longer confine themselves to the frontiers of demographic segmentation, but they move into psychographic profiling.
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What is Psychographic Customer Profiling in Machine Learning?
Bridging Psychology and Data Science. Ready to Witness the Use of Machine Learning Techniques within Your Organization
In machine learning, psychographic customer profiling is the process whereby algorithms are used to identify and describe individuals on the basis of psychological traits, including values, personality, lifestyle, interests, and motivations, by looking at patterns within the data concerned.
In contrast to demographic profiling, which helps one to answer the specific question of who the customer is, psychographic profiling can in some sense explain why they behave in a particular way.
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The Challenges of Traditional Psychographic Analysis
Limited Scalability and Manual Processes
Traditional psychographic analyses making use of surveys, focus groups, and in-depth interviews gather insight about consumers’ personalities, values, interests and lifestyles.
While qualitative data can be rich, these methods are long-winded, expensive, and labour-intensive.
Scaling such methods to very large or specific audiences is not feasible; thus, the application of such insights by brands is compromised in terms of generalization or real-time application.
Manual collection and interpretation of data could, then, in theory, lead to a great deal of human-error bias and less frequent updates of customer profiles.
Data Reliability and Self-Reporting Bias
Psychographic profiles are largely dependent on self-reporting, which is inherently subjective.
People may consciously or unconsciously report false interests because it is socially desirable or simply due to self-unawareness. Therefore, the findings may not represent true behaviour or preference.
This disjunction might mislead insights and in the end distort the effectiveness of segmentation and targeted campaigns.
Static and Outdated Profiles
One of the largest issues with traditional psychographic analyses is that they are static. Profiles built from one-time surveys or focus groups do not change with the customer.
In the fast-moving digital environment of today, consumer preferences can shift rapidly with trends, life events, or social influences; hence, in the absence of an active process to input continuous streams of data into analysis, traditional psychographic profiles can soon become outdated and irrelevant.
Limited Integration with Digital Behavior
Most of the psychographic techniques used today operate separately from behavior and transactional data-based systems.
This separation locks marketers from using an entirely integrated psychographic insight in a digital environment where real-time personalization is concerned.
The only thing left is make-theory by itself if psychographic data is not integrated into these digital touchpoints, such as e-commerce, social media, and mobile applications.
Difficulty in Measuring ROI
The other problem is directly linking psychographic segmentation with business results.
Though the psychographic insights would give a good foundation for creating and advertising, linking the changes created in conversion rates, customer retention, or revenue growth is not easy with conventional tools.
A lot can be lost in the way of justification for ongoing investments in traditional psychographic research based on a balance sheet of those results, not yet quantifiable.
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Retail and E-Commerce
How Industries Use ML-Driven Psychographics
Machine learning psychographic profiling has completely transformed the understanding and service of consumers by brands in the retail industry.
By monitoring customer’s shopping behaviour, product affinity, and online activity, retailers would know not just what customers buy but why they buy.
For instance, a fashion retailer may differentiate between trend-conscious buyers and sustainability-conscious buyers in their messaging.
Such customers can be dynamically segmented through ML models into sub-groups in real-time to allow for tailored campaigns, dynamic website content and custom recommendations that reflect the values and lifestyle of the customer.
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Media and Entertainment
How Industries Use ML-Driven Psychographics
To recommend content, streaming platforms and digital content services exploit psychographics beyond generations of viewing habits.
They feed ML with user engagement patterns, viewing times, and sentiment revealed via reviews or even social media to infer the emotional states of the user and the preferences for the content.
Trend increasingly allows Netflix or Spotify, for example, to recommend when a user needs to be inspired, entertained, or comforted-and thus resulting in long watch times and even more important watch retention from users.
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Financial Services
How Industries Use ML-Driven Psychographics
Banks, fintech startups and insurers are capitalizing on psychographic profiling to better predict how the middle class makes its financial decisions and how it thinks about risk.
The digital behavior of the customers is coupled with analysis of their spending habits, how they use apps, and their reactions to financial content to categorize them into risk-takers, savers, planners, and spontaneous spenders.
This then dictates the way a product is presented, whether it is a credit card, investment tool, or a savings plan:
A retirement planning tool will be shown to a user driven by long-term goals, while another focused on instant gratification may be shown short-term budgeting apps.
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