When an entrepreneur is asked what aspects of the business he would like to improve, very often, there is a tangible need to get to know his customers better, to define the reference target of his offer more precisely and to understand how to guide the company’s communication activities.
In this context, customer profiling takes on unprecedented strategic value, to the point of requiring a profound evolution of technology capable of making traditional profiling forms definitively obsolete in favor of automated processes capable of analyzing data in real-time to deliver actionable insights to lines of business that engage with customers to meet different business objectives.
What Is Customer Profiling?
Customer profiling is a discipline that deals with organizing a company’s customers into categories sharing one or more characteristics to get to know them better and make activities in marketing and sales more efficient and profitable. And customer care, to mention the business lines most involved in this kind of relationship.
Among the characteristics frequently used for customer profiling, we could, for example, mention personal data (age, education, occupation, origin), psychographic data (personality, interests), and the main aspects of connection with the brand (interests, problems, barriers to purchase, decision-making criteria, customer journey methods).
In the digital age, the leading resource for customer profiling is data, which can be acquired through user interactions with company channels: e-commerce, in-store touch-points, customer care, etc. Customer profiles primarily allow you to describe a group of consumers. With the technologies currently available, such as data analysis applications based on machine learning, it is possible to manage and analyze large amounts of customer information, profile them, and progressively refine their knowledge over time.
Customer profiling helps product companies and service providers adopt a customer-centric approach right from the constitution of their offer to be attractive to the most profitable stakeholders, those considered more functional for satisfying the business goals. Customer profiling is often mistaken for customer segmentation. These two activities are often implemented while maintaining some distinctive characteristics. Customer profiling, as we have seen, aims to describe customers thanks to demographic characteristics, behavior, attention towards the brand, and products positioned in a specific market segment.
Customer segmentation instead deals with classifying existing customers into a series of smaller subgroups based on some specificities, usually related to the creation of a campaign or an activity dating back to the business lines in more direct contact with the customer: marketing, sales, and customer care. These simple definitions allow you to learn how customer profiling and segmentation, if implemented consciously, are complementary and can be implemented in practically any commercial context.
How Customer Profiling Helps Companies
Among the aspects that companies must consider when it comes to the customer portfolio, there is undoubtedly the fact that maintaining an acquired customer can give rise to a series of significant advantages. According to some research data published by the Harvard Business Review, a 5% increase in customer loyalty can correspond to a rise in profitability ranging from 25% to 95% compared to the starting figure.
This is a significant added value for the business, triggered precisely by the greater ease in conversions that derives from the ongoing relationship of trust between the customer and the brand. The measurement of a customer’s loyalty to the brand is usually expressed by the customer retention rate, whose values represent the level of commitment through the percentage of customers who continue to purchase and have a relationship with the brand over a given period.
In other words, the customer retention rate is an index that expresses, above all, from a qualitative point of view, the ability of a company to make its customers more profitable thanks to activities aimed at their retention, both through the introduction of the market of products that respond to their wishes and in the quality of communication that the brand can establish thanks to the services offered to them. What characterizes a successful customer journey is the quality of the customer’s experience when using the contents and services the brand makes available through various channels.
According to this perspective, customer profiling represents a significant opportunity to improve knowledge of the target and personalize the digital experience. Effective profiling makes the customer experience simple and intuitive through increasingly advanced interfaces and, at the same time, ideally in line with the needs and interests of customers. Through an effective customer profiling strategy, for example, it is possible to:
- create new products capable of natively transposing customer needs;
- propose product variants capable of raising the level of customization of the offer;
- create customized marketing and sales campaigns;
- reposition the brand to intercept more profitable customers;
- improve direct communication with customers and the digital experience across all brand channels in complete coherence with the brand’s omnichannel strategies.
How To Profile Your Customers
Customer profiling was once done manually using special forms, which were then converted into Excel forms with a similar role. Today, many procedures that allow user profiling are automated thanks to special software enabling you to interface directly with company systems, especially CRMs, to analyze customer interaction data. Customer profiling activities do not follow a single pattern. Over the years, various frameworks that take advantage of data science have been developed by applying different analytical methods, including clustering, cohort analysis, and RFM analysis.
Clustering (Cluster Analysis)
Clustering, or cluster analysis, corresponds to a set of multivariate and descriptive data analysis techniques to select and group information based on homogeneous elements. In the analytical context, the “cluster” represents a set of subjects, which in the case of customer profiling, corresponds to customers with characteristics that are similar to each other and differentiate from the other clusters. The factors that define the groups are selected from the mathematical model chosen for data analysis.
Cohort analysis focuses on the behavior of a specific cohort—a group of people who share one or more characteristics at a given moment or over a certain period. Cohort analysis, therefore, constitutes a type of behavioral analysis that uses data from groups of users with similar socio-demographic, psychographic, and behavioral profiles, with rules established a priori to respond to specific business needs. Cohort analyses have been used for several years, mainly in the context of statistical surveys in the social and medical fields, before successfully adapting to corporate strategies based on customer centricity.
RFM analysis aims to describe customers based on recency, frequency, and monetary. Recency is the amount of time since the customer’s last action. In other words, it indicates how long the customer has yet to buy. The frequency represents the frequency of purchases for each customer, while the monetary corresponds to his attitude toward spending.
The RFM analysis uses the combination of recency, frequency, and monetary values to develop a behavioral profile. This approach makes it a very flexible analysis methodology that can be adapted to a large variety of contexts, which analysts must be able to accurately identify and describe so that the profiling results are responsive to the profiling needs of a specific business strategy.