Overhauling the Traditional Customer Segmentation Approach

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Traditional segmentation methods are based on factors including, demographic profile, income, and total customer spend. Until the recent past, these methods provided a means for devising segment-specific strategies. However, traditional segmentation methods are no longer relevant or effective with the rapid advent of data, advancements in technology, the emergence of disruptive tech startups, and changing client behavior in the digital age.

It is no longer sufficient to have a traditional segmentation strategy that:

• Relies on only the basic demographic and financial profile of customers
• Is time-consuming and effort-intensive to create
• Is static and does not change with time

This is especially true in the case of the financial services sector, where the need to evolve has been accelerated by the emergence of fintech companies that are disrupting the traditional business model for banks, investment managers, and insurance firms. The success of robo-advisors is a prime example where the likes of Betterment and Wealthfront have forced the traditional managers to change their approach toward targeting and servicing customers.

Many marketing teams now rely on behavioral data as a primary determinant to create accurate customer segments. Each customer has a certain level of purchase intent that determines their behavior on a digital channel. If they don't find what they're looking for, they bounce back immediately. Banks, financial services, and insurance companies try to segment customers based on their digital behavior and then adapt their products and services accordingly.

However, behavioral segmentation is primarily descriptive in nature and does not provide insights into what a customer is likely to do and what action needs to be taken to encourage a specific action.

This can be overcome by a data-driven predictive segmentation approach. Predictive segmentation can automatically segment customers based on multiple factors, such as propensity to engage, propensity to take a specific action (e.g., purchase, churn), client behavior, etc. It allows marketing teams to gain meaningful insights into the performance of each segment. They can optimize their marketing spend, and design customized campaigns for users across different segments and channels.

A predictive approach to customer segmentation will enable a company to:

• Tweak its product portfolio based on specific client needs. They will be able to launch new products that are aligned to changing client needs or address shortcomings of their existing product portfolio
• Have a more targeted communication based on the client’s past behavior, channel preference (mobile, web, in-person, etc.), risk appetite, recent events, etc.

Challenges in Predictive Customer Segmentation

While a data-driven approach offers tremendous opportunity for financial services firms, they also need to be wary about and plan for issues related to compliance with regulations related to data security and privacy. In addition to this, they need to establish a control framework that ensures that while vying for hyper-personalization, they remain compliant with policies related to product information, risk disclosures, and appropriate mapping of product recommendations with the customer’s risk profile.

Conclusion

Client expectations are evolving rapidly and with increasing exposure to digital channels, they expect higher benchmarks for overall experience and service. Using predictive segmentation allows financial marketers to predict customer preferences and create a differentiated experience. Brand engagement and digital conversions are also enhanced by personalizing the experience across users' preferred channels and devices.

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A data-driven predictive segmentation approach provides the following benefits:

• Dynamic – Customer preferences change with time and predictive segmentation adapts accordingly; customers can shift from one segment to the other based on their changing behavior
• Personalized – AI-driven segmentation allows marketing teams to achieve hyper-personalization and address the individual preferences of each customer across channels and devices
• Accelerated – Data can be analyzed and converted into insights at a rapid pace, making it a very useful tool for marketing teams when they are planning a new program or campaign
• Action Oriented – Custom actions with a higher success probability can be recommended resulting in a higher conversion rate, lower cost per customer acquisition, and lower churn

The future of marketing for financial services will depend heavily on artificial intelligence. Investing in capabilities to create predictive segments will enable firms to create a competitive advantage by optimizing marketing spending, planning campaigns, providing insights on customer preferences and channels, designing new products/services, and enhancing the overall client experience.