Customer segmentation helps banks get to know their customers on a more granular level. Segmentation reveals specific intelligence that could otherwise be obscured by the sheer volume of data. These insights, in turn, inform messaging strategies for marketing and customer service strategies. Segmentation can also help banks better understand the customer lifecycle and predict customer behavior.
Examples of common customer segmentation criteria:
- Customer value – How many products & services customers purchase and what kind of revenue that generates for the bank – past, current, and predicted for the future
- Demographics – Age, geography, gender, generation (e.g. Millennials and Baby Boomers), income level, marital status, and other “vital statistics”
- Life stage – Slightly different from age, focused instead on customers’ journeys through various milestones and markers; for example, graduating college or starting a family
- Attitude – Customers’ subjective stances on things like the financial industry as a whole, online and mobile banking, the economy, and their satisfaction with their bank
- Behavior – Interactions and transactions between customers and their bank, which channels they use and how often, and which products they adopt
Similar criteria can be applied to banks’ business customers – profitability, number of employees, “life” stage (start-up, established, legacy), and so forth.
These are the traditional ways that customers have been segmented for decades. However, relying just on these categories is not going to yield many actionable insights.
In the age of Big Data, you sometimes have to think small. The real power of segmentation is not the quantity of data you can collect – which, with today’s technology and methods, is virtually infinite. It’s in the ability to drill down to the information that actually teaches you something about your customers.
Often it’s not the segments themselves, but where they overlap, where you’ll find the most valuable intelligence.
To understand how this can come in handy for banks, just think about the sometimes bizarre categories that show up in your Netflix queue based on what you’ve been watching lately. Vintage sci-fi with a strong female lead? Critically acclaimed British nature documentaries? Criminal investigation murder mysteries based on books? The more they know your tastes, the more likely you are to keep using their service based on their recommendations.
The options for how segments can overlap are nearly limitless.
Nearly. There is a bell curve to the usefulness of segmentation. Too broad, and the results are less than insightful. Too narrow, and the value of the insights gained will have minimal bottom-line impact.
This is where it helps to have experienced data scientists on your side. The purpose and advantages of segmentation are easy to enough to grasp, but the farther you get into analytic methodology, the more highly technical it becomes, and the more you need to understand about mathematical models and formulas. If things like our guide to data visualization make your eyes glaze over, chances are that the nuances of segmentation will put you right to sleep, too.
Developing a Robust Segmentation Strategy
Creating a strong segmentation strategy needs careful planning. The first step is to set clear, measurable goals. These goals help guide the process and ensure efforts meet desired outcomes. Also, using different data collection methods can make the strategy more accurate and effective.
Data Collection Techniques
Choosing the right data collection methods is vital for a segmentation strategy’s success. Banks have several options, such as:
- Surveys: Getting direct feedback from customers helps understand their likes and behaviors.
- Transaction History: Looking at customers’ transaction patterns shows their financial habits and preferences.
- Digital Footprints: Tracking online activities shows what customers are interested in and need.
Using a mix of these methods gives a full view of the customer landscape. This supports effective segmentation.
Advanced Techniques for Market Segmentation
In the world of banking, new ways to segment markets are key. They help firms understand their customers better. By using machine learning and predictive analytics, banks can get very precise with their customer profiles.
These methods let banks go beyond simple categorization. They use lots of data to find segments that fit new trends and changes in what customers want. This way, banks can really understand their customers’ needs and meet them better.
- Hierarchical clustering for nuanced group identification
- Decision trees to predict customer behavior based on attributes
- Regression analysis for understanding financial product preferences
- Support vector machines to classify customer segments effectively
With these methods, banks can make marketing that really speaks to their customers. Advanced segmentation techniques change how banks analyze markets. They help banks stay competitive and make customers happier.
Segmentation Strategy: Finding the Right Balance
In the banking world, finding the right balance in segmentation is key. Broad and narrow strategies each have their pros and cons. A broad approach might miss the detailed insights needed for effective marketing. Yet, a narrow focus might overlook valuable segments that could boost profits.
Banks need a multi-faceted strategy for effective segmentation. A balanced approach helps find profitable segments while keeping an eye on diverse customer data. By analyzing customer behaviors, demographics, and psychographics, banks can make their marketing more effective.
Here are some ways to improve segmentation:
- Regularly check how segments perform to meet changing customer needs.
- Use data analytics tools to get insights from various customer interactions.
- Listen to customer feedback to adjust marketing plans as needed.
Getting the balance right leads to better engagement and retention. This means higher customer satisfaction and loyalty. Understanding the details of customer segments is crucial for impactful marketing. Check out our PDF on ‘The Path to Continuous CX Excellence.’
The Future of Customer Segmentation in Banking
The future of banking segmentation is bright, thanks to new technologies. Banks are now using these tools to better understand and serve their customers. They must keep up with changing customer needs by using real-time data and feedback.
Artificial intelligence and machine learning will help banks analyze huge amounts of data. This will lead to more personalized banking experiences. As customer tastes change, so will the ways banks segment their customers.
- Personalized banking experiences will be achieved through enhanced data analysis.
- Predictive analytics will offer foresight into customer behaviors and trends.
- Dynamic segmentation will allow real-time adjustments based on changing customer expectations.
The banking world is changing fast, and banks need to keep up. Those that adapt their segmentation strategies will thrive in the future.
Conclusion
Customer segmentation is key in today’s banking world. It helps banks improve customer value by using big data. This way, they can offer services that meet their clients’ specific needs. Banks need to keep updating their strategies. As customer wants and the market change, so should their methods. This keeps them ahead in a fast-changing financial scene. Using big data and smart segmentation boosts customer loyalty and improves the overall experience. It helps banks understand their customers better. If your bank is ready to understand your customers, contact us at CSP to start your custom CX program.
