Retail banking, also known as consumer banking, offers financial services to the general public. Typical services offered by retail banks include checking and savings accounts, personal loans, credit card access, and mortgage loans.
This guide covers definitions of retail banking and customer segmentation and a discussion exploring common types of retail banking customer segmentation, how data analytics are used in customer segmentation and the benefits of segmentation.
Retail banking services are commonly provided by financial institutions at physical locations, or branches, where customers can manage their money and speak in-person with a banking agent regarding other financial services or products offered.
Most services can be provided at ATMs or through mobile banking platforms, which in recent years have gained substantial traction. Since these institutions have a broad customer base, banks often group their customers into categories based on similar traits, a process known as customer segmentation.
Customers that make up a retail bank’s user base can vary widely by numerous factors including age, gender, income, lifestyle, etc. Banks can segment their customers into lists dividing their consumers into groups based on certain key characteristics and take actions that better align with each segment.
Obtaining and acting on customer data through the lens of segmentation can have a massive impact on marketing and sales, retention efforts, customer service, and more.
Carefully analyzing such a high volume of customer data can be daunting. By using tools and software like WE Analyze, retail banks can easily capture data such as spending habits, frequency, and capacity, and then use this information to identify the most appropriate time to make a loan offer. In turn, this targeted action improves the likelihood of retail banks earning increased revenue through customer loans.
A bank’s customer segmentation approach can vary widely and must be based upon the organization’s business model and priorities. Segments can be quantitative, such as by age and gender, or they can be qualitative, such as separation by values and interests.
Maximum value is obtained when banks merge both types of data to better understand the wants and needs of their customer segments, allowing them to offer the right product or service at the right time.
There are numerous ways to segment customers. Traditionally, segments are demographic, geographic, or product based. With basic demographic and geographic information, a retail bank can tailor its marketing efforts so they are personalized to meet consumer demand.
Here are some of the more traditional segmentation categories retail banks may consider:
Once this information is gathered, banks refine these segments by analyzing the spending habits and capacity of their customers to increase revenue by knowing which product or service should be offered and when. In recent years, more emphasis has been placed on segments that incorporate customer spending behavior or interests, often getting quite granular with the variables, as there are many factors that impact a customer’s willingness to spend.
Once a bank is able to categorize and understand the customer they are working with, they can use software to learn how to best assist them. These are three examples of retail banking segments and how they might be approached for relevant services and marketing:
For banks looking to get the most out of their segmentation, knowing how to use wealth and lifestyle information to target the right audience with the correct services is key to retaining customers, and predicting their needs.
Once retail banks begin collecting and screening key data from their user base, analytics can be used to turn customer data into actionable insights for each of their consumer segments. As previously stated, data analytics are most commonly used in retail banking customer segmentation to identify common traits or characteristics among customers to personalize service or product offers.
Marketing software helps companies fill in the gaps in their customer database by using data enrichment, data cleansing, secure delivery and real-time updates to maintain high-quality data. Automation offers increased efficiency in comparison to resources lost when spent manually maintaining and updating databases, allowing more time to be allocated toward building stronger relationships with each customer segment.
Wealth screening through WE Screen uses proprietary wealth scores and ratings and merges them with current customer data, enabling companies to know more about consumers’ interests, political affiliations, net worth, and capacity to spend. These insights can be applied to segments to create a variety of initiatives such as reducing churn rates, improving satisfaction, and more.
With WE Screen, banks can gather analytics on customers from their lifestyle segment using affinity scores applied to their data.
Using segmentation and affinity scores, banks can rank consumers by variables such as net worth or cash on hand to identify their most (and least) valuable customer segments, allowing them to concentrate special marketing efforts directly to their top consumers.
Creating a look-alike model for these customers takes this application of data analytics further, allowing banks to target prospect segments they know will yield a higher profit. Look-alike modeling allows banks to gather and identify common traits from a certain customer segment and find new prospects who match those same criteria.
Banks can use this information to create personalized messaging for potential customers who resonate with them from the very first interaction based on the segment(s) they fall into. This often increases conversions and builds stronger relationships with consumers.
Retail banks can use other basic consumer information to more quickly identify trends among customer segments and use it to further personalize interactions. Some of these data points include:
Because there are so many pieces of customer data that can be analyzed, data mining is becoming increasingly popular for larger financial institutions. Banks use data mining to apply extensive analytics to current data and to spot trends that may not otherwise stand out.
For instance, a bank can use data mining analytics to discover the top 5 attributes shared by customers with the highest lifetime value (LTV). Knowing those key characteristics, banks can concentrate their marketing efforts by creating personalized campaigns targeting high-value customers.
Data analytics performed on customer segments can also be used to create more efficient predictive models for retail banks. When machine learning is integrated, it can use these models to create a smoother customer experience by better forecasting what customers need and when.
Machine learning is gaining traction and is predicted to have a positive impact on nearly all aspects of larger technology-driven organizations, with 57% of technology professionals expecting machine learning to contribute toward improved customer experience.
Through a solid understanding of their customer segments, retail banks can personalize consumer experiences and quickly form genuine relationships with new and existing customers. Improving these efforts leads to reduced costs and increased revenue. A list of common benefits derived from customer segmentation follows:
Through customer segmentation, banks can deploy more personalized initiatives that increase the likelihood of prospects becoming customers. Banks can also generate specialized efforts toward segments that yield the highest profitability. One way this can be achieved is by using a look-alike model.
By knowing customer interests, habits, and desires, banks can offer customers exactly what they are looking for when they need it the most, leading to increased revenue.
CLV helps banks identify their most valuable customer segments so they can focus on acquiring customers who generate the most revenue over time.
Creating a personalized experience for retail customer segments increases customer satisfaction, often leading to increased customer retention and brand loyalty, decreasing churn rate.
Using customer segments, retail banks can determine the best way to attract new customers, build brand loyalty, and promote specific products. Having a better understanding of the target segments will lead to increased conversion rates.
Customer segmentation makes marketing, product development, and even customer service more effective by helping retail banks gain further insight into specific groupings within their customer base.
To begin segmenting your customer list, visit WealthEngine today to see all of the powerful tools our platform offers to help organizations turn data into action.
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