In this environment, consumers have become adept at filtering relevant, meaningful offers from the rest. Instead of blanketing customers with a barrage of generic messages, tailor your communications with targeted and personalized messages that grab and hold the customer’s interest. In order to deliver highly relevant and beneficial offers to clients and prospects, bank marketers must first truly understand individual consumers and their specific needs. This can be a daunting challenge. The advent of predictive analytics supported by location intelligence enhances the customer information that banks already maintain and strengthens their ability to deliver messages with impact.
Analyzing customer behaviors
The first step in predicting future customer behavior is to analyze and understand current and past behavior. The rich datasets maintained by financial institutions on the history and patterns of account holdings and channel usage are extremely useful in predicting the needs of similar customers. Consider, for example, an analysis of the patterns of account ownership for customers with money market accounts. The table summarizes hypothetical combinations of product ownership, and the percentage of households that purchased a money market account. Clearly, this analysis suggests that customers who own CD and IRA products are good candidates for money market offers.
|Customer Holdings (excluding Money Market)||Percent with Money Market|
|Checking - CD - IRA||33%|
|CD - IRA||16%|
|Checking - Mortgage||8%|
|Savings – CD||3%|
Information contained in these simple account patterns can be greatly improved by considering demographic or behavioral segmentation data associated with the customer’s geographic location (address or neighborhood). By knowing where the customer resides, marketing analysts can associate other relevant data such as age, income, stage of life, leisure activities, and shopping preferences. This information enables the development of even more targeted messages, and more relevant and useful products offers to current and potential customers. Continuing with the example above, it may be shown that while CD-IRA customers are good candidates for money market offers, consumers between 40-55 years old are particularly likely to purchase.
The importance of convenience
Beyond product and service satisfaction, the majority of banking consumers place a high premium on convenience to physical outlets. Strategically placed branch and ATM networks continue to be the best way for banks to differentiate from the competition. Despite the calls for the end of the branch, the physical distribution network remains vitally important.
Convenience is a relative term and it varies across different geographies and even across product offerings. Our research has shown, for example, that in suburban areas the typical time-account customer lives within three miles of her bank branch. In rural areas, this distance increases to almost five miles. Suburban mortgage customers, on the other hand, tend to live more than seven miles from their bank. Clearly, location matters when consumers make banking decisions. To accurately present relevant messages to meet the customer’s needs, marketers must understand the impact of location, and factor in the customer’s proximity to the bank.
Retail Banking Strategies recently stated, “We’ve interviewed bankers around the world to try and get a perspective on future bank branch trends. Here’s our take: If you don’t grow your distribution network, you are not likely to grow customers and revenue. Time after time, we heard stories from bankers in the U.S., U.K., Spain, Sweden and other countries who tried to pare the network only to discover that growth stopped. Customers want convenience, and that means having a convenient physical facility they can go to for sales and service.”
Savvy marketing analysts take measures of locational convenience into account when determining which offer to make to a given customer.
Sample study results
To demonstrate the ability of location intelligent models to accurately predict future customer purchase behaviors (and thus assist in designing targeted marketing activities), we analyzed bank households and their observed purchasing behavior over a 12 month period.
Location-intelligent statistical models were created to assess the likelihood of a given household to purchase a checking account, money market, time account, or home equity loan. These models were constructed using categorical data analysis methods and logistic regression techniques. The likelihood of a household purchasing one of these banking products was examined taking into consideration:
• Existing accounts the household had with the bank;
• The customer’s demographic segment; and
• The distance between the household and their primary branch
These relatively simple predictive models were used to score each household’s likelihood of purchasing one, or any, of the three products examined. The findings clearly demonstrate the value of predictive analytics based on location intelligence in target marketing. Typical households scoring in the top decile were 2 to 9 times more likely to purchase these accounts than the average bank customer during the same time period.
Armed with this information, a bank marketer becomes much more effective with their resources. For example, to reach 500 home equity purchasers in its current customer base with a direct mail offer, a bank would need to send approximately 50,000 mail pieces with a one percent response rate. By focusing only on those customers scoring in the top decile for home equity need, the size of this mailing can be reduced almost nine-fold to approximately 6,000 mail pieces. These savings can be used to support other initiatives and campaigns, or used to generate additional home equity opportunities. Perhaps as importantly, relevant messaging strengthens the sense that the financial institution understands its customers’ needs.
Topics: Retail Banking,