Increasing Customer Acquisition and Retention with Predictive Analytics


Imagine if your branch bankers could determine which customer is most likely to buy if offered a special promotion in the teller line?  Predictive analytics can provide banks the ability to leverage data to positively impact business. By connecting data to action, banks can better understand customer behavior, anticipate future events, and gain new insights to retain customers and build more valuable relationships.
Asking the Right Questions
Data mining is a new way of thinking about your business, using data to ask questions and investigate hypotheses. In the context of acquisition and retention, the questions can range from sales and marketing issues to operational issues. Here are a few questions a bank may typically ask:

  • What products sell well together and what products are customers most likely to purchase in a package?
  • What campaigns and offers have the highest chance of success and what types of customers should a promotion be targeting?
  • What defects occur in the credit card fulfillment process and is there a way to prevent the most frequent issues?
  • Can a bank forecast call center staffing levels based on the expected response from marketing campaigns?

Customer Acquisition and the Customer Lifecycle

There’s a natural progression in a customer relationship and it helps to systematically define this customer lifecycle for your business. For a bank, you could view interactions across five typical stages: awareness, presales, sale, customer service and advocacy. Analytics can boost your marketing effectiveness at each stage.

Tailoring predictive analytics to each stage of the lifecycle can help you to prioritize your marketing and focus your resources where they will have the most impact. In the early stages of the customer relationship, you can identify trends and patterns of behavior that indicate which prospects are most likely to respond. You can also assess how certain factors shape the decision to buy, with data-driven segmentation that fits each individual customer.

As your relationship with a customer matures, predictive analytics can help to identify which customers are at risk of attrition. By continually learning about, and meeting, customer needs your company builds brand advocates among satisfied customers. Ultimately, highly satisfied customers can help to positively influence the buying decisions of others.

Turning Data Into Action

Understanding the data across the customer lifecycle enables banks to create more actionable segments and take targeted action tailored for each segment. For example, in a recent webinar, we illustrated how predictive analytics could reveal that married couples with a mortgage through the bank, but not a credit card, are more likely to respond to a home equity line of credit offer.

Once a bank gathers their data and generates the insights into how customers are likely to behave, the final step is to act on those insights. Analytics won’t create value until a bank acts on the information. While every situation will be unique, a few common actions can include:

  • Personalize offers based on the predictive model variables
  • Send regular emails to prospects identified as most likely to purchase
  • Re-mail to people who didn’t respond but should have
  • Re-mail based on web/mobile click-through
  • Email those least likely to accept the offer less frequently, to minimize opt-outs and keep them in the campaign pool
  • Consider incentives for long-tenured customers acknowledging their loyalty

Analytics Unlock the Value of Big Data

While big data is not a prerequisite to success with predictive analytics, big data can help banks to create broader and deeper customer insights. In other words, predictive analytics can help financial institutions make sense out of big data.

Despite the recent attention on big data, many companies are still struggling with ROI and the business case for their organization. By using big data to fuel customer acquisition and retention efforts, banks can begin to quantify the revenue gains.

Big data is not just a reference to the size of a company’s database. There are three primary challenges that a company must tackle: the volume, velocity and variety of data.

In the context of predictive analytics, these are practical issues to address. Is the data complete, and has enough data been collected to draw statistically valid conclusions? Is the data broad enough to encompass customer behavior over time and across product holdings? How fast does the data come in? Data can be nearly real-time as companies collect everything from transaction data to service interactions to online web browsing patterns.

Data can also be either structured or unstructured. Structured data is numeric, fits pre-determined fields, and is useful in segmentation or analyzing transaction trends. Unstructured data is free-form text and can come from a variety of sources, from customer service interactions, to social media conversations. Fortunately, banks can address the complexity of these issues incrementally. Predictive analytics provides a place to get started with a relatively quick payoff.

Whether it’s retaining customers or increasing marketing effectiveness, customer-centric data and predictive analytics can positively improve a bank’s bottom line.

 

Steven J. Ramirez is CEO of Beyond the Arc, Inc., a customer experience and advanced analytics firm helps financial services clients retain customers and increase marketing effectiveness. The company’s social media data mining helps clients improve their customer experience across products, channels, and touch points.


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