Turning Big Data Into Big Revenue

Segmentation Tips for Relevant Member-Centric Marketing

The term “Big Data” can be an anxiety inducing one for many financial services organizations and it is easy to become overwhelmed with the sheer volume and velocity of data available today. Yet, many would agree that if leveraged well, Big Data can become a substantial competitive advantage.

In addition to valuable internal data (ex. member balances, products, demographics, etc.), there are many rich external data sources that can help firms empower their marketing efforts by creating a more complete picture of their members and prospects.

The key to benefiting from Big Data is to find ways not to drown in it.

How Does Big Data Play Into Actionable Segmentation Strategies?
One common approach marketing organizations across the Financial Services industry rely on to gather and distill disparate data is segmentation. Many firms segment their existing members as well as the markets in which they do business in some fashion today, but often find it a struggle to do so in a way that will be effective and actionable. Also, they may not know where to start when it comes to combining their own internal data with the vast amounts of rich, well-managed external data to gather actionable insights for better decision-making.

Here are four tips to help firms get started implementing actionable segmentation strategies for both traditional data sets and Big Data from internal or external sources:

  1. Begin with the end in mind. Get agreement on the business goal and then design a segmentation approach around that goal.
  2. Build only what can be operationalized. Define the steps for turning segmentation data into insights and then into decisions. Make sure each step is feasible, affordable, and compliant with regulations.
  3. Use data that replicates the member’s view, not just the credit union’s view. For example, a credit union needs to look beyond a member’s accounts at the credit union and consider the member’s total wallet and needs. Share the insights gained from segmentation. Often times, segment definitions can be applied without substantial changes by other core systems and member-facing personnel.
  4. Minimize expenses and optimize investment. Understand cost to serve for key segments and use this as guidance for fee refund decisions, rate offers and pricing, member experience improvements, and other decisions involving expenses and investments in Big Data assets.

Segmentation schemes can be applied in the following valuable ways:

  • Define member segments for cross-sell strategies
  • Identify high-potential prospects for new member acquisition efforts
  • Size markets and identify growth opportunities for key target segments
  • Tailor online messaging by differentiating online visitors
  • Improve CRM and loyalty efforts by delivering communications using the right channels and relevant messages 

Ultimately, a credit union that does not use available data assets for a broad, deep and timely view of its members and prospects will lose business to its competitors. Now is the time to start thinking of Big Data as a big opportunity rather than a big effort.

Incorporating External Big Data Sources to Super-Charge Segmentation
There are numerous ways to segment a population to reach specific goals and get the desired outcomes. Depending on the objective, credit unions can segment existing members solely by internal behavioral data. However, without a lens into the full member wallet and financial situation, credit unions will not fully understand the relationship potential that exists.

Does a particular firm likely hold 80% of a household’s assets? Or is that number closer to 20%? By having a better understanding of their estimated share of wallet, credit unions can develop more actionable segmentation strategies.

Which one is the more accurate share of wallet?

Examples of external data sources that can help credit unions piece together a better picture of a member’s or prospect’s likely financial capacity and behaviors are estimates of total investable assets, income, credit, and likely investment preferences, to name a few. Firms can also incorporate more granular external data that distills anonymous wealth data from millions of U.S. households to estimates of deposits, stocks, bonds, mutual funds, annuities, and other assets that a household may hold in their full portfolio, not just their assets under management with a particular credit union. Estimates of holdings in various deposit product categories (interest checking, CDs, etc.) add additional insight.

Sample Scenario: Using Big Data to Narrow the Target Audience for a Cross-sell Campaign

Credit Union XYZ would like to implement a targeted marketing campaign to cross-sell deposit products to existing customers. Using a combination of internal and external data sources, they are able to narrow in on their target audience and come up with meaningful segments for their campaign:

Credit Union XYZ is able to make the most out of their limited marketing budget by narrowing their target audience to members who are the best fit for the products they are hoping to cross-sell. They have also developed segments that allow them to version their campaign to suit the channel preferences of their target members (ex. in-person or online). Successful segmentation helps marketers deliver the right message, to the right household, via the right channel.

NOTE: The data in this document is for representative purposes only
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