Data Driven Marketing

3 Ways “Good Enough” Analytics May Be Hurting Your Financial Services Organization

3 Ways “Good Enough” Analytics May Be Hurting Your Financial Services Organization

February 15, 2018 | Sherrie Tanner

Most financial services organizations already have adopted data and advanced analytics strategies. They typically use data to identify potential customers, segment customers using existing data sets, and implement retention programs. But alternative financial institutions, including online competitors, are turning up the pressure on the rest of the industry.

In the face of competition from these industry game-changers — as well as traditional financial institutions — marketers, data analysts, and risk managers have been puzzling over new acquisition and engagement models to attract long-term, high-value customers, reduce churn, and market to existing customers in ways that will resonate.

The truth is, existing data and analytics strategies may not be enough. The current competitive climate requires more than a “good enough” approach; it demands a finely tuned, comprehensive approach that maximizes marketing dollars and reduces risk. What you’re currently doing may not be powerful enough to support your growth goals and may be leaving competitors to fill potential voids in the market — which could erode some of your market share. Here are three ways this may be happening to your organization.

#1: Your Data May Be Siloed and Disconnected

The most critical component of your customer acquisition and engagement strategies is the data which helps to shape insights. Your organization may have data scattered throughout the enterprise: for instance, one set of customer data for mortgages, another for deposit accounts, and another for credit cards, all living in different departments and lines of business. As the structure of the business evolves through mergers and reorganizations, these data sets become more fragmented and are less likely to be standardized. As a result, you may be working with only one small subset of data — which will not accurately predict which customers are more likely to purchase additional products, or identify which customers or prospects are high value.

To run effective analytics models and gain the robust insights needed, you need to start with relevant customer data. You’ll need to link disparate data sets of customer information, which requires linking and standardizing the data throughout the organization. This also helps you eliminate duplicates and streamline the data so you can get a more complete picture of the customer’s past, present, and future with your organization.

#2: You Do Not Have a Clear View of Your Best Customers

Standardized and cleansed data is just the beginning. Who are your best customers? What markets have the most potential? If you can’t answer these questions, your strategy may be “good enough,” but it’s not providing the ROI and results you expect. You may need to enhance your data sets with direct-measured household economic data to help get a full view of your existing customers and create predictive analytics that take into account credit data, wealth estimates, risk tolerance, and other indicators of financial potential. This not only allows you to pinpoint the customers who are likely to purchase new products from your organization, but also generate lookalike customers.

Even better, you can use the insights derived from the unique data and advanced analytics to build personalized offers and experiences for your high-value customers — for example, inviting them to apply for a new credit card product based on their history and financial potential, rather than waiting for them to research and find you. You can also use this data to determine how you’ll market to current and potential customers in their preferred channels — online and offline.

#3: You’re Not Reaching the Right People, in the Right Channel

When a customer applies for a financial product they thought was within reach because they received an offer, only to learn they do not truly qualify, the effect can be irreversibly damaging to a potential long-term customer relationship. An experience of this sort lingers — and could undermine your efforts to foster customer loyalty. Additionally, targeting these customers without the proper insights may not yield a return on investment for your efforts. But what if you could better segment prospects, and place the right offers in front of them through the most appropriate channels, at times that align with their specific life stages?

Your customer acquisition strategy should include advanced analytics to help reveal the customers and prospects who are likely to qualify for your products and services; otherwise, your organization is missing potentially lucrative opportunities.

Robust analytics and actionable insights powered by linked, standardized, and unique data are critical for effective customer acquisition and engagement strategies. Without it, identifying potentially high-value, low-risk customers, marketing to them, and retaining them becomes even more difficult in the face of fierce competition.

To learn more about how unique data and analytics can help your financial services organization, check out our guide, “4 Steps Financial Services Firms Can Take to Address the New Realities of Customer Acquisition & Engagement.”