For financial services organizations, competition from nontraditional market players is eating into revenue. According to PricewaterhouseCoopers, financial institutions face losing 24 percent of revenue to these competitors in the next three to five years. As they grapple with a low-growth, low-margin, highly-regulated environment, financial institutions must be able to tap into new sources of revenue while containing costs. Big data and predictive analytics play a large role in recapturing that revenue while managing risk.
Big data and predictive analytics can provide customer insights that help drive acquisitions, increase customer engagement and loyalty, and overall improve customer lifetime value. The insights derived from unique data and innovative analytics could help you deliver the right offering to the right customer, in the right channels. Here are three keys to leveraging data and analytics to keep your financial services organization ahead of the changing customer behavior.
- Obtain a single, actionable view of customer relationships.
By bringing together internal, disparate data, you can gain a single, actionable view of your customer relationships. But, if you could add in consumer spending, income, and credit insight, you could better identify potentially high-value, low-risk customers.
Then, it is important to connect the dots between the consumer data and behavior. According to the FIS 2016 PACE Index, 70 percent of consumers expect a major life event to affect their finances in the next three years. Data from third-party sources, combined with predictive analytics models that take into account likely consumer life events and behavior, can help you better attract and retain high-value customers, stay ahead of customer needs and determine the most effective next steps for deepening the customer relationship.
You may think that customer acquisition and risk management are mutually exclusive goals, but they don’t have to be. The insights you can gain from predictive analytics based on reliable consumer data can help mitigate risk — an important factor for financial institutions already competing in a low-margin industry.
- Leverage predictive analytics to understand customer behavior.
Predictive analytics help identify new customer opportunities, as well as opportunities to upsell and cross-sell to existing customers. According to Aberdeen Group, financial services companies that use predictive analytics achieve an 11 percent increase in their total number of customers when they start using predictive analytics, as well as an 8 percent increase in upsell and cross-sell revenue. Compared to companies that don’t use predictive analytics at all, these companies achieved a 167 percent increase in cross-sell and upsell revenue.
Most financial institutions are poised already to leverage predictive analytics. Today, it’s likely that your organization uses predictive analytics to some degree. For example, if you’ve created a customer lifetime value (CLTV) measure, you’ve created a predictive analytics model to determine how much revenue the customer will generate over time. When you take that a step further and use existing data from a variety of sources, aggregated and analyzed, you’re in a better position to predict what your prospect or customer needs and how to market to them.
- Base insights on consumer financial patterns derived from real data.
A predictive analytics model is only as good as the data that goes into it. Financial institutions need a complete view of the customer, gleaned from unique data assets, to help predict consumer behavior and understand risk. For example, according to McKinsey & Company, a consumer who is issued a store credit card in one location but frequently uses the card in locations other than the issuing store may be more of a credit risk. This information isn’t found in your existing customer databases, but it is critical to gain better insights on consumers. Often, it can be acquired through trusted third-party sources with proven track records, as well as data derived from granular and verified sources.
Additionally, financial institutions may be limited in other ways when it comes to using data. Legacy infrastructure, siloed lines of business, and duplicate records may hamper data collection and analysis. This poses a real problem for deriving insight but can be mitigated by approaching data acquisition in a new way.
By using predictive analytics and third-party data, your financial services organization can successfully balance both customer acquisition and risk management. As you choose a solution, look for a partner that can leverage unique data, analytical expertise, and innovative technology to power your critical decision-making.
Download our checklist, “Sizing Up Your Customer Acquisition Strategy: A Guided Self-Assessment for Financial Services” to take your organization’s pulse, then contact us to learn more about what Data-driven Marketing solutions from Equifax can do for your customer acquisition and retention strategies — along with your bottom line.
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