Financial institutions are cashing in on big data! The most progressive companies in financial services are rapidly deploying big data analytics to yield big results around understanding customer behavior, preventing fraud and delivering more targeted advertisement.
By far, understanding customer interactions is at the forefront of these use cases with 48 percent of Datameer FINS customers leveraging big data analytics to combine interactions that occur through the Web, digital ads, mobile apps and social media with CRM and marketing automation data. Years ago, companies only needed to analyze structured data such as CRM, transaction and point of sales. But today, customers interact with companies through so many channels that this structured data must be combined with unstructured data (e.g. Web logs, social media, digital ads and mobile interactions) to gain a deeper understanding of the customer journey. Big data analytics gives companies access to new insights that can significantly increase customer loyalty and acquisition.
In working with over 200 companies, we have identified three use cases that bubble to the top when using big data in financial services. These use cases are:
• Predict and reduce customer churn
• Reduce customer acquisition costs
• Clickstream analysis for digital advertisement
Take a look at these real-life examples to see how financial services companies are tapping into a wealth of customer interaction data.
Track Customer Behavior to Preemptively Avoid Churn
As part of a strategic initiative to reduce the amount of funds being transferred after customers reached retirement, a leading financial services company set out to understand which client behaviors signaled churn.
The company knew that if they preemptively engaged with customers who were considering moving their money, they had a 50 percent chance of retaining them. Given this knowledge, they wanted to analyze customer activity leading up to a withdrawal of their pension.
First, they tracked activities such as customer calls to their call center; a change in address, workplace, or power of attorney; and if the client had recently been browsing on the company site for forms. After pulling multiple data sources together, they were able to build out activity paths for each client and determine with statistical relevance which activities or combination of activities signaled that the customer is at risk of moving their funds.
By identifying patterns of customers at risk of churn and proactively reaching out to offer their wealth management products, the company has reduced customer churn by 50 percent.
Reduce Customer Acquisition Costs with Targeted Promotions
A McKinsey Global Institute report shows that marketing and sales consume about 15 percent of costs for bank and insurance companies. Institutions spend hundreds of dollars to acquire each new customer. Credit card issuers spend billions of dollars annually on direct mail and mass-market advertising to attract new customers. Yet most of these tactics simply rely on a ‘spray and pray’ mentality that lacks strategy and targeted campaigns.
A major credit card company wanted to gain a deeper understanding of their high-value customers to increase wallet share by delivering much more targeted campaigns. Turning to big data, this company correlated customer purchase history, customer profile data and customer behavior on social media sites that indicated areas of interest. For example, they realized that a large percentage of their high-value customers watch the Food Network and shop at Whole Foods. Armed with those insights the company created target advertisement on the Food Network with special promotions for Whole Foods.
As a result, they improved conversion by 25 percent and at the same time reduced their advertisement budget by $3.5 million per year.
Accelerate Customer Acquisition with Clickstream Analysis of Digital Ads
A global financial services company uses big data to perform clickstream analysis on their digital advertisement. Their goal was to create more targeted ads that improve conversion and reduces customer acquisition cost.
The company ingests four to five billion advertisement records per month across all business units to better understand ad exposure for each consumer segment. What they found was that 60 percent of their advertisement budget was focused on only 4 percent of consumer segments.
The company reallocated their budget to other consumer segments to increase ad conversion. Now they can trace 50 to 60 percent of people who receive their ads and have significantly improved advertisement conversion by creating more targeted ads for each consumer segment.
As financial services companies continue to generate and compile more and more customer interaction data each year, the move toward data-driven approaches is becoming vital as a competitive advantage. Soon every top credit card company, global bank, wealth management and insurance company will use data analytics to better understand their customers. Those that do not will fall by the wayside.
To learn by example and understand how leading financial services companies are gaining momentum in big data analytics and getting results, download this eBook, “Top 3 Big Data Use Cases in Financial Services.”