Is Predictive Analytics For Everyone? Or Just Megabanks?
Many believe predictive analytics will give some banks a big edge by identifying patterns well before their competitors. But is this power only going to be available to a privileged few? Or will the potential of artificial intelligence and advanced statistical analysis be something that any financial institution can leverage — even community banks and credit unions?
As banking evolves to meet consumer demands, financial marketers face mounting pressure to understand what works and where they need to improve. Consumers expect convenience and simple solutions. They want an immediate, personalized experience. These are today’s table stakes, not “value-added options.” But it’s virtually impossible to tailor any kind of experience — much less in real time — if you don’t know who you’re talking to.
Many in the banking industry believe the answer to these problems can be found in advanced statistical analytics — what is often called “artificial intelligence.”
One of those is Steven Ramirez, CEO of Beyond the Arc.
“For financial marketers who are often asked to do more with less, predictive analytics can be the key to unlocking greater ROI,” says Ramirez. “The trick is how to interpret all the data that’s available so you can answer strategic critical questions. Who are the most likely prospects? How do you identify which customers are at the greatest risk of leaving? What offers work best?”
With predictive analytic technologies, you can identify patterns in data that were previously hidden (or extremely difficult to spot using more traditional methods). Based on these statistical correlations, you can predict the likelihood of future outcomes. In the past, financial marketers may have estimated forecasts for broad customer segments like “Millennials” or “Single Moms.” Now, Ramirez says bankers can predict the behavior of each and every individual, and it can be done for a database with millions of people.
What makes this possible is leveraging a broad range of customer data:
If, for example, the goal is retention, analysis usually includes consumer behavior like calls to the contact center or visits to a branch. Analysis to support marketing offers might also include credit scores, social media, transactions, census data, and more.
According to Ramirez, the asset size of the institution doesn’t matter. While advanced analytics once required complex IT systems, expensive software and PhD-level statisticians, today there are plenty of low-cost and open source tools that can empower a bank or credit union with predictive engines.
“You don’t need an enterprise data warehouse, nor millions of rows of data,” Ramirez explains. “A propensity model to predict who is most likely to accept a home equity offer can use data in a spreadsheet, and the model can be constructed and run on a laptop.”
However, Ramirez notes that the more sophisticated the modeling desired, the greater the requirement for a complete view of the customer — and that will need to extend across all of the institution’s silos.
