While predicting the ups and downs of capricious financial markets may be next to impossible, financial services companies can tap analytics to confidently predict customer behavior to outperform their competitors.
Financial services firms that have adopted predictive analytics performed better in identifying new customer opportunities, increasing total numbers of customer and boosting cross-sell and upsell revenue, according to a new research report from Aberdeen Group.
The companies that adopted predictive analytics realized a 10 percent increase in new customer opportunities over a year, according to the report.
“This goes beyond basic demographic segmentation; predictive analytics reveals untapped market segments full of prospects to business development decision-makers,” the report notes. “Predictive models also identify the traits in potential customers that bear out to longstanding, profitable relationships.”
Additionally, those firms that adopted predictive analytics increased their total number of customers by 11 percent, compared to the eight percent growth of those companies not using the technology, Aberdeen notes.
“This level of customer acquisition performance requires that firms not only land a high percentage of prospects, but also retain current customers,” according to the report. “Predictive analytics adopters can run multiple ‘what-if’ scenarios to see which offerings and messaging appeal to both old and new customers.”
Furthermore, companies with predictive analytics averaged an eight percent increase in cross-sell and upsell revenue compared to a three percent gain of non-adopters.
“Financial services firms with predictive analytics are also more than twice as likely as those without to have real-time analytical capabilities,” the report notes. “In the financial world, predictions based on stale data simply won’t cut it.”
In addition to analyzing data in real-time, financial services firms using predictive analytics are better able to handle the vast amounts of data flooding their networks from multiple, disparate sources.
For example, 72 percent of adopters noted that they used data integration tools, compared to 40 percent of those without predictive analytics.
“Companies with predictive analytics leverage integration tools to pool all available information – from internal and external sources – and create the most comprehensive model possible,” the report notes. “Finally, predictive analytics adopters recognize the importance of feeding only high quality data into their models; they are nearly three times as likely as non adopters to have data cleansing technology.”
