The secret to at-scale personalization isn’t just the analytics. It’s the ecosystem organizations set up to use the analytics well.
Key takeaways
- Despite significant investment in AI, only 8 percent of banks are able to apply predictive insights from their machine-learning (ML) models to inform campaigns.
- Although banks know that time to insight matters, just 16 percent have standard protocols for algorithm development.
- By codifying, unifying, and centralizing key analytics and supporting processes, these organizations generate 5 to 15 percent higher revenue from their campaigns and launch them two-to-four times faster.
A large retail bank invested in ML technology to take its personalization initiatives to the next level. The goal was greater predictive power and efficiency in designing and automating customer campaigns. But two years later, the bank was still managing its personalization program much as it always had: manually and in silos. Although it had acquired a sophisticated analytics engine, the bank had overlooked the elements needed to turn that engine into a smoothly functioning “brain.” The result was a perpetual cycle of subscale efforts.
This bank is not alone. We see many well-intentioned personalization programs stumble from a mix of inconsistent data gathering, sluggish production models, and bespoke algorithms that are hard to update and share.
There are organizational issues as well. Too many businesses continue to view personalization as either a marketing initiative or an analytics initiative, when it needs to be managed as a joint initiative across the business. In the bank’s case, the marketing team was tasked with managing the program, but marketers had only sporadic access to analytics resources, forcing them to fall back on basic heuristics that were easier to manage but less effective at personalization. The bank’s aspiration also proved narrow, with a heavy focus on boosting click rates and conversions rather than on long-term drivers of customer value. As a result, the institution struggled to meet its retention and satisfaction targets for key segments.
There is a way to crack these challenges, and it starts by reframing them. We’ve seen organizations across the banking sector test a model that puts customer value at the center of personalization efforts. The outputs are individualized, but the inputs and algorithms that produce them are codified, unified, and centralized. Companies that have employed this model have generated 5 to 15 percent higher revenues from their enhanced campaigns and have halved or quartered their time to market. The bank referenced above, for example, learned to shift from equating value with mortgages sold and accounts opened to measuring it based on customer outcomes, such as retention and willingness to recommend. Instead of sales messaging such as “Try our zero percent introductory rate,” they developed journey-based communications, such as “Here’s how to make your holiday stress free.” Instead of monthly or quarterly campaign releases, they shifted to a daily or weekly tempo. Other companies can do the same.
