Leveraging a richer data model to surface Next Best Action

Leveraging a richer data model to surface Next Best Action

Customer Experience
Banks today are facing new challenges as they try to maintain wallet share and prevent customer attrition. Most banks have made the shift toward providing digital banking options, but are realizing they'll need to differentiate themselves more effectively if they want to grow and succeed. The ability to deliver a personalized customer experience is key to effective differentiation in the digital age.

This reality is not lost on financial institutions. For years, banks have been trying to update their data modeling systems to suggest tailored next best actions or next best conversations to customers. However, these homegrown solutions don't come without their challenges.

Homegrown data modeling is time consuming, costly, and generates incomplete recommendations

The up-front investment to develop the infrastructure needed to generate customized daily offers is two-fold, requiring both time and money. Designing, developing, and implementing a data modeling system capable of surfacing next best action is time-intensive and could require multiple dedicated teams. Ongoing support and maintenance of these data modeling systems is costly as well. A bank's infrastructure needs to be powerful enough to process heavy computing workloads and must store massive amounts of data. Even after the infrastructure is stood up, these models are prone to being polluted by domain logic that is focused on solving a narrow scope of business problems. This leads to an incomplete picture of the bank's portfolio and a model that can't be reused to surface a wider or different scope of recommendations.

Not only is the resulting portfolio view incomplete, homegrown systems often don't achieve their ultimate goal – which is a more personalized banking experience for each customer. What makes this more troubling is the fact that personalization is the number one thing that must be mastered to drive loyalty and improve a bank's Net Promoter Score (NPS).[1] Unless recommendations are targeted more specifically toward individual customers, the return on investment in homegrown modeling systems is likely to remain low.

When you've already invested a lot of money in a business approach, you might feel obligated to make it work in order to ensure your investment in pays off. While that's an option, there's another approach to consider.

VeriPark gives banks a more complete picture

VeriPark Next Best Action offers banks a comprehensive modeling system that's capable of analyzing their entire portfolio. By leveraging VeriPark's rich data modeling solution, banks gain the complete portfolio views needed to generate offers that are most relevant to individual customer needs.

The solution's rich data model integrates with existing systems so institutions don't have to break the bank to better understand their business. VeriPark's SaaS approach eliminates the need for huge up-front investments in modeling hardware and software along with the ongoing cost of keeping servers up and running. Since VeriPark's solution logic is fueled by industry data and focused on a wide scope of business issues, it is reusable across organizations. The solution surfaces insights that enrich operations from multiple perspectives and take customer recommendations to the next level.

VeriPark's rich data model is what sets the Next Best Action solution apart. The solution eliminates the shortcomings of homegrown modeling systems and elevates a bank's ability to provide deeper customer insights, surface potential compliance issues, and tailor product and service offers. This enables employees to cross-sell and up-sell more effectively, and enhances the customer experience, which, in turn, improves retention. The solution also delivers powerful new insights into the effectiveness of sales campaigns, enabling the marketing department to push personalized, targeted ads to customers based on factors such as events and lifecycle.