In ZineOne’s Thought Leadership Interview series, we sit down with experts from across industries to discuss the latest movements and technologies transforming the customer experience.
When it comes to personalization, understanding customer intent is only half the battle. Consider the following:
What happened here is a common example of a bank’s ability to understand customer intent, but failure to utilize data science to intervene only for qualified customers. It is essential that enterprises match the right offer with the right customer to ensure engaging customer experiences from end to end.
To do so, financial institutions are increasingly relying on artificial intelligence (AI), data science, and prescriptive analytics to bring automated, relevant intervention to the customer journey. Ravi Santhanam, Chief Marketing Officer at HDFC Bank, recently sat down with Debjani Deb, CEO and Co-founder of ZineOne, to discuss how HDFC Bank injects AI and ML into their day-to-day operations, and what others can learn from their success. In the discussion, he covered:
- Finding success in the fulfillment journey
- How to use prescriptive analytics for better results
- The importance of data science for personalization
The Three Steps to Transition a Lead into a Conversion
In order to offer personalized outreach to customers, financial institutions must complete three key steps. First, banks must consider how to reach relevant customers to convert their audience into traffic on their website. This requires a two-pronged effort, first identifying where best to reach a customer (e.g. banking app, call center, website) and how to identify prospects ideal for intervention. At HDFC Bank, more and more of these interactions are taking place on mobile devices, with 90% of the bank’s transactions taking place digitally.
Once the relevant customer and best-fit channel have been identified, the second step involves understanding what message to convey and how best to convey it in order to convert traffic into leads. For instance, a new college graduate browsing their bank’s website may not be a prime target for outreach about a mortgage, but they may be interested in discussing long-term retirement planning initiatives.
Last, it’s important to consider intervention actions that may nudge intention into action to convert leads into fulfillment. This involves 1:1 personalization—from messaging to custom landing pages—based on collected data in order to ensure the offering is tailored exactly to the customer’s need. As part of this stage, it is also critical to revisit individuals who dropped off in an earlier phase to understand why they did not continue and how you can more effectively personalize their journey so they reach fulfillment.
The Future of Journey Analytics
Part of optimizing the customer experience from end to end is taking an intentional approach to the customer journey, one that allows for in-the-moment intervention. Where in the past, the customer journey was guided by predictive analytics—analyzing historical factors to predict future decisions—today’s customer journeys are more often informed by prescriptive analytics. While predictive analytics is passive, prescriptive analytics utilizes real-time behavior to anticipate what action a customer will take based on their in-session behavior. It empowers enterprises to react instantly to customer actions, serving up highly relevant offerings based on their current needs.
Prescriptive analytics can also continually analyze new data to re-prescribe decisions, which automatically improves accuracy. This allows organizations to make the best decisions possible for their customers and to offer engaging, personalized customer journeys.
Investing in Data Science to Deliver 1:1 Personalization
The difference between the best salesperson and the next best salesperson is all about the relationship that they build with the customer. Where once personalization was beholden to the human salesperson, today the AI-enabled digital salesperson can utilize data science to automate the customer journey to offer 1:1 experiences tailored to each individual customer. As digital adoption continues to rise, this allows banks to utilize machine learning (ML) to create seamless, end to end customer experiences that take into account factors such as a customer’s current mood, ideal time for intervention, and the optimal channel for outreach to create a personal, and even emotional, connection.
Interested in learning more? Check out the full interview with Ravi Santhanam below to learn firsthand how data can be used to improve the customer experience.
The Importance of Data Science for Personalization
