You want to maximize the value of your customers but they don’t interact with you very often. With so few opportunities to cross-sell or up-sell, you want to maximize the value of each one. Determining the next best offer for each customer each time you get the opportunity is critical. A customer-centric selection of the most appropriate, most valuable offer focuses your product portfolio to something that is relevant, appealing and easy to consume. With next best offer, every customer interaction becomes an opportunity to cross-sell or up-sell a customer effectively.

In insurance, next best offer means balancing regulations, eligibility, segmentation and propensity to purchase analytics. It means correctly identifying the kind of customer someone is from their behavior and then using historical data to identify the products they are most likely to buy next. These analytic insights must be constrained by legal and eligibility requirements. Channel context as well as recent customer actions must be considered. And the resulting offer has to be delivered in real-time in whichever channel the customer is using.

Digital decisioning automates the next best offer decision independently of your legacy systems. The automated decision plugs into all your channels to ensure consistency and excellence everywhere. An automated next best offer decision applies machine learning to focus on the best offers while also managing fraud, ensuring compliance and managing eligibility.

OCR for Next Best Offer

Data required for personalized customer interaction and for creating the best Next Best Action (NBA) for them is still frequently on paper. This data may be on forms that customers fill out or on various internal forms that businesses complete to support business processes. An effective way to minimize that work on both sides is to deploy OCR, or Optical Character Recognition, in conjunction with a decision management service. The decision management service allows the business to key into exactly which pieces of data will help effectively suggest the Next Best Offer and OCR captures that data. 

For retail, most transactions occur blindly- a transaction is associated with a time, a place, an amount, some products, etc., but often the customer information is underutilized if used at all. Being able to connect a person to a transaction (date, time, place, etc.) via scanned receipts or QR codes would allow significantly greater insight into individual customers’ behavior and therefore help decide which offer will be most effective for that customer.

In insurance underwriting, a lot of information provided in support of the application is on paper or pictures of paper. Underwriting decisions rely on income, health, occupation and related data that is trapped in this paper. Decision modeling identifies the specific data required for underwriting so that OCR can be applied more precisely – to create digital data that powers Instant Underwriting and Straight Through Processing (STP), for example. This OCR-driven decision management not only improves turnaround time, but it can also be used to recommend personalized policy elements for each customer – i.e. decide the next best underwriting action for this customer.