The panel for ‘Member Experience Analytics’ consisted of Manoj Rai who elaborated on how to keep Member Experience Analytics people-centric, Rob Guilfoyle and Mathy Hogan working in collaboration and Joseph W. Mclean who talked about member interactions in the context of fraud.
Analytics Driven Member Experience provides a member-centric approach to analytics. This includes a synchronized presentation, investments, and insights-driven action however certain challenges are faced while achieving this objective. The first is siloed function leads to fragmented experience, the second is the pursuit of perfection leading to inaction and last revenues and the last is action based on data has not been a priority. It is important to know your member through internal and external data. Internal data includes Member Transactions and Channel Interactions and external data includes Financial Profile, Employment Profile, and Social Profile. It gives a 360-degree view of a member from the data perspective to design individual experience for all of them. Internal data is easy to manage, and lower cost is associated with it. Additionally, more freedom is available to use it when organizing. On the other hand, external data is concerned with the incremental cost of bringing in-house is higher. There are additional restrictions as to when it can be used.
The lifecycle of a member journey through a credit union can be represented as:
Discover > Explore > Purchase > Deliver > Support > Advocate > Churn
It starts from the realization of need, shopping cart product comparison, pulling the trigger, providing online and offline support, contact centers, bill payments, and e-statements to social influence or detraction and end of the lifecycle. It leads to lifetime value, product mix, and credit risk exposure. We need to align business by recognizing why the problem needs to be solved, who needs it and who is it benefitting, what is the priority of the solution and how would the solution be implemented.
The importance of Artificial Intelligence was emphasized in Crawling with Artificial Intelligence. According to a study, 69% of Financial Institutions plan to implement an AI-Powered chatbot and 71% of Financial Institutions don’t know how to get started or what options exist. The answer to the question of where to start with lies in increasing the level of investment with increasing level of impact. Implementation of AI is made possible and monitored through the crawl-walk-run approach. Access to AI is possible through Virtual Advisors such as Amazon Echo, Google Home, and Facebook Messages. We need to learn from the members what is most popular amongst them. We need providing short, concise and pro-active answers with accuracy, relevance, and retention so that each device is independently optimized. Acquisition and support engagement is required for success. There rest unique analytics opportunities of conversational data which is to distill information into a product.
The product will tell what features to implement or changes to be made according to user demands saying and potentially what context there’s under.
The next important aspect is that of Fraud: A Moment That Matters. Once the fraud has been identified, the subsequent step is to see how we are treating that member and the experiences they are going through. The current member experiences are somewhat traumatic and they feel like a suspect rather than a victim with the lengthy resolution timeframes and inefficient processing. To solve the problem of frauds, a Member First Approach should be followed to show that we care about the member. It is required to gather pertinent data and understand the data required to resolve the situation and use existing data sources to fill the gaps. It is imperative to know the end result from the very start so as to ascertain fraud, recover and give the member results immediately. We can adopt multi-channel processing where intake may be in a branch, a contact center or digital self-service, provide the ability to switch between channels.
With the data at your disposal, you need an intelligent question and answer service, transaction or authentication details from core system, member history for fraud and transaction system, supplement with association data such as visa and MasterCard, understand recovery history with chargeback stick rates and representation rates and merchant collaboration such as Ethoch, Verifi and Visa NPI. The data should be used wisely because it serves a number of purposes and can be helping in determining fraud probability and recovery probability, high or low fraud probability and recovery possibility and incorporating AI for future proofing.
There are a number of benefits to the members and credit unions. For the members it is a simplified and consistent process, it provides a channel of choice, faster turnaround time on the outcome, no hassle in high fraud or high recovery scenarios, single contact resolution and money back into accounts later. For the credit unions, it is less expensive during the fraud intake process and less expense in back office processing, more satisfied members and reduction in regulatory non-compliance.