The customer satisfaction for a particular product recommendation has always fared based on the ability to make inroads into the mind of the customer. However, banks have relied on the experience of the financial advisor to make the case for the customer.
Reality Check: It is time to stop guessing and start predicting customer behavior. The reality is that customer has one interaction with the bank and there is one right way to get the best experience for the customer while there are many wrong ways. Get your recommendation right, and customer retention and loyalty is never a problem. Get your recommendation wrong and your competitors will be thanking you for a very long time.
So how can you obtain and suggest superior product recommendations for your customer? – Through Technology. When it is available on a platter, why hesitate to use it?
The Banking Dilemma – Human Based Wealth Managers
Artificial Intelligence has taken the banking space by storm. There has been a sudden and unusual pressure on banks to adopt technology based wealth managers. With non-financial organizations and neo-banks breathing down the necks of traditional global banks, the intensity of the pressure faced by banks makes sense.
According to a Forbes Insight, 82% of customers and organizational C-level executives believe that increased personalization of products for customers will be critical to having better goals and investments. However, at the same time only 45% globally feel that financial guidance might change because of AI.
Banks have struggled in their aim to provide heightened customer experiences at banks. Long paperwork during onboarding, cybersecurity, risks over loans and payments still exist. Operational back office operations still act as liabilities. AI can change all of that and it starts with product recommendations.
Recommendation Engines based on technology such as AI and ML are doing wonders in today’s business landscape. The retail and entertainment industries have implemented them to perfection, and have customers falling for the customer-enriched experiences that they have. However, with legacy architectures handicapping banks, they have not been able to create the experiences that customers have been demanding.
The Next Generation Product Recommending Technique
Banks are looking at acquiring affluent clients and according to Forbes Insights, customer analytics (36%) and technologies (43%) are the top ways to attract this segment.
How does a recommendation engine work and why is it in high demand amongst thought leaders in the financial space? The pipeline to having a powerful recommendation engine starts with obtaining data from different paths, channels and browsers that a customer might visit. It includes tracing the customer’s behavior on both online and offline platforms such as payments, transactions, bills, complaints, feedbacks, suggestions, past visits, history, and the list keeps going on. Once data is collected and then cleansing process of the data takes place. After this, the segregation of customers into several taste groups takes place based on the customer behavior.
Reality Check: Banks currently have minimal taste groups to segregate their customers. This taste groups are usually classified under broad demographics like age, race, sex, etc. For each taste group, the bank offers recommendations, which often does not match the customer’s exact needs. With the customer data, the machine-learning model takes the cleansed data and starts a process of self-learning to obtain accurate recommendations. This sort of recommendation engine also helps the model to adapt and provide accurate recommendations even if the customer behavior takes a different turn.
Once Recommendations and Insights come in, The Next Best Action
Through the insights derived from recommendation engines, real time data of customer needs and requirements come in. Also, based on the enormous amounts of customer data that flows in, several potential recommendations would come in. Banks can choose the next best action from the several options and drive customer experiences across platforms and channels.
Each of these customer experiences also help banks to set up appropriate budgets for each cluster of segregated customers. The calculation of each budget depends on the monetary value that the customer brings to the bank. Hence, banks can finalize on the budgets for each class of customers and remove the risk involved with money laundering at the same time, through an intensive product recommendation formula.
The Journey Ahead – 2020 and Beyond
It is the need of the hour that banks take the rope to get a new leash for business. With the enormous build-up and success behind challenger banks in the UK and in other areas globally, they are going to be a hefty challenge to deal with. At the same time, non-financial entrants also vie for the customers, whom banks had easily held on to until now. Customers expect to experience engaging opportunities, which help them in wealth management and wealth accumulation. With AI, ML and a proper data strategy, banks can slowly retain back some of their customers. Customers would get the traditional face connect with the bank and would be able to derive delightful experiences through the new technology based wealth managers.
Time will tell, if banks are able to pull off a technological implementation which goes beyond complex chatbots within the front office and automation in the middle and back office operations. Time will tell, if banks can build the bridge of trust with their customers again.