I log into my banking website and a chat bot pops up asking me how they can assist – that’s personal! I walk into my local bank branch and the teller comments that my account balances are high and asks if I would like to speak to a financial advisor – that’s personal!
#nope. These are generic interactions and while they are nice things to do for your customers, they are not personalized to me, and they are not enough to earn my trust and loyalty – not anymore. Customers are demanding more and being offered more by the disruptive fintechs and challenger banks. Today’s retail banks can’t just keep up, they need to catapult themselves past the competition and hyper-personalization is the key.
So, what is hyper-personalization? It’s not about selling, it’s not about the next best offer, it’s about knowing your customer and being there for them with the information they need when then need it, sometimes before they know they need it. It may be a service, offering, or recommendation on how a customer should invest, save or modify their banking portfolio, and it’s based upon the holistic view of customer information across their life-cycle including personal interests, current situation, and future needs. Hyper-personalization requires deep intelligence about each customer so that banks can deliver the right offer to each person at the right time and place along their connected physical & digital journey. Building hyper-personalized relationships requires gathering, analyzing and understanding customer insights (CI). “A life-cycle view of the customer forces CI pros to build customer relationships rather than manage campaign execution.” [Source: Forrester – How Analytics Drives Customer Life Cycle Management, February 1, 2019]
Hyper-personalization allows banks to know their customers better, but also provide the customer more flexibility in the products and services they can acquire. Consider this situation – you’re ready to buy a new home and you go to the bank website and review the loan offerings available. None of them are quite what you require. You get a pop-up window that advises, based on your relationship with the bank, your credit standing, and asset base, how you can design a loan to fit your needs. You’re able to choose from categories of interest, length, payment schedule, and down payment amounts to design your perfect loan – to meet your needs. This is just one example of how banks can leverage data from multiple sources to deliver hyper-personalization. So how do banks get there? It all starts with data, banks need to be able to gather and analyze data from a wide variety of sources, including customer interaction (physical and digital), websites, apps, and streaming/real-time sources. Analyzing this data and creating insights then allows banks to hyper-personalize interactions into holistic connected experiences that drive differentiation and strengthen customer loyalty.
Three things need to be considered to start the hyper-personalization journey.
- Personalized to the individual: Interact with an audience of one, with a rich understanding of the customer, including information such as: profile, preferences and affinities, sentiments, banking relationship history, etc. Furthermore, the interactions should be delivered via the right combination of a customer’s preferred channels (whether that is by phone, email, text, mail, website, mobile app or in-person).
- Contextual to the situation: Tailor offers and actions using a contextual understanding of what the customer is trying to do (their overall goal) and where they currently are on their physical & digital journey.
- Timely: Catch the customer in the act of deciding, surface timely recommendations for interactions and offers and deliver them when they matter most.
To learn how you can deliver the kinds of hyper-personalized experiences your customers expect download the report featuring research from Forrester: Hyper-personalization: The New Competitive Battleground for Retail Banks.
TCS Customer Intelligence & Insights (CI&I) for Banking is a software product that applies machine learning and adaptive analytics models to improve targeting, personalize experience and drive loyalty at every point of engagement. The pre-built customer analytics use cases accelerate time to value for banks’ customer centricity initiatives. The solution determines key customer personas, builds dynamic segments, monitors and analyzes the customer base, streamlines customer facing operations, and surfaces prescriptive next-best actions for each customer.