Extreme Personalization through Dynamic Segmentation in real-time
When was the last time your bank saw success with product campaigns without a robust target customer strategy? Today’s customer is looking for transformational experiences from banks. And this experience has to be continuous and adaptable to the ‘quicker-than-ever’ changing requirements of the new-age customer. An ideal bank in this age would be the one who is able to partner with the customer, understands their financial goals and limitations in achieving these goals, and at the end of the day is able to help the customer achieve them to the best possible extent. It is all about gaining customer’s trust and then retaining it.
Banks need to work hard towards the customers’ impression about them as the one-stop solution for all their financial goals. They need to figure newer ways of capturing the customers’ attention and wallet share. It is not just about offering customers with financial products and services. It is about understanding the entire customer life journey, their various financial and non-financial requirements at various stages and contexts, and to be able to bring them all together as a complete value provider. These experiences have to be seamlessly orchestrated across the multiple customer touch-points in order to provide a transformational omnichannel experience to the customers. The Banking Customer 2020 report, by Accenture Strategy, depicts an ideal picture of an Everyday Bank (Figure 1)
With digitalization at its peak, the digital-savvy customers know their options and are intolerant. In the “Banking 2020” point of view, Accenture estimates that 35% of banking revenues will be at risk by 2020 due to disruption in the financial sector.
In order to achieve customer centricity in this scheme of things, banks need to be able to dynamically understand the customer context in real-time, pre-empt their behavior and requirements and be able to roll-out quick but perfectly suitable offers for the customers. This level of perfectionism and personalization can be achieved only if the bank is able to treat each customer as a unique individual with unique requirements. Traditional methods of targeting customers, with products and services, involved segmenting them based on very high-level parameters like geographic, demographic, psychographic and behavioral. The market has evolved from traditional segmentation methods into the concept of micro-segmentation. The “Banking 2016” report by Accenture defines a micro-segment as “the smallest set of customers with uniform demographics and social behaviors, and form the basis for defining strategic profit pools”. With micro-segmentation, banks can create smaller groups of personas which take many more parameters into account while defining them. The Intelligent Customer Experience report by EY identifies the following parameters for defining personas:
- Customer characteristics: income, age, marital status, employment or education
- Customer emotions: life aspirations, fears, dreams, brand loyalties, advocacy and trust
- Banking behaviors: usage frequency, usage volume and preferred channels
- Financial factors: asset level, revenue level or profitability
- Digital factors: mobile usage or online shopping
In order to achieve this, the banks need to be able to make sense of the large troves of customer data that they possess. There are a huge number of platforms available in the market today which promise faster and more profitable business decisions through analytical insights. But all the micro-segmentation efforts to arrive at profitable customer pools can be futile if done in a static methodology. With the ever-changing personas and contexts of customers, banks are required to constantly and dynamically review the requirements of the customer in real-time and roll-out suitable offers to retain them. Banks need to adopt dynamic segmentation methods in order to cater to the rapidly evolving customer personas. The Intelligent Customer Experience Report by EY explains an intuitive way of predicting customer behavior through personas. As per the report, a set of personas are created and plotted in a map. Each persona is assigned with a reference point or “centroid” against which the customer expectations and requirements are mapped, as seen in Figure 2. The behavior of any customer is determined by the relative position of the individual against the reference persona centroids. This, coupled with data analytics, is used to assess the preferences of the customer at any point in time and roll-out suitable offer to cater to their needs.
Figure 2: Persona-based dynamic segmentation (Source: Intelligent Customer Experience, EY)
Many banks today are custodians of big data on customers, but are unable to leverage its potential owing to challenges posed by information silos, legacy systems, and lack of an achievable customer experience strategy. An orchestration layer that can bring all the platforms and applications in the banks’ technology landscape together and enable the bank to achieve a unified view of the customers and products in the bank might be the first step that these organizations might want to take to be able to leverage the benefits of the historical data in store with them. This layer should empower the bank to cater to the entire value chain of the customer by orchestrating various transactions across multiple channels and third-party applications. The bank should be able to personalize and roll-out offers in real-time, for each customer persona identified, in order to achieve ultimate customer experience orchestration and lifetime value.
It would be nirvana state for banks the day they are able to achieve this degree of extreme personalization for their customers, by leveraging the customer information to dynamically segment them in real-time and predict their requirements and preferences.
Imagine the day when a customer walks into the bank and even before he reaches the counter, the banker knows that the customer is looking for a new investment fund – wouldn’t that be an AHA moment for the customer!! It would also save the banker the effort of shooting in the dark by extending the offer to every other customer walking in, and also the embarrassment and bad publicity that would follow when some irate customers retaliate in a negative manner. The CSAT scores would soar higher when customers experience that the bank cares about them and understands their requirements. The day banks are able to prove that they know their customers, better than the customers themselves, would be a high-point for gaining customer trust, loyalty and wallet-share.