There is no doubt that when an enterprise’s customer base and product portfolio grows, it becomes even more complicated to manage multiple relationships with the customer across products, channels and geographies. Most organizations have a simplistic approach to manage this challenge: Customer Single View .
Customer single view, as a concept, has been around for many years now. However, it has been more of a static endeavor with updates made once a fortnight or at best once a week. Adding new channels or products is a project in itself that leaves the users frustrated and discontent. The lack of real time updates, and multiple views across different channels add to the chaos. Moreover, the customer single view may or may not be driven by any KPIs.
For Banking and Insurance especially, it has become more important than ever to be as closely aligned with the customer as possible. This alignment could come in terms of knowing what the customer may need next (cross sell opportunity), understanding patterns in interaction and reactions (co-relation), mapping recurrences, and generally being ready before the customer connect happens.
The traditional customer single view solutions are just not geared up to meet the challenges of the new age business. Insurance and banks have multiple channels that allow a customer to transact and interact. Moreover, social channels are now the preferred channel of customer complaints. Sure, email and call center work, but Twitter and Facebook have a greater ability to sway public sentiment for, or against your establishment. Customers expect, nee, rather demand that their problems be solved in real time. A recent engagement with one of our banking customers showed that they have over 15 channels of customer interaction – the customer can request a service or raise a complaint on any of these channels!
Customer single view for big data solves these problems largely based on its ability to process data in real time, and using complex data models that can work across products, channels and geographies. It is thisreal time capability that is the real differentiator. End users do not have to wait for arduous process driven reports or metrics to make decisions pertaining to a customer. We like to call this ‘analytics at the point of decision’ – insights whenever you need and where ever you need.
Customer single view at the point of decision need to be driven by KPIs that are either decided by business or derived by smart algorithms powered by big data. Either way, these KPIs are a critical aspect of decision making. The KPIs could be dynamic, or static. The advantages of having a KPI driven customer single view are many. For one, there is always a benchmark to analyze customer behavior, and secondly the end user will always have perspective of the customer while interacting.
Because of the large volumes of data, it is possible to find relevant correlation and recurrence patterns that are critical to analytical insights. These insights from the single view can be pushed into other transactional and intelligence systems such as retention, campaigns to drive meaningful decision making.
Moreover, large organizations may have multiple teams to manage the various channels of communication with the customer. It is important that all the teams see the same view for the same customer, to ensure that she has a seamless experience.
Customer single view is not an end in itself. It is however a catalyst that helps to drive deeper customer engagement.
To know more about how customer single view can help you, reach out to us today using the link below.