As we transition from manual to self-service and from there on to the promise of unified experience, the requirement for reduced BVL becomes even more intense. In an ideal world, BVL should be near zero.

Why BVL is important and more so today?
The current generation lives on Face Book, has a presence on 2nd Life, is never seen without a mobile phone and lives and breathes technology. To this generation, the only thing that would appeal is a unified experience – real-time, on-demand.

A Gartner study (in figure below) attests to the fact that customers expect a much shorter turnaround time from businesses.

Catering to such a customer base means that the time needed for businesses to react has come down drastically. Consequently, business leaders today are under tremendous pressure to make fast decisions – decisions that require information as current as possible. Any delay in the availability of information is therefore not acceptable and could have a catastrophic impact on the business.

What does this mean to CRM
BVL becomes important when servicing customers. Having all information about a customer at the touchpoint greatly empowers the sales person in serving the customer better.

Today’s CRM systems are mostly after the fact kind of systems. They rely and function more on analytics of past / historical data rather than predictive or proactive modeling. Be it an airline or a retailer, all analysis is typically done after the passenger has flown or the purchase has been made. And typically this is done once a month or at best once a week. By which time, your opportunity to serve the customer better has already gone by.

Reducing BVL through Predictive Modeling
iLoyal is the latest new generation product from IBS and joins a growing list of new-generation business applications to cater to the emerging needs of the global marketplace. iLoyal helps the Loyalty Manager and the front-end agents by giving them a deeper and more current understanding of the customer value.

iLoyal’s Customer Analytics allows a business user to gain greater insight into customer value and behavior through the analysis of data. While it is difficult to ascertain any pattern from raw transaction data, iLoyal synthesizes data into specific variables and with analytical models, presenting a much clearer picture of value and behavior. iLoyal achieves it primarily through two predictive models that are built as part of the product.

Current Value Model: The current value score in iLoyal tells to what extent the customer is valuable and profitable. In order to arrive at this score a variety of data is used which includes customer behavior attributes and aggregates (derived attributes).

Life Time Value Model: This model predicts the long term value of a customer based on a combination of variables which typically include demographics (job, industry, age, place of residence) and recent value contribution of activity pattern (e.g. flying frequently in first class, regular usage of credit cards in retail shops etc.).

Churn Model: This model predicts the probability of a member churning out to a competitor. This is arrived at by analyzing the previous activity trends, the frequency of activities and maps it across a timeline.

These customer value scores and list of potential churns can be generated up-front. With a small interfacing, these customer value scores can be displayed to the agent at the customer touch-point – be it a check-in agent at an airline or an agent at a POS of a retailer. By doing so, the agent now has a complete understanding of the value of the customer – both short term as well as long term – and can view the current transaction in light of that information and provide a more appropriate service to the customer. It also gives the agent the ability to win back the customer who is likely to churn away. All this happens in real time.

Conclusion
With such an approach the benefit is that you now have near real time information about the customer’s value and combining it with the value / nature of the transaction that is happening you can greatly reduce the BVL. Decision making is pushed further down the line – where it actually matters – and that ensures that you have a delighted customer. All that you need is a combination of the right technology and human intelligence.