Understanding what customer actions predict their attrition, knowing what negative experiences customers remember when responding to NPS/CSAT surveys, or recognizing the moments that matter when deciding what next product to buy – this is the power behind understanding the customer journey.
All four of the major US wireless carriers list customer churn as one of their greatest risks, but one of these carriers has been able to decrease churn by a significant margin using omni-channel customer behaviors as input into their attrition models. Transforming journey attributes into model variables illuminated customer behavior patterns that were continuously repeated before the final call came to switch to a competitor. Uncovering those patterns enabled this carrier to implement intervention techniques to retain their business when a customer started down a journey leading to churn.
Financial institutions around the world vie for top NPS scores as a measure of client trust and a predictor of wallet-share success. With financial systems becoming progressively complex and the number of customer interactions and channels increasing, banks are faced with the challenge of determining where to place their focus and dollars in order to maximize client satisfaction. Instead of qualitatively assessing processes and systems channel by channel – one bank has prioritized their efforts by using their customers’ journeys to quantitatively determine what interactions drive detractorship. The customer journeys revealed it was not broken processes but rather mediocre experiences leading to customer apathy, or even worse, anger. Remodeling those moments that matter to be more intuitive for their customers has led to an increase in wallet share and increasing NPS scores.
Customer actions build upon themselves – one action generally foreshadows another. Being able to digest customer behaviors, though, has historically been an impossible challenge. It is not just that the customer failed to make a payment online but that the failure then led to a call with a seemingly interminable hold time, dropped call, and then late fee that resulted in the “0” NPS score or worse yet their finding a new service provider. Past models of studying client behaviors have failed to produce results because the client context has not been captured. Analyzing actions in isolation from one another removes their predictive power.
The ClickFox Journey data set captures and links customers’ experiences. While in the last installment, we established that journey data sets can provide better cues to CSRs that is just where the possibilities begin. Journey data sets reveal the patterns preceding different business outcomes. Being able to intervene just in the nick of time and save a customer relationship, or prioritize web enhancements for greatest increase in client delight, are just two examples of what is enabled when companies can leverage a customer’s journey. While many companies are still struggling to unlock the power of big data, journey data sets and journey science initiatives are already paying huge dividends and the exploration of their power has only begun.