Customer journeys help to increase positive results in all customer related KPIs, but in large enterprises and public organisations, the rule is if you cannot measure it, you cannot improve it. How do we measure commitment to a brand and products – and how do we score KPIs that need to be improved upon?
A recent report by McKinsey indicates that customer journeys are 30% more predictive of customer satisfaction than measuring individual interactions, and that using customer journeys can increase customer satisfaction by up to 20%, leading to significant revenue gains and lower costs. In another study, a premier automotive OEM said that every 1% increase in sales retention translates to a $700 million increase in revenue annually – an average of $150,000 per dealer.
Customer journeys can be understood as a discrete, unevenly sampled time series of customer events that contain both unambiguous signals of commitment—like buying a new car—as well as more ambiguous signals of commitment, such as a monthly series of credit card purchases or contact with customer service.
Machine learning comes into the picture
How do we unleash profitable growth for these companies using customer journeys? Since we are trying to predict an outcome, statistically we need to gather lots and lots of customer journeys to arrive at an answer. A larger data set will most often yield a better result than a smaller one. This is the revenge of the large enterprise because 10 million customers can, ostensibly, produce billions of data points.
However, the sheer size of the data set means nothing. What really matters is how these data points interact with each other. It is simply impossible for a human brain to comprehend all of the patterns that could be discernable in this volume of data. Machine learning solves this problem.
Machine learning models are good for making predictions. The easiest case would be a sale. The average vehicle sold in the U.S. and Canada costs roughly $35,000. That’s easy to see in a data set, but there might be 10 to 20, or even 100 events before a customer finally buys that SUV they always wanted.
The machine learning model not only looks at the customer, but it looks at all customers, especially those with journeys close to the customer in question, to understand what events are most important in driving an outcome. This requires a lot of number crunching. It is not unusual for us to do 5-10 trillion calculations to solve this type of problem. In changing the weights assigned to events in a model in this way, the machine learning model – learns.
Why it matters when measuring customer experience
Cerebri AI has filed numerous patents on how all this works, but why does this matter? Assume we have two buyers of SUVs, one paid $30,000 and one paid $60,000, and each had ten events leading to the actual purchase. In the first case, all ten events are valued adding up to $10,000. In its simplest form, if each event was equally weighted by our models, then each event was worth $3,000 to the end goal, the purchase of an SUV. In the second case, under similar circumstances, each event was worth $6,000, or double.
How does that work? If events lead to a bigger purchase from an enterprise point of view, the events are simply more valuable and more events like these lead to a larger purchase. In other words, if you go to a vehicle OEM website to decide on a car and your purchase ends up being twice the standard price, then you visit is twice as valuable to the vehicle OEM.
It’s simple. Everyone understands money, we use it every day. But that simplicity masks the real power of the system, and that’s why Cerebri AI has applied this approach across markets where customer experience is critical to predicting growth. More than anything else, it’s an approach that is easy to introduce, using technology as a platform, with little disruption in time and training.
It provides the post-purchase means to track a customer’s commitment to a brand or product, one subsequent event at a time. If an event increases a customer’s commitment, then their ‘value’ will be higher and once we have a lock on every customer’s commitment, it is possible to swing into action.
For instance, in a case where a value goes up and down, we can gain visibility through these values at what events have a positive impact, and what events have a negative impact such as a marketing campaign. By pressing a button, it is possible to build a cohort of similar customers where we expect to see a similar positive result.
Customer value as the most important KPI
Of course, the action we identify and recommend can then be tracked through to successful sales. The customer’s value is the first and most important KPI every company wants to measure without fail. Many executive bonuses are now tied in part to NPS scores and their movement over time.
A successful values system is built to operate in real time. The driver for this is two-fold. First more and more buyers want to buy on the web. Buyers also want to source valued information on the web. These web site visits may last a few seconds or hours, but making an offer to a customer clicking through a website has to be adjudicated very fast or you risk losing the transaction to a competitor. So, speed does not kill in this scenario, speed is a necessary condition for success.
The second reason why speed is a critical KPI for buyers today decide how they interact with companies. If I am a bank customer, I want the offers the bank presents the same in similar circumstances if I present myself on the web, or in a branch, or via a call to a support center.
In the end, measuring customer value means the customer decides, and AI techniques provide the key to understanding each customer’s journey as we look to quantify the customer experience.
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