How Predictive Analytics drives Customer Lifetime Value

Published On - July 3, 2018 channeleyes     Analytics

A customer is a collection of purchases over some period. The same entity, purchasing multiple items in multiple transactions from you has a lifetime customer value. Our goal is to maximize the value of that relationship in two critical ways:

1. Accelerate the revenue – Money has an intrinsic value which is based on how long it takes you to get the money (revenue now is better than revenue a year from now)
2. Increase the number of products/services that the entity purchases from us (and not from our competition)

Let’s consider the current model, where a customer makes a purchase and then we wait until they initiate another purchase and so on. We’ll call this a reactive model. We are reacting to the customers’ needs as they expose them to us.

Now let’s suppose we could understand what it was the customer wanted – before they did. Regardless of how we do this, it gives us a few significant advantages. Let’s look at a simple example. A customer has just purchased a laptop, and we know that 90% of customers will buy Microsoft Office within 30 days (when they realize they don’t have it). Given this insight, we can certainly construct a chain of marketing/sales events which converts some of our buyers into Microsoft office buyers earlier. We’ve accelerated revenue.

Being able to identify the buyer’s next purchase ask gives us a leg up on the product side. First, we may identify a product the customer wasn’t even aware of but wants. We call this “novel product identification.” We can direct information to them and have made a sale which we otherwise would not have. Even when the customer is aware of the need, the ability to anticipate and “pre-market” to the buyer gives us a head start on the competition.

Amazon is a prime example of “pro-active” marketing. Whenever you make a purchase on their website, they recommend other products to you. Some are obvious (I bought golf clubs and now they are advertising golf balls), but some are not (tires). It’s this ability to identify and expose this “novel” product to their customers that accounts for most of the 20-25% revenue lift that Amazon gets from their recommendation engine.

Just looking at these simple examples only scratches the surface of some of the advantages we can gain in the Sales and Marketing space from predictive analytics.

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