Calculating the Expected Future Value of Anonymous Visitors

When a visitor arrives on your site, many questions may arise as your technology stack works to match them with your CRM:

• Are they a returning shopper or an anonymous, “net-new” customer?
• What do they expect from their shopping experience?
• How much could this customer be worth?

If the CRM cannot match them with an existing customer (as is this case with 90% of e-commerce site visitors,) to answer these questions, we look at macro data. For example, we may learn that this shopper:

By analyzing this macro data, you can establish their Expected Present Value based on others who exactly matched these data points. There is, however, a wide variety of digital body language that could affect the visitor’s Expected Future Value.

Expected Value Over Session

Using macro data, you not only determine the visitor’s potential Expected Future Value but also the probability of their variation. To maximize each visitor’s value, it’s important to compare their Expected Present Value to their Expected Future Value. If their Expected Future Value exceeds their Expected Present Value, it’s best to wait and see what they do next. On the other hand, if it’s determined that Expected Future Value won’t surpass Expected Present Value, it is crucial to convert the customer at that moment, when they’re at their highest value.

Now what if you could do this automatically, at scale, for every shopper on your site? What if you could influence and maximize a shopper’s Expected Future Value in real time? Granify provides just this solution for online retailers to increase conversion rates and drive incremental revenue.