Precomputed List of Next Best Offers = Bad Idea

What is the difference between having a batch process that computes the Next Best Offer for every customer every night and computing the best offer in real time?

It is all about context. Any precomputed offer list can not possibly take into account the context of the interaction between the customer and the company. Examples of attributes that can not be taken into account in a prebuilt list:

  • Call Reason
  • Recent and Last Transaction
  • Exact state of the account
  • Time of the interaction
  • User Agent (iPhone, Computer, Phone, etc.)
  • Call center agent answering the call
Without utilizing this kind of information you are certain to make the wrong decision in many cases. For example, a customer may be amenable to listening and accepting an offer if they are calling the service call center in the evening and have received a satisfactory resolution for a service call, while the same customer when accessing the site at 10:30 in the morning with the iPhone browser would more likely not be open to any offers at that time.

It has been my experience that in Real Time Marketing implementations in call centers the actual agent answering the call is always in the top 5 predictors that influence the selection of the best offer. Similarly, the call reason and the time of the call tend to be very good predictors.

It is important to understand the difference between inbound and outbound marketing. In addition to the obvious difference in the attitude of the customer and their openness to interact with the company, there is a fundamental difference from the point of view of the customer data. In outbound marketing I can compute the best offer for a customer and then call them a few hours or days later and there is no reason to assume the customer's data would have changed significantly in most cases - only the statistically regular changes apply. In contrast, in inbound marketing I am assured that the customer's data will have changed by the time I am ready to make an offer at the tail end of a call, after all, 100% of those callers decided to call the company for some reason.
Comments:

In some cases you might actually want to exclude the Agent ID as an input to the model as you are placing a bias on what is being recommended. This bias may not be inline with the main business objectives or KPIs. You may also end up in a situation where agents continue to see the same recommendations again and again no matter which customer they are talking to. This can then have a negative impact on user adoption as they feel that the system is providing little benefit to them by continually recommending the same products and services. This can be negated by removing the Agent ID from the prediction model or by ensuring that the decision utilises a number of other Performance Goals to ensure the overall score is a well blended one that reflects the real KPIs of the business.

Posted by Steven Roberts on January 28, 2010 at 11:40 PM PST #

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