The Key to Personalized Marketing: Be Analytical, Not Personal

analytical

Personalized marketing sounds like a marketing dream come true — if you can send each customer exactly the right message at exactly the right time that gets them to buy more, you will maximize revenue. The trick, of course, is figuring out what message to send and when. For that, you need some sort of advanced analytics to predict customer interests and behavior. The problem is that’s impossible. Each individual customer and customer context is too complex to predict their interest or behavior with any real certainty. So what can be done?

No technology or person can predict the future.

Be careful of marketing platforms that put too much emphasis on watching and responding to an individual customer’s behavior in real-time. What the system might infer about that customer could well be annoying to the customer. Systems that “personalize on the fly” have a good chance of getting it wrong for any given interaction or customer experience. A deeper analysis of actual purchasing behavior and patterns among all customers is needed.

When it comes to making predictions, all anyone can do is assess the probabilities of various outcomes. That may sound simple or obvious, but I’ve talked to a lot of marketers who do not understand what that means. They think a mathematically derived probability of a customer to buy a certain product is a prediction of whether or not they will buy. If a customer has a high probability, and they buy, the prediction was good. If they have a high probability and do not buy, the prediction was bad.

Confusion about probability leads to all kinds of errors in marketing judgment.

For example, if a customer has a low probability to buy a certain product, some marketers automatically conclude that it’s not worth promoting or recommending that product to the customer. They also might look at a high probability for an individual customer to buy a certain product and, based on their knowledge of that one particular customer, know that the predicted probability cannot be correct. So they conclude that the scoring algorithms used to calculate probabilities will not be useful for marketing.

The secret to making predictive analytics work for marketers is using it at scale.

That means, forget about getting the message or offer right for each individual customer. You will not. The goal is only to improve your batting average – just get more customers to buy. And concentrate most of your limited analytics budget on buying behavior, not propensities or behaviors less directly associated with actual purchases.

Probabilities, calculated properly, work very well across populations of customers, not for individual customers. If you can find a group of customers who have a 1-2% probability of buying a certain product, that’s a very low probability for any individual customer. But you will get 1-2% of that group to buy. If your typical product promotion gets less than 1% buyer response, you could get significant revenue uplift from promoting that product to this group.

The key to optimizing revenue lift is to offer each customer the products with the highest probability of being purchased, even if the actual probability for any given customer-product combination is low.

If you recommend products with the highest probability of purchase to each individual customer, and do that with a lot of customers, you’ll see increased buying across the population, even if many customers do not behave as predicted.

New services and technologies are making it easy and affordable to “predict” the loyalty, likelihood to buy, and the probability to buy each available product for every customer at the individual customer level. However, these predictions are most effective when applied across a large body of customers. So you need a process that scores and makes recommendations at once for every customer in a campaign or be ready with recommendations for any customer who “might” show up online or elsewhere .

If an email campaign promoting three personalized product offers to each of a million customers at once could increase revenue from $1.00 to $1.20 per email, despite many customers not buying as predicted, would that be a successful campaign? That’s exactly the sort of thing that is possible.

Platforms that rely on calculated predictions to personalize messaging must work with many customers to amplify the impact of getting it right more often, but not all the time. Nothing gets it right all the time. And if you focus your analytics on predicting buying behavior rather than chasing the dream of the perfect “customer experience,” you can make a big difference to your bottom line.

Peter Moloney is CEO of Loyalty Builders, whose Marketing Lift Service offers a simple, cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.

 

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