A lot of marketing technology and work has gone into “how and when” to successfully reach customers. Maybe too successfully. These processes have been created then automated until they start to flood customers with messages coming from all different directions – too often messages that customers do not want. It’s time to take the conversation to the next level – automating “what” will motivate customers to buy.

Knowing What to Say

The paramount importance of “what” to say to customers in your communications, however or whenever delivered, has too often been overlooked. This is because while the how and when are relatively easy to automate, figuring out exactly what to say to each individual customer — the right message or offer that will improve that specific customer’s experience and loyalty — is really hard. It’s a lot easier to send everyone, or everyone with a certain profile, or everyone with the same recent activity, a similar communication and call it a personalized experience.

Research shows that sending the right message motivates customers to buy far more effectively than how you reach them or when. That’s backed up by real campaigning. Fewer but more relevant messages consistently beat more frequent and general messaging. The message is everything; it’s just a matter of getting it right. The how and when of getting it to them is important, but it’s more important not to squander with an irrelevant message those increasingly rare moments when you might have the customer’s attention.

Making Personalization Practical

While profiling and segmenting customers are a step in the right direction, determining the best possible message or recommendation at the individual customer level requires a deep analysis of each customer over time. A customer’s actions at the present moment can provide indicators, but they are often misleading or unrepresentative of their true or deeper loyalty and interests.

Figuring out the best “what” to say is an essential nest step in marketing technology. But to make it practical at scale, we have to give it the same treatment as what we did the “how” and “when.” We have to automate it.

It will not be as easy; working with data is always hard. It has to be sourced, cleansed, integrated, normalized, and governed, and that’s all before it can be analyzed, mined, and modeled for predicting what each customer might want. Fortunately, there is a lot of innovation happening in this area that can add focus and simplicity to the process.

One approach is the idea of packaging up specific analytic problems for examination and solutions, rather than trying to answer every question data raises. Data science can now limit the source data blocking and bloating analytic objectives, then allow marketers to focus on key data like product recommendations for each customer. With information like that nailed down, the process can be automated.

The idea there is to work with a well-defined set of data and get back customer-specific answers on what incentives or products to offer. In this way, the power of truly personalized product recommendations and offers may soon become as efficient and affordable as the “how and when”’ of sending messages has already become.

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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