Is Your Marketing Biased Against Action?

Over time, having more data has helped us create a higher-resolution model of our customers. Despite the richness of the information we collect, we have only a few choices for taking action. We must increase our ability to take action, building a feedback loop that informs more targeted actions.

Today’s marketing: M.I.A. (missing impactful action)

It’s common for marketers to have dashboards and execution tools for campaigns, but the two areas are disconnected. Marketers can’t get enough granularity from reports to take meaningful action, and there is no way to experiment with targeted strategies and determine whether they are working. How can we fix this?

Putting the action in your marketing program

I’ve spent my career thinking about how to close this feedback loop. I’ve developed key approaches to bringing the action back into marketing strategy.



Intelligent analysis

Many marketers collect intelligence, but don’t put it to good use. Here are a few key strategies for getting more from the data you collect.

Divide to conquer. Increase your understanding of new customers. Don’t waste energy acquiring low-spend or low-loyalty customers. Often, first-time buyers don’t return. Understand which customers are likely to return before you spend money to acquire them.

Be aware of the buyer(s). Customers can be segmented into “personas.” For example, a company that sells vitamins could develop different personas for customers who buy athletic dietary supplements (“athletes”), as opposed to heart medication and joint pain relievers (“seniors”). These personas can then be offered related items or an intensification of the relationship in some other dimension.

Don’t stare at stills - watch the movie. Traditional approaches to business intelligence and reporting are static. Using a real-time system that detects change and offers suggestions for action is like watching a movie. 

Practice simple heuristics, not mental gymnastics. Even without a sophisticated model, marketers can use simple heuristics to identify customers who are likely to leave, based on two factors.

  1. Breadth of the relationship. For example, if customers who only buy jeans are more likely to go somewhere else than customers who buy shirts, shoes, and jeans. Even if they spend the same amounts with the same frequency, marketing dollars are better spent on multi-item purchasers.
  2. Message relevancy reveals the degree of customer engagement. To measure this, track the number of opened emails or redeemed coupons for a given customer. 

Into Action

Armed with some concrete ideas, you’re ready to take action based on what you know.

Say “hey!” to the big spenders. Marketers often miss the two dimensions of customer loss. Businesses make 80% of their earnings from the top 20% of customers. Develop a model that can identify the 20% in your current and future customer base:

  1. For current customers, look at churn value. Devote retention efforts to big spenders.
  2. For prospects, look at potential value. Consider prospects and their potential for spending with you. Identify customers with high spending potential.

Lasso the drifters. Reengage the marginally committed. Marketers focus too heavily on acquiring customers. It takes less energy to retain customers. For example, in many subscription businesses, 50% of customers don’t renew. Reengage customers before they are lost.

With the right predictive analytics tools, marketers can discern signals surrounding customers who are about to churn. Someone who hasn’t bought for two years is difficult to reacquire. Someone who hasn’t bought for three months might be trying a competing product or dissatisfied with their last experience, but could be reengaged cost effectively. Cross-referencing the customer’s last purchase with his persona, geographical location, or income can help you craft an offer that is likely to pay off. Many marketers fail to notice this critical middle spectrum of customers.


There’s no question that the amount of data being ingested has great potential, but its value is realized only if analytics and execution are seamlessly integrated. In most companies, there is still a heavy bias toward information disconnected from action. To pull ahead, our actions must match the quality of our data.


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