4 Steps in Blending Customer Behavior and Customer Analytics for Better Insights

By Angela Hausman, PhD

Image courtesy of ExasolAG

Customer analytics come from a variety of sources:

  • Google Analytics – which becomes more robust every year
  • Primary research
  • Internal sales databases
  • Login databases
  • Customer service
  • Potentially other sources specific to a particular company

Harvesting this data and successfully utilizing it relies on doing 4 things really well:

  1. Understanding concepts of consumer (customer) behavior
  2. Gathering data across functional silos and disparate sources
  3. Gleaning insights from a blending of data and relevant concepts from consumer behavior
  4. Implementing change based on your insights

And, based on a survey of marketing managers, alteryx finds more than half feel these challenges keep them from optimizing long-run ROI. So, let’s take a look at each of these challenges and ways to overcome them. See the infographic they produced based on this survey at the bottom of this post.

Customer analytics problem 1: consumer behavior

I’ve been a marketing professor for over 20 years. Most of the schools that I know of require marketing students take a consumer behavior course or, at least, strongly suggests they take one. So, marketing students come out knowing a lot about the decision-making process that ended with consumers either buying or not buying their products. They understand how peers and other influencers, memory and learned behaviors, and cultural beliefs impact this decision-making process. Thus, they know what variables likely impact purchase decisions, so they know which data is important and which has little to no impact on buying decisions.

Even price is a poor predictor of purchase behavior. For instance, Apple sells a ton of PCs, tablets, and other devices despite pricing their products substantially higher than competitors. And, the decisions have little to do with other factors we commonly think of as driving customer purchase, such as quality, availability, etc. And, Apple isn’t the only case where consumers make decisions that don’t fit with our economic notions of what drives behavior.

The problem occurs that these same marketing students who have such a clear grasp of the consumer behavior process as it relates to purchase decisions have poor analytical skills and lack skills in related aspects necessary to derive meaning from data, such as SQL, which we’ll discuss in a few minutes.

The same is true for folks trained in analytics, only in reverse. They’re trained in deriving business intelligence (BI) from data, but, because they have no clue about consumer behavior, they have little clue about what to look for, beyond superficial types of data like demographics, which often explain little of why consumers made specific decisions. Without this information to guide their queries, they’re ill-prepared to develop actionable insights that improve ROI, even in the short-run.


The obvious and most practical solution is to train marketing students more thoroughly in customer analytics. We could think about integrating customer analytics into existing marketing courses but, there are a couple of problems with that solution. First, students self-select marketing, at least in large part, because it’s not reliant on math. A related problem is that most BI courses don’t include enough on customer analytics, instead focusing on finance or operations. A second Go to the full article.

Source:: Business 2 Community

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