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Data Is Useless (at Least on Its Own)

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Personalization is a big deal—a really big deal if you are to believe all the extant posts, articles, and punditry out there. I would tend to agree, though I’d want to make sure that personalization is just one element of a larger effort to engage customers at the level they need to be to keep them interacting—and transacting—with my business.

With that caveat in place, ultimately what is needed to personalize interactions is more than just data and more than just technology. At the same time, when you are trying to personalize at scale the offers and interactions of hundreds of thousands or even millions of customers, technology becomes a necessary component.

But gathering the data you need is only the very first step in identifying the optimal approach to each of those potentially millions of customers. Because without taking some further steps, gathering data—actually, no, data itself—is pretty useless.

Huh? Just bear with me.

PERSONALIZATION AT SCALE: THE TECHNOLOGY

In an era when engagement is a key strategy, and when customers expect you to provide them with what they need in order to do whatever it is they’re doing with you, data and insight become incredibly important for providing a personalized experience. But more than just identifying the products, services, tools, and consumable experiences necessary for customers to sculpt their own choice of engagement with the company, businesses are now in a position where they must anticipate customer behavior and then develop optimized offers or programs for those individual customers in real time (or close to it). While algorithms never determine those insights, the data analyzed and presented in the right way provides the basis for them. How can these insights be used?

The difficulties inherent in identifying an insight are increased by orders of magnitude when two other factors come into play. Scale (i.e., the size of your customer base) and speed (i.e., the expectation that the action suggested by the insight will be taken live rather than later) more often than not add complexity to a situation that was already complex. As the customers are on their thousands or even millions of journeys, how do you find out what they are doing, interpret what you know about their activities, decide how you are going to respond to them, build the responses, and communicate those responses—all while the customers are still on their various journeys? Equally important, how are you going to do this in a meaningful, individualized way for those thousands, maybe millions, of people?

This is the dilemma of personalization at scale. What you have to do, though, is not lament but solve the dilemma. Technology in this case is an enabler of the solution, though not the driver of it. But to realize the value of the analytics capabilities you’re going to need, it is imperative—and I’m not using that word lightly or metaphorically—that you see the clear distinction between data and what it has to become to further your knowledge of those thousands (or millions) of customers.

FROM DATA TO INFORMATION TO KNOWLEDGE TO INSIGHT

When I said data is useless, I wasn’t kidding or being facetious. But I did leave something out. Data is useless…until it is used in context. Think of it this way: How often has someone told you something, and your response, if you weren’t being nice, was, “So what? Why’d you tell me that?” Whoever imparted the information, of course, thought they were telling you something important. But you had no idea why they shared it with you. The person who told you the thing had context—in other words, they had a reason to tell you. But you had no context, and so you didn’t understand what (or why) you were being told.

What you received was information without context and, thus, no meaningful actions could be taken. That is data. It is the raw ingredient that leads ultimately to the insights you need to personalize the relationship you have with your customers.

Here’s the sequence.

  1. Data becomes information when you give it context.
  2. Information becomes knowledge when you define its value and its purpose.
  3. Knowledge can be used for insight when you figure out how to use it effectively.

Think of it like a recipe.

THE RECIPE FOR INSIGHT

This example is excerpted from my new book on customer engagement The Commonwealth of Self Interest: Business Success Through Customer Engagement (2019).

Data:

  • 17.5 ounces white flour, plus extra for dusting
  • 2 tsp salt
  • ¼ ounce fast-action yeast
  • 3 tbsp olive oil
  • 10.5 fluid ounces water

Information:

This is a recipe for making bread.

Knowledge:

Here’s how you make the bread.

Insight:

Judging from the comments on those who ate the bread made with this recipe, this is a good-tasting bread that can be made even healthier and would taste just as good with multigrain flour instead of white flour, but it would need 1 tbsp more of olive oil.

Of course, it’s not easy to figure out how to take data and turn it into useful knowledge and ultimately gain some actionable insights. The technologies are there to parse the data and then run the algorithms, thus providing you with what you need to see in order to gain the insights (so long as you are not overwhelmed by the amounts of data available to you). Scale can be frightening. But the approach to handling Big Data coming at you at high velocity is simple: Take control of it. Follow these steps so that you can gain value from the data you have.

  1. Don’t treat it as Big Data; remember that you want something from it.
  2. Decide what it is you’re looking for.
  3. Develop a hypothesis.
  4. Decide on what specific information is going to be needed.
  5. Plan accordingly.
  6. Gather the information—which means find the data, organize it, and build the reports that provide it to you in the form you need.
  7. Run the analytics on the data—the analytics can be descriptive, predictive, or prescriptive.
  8. Use the knowledge you get from this process to produce the insights you need.
  9. Apply those insights (e.g., make an optimized offer to a particular customer).

I could obviously go into much more depth than I have here. But hopefully, this provides some clarity on what to do and how to begin to approach it. And if it does, well, then the data I had on you is not so useless after all. 

Paul Greenberg is the author of CRM at the Speed of Light, called the “Bible of CRM.” His new book, The Commonwealth of Self Interest: Business Success Through Customer Engagement (2019) is available on Amazon and anywhere otherwise books are sold.

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