Demand Insight and the Data Value Chain

An engineer has been defined as “someone who measures with a micrometer, marks with chalk, and cuts with an ax.” The message of this quip is simple: If the tool to take action is blunt, fine measurement means little. It’s a lesson that holds true for marketers as well as engineers.

In today’s data-rich environments, precise measurement can be useful, of course, but only when there’s an understanding as to why you’re analyzing the data and which actions can result from an analysis. Rather than looking at whether enough data exist, ask “What’s the point of collecting the data?” After all, it’s the why that will help you to understand what actions to take.

The data value chain
Collecting all the data in the world does not make for success. In any business setting, to make use of measurements, you need to consider five elements:

1) Data–the raw numeric or alphanumeric values associated with specific measured events.
2) Information–the data collected into tables or other organized areas so that they can be used in a meaningful way.
3) Analytics–information that has been sorted through, using a range of algorithms and programs, so that aggregated trends or results are visible.
4) Insights–key learnings from the analytics, identified in terms of meaningful business conclusions that can be drawn.
5) Actions–based on insights, business actions are taken to correct or exploit the results of all the work done in the data/information/analytics/insights value chain so far.

While there is a story in the data somewhere, without a well-understood process of uncovering the relationships between the data and the context in which they were collected the story will most likely remain hidden. The very first step is to identify what the intended use of the information may be. For example, in a retail setting you might look to improve response to consumer demand or fight competitive pressures such as price deflation and eroding gross margins. Only after you’ve identified the objective can you look at what’s possible to measure, and that, in turn, can lead to actions.

Capturing—and using—demand insight
Brick-and-mortar retailers can capture all sorts of illuminating data, such as customer demographics, transaction history, merchandise turns, and promotional tie-ins. Online marketers, however, have access to an additional critical category of data. Often underutilized, these data are prepurchase customer behaviors: where a Website visitor has been; what search terms he used; how long he spent looking at products, doing comparisons, and exploring features. Think of these data as “demand insight.”

If in-store retailers could better understand what drives demand (specifically, product demand) and how a customer’s prepurchase behavior affects (or even predicts) such demand, they could make highly accurate merchandising decisions to improve customer satisfaction, yielding much better inventory turn rates and improving overall margins.

While making better merchandising decisions is inherently good for the retailer, it is, by implication, also better for the consumer. If it were possible to see, through prepurchase behavior, what moved the consumer–learning to change the batteries? feeling a product’s weight? realizing how easy the product is to clean?–retailers and other marketers could better reflect those areas of interest and highlight them. The transfer of knowledge naturally leads to better-informed consumers who are more apt to buy.

So what data could be captured–both online and offline–that would lead to information, facilitate analytics, and drive insights around prepurchase customer behavior? Here are some suggestions:

* Which products a customer looked at before purchasing the selected model?
* Which product features were important to the customer in his selection? (For example, do customers who care more about ease of use choose product A over product B?)
* How many products a customer looked at before purchasing his selection?
* How long the customer spent making his decision?
* Which channels the customer used for research prior to purchasing?

Analysis on these data might show trends. For example, customers who buy high-end digital camcorders tend to focus more on user features and product capabilities, whereas customers who purchase lower-end models focus more on size and weight. Insights and actions that would follow could include merchandising products based on price category; demonstrating key feature differentiation at the high end; and focusing on in-store marketing that highlights size and weight benefits/differences at the lower end. In some cases, the merchant might decide to reduce or increase the product variety at one or another end of the price spectrum to be able to drive purchase behavior based on insights obtained in this process.

While measuring this kind of raw prepurchase data is easy online, with today’s cutting-edge technology it can also be accomplished in an in-store environment. Innovative in-store interactive appliances such as kiosks and interactive computer screens not only create compelling customer experiences but also capture key customer prepurchase behavior data. No longer will the cross-channel analysis be limited to conversion and traffic comparisons; it can now take into account what customers look for, how they evaluate products, and whether their actions differ when they are shopping online or offline.

Being able to use the data you’ve collected is more important than simply aggregating enormous amounts of data and not knowing what to do with them. If decisions are to be made on the basis of insights, ensure that the data collected, and the analyses performed, lead to the conclusions that will allow for meaningful business insights.

Gavin Finn is president/CEO of Kaon Interactive (, a Maynard, MA-based provider of three-dimensional interactive marketing solutions.

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