Marketing Success Sans ‘Every Breath They Take, Every Move They Make’

Last month, I talked about how to measure success when there are many conflicting goals and available metrics flying around (refer to “Marketing Success Metrics: Response or Dollars?”). This time, let’s start thinking about how to act on data and intelligence that we’ve gathered. And that means we get to touch different kinds of advanced analytics.

marketing success

The ‘When’ of Buying Behavior

Then there is the question of “when,” and such a prediction is not easy. For instance, you may be able to predict if a certain family is very likely to go on a luxury cruise “at some point.” Maybe their demographics and past behavior data are very revealing in that regard. But will they go on a cruise “this year”? Now, that is a very different question. You may need to procure costly “trigger” or real-time data (such as, someone visited travel sites and browsed a series of luxury cruise options). If such explicit data are not available, they you may have to put some educated guesses in based on the “date of last vacation,” and “average days between vacations” for families with similar profiles. Difficult, but not impossible.

The ‘Why’ of Buying Behavior

The most difficult prediction would be “why.” Why does anyone do anything? Heck, we often have a hard time figuring out why even a close friend does anything. We can be total snoops and watch for signs and plow through every comment, but is “figuring out everyone” even feasible on a mass scale? Even so, do you believe every Facebook posting? We may get a glimpse of such data on a social media, but how do we get to use them without looking totally creepy and triggering many alarms along the way? If the goal is to set up the direction of new product development, yes, social data analysis can be very helpful. But that kind of study doesn’t have to be done on an individual level.

I personally gave up on that “why” part a long time ago, and settled on “high correlation.” Yes, correlation does not always equate to causality, but if the goal is to sell things to them, do we really need to know “why” someone is shopping for anything? If we get to know any strong correlation of known behavior to certain product purchases, just rejoice for such findings and send out a gentle nudge to the target individual. Don’t ask “why” every time, unless she volunteers such information in a survey.

Like you see here, each type of question calls for a different type of data.

Know What You Want to Find

And that has been the central theme of this series — that we must define clear goals before we dig into data and analytics. We should not initiate massive data collection and employ cutting-edge machine learning, just because such techniques are available to us.

A while back, a bunch of geeky analysts had this philosophical discussion. If the Genie of the Lamp shows up in front of you and promises to grant you one marketing superpower between two choices, which one should you pick?

  • Choice No. 1 is that you will obtain super vision, as if you are sitting on every shopper’s shoulder — online or offline for argument’s sake — and be able to see everything they see. Let’s call that “cameras on shoppers’ shoulders.”
  • Choice No. 2 is that you will have access to the complete purchase history data of all shoppers.

Now, if such a question were posed to me, my answer would still be “it depends.” (Hey, at least I am consistent.)

  • If the goal were to design the optimal store traffic, the obvious choice would be option No. 1.
  • If the goal were to predict each person’s product affinity, No. 2 would be the obvious choice. For something like that, we do not need every piece of movement data (such as online click-stream data), if we have the purchase history data.

We are all reflections of our past behavior, and what we look at does not always equate to what we actually purchase. Maybe a shopper just loves to look at fancy shoes, with no ability or intention of purchasing them? For many, shopping — online or offline — is not just the act of purchasing, but a form of entertainment, as well. Movement can be a good directional indicator, but not the best predictor of future purchase. The shopper’s wallet is more closely related to her past purchases.

Of course, click-stream data are useful, if purchase history is not available. After all, such a massive data collection is the reason why analytical vendors created the concept of Big Data. But why dig through all of those “indicational” data of possible purchases, if actual transaction history can be used? Not me.

Always set the goals first by which to measure success, and then look at the data list (included in this article). Do not think that you must stalk every customer and obtain every piece of data to get into the prediction business for “selling more things.” “Every breath they take, every move they make” is not a good starting point for data strategy.

Author: Stephen H. Yu

Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at

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