The Killer Media Strategy: Connecting Attribute and Live Behavior Data

April 29, 2016

Picture this: an online shopper was recently browsing purses on a retail site, then left the site without purchasing. For the next week, that shopper was served digital ads across the web for the same purses. However, the ads did not generate any conversions or even any clicks, and the shopper didn’t return to the site.

Sound familiar? Too many advertisers fall into this common pattern and believe they are implementing a people-based marketing strategy, when in reality, it lacks the true one-to-one addressability that is possible. They accept an average click-through rate of 0.06% as an adequate explanation for performance. To provide the timeliest and most engaging ads, advertisers must measure, optimize and find ways to target more efficiently. And they must do more than simply following a shopper from site to site, retargeting with a potentially meaningless message.

What Makes an Ad Meaningless?

The shopper was sending intent signals, showing obvious interest in the product, yet the retargeted ads for that product did not resonate. That’s because the messages relied solely on those intent signals. Live intent data is a very powerful marketing tool, but it can also provide a somewhat inaccurate view of the customer when used alone. While intent data is based on a specific behavior that identifies an immediate customer interest, it does not define the individual customer, resulting in only a partial view of the complete story.

What Makes an Ad More Meaningful?

Let’s take a closer look at our purse example. Yes, the interest and intent was there. But what marketers may not know about whose intent it is may hurt them. Combining the qualities, characteristics and known information tied to that shopper – or attribute data – to her live intent signals could be the difference between a successful conversion or wasted ad spend. For example:

  • Was the shopper a new or lapsed customer? Say an individual used to purchase items for a particular brand, but stopped doing so for an extended period of time. The savvy marketer with the right technology to identify this attribute could retarget an ad toward the purse shopper that leverages the individual’s past relationship with the brand to help drive another purchase, and even renewed loyalty, such as, “We’ve missed you! Enjoy 15% off next time you make a purchase with us.”
  • How much has the shopper spent in the past? If the shopper typically buys in a higher price range, she could more likely be upsold on something similar, but of higher value, and more in line with her past preferences.
  • Was the item actually purchased? Perhaps in-store or from a different device, the shopper could have converted within days or even hours of the initial online browsing. This knowledge could limit wasteful retargeting or provide a cross-sell opportunity for a coordinated item like a wallet.
  • What is the age of the shopper? If a customer over the age of 18 is browsing children’s toys, he or she is more likely shopping for a gift, a journey that often has a much shorter expiration. In this situation, marketers can combine live intent with customer attribute data to target an effective ad immediately.

[tweetable]The combination of live behavior and attribute data is a game changer.[/tweetable] Knowing who is doing the research is just as important as knowing what they are researching, as it is happening. Useful attribute data could include:

  • Gender
  • Age
  • Recent change of address
  • Past purchase behavior
  • Lifetime value scores
  • Loyalty status
  • Favorite items
  • Contract or lease expiration
  • Reviewed items

Alone, live intent data shows buying propensity, as attribute data paints a picture of shoppers and who they are. Together, live intent data and attribute data can be the bulls eye in a successful addressable media strategy.

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Jon Padilla

Jon is a data-driven marketer who's passionate about using technology to deliver innovative media strategies and create meaningful digital measurement. He the former Director of Product Strategy at Signal.

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