Your Marketing Jargon Primer

iStock_000076158045_MediumTerminology matters in today’s digital advertising. And yet, it seems that the speed of our linguistic innovation has outpaced our ability to really make good use of our shiny new acronyms and buzzwords. What’s worse still is that all the new jargon has actually led to confusion about the meaning of some of our “oldest” (it’s all relative) and most important core ideas and concepts. It’s time to step back for a moment and make sure we are all speaking the same language.

What do we mean by some of the terms we toss around so casually, blithely giving little thought to what they may communicate? If we understand the definitions, we will have a better chance implementing the right strategies for growing our businesses.

Segment targeting vs. predictive targeting
segment is a generalization based on a set of characteristics like age, income range, location and so forth. All of that is pretty well understood. What people tend to overlook or not understand about segments — with regards to more sophisticated ways of targeting — is that the segment presumes equal value among all its members. Take for example, the infamous the “Soccer Moms” segment. It assumes that everyone in that segment is as good a fit for your product or service as each of the others. The reality is, segments ignore the wide variety of individuals who fit the criteria. These individuals have different tastes, interests, beliefs, finances, priorities, etc., that influence whether a person will be a good fit for your product or even the means to act on that interest.

See how predictive targeting stacks up against segments by downloading this case study.

By contrast, predictive targeting aggregates and analyzes thousands of data points for an individual person, presuming no affinity with a larger segment or group. Predictive targeting zeroes in on consumer profiles, individualized in such a way that leaves the uniqueness of the consumer’s attributes intact, rather than subsumed by a general rubric of a segment.

As you overlay segment characteristics to pursue more accurate target audiences, you lose the scale of the audience. Predictive targeting does not reject people on the basis of one or two characteristics, thereby making audiences more scalable while using more robust and accurate targeting criteria. Segments exchange scale for specificity. With predictive targeting, you can have your cake and eat it, too.

Purchase intent vs. propensity to buy
When a consumer’s online behavior evinces direct indication of their intention to purchase, you can say they have shown purchase intent. If they browse for a product, conduct an explicit search, click on an advertisement, we can draw reasonable conclusion about their purchase intentions surrounding the product or its competitors.

The drawback with purchase intent is that the signals themselves lack a lot of key contextual information. For example, purchase intent signals don’t discern between the aspirational shopper — a 16-year-old boy searching Google for pictures of Mustangs — and another shopper that has the financial means to act on their interest.

The advantage, however, is that it’s an excellent indicator for conversion campaigns. The consumer is telling you that they are ready for that final nudge.

The concept of propensity to buy comes into play when you evolve beyond relying on the consumer’s outward behavior. A consumer’s propensity to buy is assessed on the basis of the predictive targeting outlined above. Propensity does not require that the consumer express an intention to buy, but that they possess the combination of qualities that are historically common among those who have purchased. That makes it better for upper-funnel campaigns, targeting consumers at an earlier stage of their path to purchase.

Panel-based vs. actual-based measurement
There are two types of closed-loop analysis. The first is panel-based measurement, an analysis commonly associated with comScore. comScore consistently tracks the shopping behavior of a random representative sample of all the consumers in the country.

In panel-based measurement, the brand runs the campaign with no knowledge of who exactly makes up that panel. At the termination of the campaign or test, comScore will look at the panel group that was exposed to the campaign (a sub-section of the already small representative sample of the U.S.), and will also find an audience of unexposed users that is “identical” to serve as the control (based on shopping behavior or past purchases). That allows comScore to measure how shopping habits changed after users saw the campaign. They take this percentage difference and extrapolate that the rest of users who were exposed to the campaign (those non-panel members) behaved the same.

Actual-based measurement is another type of closed-loop analysis, based on direct CRM matching instead of comparisons between panels. In this analysis, a brand must first start out with an audience based on verified profile data so there is a record of everyone who was targeted with the advertisements. Then the list of exposed users is matched back to a CRM file to see how many users converted. The number of exposed users can be compared to a baseline or control group to measure the impact of the campaign over benchmark.

Clarity matters
Are you focused on individual consumer profiles or targeting a segment? Can you appreciate the value of purchase intent and further assess the propensity of others to buy? How are you measuring effectiveness — with panel-based measurement or with actual CRM matching?

Let’s not forget the basics. These can determine whether you are pursuing the right strategy for your business.

Published 4/22/16 on

Pete LaFond

About Pete LaFond

As chief marketing officer, Pete leads TruSignal’s marketing group, including marketing strategy, brand and acquisition advertising, product and vertical marketing, events and conferences, social media and public relations.
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