Data-driven marketing is based on the idea that data provides marketers with a much clearer understanding of their consumer segments and thus more efficient advertising when reaching these segments. While this remains central to data-driven marketing, difficulties abound, including the fact that data sellers aren’t always aligned with marketer goals, and marketers aren’t always sure about what they’re buying.
This past summer, the IAB Tech Lab launched a much-needed transparency initiative in the form of its Data Transparency Standard, establishing baseline statistics on the objective attributes of a given data segment, such as refresh cadence, ID types, and segmentation criteria. The Data Label should be a big relief to data buyers, including agencies and advertisers, that are trying to wrap their heads around audiences, but it’s still only the starting point when it comes to assessing digital audiences.
For as much as the Data Label injects transparency into audience construction, it only addresses objective attributes, and not the qualitative aspects of the data segment. To get the most value out of their data investments, advertisers and agencies need to go further and develop their own standards for evaluating data, especially when it comes to quality.
Understand objective attributes first
The current Data Transparency initiative provides crucial information for ad buyers: it explains who provided the segment, the currency of IDs included in the segment, information on how the segment was constructed, where the components were sourced from, and how often the source is refreshed.
All of these attributes can impact the performance of a segment. For example, freshness matters, because consumers move in and out of purchase mindsets constantly. If it’s assumed that the industry standard refresh rate is 30 days, consumers may be hanging out in an audience for nearly an entire month after they’ve changed their mind or already made a purchase.
While this information is useful, it’s confined to quantitative measurements, and not the qualitative aspects. As it becomes harder than ever for advertisers to reach their target audiences and rise above the noise, the quality of the data matters just as much.
Yet quality is subjective, making it that much harder for any third party to come up with a standard way of assessing data and audience quality, much less infer or predict efficacy for a specific campaign or initiative. So how can advertisers assess audience quality? It starts with drilling down on the question, “Am I reaching the consumers I think I’m targeting?”
Assess the audience based on brand and campaign goals
The answer to this question depends on a brand’s goals, target audience, creative messaging, campaign KPIs, and the extent to which they are already familiar with the multiple segments within their customers and prospects.
Based on their own needs, brands can conduct a “sniff test” of sorts, asking themselves if the derived insights look and smell like they’d expect. Pre-built data segments are pretty easy to test under these parameters. A high-end retailer buying a luxury enthusiasts segment can analyze the audience to make sure the behaviors match with the description. Do the consumers within the audience segment index highly for audience segments related to other luxury signals like high-end auto or clothing, real estate or luxury home furnishings? Are they exhibiting behaviors of high net-worth individuals, or at least those aspiring to be so?
Those adjacent or overlapping segments are clear signals of quality. The brand should get worried if its so-called luxury segment indexes very high on couponing or discount shopping (though it’s worth noting that luxury and deal seeking are not mutually exclusive by any means). If those consumers are likely to never interact with a luxury brand, however, targeting them is tantamount to lighting advertising spend on fire.
This is where there’s room for the next iteration of the Data Transparency initiative to evolve and improve. While it’s unmistakably a massive step forward for the industry, even the IAB Tech Lab itself describes the standard as the “minimum disclosure requirements,” signaling that advertisers need more, and data providers can go much further. This year will be marked by a commitment to more transparency and disclosure around the qualitative elements. At the same time, advertisers will have to increase their sophistication around data in order to best harness the value of what they’re buying.