We in the data marketing business love to test — at least, we should. And what we should test for is new data categories.
Expanding the marketing universe — and stretching the marketing budget — depends on higher efficiency in our lists, offers, and creative. We should be eager to test new proofs of concepts and new categories of data sources as they enter the market … if only to know whether or not they produce incrementally or otherwise.
I’m still surprised when I hear some of my data-vendor friends say that a good number of their clients pass on testing — and just go all-in on new lists and data sources. It seems like testing is still too much work for some, or they feel the only way to test is with an entire data source. Guess these client-side folks have money to burn, or are operating very much on-the-fly.
In some ways, digital marketers have it all over offline marketers in their ability to test, cycle, test again, and so on — often, many times over by the time a direct mail or direct-response print or broadcast test cycle has run its course. Yet, in this speed, have we sacrificed some quality in our prospecting strategies?
Online audience algorithms can produce some highly categorized niche segments, based on site visits and app usage — much of it de-identified, from a personal perspective. But how do these segments really stack up against a transaction database, or response lists, or even compiled lists, based on personally identifiable information? Thankfully, we can test for this, or even overlay data! (I am not advocating re-identification here, nor should you. Oh California, please don’t force us to identify non-PII. It’s soooo anti-privacy.)
Recently, the Direct Marketing Club of New York (DMCNY) held a very interesting breakfast program titled “Beyond Demographics: The Data You Need to Max Out Marketing Performance.”
Some Fresh Categories for New Reach and Affinity Discovery
Consider some of these data sources for testing:
- Values Data — Test cohorts based on “shared values,” rather than simply choosing audiences based on demographics or psychographics. David Allison, principal, David Allison Inc., and author of “We Are All the Same Age Now,” pointed to his firm’s internal research that shows that popularly defined age groups rarely (or barely) match on what they agree upon, or value, as a generation. For example, Baby Boomers agree with each other about 13% of the time; Gen X, about 11% of the time; and Millennials, 15% of the time. Thus, targeting based on demographics alone can be extremely wasteful if the marketer is assuming some sort of shared attribute among them, other than age.However, when targeting based on shared “values” — Adventurers, Savers, and Techsters, and the like — all of a sudden affinities jump sky-high. In these cases, 89%, 76%, and 81%, respectively. These “valuegraphics” are based on “big data” segments — rather than small data (response lists, for example). Still, when compared to demographics targeting alone, shared-value targeting offers an eight-time lift! Well, that’s worth testing.
- Attitudinal Data — Another perspective on “beyond demographics” came from Mark Himmelsbach, co-founder, Episode Four, a creator of “brand hits,” such as this one for Charles Schwab. We often have stereotypical views of many demographic and other audience categories — and too many algorithms, he said. But analyze the data for unusual patterns, and suddenly you can find “who knew?” commonalities among certain audience segments that would wow any of us.Who knew that ultra-high net worth individuals are electronic dance music enthusiasts? Who knew that African-American married women are high on the e-sports genre? Or that young Hispanic/Latino adventurers are really into escape rooms? These discoveries give brands new advertising, product placement, and sponsorship opportunities, for example, which might otherwise go untapped. I’m still trying to get my head around these reported affinities, based no doubt by my own preconceptions.
- Location Data — According to the World Economic Forum, 90% of the world will soon have or already has a supercomputer in their pocket — a smartphone. We’re actually closing in on four connected devices per person, reports Jeff White, founder and CEO, Gravy Analytics. With smartphones alone, as constant companions, we have a huge opportunity to leverage responsibly use of location data. Location can provide huge “affinity” targeting opportunities.A casual wine user might search and buy online his or her wine. But a wine aficionado visits a winery (Location X), or attends a wine tasting (Location Y), and now you have a true affinity opportunity. Granted, location data has a level of sensitivity that carries, more often than not, an opt-in requirement — but the marketing lift can be a significant reward for the advertiser who strategically applies such insights from it. Makes me want to tag every latitude and longitude for some hobby or interest!
- Experiential Data — Live Nation may own concert venues, Ticketmaster, online game communities and music/culture festivals — but across these many first-party experiences, the company can provide deep analytics that help monetize its various audiences through enriched second-party relationships, said Anubhav Mehrotra, VP, Live Nation. Hilton, American Express, and Uber are just some of the brands Live Nation has teamed up with to enrich brand users with engaging experiences, such as backstage tours and “meet the artists.”
We are all trying to create and sustain customers, using data to discover new patterns, new audiences, and new prospects — and that requires a lot of testing, and innovative data sets to explore (responsibly). Let’s make it experiential, as well as experimental: I sure hope to meet some ultra-high-net-worth individuals at the next Electronic Dance Festival I attend. Or not.