It’s Easy To Forget, But Tracking Does Not Equal Measurement

Matt Voda, CEO of OptiMine Software

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Matt Voda, CEO of OptiMine Software.

There’s no denying that we’re living in a period of massive disruption to the way in which data is used for marketing.

Cookies are (finally) dying and Apple is single-handedly forcing privacy protocols in the mobile space, hobbling Facebook’s own attribution tools in the process. All of this is happening against a backdrop of privacy regulations and growing consumer awareness of the value of their data as the market urgently seeks alternatives for tracking ads across devices and consumer identities.

But there’s one very important point that’s been lost in all of this turmoil:

There are still ways to effectively measure marketing performance without relying on ad tracking.

Rethinking measurement

Despite the breathless rush to multitouch attribution and the still-perpetuated myth that a magic sequence of ads can trigger the perfect path to purchase, this mistaken notion – that tracking consumers is the only way to approach measurement – was never true to begin with.

So, how did we get here?

Digital marketing has always been considered to be the most measurable medium because marketers could get instant reads based on click behavior. A consumer sees an ad, clicks on it, makes a purchase and that ad – usually a paid search ad – gets all of the “credit.”

This seemingly clear-cut simplicity drove marketer confidence and provided an easy solution for the ongoing tracking of performance. A marketer could know that a particular ad was seen regardless of whether there was a click or if that click led to a purchase, and that ad would be attributed to the sale.

And thus began an overreliance on tracking-based approaches to performance measurement, all of which are now being disrupted.

The issue here is that tracking-based measurement was never all that accurate to begin with. Prioritizing only that a consumer saw a digital ad and made a purchase online ignores the myriad other touchpoints that could have contributed to the conversion, from brand awareness, promotions and traditional media to ad exposure, seasonality and economic conditions.

Making matters worse is that the industry appears to be doubling down on new consumer identity schemes in order to ensure continued measurement all while seeming to blissfully disregard how we got to this point of disruption: the fact that consumers are concerned about their privacy.

Oldies but goodies

In this attribution gold rush, the market forgot – or perhaps never learned – about traditional measurement approaches, such as marketing mix modeling, randomized control tests and holdout testing.

These proven measurement approaches in many cases are far more accurate than multitouch attribution, they cover both digital and traditional media channels and they measure outcomes both online and offline.

And here’s the thing: These approaches never actually went away and are even now learning new tricks and gaining agility with innovations in artificial intelligence, machine learning and high-scale computing.

Marketing mix modeling, for example, accounts for all of the nonmarketing factors that might drive a business in order to better isolate and more accurately identify the incremental lift of a marketing campaign – something that tracking-based measurement never did.

The pandemic has also driven innovation in these traditional methods by forcing models and measures to examine shorter time windows, refresh more frequently and deliver more detailed reads to the marketer.

With consumer data disruption gaining steam, marketers and their analytics teams are now – or will very soon – be looking for future-proofed marketing measurement approaches.

So let’s not forget how we got to this point of disruption, while also keeping in mind what consumers wants: their privacy. And in the ongoing search for alternative solutions, let’s also not be blind to measurement methods that we seem to have lost sight of in the first place.

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