Marketing attribution analysis was great in theory. What are the things consumers see and hear that influence and prompt them to act, specifically to buy a product or service?
It would be nice if it were simple and binary: Consumers see this, they buy that.
But let’s start with the essential data and the connections brands need to make, to support decisions that impact media planning effectiveness.
Visibility into what a consumer bought: Possible in digital (if brands own their own data or can get it easily from e-commerce distribution partners.) Harder in physical stores. In both cases brands need first party data from the retailer to know what was actually purchased.
Visibility into what the consumer did to get to the point of purchase: Did they go to a retailer’s website and search for the product? Did they see a digital ad, click on it, and it took them to an e-commerce page? Did they see the product on their neighbor’s kitchen counter and make a mental note to purchase it next time they were in the grocery store?
Visibility into what advertising campaign was seen or heard before the purchase: Was it the digital ad on their favorite app? Was it the TV ad, or the OOH ad on a bus shelter? Was it an audio ad on a podcast or on the radio during their commute from the office? If it is digital media and a digital purchase with accurate UTMs embedded and an effective tracker to see what messaging converted, an advertiser may have half a shot of making a connection of the channel and the campaign to the actual transaction. But it is not a precise media planning tool, particularly if the campaign intent was brand awareness, versus activation.
Connecting all these data points to create optimal media plans
Here’s the dilemma. Attribution modeling has been described as limited, in part because it may account for one or two online channels compared to each other, but in terms of all other media and marketing exposure it can’t definitively say which is THE ONE that can be attributed to the purchase. What is more likely is a combination of factors in aggregate, in context of other behavioral conditions, drove the consumer to put the product in their virtual or physical carts.
Folks at the digital marketing agency Atomicdust back in 2021 wrote an interesting piece provocatively entitled “Marketing Attribution is Killing Common Sense”.
Still quite relevant, it contains some of the better definitions we have seen on the types of attribution (“last interaction”, “last non-direct interaction”, “linear attribution”, “multi-touch”, etc.) commonly tracked in attribution modeling. More importantly, the author basically describes how each of these models as flawed in their own way.
Why are they flawed? Two reasons, actually.
- Attribution models are primarily limited to digital marketing, which is highly dependent on the infamous cookies that are going away due to privacy concerns.
- These models don’t reliably accommodate the attribution of sales that are conducted in physical retail settings, in which case an advertiser may have no idea what was the last (or first) ad the shopper saw.
Frankly consumer behaviors are sufficiently complicated as to defy predictions based purely on the last or first impression of the product. Everything from seasonality and weather to time of day (particularly in the case of impulse buys at quick service restaurants or convenience stores) will be influenced by the confluence of when and how an advertising message is delivered in any given media channel.
So, if not attribution modeling, what can brands employ that is more evidenced based than “common sense”, to make better media buying decisions?
The Return to a Trusted Tool: Marketing Mix Modeling
FMCG companies have relied on Marketing Mix Modeling since the 1960s. Of course, retail options and even the media channels available to advertisers were far less complicated then. Decisions on how, where and when to plan media spend could be done at a comparatively more leisurely pace than the frenetic programmatically driven speed required in today’s digital environment.
But the beauty of MMM is its inherent data analytical sophistication, capable of incredibly complicated models with far more degrees of granularity, is that it can tackle the combination of many new kinds of data feeds at different levels of specificity (target audience, time granularity, geographic unit, type of metric, etc.) And this level of complexity requires data analytics robust enough to parse the results at a much more sophisticated level to much better media planning support.
As retail has also evolved to include far more distribution outlets (including e-commerce) and media followed suit with the introduction of digital advertising, MMM provides the perfect solution to making highly complex decisions about media buys. But you need accomplished data scientists running the models, equipped with the expertise to interrogate the algorithms with a brand’s specific objectives in mind.
- MMM when done right, can provide far more detailed road maps for achieving ROAS (return on advertising spend) encompassing virtually endless variables that can impact spending.
- MMM done right can provide guidelines for spending just enough in any one media channel so as not to overspend in terms of overall return on investment.
- And we could (and will) dedicate and entire post to the unique benefit of MMM to facilitate targeting not only the many more channels in the new world of media used by people, but the situation and the journeys that got them there. Attribution never really fulfilled its promise to get a whole lot closer to this level of targeting visibility.
Brands are moving away from strictly asking themselves: “Should we spend less on TV and more in digital?”, to the much more relevant question: “What’s the role of the big-screen in my campaign and which new channels are going to replace the ubiquity of TV?” To guide the resulting media planning choices, the best MMM provider/partner will actually surface the more discrete evidence as decision support. The ideal MMM partner will reveal which of all the digital and other channels available will yield the best contributions to ROAS compared to each other, so media buying decisions can be more fine-tuned and effective.
Attribution may be letting you down. But have no fear. Our data analyst teams at in4mation insights are prepared to help you tackle media planning, delivering optimized scenarios to achieve return on your investments and ongoing growth for your brands.
