The effectiveness of digital ads is wildly oversold. A large-scale study of ads on eBay found that brand search ad effectiveness was overestimated by up to 4,100%. A similar analysis of Facebook ads threw up a number of 4,000%. For all the data we have, it seems like companies still don’t have an answer to the age old question: Which half of my company’s advertising budget is wasted?
It should be possible to answer this question, though. Because what’s getting in the way is not a lack of information but rather a fundamental confusion between correlation and causation.
The Conversion Fallacy
When marketing reps sell ad space to clients, they claim that ads will create or cause behavioral change — a phenomenon typically called lift. They back up the claim by pointing to the number of people who purchase a product after seeing the ad — typically referred to as the conversion rate.
To explain the difference between the two, imagine that, on the first day of school, you stand at the classroom door handing out leaflets advertising the class to every student who walked in. You then ask them: “What’s the conversion rate on my ads?” They always correctly reply “100%” because 100% of the people who saw the ad “bought” or enrolled in the class. Then ask: “How much did those ads change your behavior?” Since they had all already signed up for the class long before seeing the ad, they all reply, “Not at all.” So, while the conversion rate on your ad is 100%, the lift from the ad — the amount of behavior change it provokes — is zero.
Although my example is a bit simplistic, it shows why the confusion of lift and conversion can create problems for measuring marketing ROI. Big brands “target” their ads at the people most likely to buy their products. But unless the targeting is directed at customers who aren’t already prepped to buy the products, the conversion from click to cash will not generate any new revenue. The key to making advertising pay is getting people to buy your goods (or donate to a political campaign or take a vaccine) who would not otherwise have done so.
So what looks at first like a causal relationship between advertising and response might simply be a correlation induced by other factors. The challenge, then, is to control for these other factors while isolating the relationship we want to examine.
We can do this by creating a control group. If we randomly assign some students to join the class, the group that joins (the treatment group) will have, on average, the same education and skills (and age, gender, temperament, attitudes, and so on) as the group that doesn’t join (the control group). With a large enough sample, the distributions of all observable and unobservable characteristics across people assigned to treatment and control groups are the same, making the treatment itself the only remaining explanation for any differences in outcomes across the two groups. With all else equal, we can be confident that nothing other than their class interest can drive differences in their attendance. The trouble is we can’t always do this. A scientist would be hard pressed to justify a study that randomly forced students into a class. In these cases, we look for what are known as “natural experiments” — natural sources of random variation that mimic a randomized experiment.
In the past, I used the weather as a natural experiment to understand the effect of social media messaging on exercise behavior. Though people who run more tend to have friends who run more, variation in the weather helped us estimate the degree to which receiving social messages from our friends cause us to run more.
When you dig into the data and start running experiments, you quickly learn that effects of online ads are not what you might expect. In the Yahoo! study, for example researchers found that online display ads did indeed profitably increase purchases by 5%. But almost none of that increase came from loyal, repeat customers: 78% came from people who had never clicked on an ad before and 93% of the actual sales occurred later, in the retailer’s brick-and-mortar stores, rather than through direct responses online. In other words, the standard model of online ad causality — that viewing translates into click, which then leads to purchase — does not accurately describe how ads affect what consumers do.
The Benefits of Causal Marketing
Findings like that may explain why Procter & Gamble and Unilever, the granddaddies of brand marketing, were able to improve their digital marketing performance even as they slashed their digital advertising budgets. In 2017, P&G cut the company’s digital advertising budget by $200 million or 6%. In 2018, Unilever went even further, cutting its digital advertising by nearly 30%. The result? A 7.5% increase in organic sales growth for P&G in 2019 and a 3.8% gain for Unilever.
The improvements were made possible because both companies also shifted their media spend from a previous narrow focus on frequency — measured in clicks or views — to one focused on reach, the number of consumers they touched. Data had shown that they were previously hitting some of their customers with social media ads ten to twenty times a month. This level of bombardment resulted in diminishing returns, and probably even annoyed some loyal customers. So they reduced their frequency by 10% and shifted those ad dollars to reach new and infrequent customers who were not seeing ads.
They also looked very closely at first-time buyers to understand purchase motivations, enabling them to identify, quite precisely, promising groups of under-touched customers. For example, they described in their financial report that they were moving from “generic demographic targets like ‘women 18-49’” to “smart audiences” like first-time moms and first-time washing machine owners.
The tidal wave of granular, individual level, personal data created by online advertising has given us the answer to the question many have posed. It can potentially allow marketers to measure media effects precisely and to know which messages work and which don’t. Just be sure you’re distinguishing correlation from causation, as P&G and Unilever did, and not targeting people who are already your most loyal customers.
For more thoughts on digital ad return on investment, search the Library for the key word “Digital”.
Copyright ©John Trenary 2021