What is Incrementality in Marketing?
There’s a lot of buzz in the market today about measuring the “incrementality” of marketing, but what does this mean exactly? Much like Marketing Attribution, Incrementality means many different things to many people, and since OptiMine fields this question frequently, we felt it important to take a minute, turn the spotlight on “incrementality” for a moment and tighten up the definition. Clarity = confidence, and just as importantly, confidence is no substitute for clarity!
A frequent mistake by marketers is to assume that their efforts produce and deliver most of the revenue or other positive impacts for the business. For example, if a brand invests in brand paid search (SEM) and pays to have their brand name listed at the top of search results, and people click on those ads and make a purchase, is it reasonable to conclude that these brand paid search ads are now delivering new revenue streams- and that all of these purchases are the result of the search ads? No, of course not. Some portion of these sales might be from the paid search investment, but on the surface, it is impossible to know because there may be many other marketing efforts occurring at the same time.
Differentiating what an ad truly generates revenue-wise from what sales would have been obtained anyway (without the ad) gets to the nature of “incrementality”. Understanding what conversions a brand would obtain organically without marketing is essential to the equation. Further, by being able to split out sales that are driven by non-marketing factors, an analyst can then identify what was truly generated by the advertisement. The key is to be able to discern the difference between marketing and non-marketing driven sales conversions.
Marketing Incrementality is the measurement of what a marketing effort generated beyond what would have been obtained without the ad.
It’s that simple. But measuring Incrementality isn’t so simple and easy.
The Risks of Getting it Wrong
Marketing attribution = wrong conclusions (just because someone saw an ad and made a purchase doesn’t mean the ad had any effect). Many marketing attribution approaches incorrectly conclude that if an ad was shown to a consumer prior to that consumer making a purchase, then the ad must have been responsible for the sale. Worse, most attribution approaches don’t account for the effects of traditional media (TV, Radio, Print, Direct Mail, OOH) and as a result, overstate or misstate the value of lower funnel digital campaigns. Or, these approaches fail to account for critical factors such as seasonality, pricing, the economy or other elements- most of which have an equal or larger impact on sales than marketing alone.
In a marketer’s pursuit to understand incrementality, many attribution approaches make the problem worse.
What’s Required to Get it Right
Too many marketing attribution solutions mis-allocate credit for campaigns because they fail to account for some of the most fundamental sales drivers. Does a retailer’s sales increase every year during the period between Thanksgiving and Christmas because of advertising? No. Seasonality- in this case holiday seasonal lift- drives a major part of the increase in sales. And if your attribution model fails to account for this, the measurement of a campaign’s contribution is likely to be highly in accurate.
Breaking down the drivers of sales conversions, it is important to account for and filter out non-marketing contributors:
Physical stores & locations:
A retailer or bank will generate some portion of their revenue and customer acquisition simply because they’ve selected a good location for their physical presence.
Promotions & Discounts:
Many brands falsely equate the contribution of a coupon or discount with the marketing channel used to communicate and deliver the promotional price. For example, if you send a 10% discount coupon via direct mail and the redemption rate is 10%, does that mean direct mail marketing drives an incremental lift of 10%? Absolutely not. The discount itself is a primary driver of the sales.
Many brands experience different seasonal patterns where sales naturally increase or decrease for periods of time. Accounting for these seasonal patterns is essential in order to tease apart the effects of marketing during these same periods. Using historical data over time allows the analyst to split apart these effects.
Is your competitor running a major promotion during your ad campaign? Did they open a new store nearby? Are they advertising more heavily than your brand? These can all impact your sales and these signals may mask your own advertising performance.
This factor should be obvious to most, but failing to account for the broad effects of the economy- whether it is going well or poorly- will create large accuracy issues for a brand’s marketing measurement.
Of course, there are many other potential factors that drive your brand’s business volumes. These are just examples of the most common ones.
Removing Non-Marketing Factors to Identify the True Marketing Incrementality
What is Incrementality Testing? One other way to identify the incremental effects of advertising is to run randomized control tests (“RCT”). In this approach, a brand identifies and selects test and control audiences and exposes the test audience to the ad campaign and then selects some other treatment (no ad, public service announcement, or another ad creative) for the control group. Then, by comparing the sales performance of the test and control groups, the brand can measure the incremental gain – if any- that the ad campaign drove with the test group.
There are a few important points to consider with incrementality testing:
(1) Data Loss & Accuracy Issues: data loss through cookie blocking or tracking prevention (Apple iOS 14.5 killed most individual tracking across Apple devices) create major roadblocks for this type of testing. Why? Because if the brand cannot properly keep the test and control groups truly separate, the incrementality test will be completely useless. Worse, the brand may make conclusions from the test that aren’t actually accurate and may make spend decisions that damage their performance. This data loss issue will continue to get worse in the future as more technology players and US state regulations clamp down on consumer tracking and targeting.
(2) Difficult to Scale: it is very difficult to have incrementality testing on all of the time across all channels. As a result, it is at best a point-in-time view of ad performance and the measurement will not reflect changes in performance over time. Also, this testing is expensive, because media must be turned off and if the advertising does have an effect, this will reduce sales for the brand.
(3) No Guidance on Spend Levels: because incrementality tests only look at a single point in time, the tests do not provide guidance to the marketer on the proper investment levels. Where is the point of saturation or diminishing returns for a campaign? This answer won’t be provided from an incrementality test.
(4) Expensive Software Not Needed: there are software solutions in the market that allow brands to run tests. In the vast majority of cases, a brand- or their agency partner- can execute a test in a very straightforward design without the need for expensive software to do so.
Interested in learning more about how OptiMine’s Marketing Attribution solution can help you? Contact us today!
- “What is Marketing Mix Modeling (MMM)?” Blog Post
- “Major Consumer Privacy Changes from Apple and Google are Reshaping the Marketing & Measurement Landscapes” Blog Post
- OptiMine On-Demand Webinar: “Apple’s ATT Changes Are Now Live. Learn How It’s Impacting Brands’ Measurement, Performance, & How to Move Forward”