Using Marketing Incrementality to Measure Campaign Performance & Impact
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.
What Does Incrementality Mean in Marketing and Advertising?
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.
What is an Example of Marketing Incrementality?
One common example of marketing incrementality is the use of A/B testing to evaluate the effectiveness of a new advertising campaign. In this scenario, a company creates two groups: a control group and a test group. The control group is not exposed to the new ad campaign, while the test group is. By comparing the performance of these two groups over a specific period, the company can assess the incremental impact of the advertising campaign.
For instance, an e-commerce business might run an A/B test where the control group is not shown any online display ads, while the test group sees the ads. By comparing metrics like sales, conversion rates, or customer acquisition costs between the two groups, the company can determine whether the advertising campaign led to a significant increase in sales and whether the return on investment justifies the ad spend. This incremental analysis helps the company make data-driven decisions about the effectiveness of the campaign and optimize their marketing strategy accordingly.
Is Marketing Incrementality Accurate?
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.
Why is it Important to Measure Marketing Incrementality?
When OptiMine’s measurement solution is deployed for the brands we work with, one of the most awaited metrics the marketing team wants to know is what percent of sales are being driven by marketing— or “marketing contribution”. The interest stems from a few main reasons:
- The marketing team wants to know how their more difficult-to-measure channels are performing, mainly because these channels are getting missed by their web analytics (e.g. tracking) platforms. Low, or zero, click channels such as video or display show up poorly because consumers rarely click on the ads, and traditional media channels such as TV, OOH and print aren’t easily “trackable”. So, it is natural for brands to want to know how these areas perform within their mix.
- More importantly, marketing teams want to be able to prove the value of their efforts. This is especially true during periods of economic headwinds when marketing budgets are under significant pressure.
An over-reliance on “attribution” to answer these questions leads to bad outcomes— which is where marketing incrementality comes in.
What are the Benefits of Incrementality?
Marketing incrementality offers several valuable benefits for businesses and marketers:
Accurate ROI Measurement
Incrementality analysis provides a more accurate and granular understanding of a marketing campaign’s return on investment (ROI) by isolating its true impact from other factors affecting business performance.
It enables data-driven decision-making by providing empirical evidence of a campaign’s effectiveness, allowing businesses to allocate resources and budget to the most efficient marketing channels and strategies.
By identifying the elements that contribute the most to incremental gains, marketers can refine and optimize their campaigns for better results, ultimately improving marketing efficiency and effectiveness.
It offers insights into customer behavior and preferences, helping marketers understand how specific marketing efforts influence consumer actions and allowing for more targeted and relevant messaging.
Long-Term Strategy Development
Over time, incrementality analysis can inform the development of more effective long-term marketing strategies, aligning them with business objectives and maximizing growth opportunities.
Removing Non-Marketing Factors to Identify the True Marketing Incrementality
Sales in your stores spike on Saturdays during the holiday season. Does that mean the ads that ran on Saturday magically got more effective? Probably not. Accounting for non-marketing factors (examples listed below) is critical to understanding how effective your marketing campaigns are. You’ll misstate the value of ads if you aren’t accounting for these elements.
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.
How Do You Test Incrementality in Marketing?
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.
The Challenges of 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.
Measuring Campaign Impact with OptiMine
The beautiful thing about measuring the incremental effects of marketing is that once you have the measures, go-forward optimization will unlock huge value. Advanced optimization algorithms allow the marketer to run simulations, scenario plans, and what-if analyses, to find budget allocations that will drive the highest returns. And interestingly, these kinds of capabilities are most important when budgets are under reduction pressures.
The marketing incrementality approach that OptiMine uses is Marketing Mix Modeling (“MMM”)— at very high scale using advanced AI to measure campaigns’ impacts across any online or offline conversion. Marketing Mix Modeling examines ad campaigns over time to observe their incremental effects as the campaigns fluctuate, and has been around for a good 40-50 years but advances in computing and AI have taken this traditional approach and scaled it for modern use. As an added bonus, MMM fills all of the gaps of MTA and can deliver much more robust planning tools.
Interested in learning more about how OptiMine’s 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”