Understanding the effectiveness of specific marketing campaigns and advertising helps determine which methods best boost sales, revenue, and profit. Yet in today’s increasingly complex and rapidly changing digital marketplace, how do you know which programs and touchpoints contributed to a sale? Would it have occurred anyway without, for example, an online ad?
With the advent of digital marketing, ROAS (return on ad spend) became a simple core metric calculated by dividing total ad spend by total revenue. However, ROAS fails to take into account the bigger picture. Incrementality goes deeper than ROAS. Incrementality tests help marketers determine which ads work and which don’t by comparing control groups. This guides them to spend their marketing budget on campaigns that deliver the best results and ROI (return on investment).
The core concepts of incrementality are outlined below, covering what it is, what it can solve, and how to use it.
What is incrementality?
Our modern marketing landscape is dominated by data and details. Using data to evaluate marketing channels and tactics to determine the effectiveness of campaigns takes effort, but it’s worth it to discover the best marketing approach to garner consumers’ attention.
Typical metrics such as ROAS provide only high-level information. More data is required to determine the most efficient and effective digital marketing campaigns. This is because ROAS takes into account total ad spend and total revenue, directly attributing the ad spend to the revenue. But how do you know if it was the ad that resulted in conversions and revenue? Were other factors involved? Was the customer going to convert anyway, regardless of the ad?
Incrementality embraces a more evidence-based, data-driven, a scientific approach using A/B test groups. For example, control group A doesn’t see an online ad. Another group, B, is exposed to the ad. If group A spends the same amount on a product or service as group B, then ultimately the ad spend didn’t contribute to conversions and additional revenue as planned.
What does incrementality help answer?
Incrementality helps identify the lift that marketing and advertising campaigns provide above native demand (baseline sales). This incremental lift provides data on sales and revenue attributed to a marketing or advertising program that puts it above the baseline demand for a product or service.
Incrementality can be applied to a variety of products and services. For example, let’s say you sell kombucha in retail stores and are introducing a new flavor. You know some customers would have visited a store and purchased the new flavor regardless of seeing your brand’s ad. What you don’t know is if viewing an ad will convince them to go to a store and purchase the new flavor. An incrementality test can help answer this question with a control group analysis.
In another example, let’s say you sell computer monitors and decide to increase your warranty period from one to three years to boost sales. An incrementality test will help determine if this tactic worked as planned.
How can marketers test and calculate incrementality?
By now you know what incrementality is, what it measures, and why it’s a valuable tool. You can implement a basic incrementality test by outlining all the test factors, then developing an implementation plan. It takes some work up front, but this is necessary to produce accurate results and best demonstrate whether advertising efforts have worked or not.
What are the three stages of an incrementality test?
- Preparation: Identify and split an audience into A and B test groups that are statistically equivalent, properly segmented, and have the same applied testing time.
- Intervention: Expose one group to a new variable, such as an ad for a new or enhanced product or service. The control group will not see the ad. Determine how to implement this A/B incrementality testing approach successfully.
- Measurement: Analyze the performance of groups A and B, pre- and post-intervention to determine the impact of the intervention and whether or not it created an incremental lift. Assuming the intervention group and the control group is equally split, calculate lift and incrementality using these formulas.
Lift = Test (conversion rate) – Control (conversion rate) / Control (conversion rate)
And
Incrementality = Test (conversion rate) – Control (conversion rate) / Test (conversion rate)
Incrementality test vs. A/B test: The key difference
Since incrementality testing splits an addressable market or a segmented addressable market into two groups, control group A and test group B as described above, you may wonder how it differs from A/B testing in digital marketing.
A/B testing, also known as split testing, is the process of comparing two test versions of a creative, such as a digital ad, an email, or a web page, to compare variances in KPIs (key performance indicators). These metrics include but are not limited to, impressions, click-through rate, conversion rate, web page bounce and exit rates, and simple revenue lift. Comparatively, incrementality testing is applied to a launched marketing campaign, measuring the impact of that campaign on business metrics such as incremental revenue lift, ROI, new client acquisition, and upselling.
As with incrementality testing, A/B testing requires staff with the knowledge base and time to conduct meaningful A/B tests. Staff augmentation is one way to add A/B testing resources.
Conclusion/Next steps
Now that you’ve learned how incrementality tests can reveal the true impact of your marketing campaigns and help you better determine where your ROI is coming from, you’re probably eager to perform your incrementality test. Doing so requires planning and the right tools to develop, launch, manage, and measure incrementality tests. Along with this, you’ll need to allocate marketing team resources who have the expertise and knowledge required to create and conduct tests that produce accurate, meaningful results.
If you don’t have available staff, or staff with the appropriate experience and knowledge, consider staff augmentation. Staff augmentation increases the talent capacity of your team, filling the gaps with high-quality temporary workers who have the right skills.
