If you’re looking to understand incrementality and how it can impact your marketing efforts, look no further than this guide. Incrementality is a key metric that can help you understand how much of an impact your marketing efforts have on your desired outcome.
By understanding incrementality, you can make informed decisions about allocating your marketing budget and how to best grow your business. So, what exactly is incrementality? And how do you measure it? Here is the ultimate guide to incrementality in marketing.
What is incrementality?
Incrementality is the degree to which one action increases or improves another. It is the concept of attributing value to incremental changes in behavior.
In the context of marketing, incrementality measures the impact that a given marketing campaign has on the desired business outcome, such as sales or conversions. In other words, it’s a way of measuring how much an ad or campaign contributes to an overall goal.
When it comes to digital marketing or advertising, incrementality can show the overall effectiveness of an ad. Incrementality goes beyond simply measuring conversion rates, ROI, ROAS (return on ad spend), or other metrics. It captures lift that is uniquely attributable to the campaign under evaluation.
Because incrementality is such a complex metric, it can be hard to measure. However, understanding incrementality is essential for any marketer who wants to gauge the effectiveness of their campaigns.
Incrementality vs. Attribution
Google defines incrementality as “the additional positive effect that your marketing has on business outcomes above and beyond the effect that would have occurred without that marketing.”
On the other hand, attribution is the process of assigning credit for a conversion to the various touchpoints that contributed to it.
There are three main types of attribution models—first-touch, last-touch, and multi-touch.
- First-touch attribution assigns 100% of the credit for a conversion to the first customer touchpoint.
- Last-touch attribution assigns 100% of the credit for a conversion to the last touchpoint.
- Multi-touch attribution is a more sophisticated approach that assigns credit to all touchpoints that contributed to a conversion.
Under the umbrella of multi-touch though, there are also specific attribution models you might use.
- Linear – every point is assigned an equal percent of credit
- Time decay – touchpoints that happen closer to the time of conversion get more credit
- Position-based – the last and first touchpoints get 40% of the credit and the remaining 20% is spread out
- Algorithmic or data-driven – relies on machine learning, data, and algorithms to examine the full customer journey and assign credit (considered the most effective and ideal for omnichannel)
As you can see, there’s a lot that goes into attribution. Incrementality and attribution are two important concepts that every marketer should understand. Incrementality will help you ensure that your marketing efforts are actually driving growth for your business.
Attribution will help you understand which channels contribute most to your business goals. In other words, attribution answers the question, “Where did the sale come from?” Incrementality answers the question, “Would we have made the sale without our marketing activity?”
How do you measure incrementality in marketing?
Incrementality is important because it allows marketers to understand the true ROI of their campaigns and make more informed decisions about where to allocate their budgets. It also helps businesses avoid the sunk cost fallacy, which is when we continue to invest in something simply because we’ve already invested so much in it, even if it’s not actually working.
There are several methods to measure incrementality. The most common is experimental design (usually A/B testing), in which a control group is exposed to the marketing campaign while a second, similar group is not.
This approach requires that the two groups be as similar as possible in all respects except for exposure to the campaign. If there are any significant differences between the groups, it will be difficult to attribute any observed incrementality. By comparing results from the two groups, you can see how much incremental business your marketing activity generated.
Another approach is econometric modeling, which uses statistical methods to isolate the incremental impact or the effects of various factors (including marketing campaigns) on desired outcomes.
How to calculate incrementality
The incrementality formula allows you to compare the performance of two different groups to measure the effectiveness of your marketing efforts.
The formula is:
- (% Conversion Rate of Test – % Conversion Rate of Control) / % Conversion Rate of Test
By calculating incrementality, you can answer questions like:
- Which ad or campaign is contributing to my desired outcome?
- How much of a percent does each campaign contribute?
- What happens if I increase or decrease my ad budget on different platforms?
- How will launching a new campaign impact my desired outcome?
How do you do incrementality testing?
Incrementality testing is a method of marketing experimentation that measures the incremental impact of marketing activities on conversion rates. How do you go about incrementality testing?
First, define your KPI and goals. Run a campaign as usual. Then, split your audience into control and test groups. Measure the conversion rate for each group. For example, let’s say one group saw a display ad for coffee and another did not see the ad. What is the conversion rate for the group that saw the ad? (Test group)
How much incremental lift or increase in conversions did you see from the test to the control group? Calculate impact, or in other words, incrementality. Incrementality testing can help you understand how your audience responds to different marketing stimuli and fine-tune your campaigns for maximum impact.
There are two main ways to measure incrementality—incrementality per exposure and incrementality per impression.
Incrementality per exposure
This is the most common way to measure incrementality, and it simply looks at the additional effect of each marketing exposure on business outcomes. So, if you have a campaign with a 1% conversion rate and you want to know how many incremental conversions you can attribute to the campaign, you would multiply the total number of exposures by 1%.
Incrementality per impression
This is a more sophisticated way to measure incrementality that considers the fact that not all impressions are equal. For example, someone who sees your ad multiple times is more likely to convert than someone who only sees it once. This method weights each impression based on its likelihood of driving a conversion, which provides a more accurate picture of incrementality.
How does incrementality testing actually work?
Incrementality testing allows you to measure how much additional lift or increase in conversions you see from a test group (who saw your marketing activity) compared to a control group (who did not see your marketing activity). This is also sometimes referred to as the incrementality effect.
It is an essential tool for marketers because it allows you to isolate the impact of specific marketing activities and understand which ones are truly driving results. This is especially useful when running multiple marketing initiatives on different channels or having a limited budget and the need to allocate resources efficiently.
There are two main ways to do incrementality testing—live experiments and model-based approaches.
Live
Live experiments are the most accurate but also the most resource-intensive method. They involve randomly splitting your audience into control and test groups and then measuring conversion rates for each group.
Model-based
Model-based approaches use statistical models to estimate the incrementality of marketing activities. These models require historical data but are less resource-intensive than live experiments.
The incremental lift can be positive or negative. That is, a campaign may result in an increase or decrease in the desired outcome relative to what would have happened in the absence of the campaign. (Of course, if incrementality is negative, that indicates that the campaign is actually harming the business.) In general, however, marketers are interested in positive incrementality. How much can we increase sales or conversions by running this campaign?
Incrementality Test Examples – It’s Not One Size Fits All
There are many other types of incrementality tests. Some of the most common include A/B tests, conversion lift tests, multivariate tests, and brand lift tests.
Conversion lift tests
In a conversion lift test, you would track the number of customers who converted (i.e., made a purchase or took some other desired action) after being exposed to your marketing activity.
Multivariate test
A multivariate test is similar to an A/B test but involves testing multiple marketing activities simultaneously. In a multivariate test, you randomly split your customer base into two or more groups and expose each group to a different combination of marketing activities.
Brand lift tests
In a brand lift test, you would track the changes in brand awareness, brand favorability, or some other brand metric after your marketing activity has been exposed to your target audience.
These are just a few examples. In reality, there are many types of incrementality tests to consider for your specific business needs. In the testing design phase, we evaluate the most common approaches and make a recommendation of the incrementality testing type that will be the most effective to measure advertising impact for your specific business.
Incrementality is a powerful tool that can help increase your marketing results while reducing risk. Selecting the right incrementality methodology can be difficult and confusing and we’ve got you covered!
If you’re unsure which method is best for you, talk to a Goodway marketing consultant or incrementality expert. They’ll be able to help you choose the right method for your business.
Goodway can help you with customized incrementality testing design and implementation. You’ll also get in-depth insights from our data science and analytics experts.