Incrementality

One of the most challenging issues with measuring and attributing marketing activity is distinguishing how your paid marketing is impacting sales and revenue. That includes separating conversions that came from organic traffic versus those that came from paid traffic, as well as isolating which paid media sources are driving what percentage of revenue.

Without having an accurate understanding of whether conversions are coming from paid or organic traffic — or which paid sources among the multiple you use — it can be much more difficult for marketers to allocate resources and ad spend to achieve the highest possible ROI.

Incrementality solves this challenge by showing marketers the incremental impact of their advertising spend on overall conversions. In particular, incrementality is a measure of the increase on conversions that ads have on driving the marketers desired outcome — whether that’s increasing awareness, driving app installs, or conversions to paid subscriptions. By measuring the incremental lift that each marketing activity has on the target audience, this helps marketers determine which ads, marketing channels, and campaigns are contributing to their bottom line.

Let’s look at an example of incrementality to understand the concept of incrementality and incrementality testing:

• Let’s say we have two groups — Group A is the control group that is not shown any ads, meaning all conversions are from organic and has 100 conversions over a given time period.
• Group B is shown ads and results in 130 conversions for the given time period.

The example above suggests that the ad spend resulted in 30 more installs. There are two main calculations that we can derive from this test: lift and incrementality:

• Lift is the increase the ad spend caused from Group A to Group B, i.e. in this case, the ads caused a 30% increase
• Incrementality is the percentage of Group B which converted as a result of advertising, i.e. 30 ad-related conversions / 130 total conversion = 23%.

Now that we know what incrementality is, let’s look at several use cases for marketers and the questions it can help answer.

As mentioned, measuring incrementality is designed to help marketers understand the lift that ads are bringing to their overall conversions, although it can also help answer much more granular questions. For example, measuring incrementality can help marketers determine which marketing channel, campaign, or ad creative is resulting in the highest incremental lift to their desired outcome (i.e. leads, app installs, ROAS, etc.).

Incrementality can also help determine how much an increase in ad budget will contribute to conversions. If, for example, a certain campaign is delivering a below-average contribution to overall conversions, marketers can choose to turn the campaign off and allocate their ad budget more efficiently.

Another example of incrementality is determining exactly how much retargeting ads are contributing to incremental conversions vs. ads shown to cold audiences. With this data in hand, marketers can decide whether to increase or decrease their retargeting campaigns accordingly.

In order to measure incrementality, marketers can use cohort-based tests that randomly select audience groups to show ads to and a control group to measure the lift ads provide. As Measured highlights:

To measure incrementality, audiences are randomly segmented into test and control cohorts. The difference in conversion rates between the two cohorts effectively gives us incrementality and an accurate read on the marginal incremental contribution of that media channel.

In order to run these tests and accurately determine incrementality, marketers rely on attribution providers like Singular.

As discussed in our roundup on multi-touch and incrementality featuring experts from Airbnb, Stitch Fix, and Bark Box, whether you’re using multi-touch attribution, incrementality, or any other type of marketing test, it all comes down to clean data.

Clean data is what fuels marketing attribution and informs marketers on the effectiveness of each touchpoint in the user journey. Data also informs marketers about their most profitable marketing campaigns and provides accurate insights into optimization opportunities. By having a clear understanding of the ROI of each channel, this provides businesses with the insights they need to allocate resources more effectively and improve their marketing performance.

In summary, with Singular’s leading mobile attribution and marketing analytics platform, marketers have all the data they need to make informed decisions about the incremental lift their advertising is providing to their bottom line.