Validate Paid Media Efficiency with Accurate Incrementality Testing
Consult Brett Odey, the incrementality testing expert, to determine the real incremental contribution of any media mix by channel, campaign, ad set, or tactic to your business results.
The Data Science Of Incrementality Testing
Incrementality testing is an essential tool for marketers because it allows them to measure the impact of specific campaigns and channels on business outcomes and make data-driven decisions on budget allocation and campaign optimization.
What is Incrementality Testing?
Incrementality testing is a method used to determine the incremental impact of a specific marketing campaign or advertising channel on business outcomes. The goal of incrementality testing is to isolate the effect of the campaign or channel being tested from other factors affecting business outcomes.
Which Method Is Right For You?
A few different methods can be used for incrementality testing, but the most common approach is to use a control group and a test group. A control group is a group of customers or consumers who are not exposed to the campaign or channel being tested, while the test group is exposed to the campaign or channel. By comparing the outcomes for the two groups, we can estimate the campaign’s or channel’s incremental impact on business outcomes.
Types Of Incrementality Testing
- Experimental incrementality: This type of testing uses randomized controlled experiments where customers are randomly assigned to either a control group or a test group. This method is considered to be the most rigorous and accurate way to measure incrementality.
- observational incrementality: This type of testing uses observational data such as web analytics data to estimate the impact of a campaign or channel. This method can be less accurate than experimental incrementality as it relies on observational data and causality can’t be inferred directly.
- Quasi-experiment incrementality: This type of testing uses a combination of experimental and observational data, and it’s a middle ground between experimental and observational incrementality.
What Software is Your Source Of Truth?
Interpreting incrementality testing results is a complex process requiring significant data and specialized statistical expertise.
Leading Benefits of Incrementality
- Increased efficiency: By focusing on the incremental impact of changes to your website or marketing campaigns, you can avoid conducting full-scale tests that can be time-consuming and resource-intensive.
- Better decision making: Incrementality testing provides a clearer understanding of the cause-and-effect relationship between changes and results, allowing you to make more informed decisions about what to keep, change, or discard.
- Increased ROI: By focusing on incremental improvements, you can continually optimize your campaigns, leading to higher returns on investment over time.
- Better targeting: Incrementality testing allows you to test specific segments of your audience, allowing you to better target your campaigns and tailor your messaging to specific groups.
- Increased agility: By testing incrementally, you can quickly adapt to changing market conditions and customer behaviors, ensuring that your campaigns remain relevant and effective.
In summary, incrementality testing measures the incremental impact of changes to a website or marketing campaign. It involves testing small, targeted changes to determine the effect on key metrics, such as conversion rate or customer engagement, allowing for a clearer understanding of the cause-and-effect relationship. The benefits of incrementality testing include increased efficiency, better decision-making, increased ROI, better targeting, and increased agility in response to changing market conditions.
Outline a test that an e-commerce company would do incrementality testing.
E-commerce companies use incrementality testing to determine the impact of changes made to their website or marketing campaigns on customer behavior.
Here is an outline for an incrementality test for an e-commerce company:
- Define the test hypothesis: The hypothesis should state the expected impact of the change on customer behavior.
- Segment the audience: The audience should be divided into two groups, a treatment group and a control group. The treatment group will be exposed to the change, while the control group will not.
- Implement the change: The change should be implemented on the treatment group’s website or marketing campaign.
- Track customer behavior: Both groups should be monitored to track customer behavior, such as the number of visits, conversions, and average order value.
- Compare results: The results should be compared between the two groups to determine the impact of the change.
- Analyze results: The results should be analyzed to determine if the hypothesis was proven or disproven.
- Take action: Based on the results, the e-commerce company can decide whether to keep the change or revert to the original version.
Incrementality Experts for Search, Social, OTT & Display