How CTV Advertisers Can Better Measure Incrementality
In the ever-evolving world of connected TV (CTV) advertising, embracing the most rigorous approach to outcome measurement is crucial. As the landscape becomes more complex and competitive, marketers and advertisers must find reliable ways to validate the impact of their campaigns. A/B testing with incremental lift studies offers a powerful solution that is superior to alternatives like multi-touch attribution and media mix modeling (MMM).
The drawbacks of multi-touch attribution and media mix modeling
Before diving into the perks of A/B testing, let's quickly address the drawbacks of two attribution models: multi-touch attribution and MMM. Multi-touch attribution tries to cover various touchpoints in the customer journey, but falls short due to its reliance on arbitrary assumptions and focus on correlations instead of causality.
Meanwhile, MMM leans heavily on historical data to assess marketing channels but overlooks individual user-level interactions, making it susceptible to confounding factors. While both models may reveal intriguing correlations, they fail to demonstrate causality, leaving advertisers uncertain about their campaigns' true impact.
Additionally, it is important to recognize that CTV conversions may involve different devices and could be delayed compared to traditional digital advertising. To address these challenges surrounding measurements specific to CTV, advertisers can effectively leverage incremental lift studies to drive impactful and measurable campaign results.
How do CTV advertisers calculate incremental lift?
A/B testing, the same methodology used in clinical trials for vaccines and life-saving drugs, offers exceptional rigor for determining incremental lift.
In CTV advertising, A/B testing involves randomly assigning viewers to a test group (exposed to your ad campaign) or a control group (either exposed to a placebo creative or generated synthetically). These two groups are statistically identical, except for their exposure to your campaign. By comparing their behaviors and outcomes, you can establish a direct causal link between your campaign and its impact on viewer behavior.
However, this gold standard of advertising efficacy is not without its challenges. Ensuring adequate sample sizes is essential, as is focusing on experimental design and tracking relevant metrics. Advertisers must also consider factors such as suitable identifiers for accurate targeting and measurement.
Beyond measurement, failure to correctly detect incrementality can lead to inefficient budget allocation to audiences with high baseline conversion rates therefore missing the opportunity to bring new incremental conversions with CTV. It is crucial to distinguish between conversions driven by efforts such as retargeting and those that would have occurred anyway. In short, understanding incrementality is key for making smarter investments and improving campaign performance while avoiding overallocation in high baseline audiences.
Advertisers must carefully consider several execution options in CTV: PSA testing, geo testing, ghost bids, and synthetic groups. Each method has its unique advantages and drawbacks.
- PSA testing: This method is among the easiest to set up. It involves running public service announcements (PSAs) as a control, allowing for a straightforward comparison against your campaign's performance. The downside is dedicating a portion of your ad budget to non-promotional content.
- Geo testing: This approach involves splitting your target audience into test and control groups based on geographical regions. However, implementing this approach can be complex and may not always account for regional differences that could skew the results.
- Ghost bids:This advanced technique involves placing non-winning bids alongside your actual campaign to gather a clean sample without additional costs. The challenge lies in ensuring your non-winning bids accurately represent the same audience segments, which can present some technical challenges
- Synthetic groups: This method creates a control group that mirrors the test group using advanced statistical techniques and extensive user data. It aims to match demographics, behavior, and other factors for accurate campaign performance comparison against a group not exposed to the advertising.
We recommend starting with PSA testing for its simplicity and ease of implementation, particularly for those new to A/B testing and incrementality measurements in the context of CTV. As your expertise grows, consider exploring ghost bids or synthetic groups as advanced, cost-effective options for measuring the incremental lift of your CTV advertising campaigns.
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