Learn how one company leveraged LiftLab to validate Meta Awareness performance and refine its media mix model while uncovering $1.1M in incremental annual revenue opportunity.
A consumer brand wanted to confirm its media mix model’s estimates of Meta Awareness performance before making changes to its marketing budget. LiftLab spearheaded a 42-day experiment to measure the channel’s actual incremental contribution to retail and eCommerce revenue. The findings helped recalibrate the media mix model and identify approximately $1.1 million in combined incremental annual revenue opportunity with optimized spending levels.
Business Challenge
Meta Awareness was an established part of one consumer brand’s always-on media mix. While the media mix model credited the channel’s contributions to retail revenue and e-commerce revenue, the brand sought to validate the actual incremental value for each business outcome. Existing model estimates were based on historical data and correlations, but the brand wanted to determine if the revenue was correctly attributed.
The team needed a measurement of channel performance across retail revenue and e-commerce revenue to inform budget decision making. They also wanted a clearer understanding of how additional spending could impact returns.
Objectives
Establish a more reliable understanding of Meta Awareness’s contribution to retail and e-commerce value.
Determine how much to invest in Meta Awareness to maximize incremental revenue for both channels.
Improve media budget allocation by using experimental evidence to refine the media mix model.
How LiftLab Ran One Experiment for Two Business Outcomes
LiftLab used its Go Dark with Pacing methodology and Incrementality Testing Suite to measure Meta Awareness’s impact on specific business outcomes. The team paused delivery in a randomized set of markets while spending continued at its normal pace for the remainder. Control and treatment cells each represented 19% of the experiment, which took place over 42 days.
The same experiment measured independent outcomes for retail revenue and e-commerce revenue. The results were used to:
Validate incremental return on ad spend (iROAS).
Map diminishing returns curves.
Identify optimal spending levels for each outcome.
Results & Business Impact
A 1.56 iROAS was observed for retail, 7.5% higher than the model’s estimate, indicating Meta Awareness’s retail contribution had been undervalued.
A 1.64 iROAS was observed for e-commerce, 18.5% lower than the model’s estimate, indicating Meta Awareness’s e-commerce contribution had been overvalued.
The findings gave the brand evidence to use as a foundation for recalibrating its model for both business outcomes.
For retail, increasing daily spend from $648 to $1,069 would generate an estimated $1,476 in incremental daily value, representing approximately $538K in annual incremental revenue opportunity.
For e-commerce, increasing daily spend from $648 to $1,101 would generate an estimated $1,573 in incremental daily value, representing approximately $574K in annual incremental revenue opportunity.
By identifying optimal spending levels for both outcomes, LiftLab uncovered approximately $1.1 million in potential annual incremental revenue.
Key Takeaways
Model needs validation: Media mix models provide estimates while live experiments help establish whether advertising is generating incremental results.
One investment. Different outcomes: Meta Awareness was undervalued for retail and overvalued for e-commerce, demonstrating the importance of measuring business outcomes independently.
Better Measurement. Better Allocation: Live experimentation can reveal new data for making business decisions and finalizing budgets.
Methodology note: iROAS reflects the observed incremental return on ad spend measured during the Experiment window via a Control (Dark) versus Treatment (Lit) geo design. mROAS reflects the marginal, next-dollar return implied by the fitted response curve at a given spend level. Figures are drawn directly from the customer’s own Experiment output and reported in anonymized form.
Customer identity and brand details have been withheld. All figures reflect a real Experiment run on the LiftTest platform.
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FAQs about Meta Awareness Case Study
Why validate a media mix model with experimentation?
Media mix models estimate marketing contribution based on historical data and statistical relationships. A live, controlled experiment can provide evidence of incremental impact to validate and refine estimates and spending.
What is diminishing returns analysis?
Diminishing returns analysis measures how incremental returns change as advertising spend increases. This can help identify the spending level at which the next dollar delivers a return compared to other media investments.
What is Go Dark with Pacing methodology?
<a href=”https://liftlab.com/measurement-conversations/incrementality-testing-paid-media/”>Go Dark with Pacing</a> is LiftLab’s experimental method for measuring incremental media impact. It pauses delivery in selected control markets while maintaining normal delivery elsewhere, allowing measurement of the difference in business outcomes.
