For the first time, we could see the real impact of CTV on retail sales, not just what we hoped it was driving. 
The Problem
Without a clear way to measure the incremental impact of CTV within Obvi’s media mix, the Obvi team didn’t have a full picture of how CTV was actually performing.
They wanted to address these core issues:
- Connecting CTV to retail sales: CTV reach and impressions were easy to see, but they wanted to know who saw an ad and then went and bought in a store.
- Measuring what matters most: As Universal Ads delivered strong results, the team sought to isolate the incremental impact of CTV on new customer acquisition and sales.
- Making confident budget decisions: As CTV spend increased, the team didn’t know if adding more budget would drive more sales or waste ad dollars.
Their goal was clear: See whether CTV was driving in-store sales.
The secondary goal: Would spending more on CTV result in more sales.
Seeing the Bigger Picture
To understand whether CTV was driving in-store sales, Obvi partnered with Lifesight to test premium TV via Universal Ads.
Instead of relying solely on platform-reported results, Lifesight’s Incrementality testing (geo-lift experiment) compared regions with heavy CTV exposure to similar regions with little or no exposure. Ads were concentrated in states like Alabama, North Carolina, and Tennessee, while fourteen comparable states, selected through an iterative, rigorous matching process to best represent the treatment market audience at the highest confidence level, served as the control group receiving minimal to no ad spend. This made it possible to see whether sales increased because of CTV or whether those purchases would have happened anyway.
The test focused on Walmart in-store sales, over a seven-week period. By comparing results between exposed and control markets, Obvi could isolate CTV’s true impact while accounting for normal sales patterns and regional differences.
This approach answered the questions that mattered most:
If CTV runs in certain markets, do sales increase more than they would without it?
The experiment was designed to detect a minimum 5% lift in retail sales at a 95% confidence level – statistically significant levels to determine if UA was driving incremental results for Obvi.
Experiment design:
| Null hypothesis | Universal Ads drives no statistically significant lift (effect = 0). |
| Alternative hypothesis | Universal Ads drives a statistically significant lift of at least 5% (effect ≥ 5%). |
| Duration | 7-week treatment period. |
| Methodology | Geo-based experiment using a pre-validated synthetic control model. |
| Treatment markets | Seven states received heavy Universal Ads exposure: Alabama, Arkansas, North Carolina, Oklahoma, Tennessee, Virginia, and West Virginia. |
| Control markets | Fourteen comparable states (including Mississippi, Illinois, Washington, Texas, Florida, and Idaho) served as the control group and received minimal to no ad spend. |
| KPI | Walmart in-store revenue. |
The Incremental Truth
After six weeks, the experiment delivered a measurable and statistically significant outcome.
Universal Ads generated a 5% incremental lift in Walmart in-store sales, clearing the pre-defined minimum detectable threshold at 95% confidence.
- For Obvi, the test showed a clear result. CTV exposure through Universal Ads was driving in-store sales at Walmart.
- Even at a limited test, the campaign delivered a 5% lift in purchases inside Walmart stores, showing that CTV influenced buying behavior.
The Results
Before the test, increasing CTV spend meant guessing. After the test, Obvi had confidence in their ability to scale profitably via Universal Ads.
By validating CTV’s impact early, the team was able to:
- Confirm that Universal Ads drives incremental in-store retail sales
- Understand the direction and strength of CTV’s impact
- Reduce risk before committing larger budgets
- Scale budget and reach along with subsequent next test, monitoring for saturation.
Instead of guessing and hoping, Obvi can now decide how much to spend on CTV using real data. Brands often invest heavily in upper-funnel channels to see if they will work. Obvi took a different approach.
By testing first, they gained clarity on how CTV fits into their retail growth strategy and built a repeatable way to evaluate future media spend.
Lifesight’s Unified Measurement OS helps teams invest with confidence instead of assumption. To see how Lifesight can help you measure real impact and make smarter growth decisions, book a demo today.
*This case study was developed and published by Lifesight. All performance results reflect Lifesight’s analysis and are not validated by Walmart Connect. Walmart Connect has not reviewed or endorsed this content, and no guarantees of future performance should be inferred.
