Inside Saatva’s measurement approach linking TV ads to in-store sales

By Tatari

Nora Eisner, senior research data scientist, Tatari

For most retailers, measuring the success of an ad is as simple as waiting to see if it drives someone to visit a website. But what about getting shoppers to go to a brick-and-mortar store? Yes, in-store sales are still very much a thing in 2025. In fact, YouGov data shows that 37% of U.S. adults shop equally online and in-person. So for marketers with retail locations, ensuring their online advertising has an impact on offline sales is critical.

But measuring that is much more complicated.

This drove mattress brand Saatva to dig deeper. The company, known for selling luxury mattresses both online and through its 22 physical showrooms across the U.S., wanted to understand how much its ads on both streaming and linear TV actually drive foot traffic and in-store sales.

Finding the answer required more than a typical attribution model. It called for a statistical deep dive into how people see, remember and, eventually, act on TV ads, even days or weeks after seeing them.

“Over the past five years, we’ve been opening up brick-and-mortar showrooms in all of our biggest markets,” said Alex Diesbach, vp of marketing at Saatva. “Since launching our ads on TV, we’ve seen a huge correlation with branded search inquiries and website traffic. So naturally, the next step for us was to determine how our TV campaigns influenced our retail sales.”

Turning ad exposure into real-world sales data

To find the signal amid all the noise, Saatva partnered with Tatari to build a model that connects TV ad spend in specific geographic areas (known as DMAs, or designated market areas) with weekly store sales dating back to 2023.

The challenge: differentiating between sales that happened because of advertising and those that would have happened anyway. So Tatari’s data science team accounted for everything from regional shopping habits to national holiday promotions.

Beyond ad spend and store receipts, Tatari’s model also factored in:

  • The ad echo, or carryover effect. An ad’s influence doesn’t just disappear the second it’s over; it fades away slowly. Someone who sees a TV ad on Monday may not act on it until Friday, or later. As such, the ad spend from week one will still affect the sales that occur in weeks two, three and possibly even beyond.
  • Holiday sales spikes. The Tatari team made sure not to give TV ads credit for sales that were going to happen because of a huge holiday sale, isolating natural lifts around events like Memorial Day or Black Friday. These special promotional periods are handled differently from everyday, non-promotional periods.
  • Regional variation. Recognizing that a huge market like New York City, which has several Saatva stores, looks very different from a smaller city with only one location. To ensure a balance, the model accounted for the number of stores in each DMA.
  • Population density and proximity. This refers to the number of potential customers living within 10, 20 or 50 kilometers of a store. The team established a baseline by factoring in this distribution in population, relative to Saatva’s stores.
  • Seasonality and long-term growth. The model takes into account that mattress sales are often driven by seasonal trends, as well as looking at sales figures over the years.

Each of these factors helped refine a more realistic model that could quantify the real-world impact of TV advertising on retail performance by predicting the patterns in the data to forecast the total number of sales over time — not just those attributed to ads. Next, the Tatari team asked the model how it would impact sales if Saatva never ran any TV ads at all. The difference between the two figures helped produce the total number of retail sales that can be attributed to TV advertising, giving Saatva an entirely new way to measure TV.

Quantifying TV’s impact on in-store sales

Out of the total retail sales, 5.7% were directly attributed to TV ads. For a high-consideration purchase like a mattress, it’s a significant lift, and one that proves television’s reach doesn’t stop at the screen.

Beyond the topline number, the model also exposed differences across local markets. Some DMAs showed stronger ad responsiveness than others — insights that Saatva can now use to optimize where, and how heavily, to invest in future campaigns.

“Getting this kind of visibility into how our TV ads perform at the regional level has been a game changer,” said Diesbach. “We can now see which markets are more likely to translate ad exposure into store visits and purchases, and that’s helped us fine tune both our media mix and our budgeting.”

Saatva’s experiment shows that TV isn’t just a brand awareness channel; it’s a real driver of retail sales, even in an era where digital metrics dominate marketing dashboards. By blending old-school advertising with new-school analytics, Saatva turned what was once a gut feeling into actionable data.

Sponsored by Tatari