Saatva gains a 22% increase in revenue from TV ads with network-level forecasting

Learn how AI-powered marketing mix modeling (MMM) revealed which linear and CTV investments would drive maximum growth

ABOUT

Saatva is a premier omnichannel retailer of handcrafted luxury mattresses, known for its eco-friendly products for healthy and restorative sleep. Founded in 2010 as the first online company to sell mattresses directly to consumers, Saatva now also has retail stores with immersive, state-of-the-art Viewing Rooms nationwide.

CHALLENGE

Saatva’s old MMM could only provide channel-level insights, delivered every six months 

After a significant boost in sales, courtesy of COVID-19, many ecommerce brands were looking for ways to keep the momentum going. For Alex Diesbach, Vice President of Digital Marketing at Saatva, investing in TV advertising proved to be a successful strategy for driving growth in the years that followed. As he continued to scale into linear and CTV as fundamental pillars in the Saatva marketing strategy, Alex needed better insight into how to allocate increasing spend for maximum return.

Saatva had an existing solution, but it was static, cumbersome, and the long lead time for performance reports it delivered weren’t relevant by the time Alex received them. Also, Saatva was investing in a diverse portfolio of ad placements across streaming platforms and linear TV, but channel-level data and required a solution that could go down to the campaign level. Based on direct response data, streaming was more effective, but inventory on linear was much less expensive. 

Alex also noticed that investments in linear TV had a positive ripple effect (what Prescient calls “halo effects“) on Saatva’s search traffic, but the existing data couldn’t prove the direct connection. Conducting TV holdout tests would be expensive and difficult to land for Saatva, because there wasn’t enough alignment in the brand’s geography.

SOLUTION

Prescient AI delivers network-level insights for ongoing TV spend optimization

As Alex began to look for an alternative solution, several colleagues recommended Prescient AI. With access to daily, campaign-level insights, he could not only answer his burning questions about TV spend, but make informed decisions for ongoing optimization across the marketing mix. The proprietary model that powers Prescient AI’s platform could also measure halo effects, meaning he could now confirm the impact that linear TV ads had on indirect results.

Prescient AI revealed that it wasn’t a question of linear versus CTV, but how budget was being allocated within each channel. On linear, where they were investing in a broad variety of programming, Saatva learned that large broadcast news buys and content with a captive audience had the strongest results. For streaming, big category investments were more effective than placements on specific shows. 

RESULTS

Adjustments to TV investments deliver big gains in revenue and search traffic

Based on insights provided by the Prescient AI platform, Saatva reallocated spend between and within both streaming and linear channels. Shifting budget to spots where Prescient AI forecasted the best performance resulted in a 22% increase in revenue generated by TV ads. Year over year (YoY) branded search traffic also increased 75% for Saatva after implementing the platform recommendations. 

Saatva now regularly uses Prescient AI to analyze and optimize spend across a broad mix of channels, including affiliate marketing – another priority strategy for the brand that is notoriously difficult to measure. Alex is also working closely with the Prescient AI team on customizing the model to measure and forecast marketing impact on both online and in-store sales, a critical functionality as Saatva continues to expand its omnichannel presence.