Learn how Catalina Crunch leveraged Prescient to improve operating income and decrease CAC by optimizing ad spend across TikTok, Meta, Google, and Amazon.
About
Catalina Crunch is a low-carb, keto-friendly snack brand that makes delicious food healthy. The brand’s cereals, snack mix cookies, and chocolate bars are available online and at 30,000 retailers nationwide.
Challenge
Building MMM in-house was incredibly time-consuming and inefficient
Nick Osborn, Catalina Crunch’s Head of Growth, says he tried a variety of marketing mix models (MMM) throughout his five years at the company. Unfortunately, all of them were extremely overpriced relative to their values and were difficult to trust. The standard MMM didn’t even use Catalina Crunch’s data. Instead, they used a larger data set of brands in different categories and external data modeling — a tactic that would dilute any insights for Catalina Crunch.
After trying a variety of MMM without success, Nick pivoted and built his own internal model based on multipliers, last-click conversions, and post-purchase surveys. In parallel, Nick also built a multi-regression model to understand how Catalina Crunch’s DTC ad spend impacted its Amazon sales and new Amazon customers. The correlation between Meta impressions and Amazon revenue was strong, but his model just scraped the surface of the insights he wanted.
What’s more, building these projects was exceptionally time-consuming. He used to put data sets into Excel workbooks that were 600k rows long and took up to 15 minutes to open. Nick needed a lower-lift way to improve blended ad campaign revenue, so he tried Prescient.
Using Prescient, I can model out any spend. If I want more incremental ROAS for next month, the platform tells me where I should adjust my budget, spend, channels, campaigns — you name it. Prescient makes the whole process so easy.
— Nick Osborn, Head of Growth at Catalina Crunch
Solution
Prescient helps Catalina Crunch model spend for optimal campaign returns
Nick points out that Prescient was collaborative, highly responsive, and growth-minded from day one of the engagement. After onboarding, Nick gained granular visibility into Catalina Crunch’s monthly ad spend channels, from Meta to Google to TikTok — right at the campaign level. With those insights, the team at Catalina Crunch was able to improve its ability to effectively forecast and reallocate spend budgets for optimal performance.
In addition, using Prescient’s recently launched Amazon Halo-Effects, he can see how those campaigns, traditionally tracked with returns on DTC, impacted Amazon performance. The best part? Nick and his team don’t have to own any of this time-consuming work manually – instead getting access to actionable data insights at the click of a button.
Results
Catalina Crunch lowered CAC by 24% – within a single month
After implementing Prescient, Catalina Crunch was able to decrease CAC across its blended business – spanning both DTC and Amazon. In early February, Nick leveraged Prescient’s optimization engine to make simple paid spend adjustments. The result? The brand’s March CAC came in 24% lower than historical averages – yielding immediate ROI for Catalina Crunch.
Using Prescient’s intuitive interface also means Nick’s no longer downloading massive data sets from Amazon to make his models work. After onboarding, Nick points to impressive results:
- 24% decrease in CAC within 30 days
- Scaled DTC bottom-line operating income
- Validated 15% of Amazon gross sales driven by Halo-Effects
- 40+ hours saved per month manually pulling data sets
On a final note, Nick points out that he’s specifically looking forward to leveraging Prescient’s models to determine how Catalina Crunch can put more budget into Meta or TikTok to boost performance on Amazon during Prime Day.
Prescient’s initial model recommendations from early February had a massive impact on our DTC performance in March. I’m looking forward to more great outcomes like that using Prescient.
— Nick Osborn, Head of Growth at Catalina Crunch
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