Hexclad Measures Meta's Incremental Impact for a High-AOV Product with a Long Consideration Phase

Hexclad’s most popular product is a 12-piece pan set priced at $699.99. Given the high price, they understand that customers typically have a longer consideration phase in deciding to purchase. Therefore, evaluating marketing performance based solely on last-click attribution doesn’t provide a complete picture, as it misses a significant portion of the customer journey. However, evaluating platform reporting isn’t particularly useful either, as platforms typically over credit themselves for any sale whether or not it was pivotal to the buyer’s journey. 

Hexclad partnered with Haus to understand where their next dollar was best spent. To do so, they needed to understand incrementality over a longer time horizon and across all their sales channels. 

Like many companies, Hexclad invests heavily in Meta ads. They wanted to determine if they could effectively scale their spend on Meta while also assessing how these ads influenced buyers throughout the extended purchase cycle on both Shopify and Amazon. Using Haus, they set out to answer two primary questions:

1. What is the optimal investment level in Meta ads?
2. How does the incrementality of Meta ads change over time considering the high AOV product?

The Hexclad team used GeoLift to automatically set up a 3-cell optimal spend level test, dividing the United States into three statistically equivalent geographic groups based on Hexclad’s historical business data. One group got normal ad exposure, one saw higher spend, and one control group saw no ads at all (the control). Success was defined based on new orders on Shopify and Amazon. 

Using the Haus power calculator, Hexclad determined two weeks was sufficient at their spend level to run a high confidence experiment. After the two weeks were up, they would go back to their business as usual strategy (Meta ads nationwide), but they wanted to continue to monitor the regions in each test cell post-test to see if there was lagging behavior. This post test observation window, often called a “post treatment window” allows you to capture any lagging sales effects from ads exposed during the test. 

Hexclad decided on a 3 week observational window for this experiment.

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At first, the Meta ads seemed successful but not as efficient as expected in driving sales of Hexclad's $700 pan sets. Both the normal "business as usual" ad spend and the increased spend cells outperformed the no-ads control group. However, their cost-per-incremental-acquisition (CPIA) metrics were higher than anticipated.

It wasn't until incorporating the extended 3-week observational window that Hexclad saw the full picture. The normal ad spend cell's CPIA decreased by 56%, while the increased spend cell's CPIA dropped even further by 67%. This illustrated how critical an extended measurement view is to account for the entire, multi-week purchase journey customers take with a premium, big-ticket product

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Hexclad plans to test other primary channels in this same capacity so they can compare omnichannel iROAS and determine where they can most efficiently scale. With the ability to measure the causal impact of marketing across all sales channels (Shopify and Amazon) for the full duration of a customer’s purchase journey, Hexclad can now optimize their media mix to total business revenue rather than just the Shopify silo.