
How effective is your investment in Facebook really, and how can you optimize spend?
The Brand
CustomTees is a successful online clothing brand, with annual revenue exceeding $50M. Despite their success, the company's marketing team grappled with one critical question: How effective was their substantial investment in Facebook? Was there an opportunity to optimize Facebook spend to increase profitability while not impacting revenue?
The Challenge
The brand had historically relied heavily on Facebook for growth, allocating north of 40% of their marketing budget to the platform. With quarterly Facebook spend reaching $600,000, the stakes were high. Facebook's prospecting platform reported an 2.8 ROAS (Return on Ad Spend) on a 7-day click one-day view basis, but the client’s marketing team suspected this might not tell the whole story despite the previous success of the channel.
Digging Deeper
The initial analysis that was done, using a 7-day click only attribution benchmark which painted a very different picture. This method, which applies a discount to platform-reported results, suggested the brand was merely breaking even on Facebook ads. The UTM attribution calculated off of URL parameters reported an even lower ROAS. If this were true, it would imply the channel isn't profitable after considering costs of goods sold, shipping, etc. Moreover, it could be a sign that Facebook needs to be optimized and is cooling off despite its earlier success.
The brand decided to take up incrementality testing with M-Squared to uncover the ground truth behind Facebook performance.
The Approach:
Incrementality & Platform Lift Testing
To uncover the truth, M-Squared recommended two measurement approaches to test Facebook Prospecting:
- Geo Incrementality Test
- Facebook Platform Lift Study
Both tests were conducted over 30 days, with the geo test designed as a multi-cell experiment to assess incrementality across various channels, including Facebook Prospecting.
Catch a quick primer on different types of incrementality tests from the M-Squared masterclass here:
The Results:
The results were eye-opening:
- The geo incrementality test showed a total lift of about 10%, primarily driven by new orders.
- Calculating the multiplier revealed a channel multiplier of 41%.
- When applied, this showed that the ROAS on Facebook prospecting was actually around 0.6 - significantly lower than initially reported.
- The UTM attribution reported ROAS seemed to closely approximate the incremental reads coming out of the tests.
Interestingly, the Facebook platform lift study corroborated these findings, showing a multiplier of 41%.
Triangulating reads across multiple studies is a common best practice that marketers are adopting en masse.
Catch a quick primer on this best practice from M-Squared masterclass here:
Implications and Next Steps
Armed with this new data outside of platform-reported results, these consistent incrementality results provided the marketing team with a much clearer and concise direction. The data strongly suggested that cutting the Facebook budget and re-allocating resources elsewhere could be a logical next step for optimizing overall marketing performance and driving increased profitability, without harming topline revenue. M-Squared recommended testing budget re-allocation before rolling it out nationally by cutting spend on FB and re-allocating to other channels.
The Conclusion:
In the world of data-driven marketing, having multiple consistent data points is crucial for avoiding false positives and making informed strategic choices.
For this e-commerce clothing brand, the journey of advanced attribution not only revealed the true performance of their Facebook advertising but also opened up the discussion for new growth opportunities. By reallocating budget from underperforming channels, they can now explore other channels to drive more efficient growth and profitability.
This case study serves as a powerful reminder of the importance of rigorous, multi-faceted attribution in modern marketing. In an era where data drives decisions, looking beyond surface-level metrics can uncover hidden truths and unlock new paths to success.