Haus analysis reveals insights on Brand Terms in Google Performance Max

Olivia Kory, Head of Strategy at Haus, shared a meta-analysis of Google Performance Max (PMax) advertising strategies on X. The analysis, focusing on the inclusion or exclusion of brand terms in PMax campaigns, offers valuable insights for marketers navigating the complexities of this AI-driven advertising tool.

Haus, a company specializing in measuring incrementality for advertisers, conducted this research to address a common dilemma faced by marketers: whether to include or exclude brand terms in their PMax campaigns. The findings, based on data from multiple customer experiments, reveal nuanced outcomes that challenge conventional wisdom and highlight the importance of tailored strategies.

According to the analysis, the decision to include or exclude brand terms in PMax campaigns yields split results in terms of incremental revenue. Kory reports that including branded terms drives more incremental revenue than excluding them 50% of the time. However, this statistic comes with a significant caveat: when excluding branded terms proved more effective, it did so by a substantial margin. On average, excluding branded terms drove 24% more incremental revenue across all tests.

One of the most striking findings from the Haus analysis concerns customer acquisition costs (CAC). The research shows that 100% of the experiments found that excluding brand terms lowers CAC. The range of improvement was substantial, with CAC reductions varying from 19% to 60%, averaging a 40% decrease in acquisition costs when brand terms were excluded.

These results suggest a complex relationship between brand term inclusion and campaign performance. While including brand terms appears to have a disproportionate impact on existing customers, it may simultaneously divert the algorithm's focus away from new customer acquisition.

The analysis also reveals an intriguing correlation between average order value (AOV) and the effectiveness of including brand terms. Brands with an AOV over $238 saw 27% more incremental revenue when including brand terms in their PMax campaigns. This finding underscores the importance of considering individual brand characteristics when applying general strategies.

Kory hypothesizes that this AOV-related trend may be due to data scarcity. Higher AOVs often correlate with higher customer acquisition costs, which can result in less data available to fuel the algorithm. For brands in this category, or those with lower overall conversion volumes, including brand terms may provide necessary signal boosting for the PMax algorithm.

The Haus analysis aligns with previous research in the field. Kory cites an analysis from Smarter Ecommerce suggesting that the signal threshold for effective PMax campaigns likely falls in the range of 300 to 1,000 monthly conversions. This benchmark provides a useful reference point for marketers considering their PMax strategies.

Based on these findings, Kory offers several recommendations for marketers:

1. For brands prioritizing new customer acquisition, excluding branded terms from PMax campaigns appears to be a safe strategy. The analysis has not yet found a case where this approach was less efficient.
2. High-AOV brands with low transaction volumes, particularly those focused on repeat or blended revenue, may benefit from including brand terms. This strategy may be especially effective for brands in highly competitive categories.
3. For brands falling between these extremes, experimentation is key. At a minimum, Kory suggests setting up a script to monitor the search terms triggering ad appearances. If branded terms dominate click traffic, marketers should consider adjusting target efficiency levels to treat the campaign more like a bottom-of-funnel initiative than a full-funnel one.
4. Beyond the branded terms debate, Kory recommends testing shopping-only PMax campaigns against the full-placements default. This approach can help mitigate the impact of low-quality inventory blending with search and shopping performance.

This comprehensive analysis from Haus provides valuable guidance for marketers navigating the complexities of Google Performance Max. By highlighting the nuanced impacts of brand term inclusion across different metrics and brand profiles, the research emphasizes the importance of tailored strategies and ongoing experimentation in digital advertising.

Key facts from the Haus analysis of Google Performance Max campaigns

Released on August 23, 2024, via X

Conducted by Olivia Kory, Head of Strategy at Haus

Including branded terms drives more incremental revenue 50% of the time

Excluding branded terms resulted in 24% more incremental revenue on average

100% of experiments found excluding brand terms lowers customer acquisition costs

CAC reductions ranged from 19% to 60% when excluding brand terms

Brands with AOV over $238 saw 27% more incremental revenue when including brand terms

Suggested signal threshold for effective PMax campaigns: 300 to 1,000 monthly conversions

Recommendations include strategy adjustments based on brand characteristics and campaign goals

Emphasizes the importance of experimentation and monitoring in PMax campaign management

This analysis from Haus represents a significant contribution to the understanding of Google Performance Max strategies, offering data-driven insights that can help marketers optimize their campaigns for better performance and efficiency.