A Kahena Case Study:

Leaning Into Automation

Client
ClickUp
Services
Paid Search
Published

The Challenge

Results

-49.6%
Reduction in Cost Per Customer Acquisition / CAC
-75.7%
Reduction in Cost Per Account Creation
+49.8%
Increase in CTR

How we did it

Leaning Into Automation

How we took the brave pill, ditched traditional PPC optimization, and dived into machine learning

Implementation

ClickUp, Kahena, and Google collaborated to implement a strategy for a new bid signal aimed at expanding auction participation on Google Search. The goal was to enable machine-learning to identify high-value searches based on predicted lifetime value (pLTV). 

Google provided recommendations, and together Kahena and ClickUp devised a strategy leveraging tROAS/Max Conv Value bidding to target high-value users and increase ROAS. Existing campaigns were rebuilt with updated keyword variations and new themes of search intent, while previous traffic-shaping optimizations were reverted to give the algorithm freedom to locate searches with value.

Launch, Observations, and Optimizations

The pLTV bid signal was launched as a 50/50 split experiment across select campaigns, allowing for observation of its impact on ROAS-bidding and Broad Match keywords. 

Positive results were observed early on, with a reduction in cost per acquisition even before predicted lifetime value data was calculated and piped back into Google Ads..

Interestingly, most conversions came from Phrase and Exact Match keywords, suggesting the benefit of combining all three match types. Continuous optimization efforts were made, including managing ad delivery in specific locations, traffic shaping optimizations, and improving landing page experiences.

Embracing Machine-Learning

As meaningful improvements were seen across KPI’s, the pLTV rollout was expanded across more campaigns targeting diverse keyword themes. By observing trends in value-based bidding we incrementally raised the target ROAS, thereby forcing the algorithm to prioritize higher value searches within a capped budget. 

This shift in SEM strategy resulted in reduced spending, maintained customer acquisition volume, and improved CAC across both Brand and Non-Branded campaigns, and was further extended beyond Google Search to also encompass Bing.

Conclusion

The collaboration between ClickUp, Google, and Kahena laid a solid foundation for future scaling and profitability. By embracing machine-learning and making strategic adjustments, ClickUp achieved cost efficiencies while maintaining acquisition volume, demonstrating the effectiveness of a data-driven approach in SEM management.