Lift Mentality is a tool that allows you to understand channel effectiveness, allocate ad spend more efficiently, and drive better performance. Unlike publisher tools, which rely on publisher tracking tags, Lift Mentality uses your first-party business data. The tool customizes the model based on your master of record for measurement and reporting, ensuring transparency and alignment with your business. This customized approach can be implemented to measure any marketing channel, no matter which team or agency is managing it.
Where is my budget better spent, Branded or Non-Branded Search?
What is the impact of specific channels, such as programmatic display or paid social, on my bottom line?
Which marketing channels are driving incremental results?
Lift Mentality uses geographic level test and control groups to gain an understanding of the incremental value a channel provides. Using historical data, it runs through 10,000+ iterations of similarly performing test and control groups at either the DMA, City/State, or Zip Code level. Once an ideal split is found, the test is activated in the appropriate channels, and performance is ingested back into the tool for analysis.
Monitor Lift on Multiple Metrics Simultaneously
Customize Test % Split
(i.e. 20/80 vs 50/50)
Calculate Ideal Test Length for ≥90% Statistical Significance
Lift Mentality was developed based on direct feedback from top advertisers to solve for the gap that exists in other measurement solutions today. Costly real-time Multi-Touch Attribution algorithms are becoming more probabilistic than deterministic given the changing landscape around cross-device journeys, digital identity resolution, browser restrictions, and privacy. Media Mix Modeling solutions are great at producing marketing investment forecasts, but the models they rely on can quickly become out of date. Lift testing, on the other hand, produces actionable insights at a reasonable price without taking up valuable resources.
An e-commerce client wanted to understand whether spending more in Google PLAs would increase incremental top-line revenue
Using the brand's internal revenue data combined with their paid search spend from Google, a 50/50 heavy up test was executed to gauge incremental top-line revenue lift
By spending 3x more in the experiment geos vs. control geos, a 3x incremental lift in top-line revenue was observed vs. last-click attribution. This demonstrated that Google Shopping drives more revenue than is being attributed via last-click attribution
Since Google Shopping drove more revenue than previously attributed, budget investment will be increased for Q3 and Q4. The results prompted the client to test all facets of paid media to understand what is truly driving value
Get a first-hand look at how our Lift Mentality tool could help you understand the impact your budget has on your response metrics by scheduling a demo with one of our experts today!