Real Impact
Real Impact causal modelling tool allows us to prove the correlation between two disconnected data sets.
CASE STUDY
Measuring the incremental impact of podcasts
Testing new brand media channels and measuring their incremental impact on business results
Objective
Zipcar is a leading car sharing service. Tug was appointed to test new brand media channels (in particular, podcasts) and measure their incremental impact on business results. The objective was to grow Zipcar’s share of voice in the competitive car sharing space and prove the impact of brand media activity on new registrations.
Challenge
Car sharing has a variety of direct and indirect competitors. To avoid wastage Zipcar needs to target their customers at key car sharing consideration moments in life. Traditionally Zipcar has struggled to report internally on the impact of individual media channels in their media mix.
Solution
Tug used 1st & 3rd party audience data to plan an omni-channel Brand and Performance campaign including a podcast test. Tug’s data science team isolated the podcast test using our proprietary tech stack, Real Impact. By combining Zipcar’s booking data with 73 external factors such as weather, inflation and train or tube delays. Tug trained the model to predict what Zipcar’s revenue would have been had the podcast ads not run.Results
5%
Uplift in net new registrations from podcasts alone.
90%
Confidence of causal attribution of impact delivered by podcasts.
4:1
ROAS. Podcasts are now an important part of the media mix.
Tug Labs
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