Since end to end attribution is effectively dead, this provides the best view of individual marketing campaign impact. A statistically robust impact score of one set of data against another that can be used in place of attribution.
With Real Impact we take your measurement beyond correlated assumption, to scientifically proven causal based measurement.
In response to these challenges, Tug Labs has developed Real Impact - an advanced causal modelling tool that ingests omnichannel campaign data alongside control variables specific to a brand or industry, such as competitor activity, weather, inflation rate, or seasonality.
Then there’s the issue of restrictive algorithms. Most ad platforms use proprietary machine-learning models to optimize campaigns. While these models can be powerful, they don’t always align with specific goals or provide insight into how decisions are made. Real Impact gives marketers full visibility and autonomy with custom-built modelling tailored to your brand and industry.
What exactly are the benefits of such real-time access? It can lead to more efficient budget allocation, speedier responses to performance fluctuations, and an enhanced ability to capitalize on high-performing segments.
Through this new measurement technology, we’re able to isolate with confidence the role an individual channel plays among an omnichannel strategy. By providing clear, qualified evidence of marketing’s impact, Heads of Marketing from brands like Zipcar, END Clothing, and GenesisCare use Real Impact to validate channel impact, secure further budget, and strategize future marketing investments.
Tug’s Real Impact tool proved instrumental in Zipcar’s exploration of the audio advertising space, particularly as they ventured into podcast ads for the first time.
While the primary focus of the campaign was to increase brand awareness, Zipcar was also keen to measure the impact on key business metrics, particularly revenue from bookings. This is where Tug’s Real Impact model came into play. By combining Zipcar’s booking data with 73 external factors such as weather conditions and train or tube delays. Tug trained the model to predict what Zipcar’s revenue would have been had the podcast ads not run.
The results were striking, demonstrating a 5% uplift in bookings that would not have been achieved without the podcast campaign. This increase was validated with over 90% confidence, demonstrating a clear business impact at a 4:1 ROAS.
For more information or to arrange a consultation call with Tug’s Head of Data Operations, get in touch: