Applied data science & analytics to quantify indirect benefits
Amid a competitive retail landscape, our UK client, with an impressive network of over 80 retail location, faced a marketing quandary. Their initial radio advertising analysis showed no tangible link to in-store traffic, casting doubts on its value.
We ran a six-week deep-dive using a combination of statistical analysis and machine learning. Processing five years of data allowed us to create custom models to describing in specific detail how radio’s daily activity impacted footfall, PPC, web traffic, market share, and sales. These models used geospacial analysis while controlling for seasonality and collinearity across variables.
- +4 new value contributions identified
- ↑96.7% confidence in testing new media channels
- ↸1.5M threshold defined for radio impacts
- ⦿1 new geospacial methodology for analysis
Our advanced analytics uncovered the hidden value of radio advertising for Robert Dyas. Beyond the anticipated in-store footfall, we revealed four additional business areas positively influenced by radio. We established a threshold for peak radio reach (1.5M impacts) and offered a high degree of confidence in testing new channels for media synergies. This analysis not only quantifies radio’s indirect benefits but also paves the way for metrics-led media planning with confidence.