iSpot TV Conversions vs. Spike Modeling: A Direct Comparison
Inside the Report
200%
Lower CPA
When a leading direct-to-consumer (DTC) brand compared iSpot TV Conversions to spike modeling, they discovered their high-reach TV spots were delivering 200% lower CPAs than traditional models showed. This led the brand and its media agency to reexamine how large-scale TV data could better capture the true impact of TV advertising. Unlike spike models, which only account for immediate, short-term responses, the advanced approach offered a more comprehensive view—factoring in delayed conversions and broader audience behavior.
Challenge: Limitations of Traditional Spike Analysis
The brand and agency team lacked a reliable way to measure conversions driven by TV ads. They were concerned that the conventional spike analysis approach was too limited, attributing success only to immediate surges in activity after an ad aired. This narrow attribution window made it difficult to capture the full impact of TV ads over time and across various media channels.
Solution: Large-Scale TV Data via iSpot TV Conversions
To address this measurement gap, the team used iSpot to match TV ad exposures to first-party data through IP address linkage across approximately 52 million connected TVs. This method enables true closed-loop attribution, helping brands evaluate how creative, media mix, and frequency influence key outcomes such as sales. By incorporating this solution, the brand and agency were able to enhance their TV attribution capabilities and better evaluate the return on investment for high-reach TV buys.
Results: Revealing Bias in Spike Analysis Models
Using iSpot TV Conversions, the agency conducted a side-by-side comparison of low-, mid-, and high-reach TV spots. The analysis revealed a bias in traditional spike modeling that favored lower-reach placements, often misattributing performance due to activity spikes from viewers who did not convert. In contrast, smart TV data, through more precise viewer-to-conversion matching, showed that high-reach placements were actually stronger performers.
For instance, on national cable and broadcast networks, low-reach spots appeared effective under spike analysis but underperformed when validated against actual purchase behavior via modern, data-driven measurement. Conversely, high-reach placements, previously underestimated, showed significantly better results—with a CPA 200% lower than what spike analysis indicated.
Furthermore, the broader attribution window available through iSpot uncovered delayed conversion activity that spike analysis failed to capture. A notable example involved a high-profile network placement that showed a conversion peak two weeks post-airing—a signal completely missed by traditional models, which led to the network initially being misclassified as underperforming.