My monthly column for SearchEngineLand in case you missed it. The direct mail industry is enormously sophisticated. They've been on the leading edge of data modeling since the 1970s, and smart PPC advertisers and agencies would do well to study them. RKG is in the midst of a research collaboration with Digital Element and Kevin Hillstrom of MineThatData to determine if some well-known truths from the catalog industry also apply to the world of Paid Search, namely that geography matters. Catalogers have known for 50 years or more that people in rural areas respond to offers at a significantly higher rate than those in urban areas. Indeed, postal zones C & D, corresponding to semi-rural and rural areas, have always outperformed zones A & B. Is the same true in Paid Search? The early answer appears to be: "Absolutely!" Just looking at low population density states like Wyoming, Montana, Alaska, etc, the quality of the traffic appears to be more than 60% higher than that of more urban states. We're going to take a look deeper along the lines of postal codes to see if this trend is as clear in PPC as it is in catalog mailings. Another factor catalog mailers have always known: the presence of retail stores matters. Not surprisingly, if you send a catalog full of terrific products to someone who lives near a physical store selling similar products, you'll drive a lot of sales to that store. If that store is part of your retail chain, great, if not... Our study will take a look at the impact of having a retail chain store in the same zip code as the searcher. Indeed, this might allow us some insight into the elusive store spillover effect. By comparing the quality of traffic in similar zip codes with and without a physical store presence, we might conjecture that the difference is a pretty good proxy for the amount of spillover. Kevin Hillstrom has done pioneering work in the field for catalogers. We hope to find out whether the same notions hold true for retail chains and online pure-plays that don't mail books. What's the point? Measuring the phenomena doesn't necessarily mean we can act on it. Who wants to set up complete campaigns for each zip code?!? No one, and indeed, slicing that thin would leave you with no data to model. However, we hope that armed with data, we can convince the engines to give us two additional tools -- er, beyond the ones I already asked for -- that would allow us to manage programs at the next level.
- Population density settings: maybe just 4 levels, corresponding to the postal zones. This would allow us to create at most 5 variants that would capture the benefits, and we might not need that many.
- Zip Code list tagging: Let us set up a list of zip codes representing anything (our client's stores, their competitor's stores, whatever). That tagged group ("my stores") could be applied to campaigns to either establish different efficiency targets -- if I know 20% of the sales happen in my brick and mortar store rather than online I can target a different efficiency threshold for that campaign -- or simply suppress ad service to avoid driving traffic to a competitor's store.
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