| Title: | PPC: Lessons from our Parents |
| URL: | http://www.rimmkaufman.com/rkgblog/2009/08/03/ppc-dm-lessons/ |
| Printed: | September 6, 2010 |
| Source: | The Rimm-Kaufman Group Blog, info@rimmkaufman.com |
- August 3, 2009
- 17 comments
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.
Sophisticated marketing techniques allow retailers to generate more sales for their marketing dollars, and the more sophisticated the tools the more retailers can spend cost effectively. That’s good for the retailer, the engines, and the agencies that handle complex accounts well.
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Like you said, the rural effect has been known for decades, and the retail effect is simply the reverse of the rural effect.
In both cases, access to alternatives is the driving force; fewer easy access alternatives = higher response to the direct offering. Is true with TV Shopping as well.
You can counter this effect somewhat by offering unique products, which of course direct is legendary for.
Early on, a lack of web access in rural areas might have countered this effect somewhat; we saw this with TV Shopping and cable TV, where the more rural areas were the last to get cable or buy a dish.
But as long as web access in rural versus urban zips is somewhat equal, we should see the same effect.
Will be interesting to see what you folks come up with!
Thanks Jim,
We are indeed intrigued! The ip data Digital Element provides also has connection speed as an attribute. Remarkably, “Dial up” connections have significantly higher conversion rates than broadband or cable! This may simply be a mask for rural effects, I haven’t taken apart the data to see what’s correlated with what yet, but it’s pretty cool!
Stay tuned!
George
George,
Very intriguing study. Good luck! I have some “from the gut” questions.
1. Do people shop online differently (in a noticeable pattern) depending upon geography?
2. Is the higher conversion rates in rural areas due more to the makeup of the web user demographics per geographical segment rather then a different online shopping behavior per geographical segment? Might the rural segment have higher conversion due to the following equation:
Higher percentage of computer savvy users(a mixture of computer literate + a high comfort level shopping online) per total web users.
3. Does factoring in the demographics of the online shoppers per geographical segment change the way we look at the geographic segment data?
4. I would love to know more about the Population density settings. What are the additional buttons you would want to press and what are the desired outcomes of pressing these new buttons?
Hi Marc,
The answer to question 1 is pretty clearly “yes”. Questions 2 and 3 are tough, getting at why #1 is the case. We have a bunch of demographic data associated with zip codes: like average home value, average household income, average age, fraction of business vs residential addresses, etc. Digital Element also passes us the connection speed for each ip as well.
Essentially we’re going to pour all of this into the analysis and let statistics tell us what factors influence performance.
If we find that population density matters, unto itself, the settings could be used like match-types and syndication to target specific campaigns to more rural areas or more urban areas.
First things first: must dive more deeply into the data :-)
Marc,
I’d like to offer the following for #2
“Higher percentage of computer savvy users(a mixture of computer literate + a high comfort level shopping online) per total web users.”
In my past experience, this is inaccurate.
A) Many rural areas lack high speed internet. (http://www.speedmatters.info/map/working.html) Although the data isn’t granular, there is a map on this site that shows avg speed by state. Given that rural states are about half the upload/download speeds, I think it’s a safe bet that there are more dialup users.
B) (purely anecdotal) I have some customer service experience at my first “web” company, and have dealt with 1000’s of customers. Most of our traffic came from rural areas, and I found most of them were too “bright” when it came to the internet.
Billy, thanks for your thoughts!
What’s striking in the data? Dial-up users have much, much higher conversion rates than cable, T1, or broadband!
Is it because: they’re in rural areas and can’t get high speed internet, but it’s still a long drive to the store? or, flipping around from store to store on the internet doing comparison shopping is painful, so they tend to buy from the first store they visit that has what they want?
Whys are hard, but the data is convincing. More to come.
George,
Marc and I were just discussing that. I personally think it’s because getting to a store is difficult.
I live in brooklyn NY, and my neighbor constantly orders from catalogs and the internet. She is a very large woman and it is not easy for her to get around. The rural population is in the same boat, long drive to get to store.
Also, I think having a dialup connection kills their web savvy-ness. Most of web 2.0 must be impossible for them.
Billy as I pull the data apart it is fascinating! I should stop talking with you folks and finish doing the analysis :-)
However, your intuition is dead on according to our data: it’s a trough. Rural areas perform very well, but so do the most densely packed urban areas — who wants to take a TV home on the subway?
I might be doing that this week since we sell them cheaper than best buy and I don’t like driving to the city ;)
but please, finish with the data