As I was thinking about my very clever post from last week, I drove past a golf course. Now, its hard to imagine the actual course, as we are still under about 2-3 feet of snow, but I instantly had visions of golf ball maps - arcs gracefully converging to the hole. My thoughts drifted to distance decay and what map style would provide an understanding of where the troubled places for this particular hole are...
But wait - a golf course is different since it actually has 2 centers of attraction - the hole and the tee. The beginning and the end have obviously high levels of activity. Granted, the tee off area is bigger, with options for moving somewhat around - and a segmented launch pad by gender, but, at is purest form - it has a single beginning and a single end.
My thoughts then drifted to another sport with dual centers of attraction - basketball with 2 hoops (and I have all 4 Final Fours in my bracket too!). Baseball has 2 - a pitcher's mound and the batter's box, but then it gets complex with the outfield.
Golf is rather analogous to retail geography. The store is the obvious center of attraction - studied as such for nearly a century. It is typically within another center of attraction - the city, which has been studied for centuries. But the other center of attraction is where the customer lives.
Blocks and block groups simplify the customer center - very similar to the golf tee off area. Direct shopping trips may be as rare as the the hole-in-one, since customers may make a handful of stops - picking up friends, stopping by the post office, getting gas, picking up dry cleaning, etc. on the way to the ultimate destination. And once we consider the trip back, we leave golf's analogy and jump into basketball.
I'm not a golf fan, so as far as I know, this process has a well formulated analytical basis. After all, golf video games have to deal somewhat with this collection of vectors. But that is essentially what it is, a collection of vectors starting in one place ending in another. To analyze this, I'd try graph theory - a visual mathematics to explain space, vectors, and movement. Graph theory can provide solutions to the traveling salesman and the postal delivery problem, which have some similarity for our shopping trip vectors.
The result would probably end up with a gravity model subsitute, but the analytical focus would be the actual travel vectors rather than the ultimate destination's attractive measure (the Mi and Mj in the Wiki link). Or perhaps it would be a more accurate distance (Dij) value.
But the results would have another critical use - you should be able to predict where the vectors pause on the way to the destination. I'm sure golf fans can tell you where the golf balls are typically going to be on a 3 shot strategy on Augusta's 16th green. So, where would you put a store - using what business plan - with a 3 vector strategy with folks going from this neighborhood/town to a mall?
I think the billboard people do something like this for placing ads on the way to and from work. I've also heard that high traffic stores - like Walgreens/CVS or fast food places - do something like this too. Although, they'd probably be better off with decent traffic counts (and I know they use that).
So, how good is your short game? In golf that is the transition from the tee off to the putting green. In real estate, could it be what high traffic companies are doing? If not, is this an opportunity?
Sunday, March 30, 2008
Sunday, March 23, 2008
Politcal Center of Attraction
Map nerds have a love/hate affair going on with today's news. Maps of the political climate are ubiquitous - Pennsylvania is now getting attention with its big primary coming up. Thematic maps are everywhere, which map nerds must love; however, they'd also hate them since they typically feature poor cartography.
Of course, Jon Stewart has the best take on this. He does expand it though to include business intelligence charts - my favorite is the statistical lazy susan.
So let's quickly summarize the Democratic primary maps - Obama does better in urban and uppity suburbs. Hillary does better in the blue collar type neighborhoods. I may not have the generalities entirely accurate, but let's just assume that they are.
Sometimes, these maps look like trade area maps because they tend to have natural groups - spots where the colors tend to group together. This is Obama's section; this is Hillary's. Similar to: this is Store A's trade area; this is Store B's.
Trade areas have a center of attraction - a store. The further you get from the store, the less strong the attraction is. Meaning that customers are more likely to shop at their nearest store. The trade area is broadly defined at the point in which the center of attraction for one store is greater than another.
So what is a political center of attraction? Is it a strong politician like a mayor, Congressman, or state representative? Could it be a super delegate? Do distance decay curves or the gravity model help predict voter behavior or trends in political contributions?
Of course, stores rarely stand by themselves, so you have a mall or a downtown center too. What is the political equivalent of that? It'd have to be some sort of neighborhood arrangement - which hints that the center of attraction would be a good precinct captain. Heck, this'd be easy if it was Chicago in the 1920's - it'd be Al Capone or one of his friendly Alderman!
I'm mostly curious to see how well this applies. If political geography does parallel trade area theory, then you'd have something other than just demographics, prior voting behavior, and contribution data to predict votes. I've never seen the nuts and bolts of how professional political geographers do it, but it'd certainly be interesting to learn more.
Of course, Jon Stewart has the best take on this. He does expand it though to include business intelligence charts - my favorite is the statistical lazy susan.
So let's quickly summarize the Democratic primary maps - Obama does better in urban and uppity suburbs. Hillary does better in the blue collar type neighborhoods. I may not have the generalities entirely accurate, but let's just assume that they are.
Sometimes, these maps look like trade area maps because they tend to have natural groups - spots where the colors tend to group together. This is Obama's section; this is Hillary's. Similar to: this is Store A's trade area; this is Store B's.
Trade areas have a center of attraction - a store. The further you get from the store, the less strong the attraction is. Meaning that customers are more likely to shop at their nearest store. The trade area is broadly defined at the point in which the center of attraction for one store is greater than another.
So what is a political center of attraction? Is it a strong politician like a mayor, Congressman, or state representative? Could it be a super delegate? Do distance decay curves or the gravity model help predict voter behavior or trends in political contributions?
Of course, stores rarely stand by themselves, so you have a mall or a downtown center too. What is the political equivalent of that? It'd have to be some sort of neighborhood arrangement - which hints that the center of attraction would be a good precinct captain. Heck, this'd be easy if it was Chicago in the 1920's - it'd be Al Capone or one of his friendly Alderman!
I'm mostly curious to see how well this applies. If political geography does parallel trade area theory, then you'd have something other than just demographics, prior voting behavior, and contribution data to predict votes. I've never seen the nuts and bolts of how professional political geographers do it, but it'd certainly be interesting to learn more.
Sunday, March 16, 2008
Large, Large Segments
I read in DM News on Friday about a presentation at the New England Mail Association given by Peter Grebus who is in charge of Williams Sonoma's Customer Information Management group. I found it interesting since its not very often that we can read some strategic details about the mix between direct marketing statistical models and retail sales.
The complete text is here and, of course, he talks about the economy, but here's what interested me:
Already, the company has been mailing deeper into its file with smaller versions of the catalog in geographic regions such as Texas, where it thinks a significant portion of recipients are driven to retail stores via a catalog. The company “has seen great success” with this strategy, Grebus said.
I've met the Williams Sonoma statisticians and they are top rate - it is interesting that their model is either being beat or over ruled by something as simple as a state segmentation.
Usually, statistical models can beat the pants off segmentation. If you aren't familiar with it, segmentation is the direct mail strategy of breaking customers into groups (segments) and making mailing decisions based on that. For example, Peter says that Williams Sonoma has 200 million customers - of course it doesn't make sense to mail everyone every catalog.
Assume the circulation for the next catalog is 2 million - who do you mail? Traditionally, direct marketers use RFM - recency, frequency, and monetary value - to make segments. An RFM segment may be customers who have purchased at least 3 times, made their last purchase 14 months ago, and have spent at least $500.
RFM works great. It helps answer what your best customer is, but it gets complex very fast. Would you rather have a customer who spends $2000 once every 2 years or a customer who spends $50 every month? This complexity is where a statistical model can really help.
Statistically, every customer is its own segment and when you have the process fine tuned, you are able to rank every customer from good to bad. And when you need to mail 2 million people, you query the top 2 million - done.
When I first finished grad school, I was convinced that geographers were needed to help businesses discover and understand spatial relationships. One day, while working at the bank, I was talking with an MBA who managed the Seattle Seahawk credit card - and I suddenly understood that everyone is a geographer since somethings are so basic... Of course the Seattle area has more Seahawk credit card holders than any place else - duh!
So, it is shocking to me that the Williams Sonoma direct mailing models are being beat by a state segmentation. Any segment of 20-30 million is too large against individual customer data - RFM will be able to wiggle within Texans to show that Texas customer A is better than Texas customer B since A purchased $1000 more than B in the past year.
So, what's up? Perhaps Peter isn't using a model. Perhaps the retail and direct folks aren't talking to each other. I kinda doubt these - Peter mentions that they are mailing smaller catalogs to drive retail sales which means that this is a well coordinated strategy - not only are store operations involved, but the catalog folks are deciding who gets the big or the small catalog - and certainly, a model of some sort is helping them.
Assuming that Peter is using states to beat the model and nothing fishy is going on, then the challenge is likely in the data itself. RFM is probably just at the direct level and perhaps at the corporate level - thus, the model cannot figure out the differences between channels. Meaning the model can't differentiate between customers who only shop at retail. or only shop direct, or shop both.
Or confusion may exist in the definition of success. For all the statistical model bragging, they are only as clever as they are told to be. If the model is directed to only focus on direct success, then the model will have troubles with retail sales. This could make sense - the catalog folks are trying to optimize their activities, which are very measurable (each catalog has a code to track sales from) and retail sales tend to be anonymous.
Of course, it is hard to say, but it is fun to ponder...
The complete text is here and, of course, he talks about the economy, but here's what interested me:
Already, the company has been mailing deeper into its file with smaller versions of the catalog in geographic regions such as Texas, where it thinks a significant portion of recipients are driven to retail stores via a catalog. The company “has seen great success” with this strategy, Grebus said.
I've met the Williams Sonoma statisticians and they are top rate - it is interesting that their model is either being beat or over ruled by something as simple as a state segmentation.
Usually, statistical models can beat the pants off segmentation. If you aren't familiar with it, segmentation is the direct mail strategy of breaking customers into groups (segments) and making mailing decisions based on that. For example, Peter says that Williams Sonoma has 200 million customers - of course it doesn't make sense to mail everyone every catalog.
Assume the circulation for the next catalog is 2 million - who do you mail? Traditionally, direct marketers use RFM - recency, frequency, and monetary value - to make segments. An RFM segment may be customers who have purchased at least 3 times, made their last purchase 14 months ago, and have spent at least $500.
RFM works great. It helps answer what your best customer is, but it gets complex very fast. Would you rather have a customer who spends $2000 once every 2 years or a customer who spends $50 every month? This complexity is where a statistical model can really help.
Statistically, every customer is its own segment and when you have the process fine tuned, you are able to rank every customer from good to bad. And when you need to mail 2 million people, you query the top 2 million - done.
When I first finished grad school, I was convinced that geographers were needed to help businesses discover and understand spatial relationships. One day, while working at the bank, I was talking with an MBA who managed the Seattle Seahawk credit card - and I suddenly understood that everyone is a geographer since somethings are so basic... Of course the Seattle area has more Seahawk credit card holders than any place else - duh!
So, it is shocking to me that the Williams Sonoma direct mailing models are being beat by a state segmentation. Any segment of 20-30 million is too large against individual customer data - RFM will be able to wiggle within Texans to show that Texas customer A is better than Texas customer B since A purchased $1000 more than B in the past year.
So, what's up? Perhaps Peter isn't using a model. Perhaps the retail and direct folks aren't talking to each other. I kinda doubt these - Peter mentions that they are mailing smaller catalogs to drive retail sales which means that this is a well coordinated strategy - not only are store operations involved, but the catalog folks are deciding who gets the big or the small catalog - and certainly, a model of some sort is helping them.
Assuming that Peter is using states to beat the model and nothing fishy is going on, then the challenge is likely in the data itself. RFM is probably just at the direct level and perhaps at the corporate level - thus, the model cannot figure out the differences between channels. Meaning the model can't differentiate between customers who only shop at retail. or only shop direct, or shop both.
Or confusion may exist in the definition of success. For all the statistical model bragging, they are only as clever as they are told to be. If the model is directed to only focus on direct success, then the model will have troubles with retail sales. This could make sense - the catalog folks are trying to optimize their activities, which are very measurable (each catalog has a code to track sales from) and retail sales tend to be anonymous.
Of course, it is hard to say, but it is fun to ponder...
Sunday, March 9, 2008
Retailer Globe
Directions Magazine had a podcast a couple of weeks ago in which Joe Francica discussed his vision of how a retailer could use Google Maps (or some such web map engine) to organize and display business intelligence. I left a comment, but I'm still thinking about it - as I've been busy designing portals for years.
I've been involved with business intelligence portals for a long time. My first portal was a CD that I made in 1998 when working at First USA. I was working with a major retailer by doing a test by identifying customers who shopped at the retailer (from their credit card purchases), and then sending targeted offers to their customers and those who shopped the competition. This retailer was high maintenance and wanted many details. After the test was complete, I could just hear the phone ringing with various questions. So, I answered them all.
Literally, I came up with every test combination possible and made a web page for it using SAS. I saved the HTML files and built a javascript front end to direct users to files saved on the CD. It was a portable portal - no need to connect to the Internet or worry about security. If you had the disk, you were good to go.
I used this same strategy in 2000 at LifeMinders.com. We were a big permission email company - it seems like a commodity now, but it was in the dot com boom and even the back of my head was on CNBC (We also ran our Super Bowl ad too!). Millions of emails were sent daily and I tracked clicks to set content strategy, bill advertisers, etc. Again, I used SAS to make every bloody report possible - it was quite successful; the reports took 2-3 hours to run, but once available, they were blazing fast for the users.
I started learning how to make a true interactive portal next - designing web pages using ASP and talking to databases. Reports started to slow down a bit, but it scaled much better. My third portal used this approach which tracked direct marketing strategy for millions of mortgage direct mail pieces. Eventually, I started experimenting with mixing operational reporting with profit and ROI metrics.
At this point, I had interviewed with a huge retailer to manage their site location department. The goal was to build a globe portal - very similar to what Joe was talking about in his podcast - in which a deal maker could click on a global location on a map and get an instant 3 year P&L projection to provide an initial read of a potential site. If favorable, additional resources would properly investigate it - with the goal to eliminate wasteful investigations.
This still sticks out to me as the ultimate in spatial analysis - clicking anywhere in the globe and creating the appropriate trade area estimate - then mixing the right amount of culture and demographics (probably using segmentation) to select (or develop on the fly) the right functional form to project revenue and project costs - for 3 years. Wow.
I didn't get the job - which is great since it opened the doors to be here at Coldwater Creek. I've played since 2002 with various portal designs to direct real estate research and I've yet to find the right mix - the biggest challenge is justifying the resources for a small audience. Which makes me wonder about the ultimate challenge. The more I understand about real estate P&L characteristics, the more I appreciate that it is less dependant on geography and more about the deal terms.
Over the years, the portal business has gone big time. Books and consultants galore exist to tell you how to do it. In 2003, I did develop the first operational portal here and its done well over the past 5 years. Today, my peers are improving it utilizing Reporting Services and using Stephen Few's design thoughts - its so full of features, interactivity, and rich with content. My early portals were functional and highly targeted - today's are complex and built with an army. But this is the first part of Joe's portal - rich, operational metrics wrapped in a well designed system.
The second part - real estate strategy - is what I have struggled with - Blockbuster appears to have been successful - they won a MapInfo Meridian award for it. I would love to see this in action and better understand who the users are and what questions is it successfully answering.
The final part is linking the first two - which will be the toughest. After someone has clicked on the globe, how do you direct them to operational metrics, HR listings, and how this retail location is meeting their goals? And once in these details, how do you back out to understand how this store operates in the grand scheme of things? And how is the company doing overall? And vise versa?
Tough questions - especially since most users are interested not in the physical geography, but in the corporate geography - what operational zone, region, or district is the store in? Or, most importantly, what GL code? Which is one of the larger challenges for Joe's globe - getting the operational and physical geographies in sync for a highly functional portal.
See, I told you I've been thinking about it for a long time!
I've been involved with business intelligence portals for a long time. My first portal was a CD that I made in 1998 when working at First USA. I was working with a major retailer by doing a test by identifying customers who shopped at the retailer (from their credit card purchases), and then sending targeted offers to their customers and those who shopped the competition. This retailer was high maintenance and wanted many details. After the test was complete, I could just hear the phone ringing with various questions. So, I answered them all.
Literally, I came up with every test combination possible and made a web page for it using SAS. I saved the HTML files and built a javascript front end to direct users to files saved on the CD. It was a portable portal - no need to connect to the Internet or worry about security. If you had the disk, you were good to go.
I used this same strategy in 2000 at LifeMinders.com. We were a big permission email company - it seems like a commodity now, but it was in the dot com boom and even the back of my head was on CNBC (We also ran our Super Bowl ad too!). Millions of emails were sent daily and I tracked clicks to set content strategy, bill advertisers, etc. Again, I used SAS to make every bloody report possible - it was quite successful; the reports took 2-3 hours to run, but once available, they were blazing fast for the users.
I started learning how to make a true interactive portal next - designing web pages using ASP and talking to databases. Reports started to slow down a bit, but it scaled much better. My third portal used this approach which tracked direct marketing strategy for millions of mortgage direct mail pieces. Eventually, I started experimenting with mixing operational reporting with profit and ROI metrics.
At this point, I had interviewed with a huge retailer to manage their site location department. The goal was to build a globe portal - very similar to what Joe was talking about in his podcast - in which a deal maker could click on a global location on a map and get an instant 3 year P&L projection to provide an initial read of a potential site. If favorable, additional resources would properly investigate it - with the goal to eliminate wasteful investigations.
This still sticks out to me as the ultimate in spatial analysis - clicking anywhere in the globe and creating the appropriate trade area estimate - then mixing the right amount of culture and demographics (probably using segmentation) to select (or develop on the fly) the right functional form to project revenue and project costs - for 3 years. Wow.
I didn't get the job - which is great since it opened the doors to be here at Coldwater Creek. I've played since 2002 with various portal designs to direct real estate research and I've yet to find the right mix - the biggest challenge is justifying the resources for a small audience. Which makes me wonder about the ultimate challenge. The more I understand about real estate P&L characteristics, the more I appreciate that it is less dependant on geography and more about the deal terms.
Over the years, the portal business has gone big time. Books and consultants galore exist to tell you how to do it. In 2003, I did develop the first operational portal here and its done well over the past 5 years. Today, my peers are improving it utilizing Reporting Services and using Stephen Few's design thoughts - its so full of features, interactivity, and rich with content. My early portals were functional and highly targeted - today's are complex and built with an army. But this is the first part of Joe's portal - rich, operational metrics wrapped in a well designed system.
The second part - real estate strategy - is what I have struggled with - Blockbuster appears to have been successful - they won a MapInfo Meridian award for it. I would love to see this in action and better understand who the users are and what questions is it successfully answering.
The final part is linking the first two - which will be the toughest. After someone has clicked on the globe, how do you direct them to operational metrics, HR listings, and how this retail location is meeting their goals? And once in these details, how do you back out to understand how this store operates in the grand scheme of things? And how is the company doing overall? And vise versa?
Tough questions - especially since most users are interested not in the physical geography, but in the corporate geography - what operational zone, region, or district is the store in? Or, most importantly, what GL code? Which is one of the larger challenges for Joe's globe - getting the operational and physical geographies in sync for a highly functional portal.
See, I told you I've been thinking about it for a long time!
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