The Istobe eCommerce Analytics Blog

A few days ago in my post Restaurant Loyalty Programs: 10 steps to understanding your most valuable customers I talked about how to do a rudimentary best customer analysis using census demographic data. One thing I did not mention, however, is the hidden danger in using census data: the temptation to overdo it.

If you followed the steps I outlined, you may have seen that the American Factfinder site offers mountains of demographic data for every zip code in the country. You might be tempted to try to use all of it in your analysis. Resist the temptation and strive to perform your demographic segmentation using as few pieces data as possible. Why?
1) Smaller data sets are easier to manage

Data integration is 90% of modeling. The more you can cut down integration by ignoring inconsequential data the less time it will take to get actionable results.
2) A kitchen sink approach can lead to overfitting

Overfitting simply means that the data is being sliced so thinly that randomness is causing you to see things that aren’t really there. While this isn’t quite as important if you’re doing the quick and dirty analysis I outlined, it becomes quite important when using more quantitative techniques.

How do you know if you’re seeing overfitting? If weird “pockets” of data exist, you may be a victim. For example, if the data show that customers who live in zip codes where males comprise between 50.31 and 50.35 of the population are twice as valuable as zip codes where they are outside that range, you have probably overfit the data.
3) Smaller models are easier to act upon

When deciding whether to keep a variable, consider what action you would propose a company to take if the variable turned out to be meaningful. For example, if you have a data set that can tell you that your best customers live in areas where public transport usage is high, you could consider advertising on subways and busses to attract more high value customers. On the other hand, if your data set tells you that your best customers have commutes between 15-30 minutes, could you act on that? If not, drop that piece of data.
Simple is beautiful when it comes to demographic modeling. Try to resist the temptation to throw all the data available at a segmentation model. You, your database guy, and your marketing staff will be glad you did.

Restaurant loyalty programs are a great way to get customers coming back for more. But what about the vast amount of data that you are generating from your loyalty program? Are you making the most of it?

In most cases, restaurants are sitting on mountains of data that they know is valuable, but they are unsure how to use it. This should not be too surprising since crunching data may very well be the least useful skill in the restaurant business.

It’s too bad, because there are a hundred ways to turn restaurant loyalty data into money. One of the most useful is to identify the demographic attributes that correspond to loyal, high spending customers. When it comes time to open the next restaurant you can focus on areas which have profiles similar to your ideal customers.

Many data mining and geographic information system consultants will be happy to do this analysis for a healthy fee. But even if you can’t afford consulting you can do a rough analysis yourself by following these 10 steps:

1) From your loyalty program application, export the following data to an Excel spreadsheet:

Customer zip code
First visit date
Last visit date
Total dollars spent over lifetime

2) Create a column called “Dollars Spent Per Day” in the spreadsheet and calculate it by dividing total dollars spent over lifetime by last visit data minus first visit date.

3) Create a pivot table that uses zip code as the row label and the average of Dollars Spent Per Day as the value. You now see which zip codes produce the highest spending customers on average.

4) Ignoring zip codes with only a few data points, find the two or three zip codes with the highest average spends.

5) Go to the American Factfinder site at census.gov and select Data Sets->Decennial Census from the left hand navigation bar.

6) Select “Census 2000 Summary File 1 (SF 1) 100-Percent Data” and then click “Quick Tables”

7) In the “Select a geographic type” drop down, choose “5-Digit Zip Code Tabulation Areas” and select the two or three zip codes you chose above. Click the “Add” button to add those zip codes to the result set and press “Next”.

Select the 5-10 most relevant tables and press “Show Result”. You’ll see a breakdown of demographics for this zip code. You may want export this to Excel.

9) Repeat step 8 for the other one or two zip codes you chose. Also, try going back to the American Factfinder main page and repeat the process using “Census 2000 Summary File 3 (SF 3) - Sample Data” information.

10) You now have detailed demographic breakdowns for your best performing zip codes. The next time you’re looking to open a restaurant, compare the demographics of your prospective locations to this gold standard. If it looks similar, you may have a winner. If it looks very different, however you may want to think twice about expanding into that area.
It is important to note that this technique is far from perfect but it’s not bad to use as a rough cut for which areas look good and which not so good for expansion. If you need more precise (and statistically sound) analysis, it may be worth the money to hire a consultant who can develop more formal estimation models using additional data sources.