The Istobe eCommerce Analytics Blog

I’ve been reading a lot recently about the current squeeze on baby boomers and how this is affecting the traditional targeting methods used by many retailers. Noreen O’Leary over at AdWeek had a pretty good article last week on how the recession is weighing on the minds of boomers getting ready to retire – so much so that many have already curbed spending and are starting to discard brands that now seem too expensive or luxurious.  This is a huge problem for many retailers – baby boomers are the heart of many businesses and represent the best and most profitable segments. So what’s a retailer to do?

Read Appealing to Baby Boomers through Enhanced Cluster Analysis »

First things first, no matter who you support, I hope all of you have either gone out to vote today, or are planning to go out to vote. Although I’m usually not this dedicated, I woke up at 6:30 this morning to try and make it to the polls by 7. I was hoping I might be able to get their before the crowds, but by the time I got there at 6:55, the line was already two blocks long. I’ve voted at the same polling station for over 10 years now, and I have to admit that I’ve never seen such an incredible turn out for any election thus far. It took me about an hour to make it through, which was actually less than what I originally thought, but glad I beat the long lines I know will be there later this evening.

Read Obama Leading in the Polls? Perhaps it’s Due to Better Marketing »

I wanted to briefly build upon Doug’s posts about customer analytics over the past few days (see Customer Analytics: A Guide To Getting Started). I try to keep abreast of the latest research about customer analytics that gets published and, just this week, came across the new Aberdeen Group’s report on the subject (Customer Analytics: Segmentation Beyond Demographics). While I encourage you to read the whole report, I wanted to point out some of the info and metrics in the article that I found most compelling.

First, and most impressive, companies with full customer analytics implementations saw incredible gains across the board, including:

• 43% year over year increase in annual revenue
• 42% year over year increase in customer profitability
• 35% year over year increase in average order value
• 25% year over year increase in market share growth

And lest you think that only those companies that completely overhauled their systems saw improvements, companies that have started down the customer analytics path saw, on average, a 7% year over year increase in annual revenue and a 3% year over year increase in customer profitability. While these might seem like modest gains, companies without a customer analytics system in place actually saw a decrease in customer profitability year over year.

Second, best in class organizations are using a wider range of data in more ways than other organizations. What do I mean by this? Well, to begin with, they’re collecting more data about their customers -demographic and behavioral information from web analytics, crm, email marketing, and customer feedback tools, all of it stored in one easily accessible place. And, they’re making better use of this data through the creation of enhanced segmentation (meaning segmentation that uses more than just behavioral or demographic info to assign customer to groups) and more relevant indicator metrics (i.e. CLV) that better inform sales and marketing staff about their customers.

Lastly, the report highlights how important it is to invest in customer analytics now. With over half of all the companies the Aberdeen Group talked to for this report planning on increasing their spending budget for customer analytics in the next year (that number goes up to 60% for companies that are considered best in class), if you haven’t made an investment in a customer analytics yet, you simply can’t wait any longer or you’ll soon find yourself far behind competitors.

Luckily, Doug’s posts can walk you through the basics on getting started, but I wanted to make sure to point out some of the most recent information on how important it is to get up and running now.

Someone asked me the other day, in response to my assertion that one-to-one marketing on a massive scale was the wave of the future, how a company could possibly send out so many personally-tailored emails. Being in the local Irish Pub, The Burren, I almost laughed Guinness out of my nostrils. But I couldn’t avoid the underlying message. One-to-one marketing never really has been embraced because no one really thinks that they do customer segmentation very well, that there are too many obstacles to customer segmentation for it to be entirely useful. Ultimately, this means that few believe they have homogenous enough segments to deliver the personalized goods.

What this also means is that one-to-one marketing is complex due to the fallacies of profiling. I once worked at a company that had such in-depth profiles for each segment that the profiles read like a Faulknerian novels. At this company, I learned that our target female customer in the 35-40 range probably once wanted to visit France but was now stuck with two kids in middle America and made meatloaf once a month for a husband she rarely saw. She obviously consoled herself by buying our software.

What’s my point with all this? This kind of profiling is for low-transaction sales, nothing more. Direct marketing units with high transaction rates should never take the tack of email blasting a segment based on their demographics. Nevermind writing fanciful biographies for said segment. Instead, direct marketing should ignore demographic profiles and concentrate on profiles that accomplish an immediate business goal (see below). Given the immediate needs that direct marketing normally serves, it needs to have a shortsighted, tactical approach, not the strategic approach that profiling represents. Below I look at the goal of getting rid of an overstock of shorts via the email channel. In doing so, I explore two, important dimensions of personalization: what the segment is willing to buy cross-referenced by when that segment most likely opens email.

The Group that Will Likely Buy Shorts Next

The truth is, the customer is not out there to buy from your company. They’re out there to purchase the product they want next and you’re merely there as a direct marketer to insinuate yourself into the buying equation. So which segment of your customers is likely to buy shorts next because that’s the group you want to reach when your shorts have been sitting in inventory for way too long and the leaves are already falling from the trees. So is this a profiling problem? In other words, is it time to blast every demographic who might wear shorts. I suppose you could. But then you’re likely to turn some people off. If you ran your customers past transactions through a classification data mining task, what you’d come up with is a list of people who are likely to buy discounted shorts at that time of the year. In fact, you’d probably come up with a few segments that demonstrate such a propensity. And they would definitely cut across your demographic profiles. You’ll have some moms buying shorts for their sons and some dads buying shorts for next summer’s Hawaii trip.

When Is the Best Time to Reach My Shorts Group?

Almost everyone out there sends me email blasts on Tuesdays and Thursdays. Why? Well, the general belief is that it adheres to the customers’ work/open schedule. I have seen elsewhere that most emails are opened on Sundays. That’s a compelling argument. But I tend to believe that each of your potential shorts purchasers has a more personalized schedule as to when they open and read emails. And that leads to the answer to the question in the subtitle. There are many times to best reach your customers who will buy your clearance shorts. The best web article I’ve read on this is by Bill Nussey of silverPOP who argues for tuning your send times per customer based on their last-recorded response. Couldn’t agree more. In fact, I believe that timing is the hidden axis of personalization. I would actually alter Nussey’s belief just slightly. And that’s simply to say that I would average their responses - and give the most recent responses just a bit more weight - to triangulate on the time your shorts buyers are most likely to open your email. For ease of use, you can bucket this into days or half-days so you don’t have to schedule an email every hour. If you record response data to your email blasts (opens), then this really shouldn’t be a problem.

So what do you ultimately have? You have customers that are most likely to want discount shorts and you have the best time to contact each of them. Now that’s personalized marketing.

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.