Every year just before the holiday season we take a look at the customer database for the Lab Store – the online retail biz my wife runs – and see what’s up with 10x or more buyers.
I often prefer to look at “worst case” data when doing customer analysis; this way you don’t over-estimate the Potential Value of the business going forward. The beginning of the 4th Quarter is a good time to do this since “holiday” really hasn’t kicked in yet, so you don’t have those buying influences skewing the natural activity in the customer database.
At the end of September, we took a look at all customers, no matter when they became customers, who have purchased from us at least 10 times – a best customer analysis. Considering a “year” to be 9/30 to 9/29, we bucketed them by when their last purchase was – past year, 2 years ago, 3 years ago, 4 or more years ago (the business started 5 years ago).
Here are the results:
Last purchase date was in Percentage of all 10x or more Buyers
9/30/07 – 9/29/08 75%
9/30/06 – 9/29/07 12%
9/30/05 – 9/29/06 8%
9/30/04 – 9/29/05 5%
If this data is still confusing, the second line above would read, “Of all customers who have ever bought 10x or more, 12% last purchased in the period 9/30/06 – 9/29/07.
75% of the best, most productive customers (all else equal, like margins, service costs, etc.) we have ever had since the busines started are still active buyers with us.
Of course, this raises the question: What’s up with the rest of them?
When you segment like this, using the actual customer behavior, an analysis of each segment can be quite revealing. What you are seeing is essentially a LifeCycle model, the attrition pattern of these best buyers. No doubt some will come back, especially out of the “last year” 12% group, but here’s the thing – the longer they are inactive, the less likely they are to come back.
So if you’re going to “do something” with this 12% Recent best defector group, the time to do it is now, going into holiday season, because you have buying momentum running with you. That’s another reason we do this analysis at the end of September ;)
To optimize this action plan, you should know something about who these defectors are. And here is where people make a huge mistake: because they’re inactive buyers, or in the case of non-commerce sites, inactive visitors, they are not being surveyed by the usual survey pops.
Let me say that again: arguably the most important segment of the customer base, dis-engaging best customers, lacks voice under most approaches to VOC. You have to reach out proactively to these people and find out what you did wrong; otherwise you will just keep doing it wrong and continue to churn off your best customers / visitors.
A segmentation technique that is very helpful in this kind of analysis is to think in terms of controllable and uncontrollable customer defection. Since the Lab Store is in the animal supplies business, the sale or death of the animal would create an uncontrollable defection – no amount of “Marketing” is going to get that customer back. Other examples of uncontrollable defection would be when a cable or local newspaper customer moves out of the area serviced by the company, or a manufacturer ceases to produce a line and no longer needs parts.
We know from previous surveys of these defectors this uncontrollable population is about 4% a year. So for each time span above, you can knock 4% off and get to the percent controllable defectors. These are the people you really want to know about, because they are actionable. So as far as the 12% “last year” group, it’s now about 8% of best buyers.
The first controllable defection trigger we would look for in the group would be Product. Product drives everything in a commerce model; it’s why you exist as a commerce entity, and is fundamental to the Brand Promise. If you have a product issue, no amount of Service or other drivers will fix the problem. So Product is where you start. Plus, you should have all the data you need, so it’s easier to get started on right away (no lag time).
Did these defecting best customers buy similar products which resulted in their defection? In other words, something about the packaging, pricing, or performance of the product may be defective. This type of analysis is how we discovered and cured the staple food problem.
The product analysis turned up an interesting trend, or non-trend, actually: while in general there were no hot spots among categories or products purchased by these folks, there was a surprising lack of purchases in the Toy category for the past year. My first question upon seeing this: what toys have they bought from us in the past?
The answer: All of them. About 70% of these defectors had purchased all the toys we carried at some point.
Now, there’s several reasons we don’t carry many toys. The community we serve is extremely sensitive to the issue of materials used in products they expose their animals to, and most animal toys don’t make that cut. Then there is our general philosopy on avoiding commodity products and keeping product selection trimmed to what people want to buy.
But clearly, this looked like a specific, nailed down opportunity to put some effort into this area.
To confirm this idea, we sat down and invented a couple of products, contacted a sample of these defected best customers, and showed them what we were thinking in terms of the new toys. This action confirmed two things for us:
1. Indeed, one of the reasons they stopped buying from us is they already owned most of our toys, and their animals were not on our staple diet, so there was no recurring purchase need.
2. They thought the new toys were very cool ideas – ultra safe materials, fair pricing, unique, etc.
So there you have it – our holiday best customer reactivation plan is set. The items are up and we’ll be dropping a promotional e-mail to just these defected best customers with an “insider deal” on the new toys. The sales generated should be almost completely incremental due to the research we did, but we’ll use a control group anyway, just to make sure we didn’t screw something up.
Now, I know a lot of people in this community would be running all kinds of statistical models and such against this defector data looking for the silver bullet, but what’s interesting about this case is:
1. Critically, the “answer” was not something in the data, the answer was NOT in the data – a lack of toy purchase. If you think this through a minute, you will realize a fundamental flaw in much of digital measurement today: due to the nature of the collection methods, digital analytics is ultra-focused on what happens, forgets what stopped happening.
Opportunity: there’s a ton of money to be made by understanding LACK of activity, why it stopped, and how to address it properly. Hint: marketing is usually not root cause and cannot be used to fix the problem, only to reduce or help correct the problem.
2. The segmentation was totally simple to do with a spreadsheet: Frequency cut with Recency dimension. That’s it. Anybody can do that, anybody. No advanced stats needed. Question is, would someone on your team think of approaching best buyer defection this way? Could they know where to look for a hidden problem like this (what data / reporting config), then see and understand the behavioral implications driving the customer?
Sure, there are ways to set up the modeling to find this out, but you would have to come up with the idea of “missing category” before you constructed the model. Then you would have to make a judgement call on whether the 70% “purchased all toys” number was significant based on intimate knowledge of past customer analysis.
These are the kinds of skills Marketing should be bringing to the party, because the Tech side is often so very stuck in binary thinking. To optimize these Marketing systems, someone has to control for the problems caused by behavior chains like:
did not buy->
so did not get surveyed->
so did not uncover “lack of toys” problem
because the Tech side is always focused on what has happened, as opposed to what has not happened.
Might as well be Marketing who takes up this role, since solving “did not happen” problems almost always has a much higher ROI than looking at what did happen. After all, a 4% conversion rate means 96% did not convert.
In solving this “did not happen” problem, we have both increased the potential for business with dis-engaged best customers and corrected a flaw in the system that would have continued to flush best buyers in the future. One campaign, with both Current and Potential Value.
I’m guessing the response rate and ROI on this best defector holiday campaign will be huge. What do you think?
Update: These toys completely sold out – mostly to the customers who had stopped buying. And 180 days later, about 25% of these “dead” best customers are still 90-day active. The return on investment for this effort is essentially infinity at this point…Follow: