Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)
Topic Overview
Hi again folks, Jim Novo here.
HIgh end hardgoods. One of the most difficult retail categories to deal with from a customer retention perspective, both offline and online. Only vehicles are tougher. In some ways, the category can be easier online, but perhaps not for a single local store due to competition. So what’s the best way to attack the repeat purchase peoblem? Focus on where you have the highest likelihood of success – 2nd purchase Latency. Ready for the Drillin’?
Q: I loved your book, thanks. Armed with it, I feel like I can achieve much more than most small retailers in terms of CRM.
A: Thanks for the kind words!
Q: I have a question though. I sell relatively high-priced furniture and design items, and as this is our first year of business, our inventory is pretty small. As a result, my Frequency totals range from 1 to 4. That’s it, after a year of business. About 75% of our customers have bought once and it “ramps up” to 4 from there. I use “ramp” in the broadest sense of the word.
A: Yep. That’s the hardgoods business, especially on the web. Don’t beat yourself up, it’s early in your game with lines like these, and don’t blame it too much on inventory either. In the long run, it’s better to sell the *right* stuff than everything you can find, trust me.
Q: So when I compute RF quintiles, the totals don’t cleanly fit within quintiles. In other words, for RF scores of X1 X4, customers have purchased once. X5 customers have purchased 2, 3, 4, or 5 times. If I raise the hurdle and only look at customers who have purchased more than once, I still can’t fit them cleanly in five quintiles.
A: That’s one problem with RFM, it’s a bit robotic and works best with larger (usually meaning older) databases…
Q: I read your article on durable goods purchases and avoiding the one-time-buyer problem. I guess I’m looking for advice on how to make the “F number” significant until we’ve been in business long enough to get a broader range of frequency options.
A: Well, Latency, Recency, RFM, and my own LifeCycle Grids are really just tools that help people recognize “patterns” in customer data and take advantage of these patterns to increase profits. Personally, I would go with more of a Latency or LifeCycle Grid approach on this until the database becomes large enough and you have had a chance to see the LifeCycle play out a little longer. For example, next year, hopefully 1/3 of those 1x’ers become 2x’ers. This is typical in higher-end hardgoods, especially “aspirational” stuff. Many people have to save up for quite a while for these purchases.
At this stage of the game I think I would concentrate on those 25% who are multibuyers and try to find commonality. You do already have examples. One of the easiest is 2nd Purchase Latency. Take the existing multi-buyers, and ask, “On average, how many days / weeks are there between first purchase and second purchase?”
Generically, let’s say the average multi makes a 2nd purchase within 5 weeks of the first purchase. So every 40 days or so, scan for 1x’ers whose last purchase was over 40 days ago and send them a 10% (or whatever, test it) discount. If you don’t convert them at that point, there is a good chance they won’t convert for a very long time, if ever. This is a very simple idea, easy to manage, and once you get a multi, they are about 5 -10x more likely to make a 3rd purchase than a 1x’er is to make a 2nd purchase. Just work it.
Once you get some experience and fine tune your “days since first purchase” drop date and discount for the best tradeoff between response and profit, you start to segment. Does “how many days between first purchase and second purchase” vary by category of first purchase? First purchase price? Line these multi’s up in a spreadsheet or something and just keep asking yourself questions until you find more patterns, something that tells you about becoming a multi-buyer on your site. Then design your campaigns around these multi-buyer patterns.
For example, you find 1st time Nelson Clock buyers who become multi-buyers tend to buy a storage accessory within 6 weeks of the clock purchase. So you set up a scan that says “give me all 1x Nelson Clock buyers” and look at how long it has been since they bought the first time. If it’s 7 weeks or so and they still have not made a 2nd purchase, send a discount for any accessory, or, if the “Nelson Multis” tend to buy Media Storage, for that particular accessory.
Follow? Look for patterns in customers clustered by category or item or price of 1st purchase.
You could end up with a bunch of campaigns like this. The great thing about approaching web marketing this way is these campaigns can easily be automated because they all use simple math (item number, # of purchases, days since purchase) to generate the lists. If you’re not a programmer and are using Excel or Access for the customer database you could probably find a programmer who could create these scanning routines for you dirt cheap. Then you could just run them with the click of a button and create your campaigns.
The process above is how the LifeCycle Grids are created. The 40 day and 6 week cutoffs mentioned above become “boundaries” in your Grid; you can track the retention of buyers overall and of “first time Nelson Clock buyers” and adjust campaigns as needed to maximize customer value / decrease discount expense.
By the way, these campaigns would be in addition to whatever generic newsletter thing you do, because they are customized to a particular segment. When you are doing a plan like this, the generic newsletter becomes “air cover”, sort of a background “friendly contact” or awareness approach. The real promotional work gets done in these behaviorally segmented campaigns. If you are doing these campaigns, you *do not* want to put discounts in the newsletter.
Do you know why?
I mean, why bother with all this, why not send the same discount to everybody in a generic newsletter? Since you bought my book you probably know the answer already, but just to wrap this reply up in a complete package, think about this:
Let’s say from the example above that you do find your multi’s usually buy the 2nd purchase within 40 days of the first. And let’s say you do a *monthly* (every 30 days) newsletter with discounts in it. What is happening to the business and customer base? Two things:
1. You are throwing margin away. All the future multi’s who would have bought anyway 10 days after the newsletter drop at full margin are responding to the newsletter discount. Sure, great response, but *is it really* if you were going to get the sales at full price anyway? Prove it to yourself. Next newsletter, look at the actual responders. You will find the majority of them are multi’s – the people most likely to buy again *anyway* without a discount. There are literally millions of dollars being left on the table this way in web retail. E-mail, at least in web retailing, is not “free” by a long shot.
2. You are teaching your future multi-buyers what is called “coupon proneness”. You are teaching them to always buy on deal. If they get their 2nd purchase on deal, they tend not to make their 3rd until they get another deal and so forth. Remember, these multi’s are your future best customers, they are the 10% that will drive 90% of your profits. Do you really want to teach them to buy everything on deal right up front? Really? Like Bed Bath and Beyond?
If that’s not enough work for you, don’t forget you have the marketing patterns to look for, e.g. where did the multi’s come from – which ads, what offers, search engines? Certain search phrases will tend to create multi-buyers and others will not. You should heavy up the spend on the phrases that create multi-buyers and back down on the phrases that tend to create 1x buyers to maximize the value of any search campaigns. Also, feature products that create multi’s in your newsletter and on the home page and back away from promoting any product that tends to create 1x buyers.
Good luck with it, and thanks for being a customer!
Jim
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