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.
This month in the newsletter we answer questions on the nitty gritty of the actual discovery work by taking a very deep look into the whys and hows of segmenting customers. Straight-up and to the point, put on those data shoes and Let’s do some Drillin’!
Q: Hi Jim, I’m a great fan of your work!
A: Well, thanks for your kind words.
Q: I have a basic question for you. We are an online retailer and thus use email as the primary marketing communication channel (we do use Direct Mail to our best customers around holidays).
A: Those are smart choices. I’ve seen some stats on using direct mail to drive lapsed online customers either back online or into a store that are very encouraging, real money-makers for retail. Definitely worth testing, though in both cases, the product mix averaged higher ticket than your category typically does.
Q: However, we don’t have a set customer segmentation technique and thus no specific customer segments. One outside consultant, a statistician, had suggested looking at a new customer’s activity in the first 30 days and then classifying them into High Spender, Frequent Transactor, etc. segments. Not sure how well it works.
A: That’s quite unusual, I think. It would work in the first 30 days, but I think you would have to re-classify every 30 days using a scheme like that. Considering web-only behavior, the typical retail lifecycle beyond 2nd purchase (many buy only one time) is a ramping to a peak and then a more gradual, but still steep, falloff in purchases. The model above would not take this into account, and while the initial label might be accurate, it soon would not be. That’s not to say these kinds of models don’t work, but it usually takes years of testing and study to perfect them. “Data miners” often believe the numbers will simply tell them things like this, but they don’t take into account the human behavioral and other mitigating factors which may not be in the data.
For example, Recency and Latency are really “meta-data” about customer behavior; they are data created from other data. You can’t just look at the first 30 days of transactions and give a customer a label; customers have LifeCycles and you drive the highest ROI when you take advantage of knowing these cycles and acting on them to increase profits.
Q: I feel that we target our customers primarily by their category purchases, and not by any kind of behavioral model.
Continue reading Customer Segmentation: Tangible vs Intangible Cost, Let Data Define Segments