You seldom read common-sense logic that explains what you should do, partly because it is hard work, partly because vendors want to monetize this process by doing the work for you.
Let's review a simple example, one that clearly outlines the challenges we all face.
The table below illustrates seven e-mail subscribers, and the amount each customer spent on Mens and Womens merchandise over the past year.
| Cust # | Mens | Womens |
| One | $50 | $0 |
| Two | $0 | $900 |
| Three | $400 | $200 |
| Four | $0 | $0 |
| Five | $1,335 | $1,335 |
| Six | $0 | $600 |
| Seven | $150 | $150 |
Based on the purchase habits of these seven individuals, we have to assign three customers to the Mens version, and four customers to the Womens version.
Method #1 = Prioritization: One of the easiest strategies is to prioritize one version of the e-mail campaign over another. In other words, the database marketer may decide that the Mens version receives top priority. In that situation, the three highest-spending Mens customers get the Mens version, with everybody else receiving the Womens version.
- Mens Version = Customer Number Three, Five and Seven.
- Womens Version = Customer Number One, Two, Four and Six.
- Mens Version = Customer Number One, Four and Seven
- Womens Version = Customer Number Two, Three, Five and Six
Method #2 = Versions Based On Spend: In this case, customers are assigned to versions based on which merchandise division the customer prefers. This works well in theory. Notice in our case that customer numbers four, five and seven spent equal amounts in each version. Therefore, we have the following situation:
- Mens Version = Customer Number One and Three.
- Womens Version = Customer Number Two and Six.
- Ties = Customer Number Four, Five and Seven.
Method #3 = Versions Based On Expected ROI: Experienced e-mail database marketers assign an expected ROI to each customer, for each version of an e-mail campaign. In this case, each customer is "scored" based on the demand per e-mail / ROI expected from the customer, if mailed either version of the e-mail campaign. The Womens version of the e-mail campaign is expected to perform twice as well as the Mens version of the e-mail campaign. Let's look at the "scores":
| Cust # | Mens | Womens |
| One | $0.106 | $0.163 |
| Two | $0.085 | $0.329 |
| Three | $0.164 | $0.213 |
| Four | $0.085 | $0.163 |
| Five | $0.397 | $0.794 |
| Six | $0.085 | $0.252 |
| Seven | $0.126 | $0.187 |
When we score each customer based on expected sales per e-mail sent, we see a problem, don't we?
The Mens version is expected to perform half as well as the Womens version. So, when evaluating each version of the e-mail campaign, we come to the logical conclusion that we should only have one version of the e-mail campaign ... the Womens version ... right?
Now go sit down in the office of the Mens Merchandise Executive, and tell him/her that we won't be sending an a Mens e-mail to our customers, because customers don't respond to Mens merchandise. After you make that statement, cover your ears and duck!!!!
So, you're left with a tough choice. What you have to do is assign the best customers to versions, "subject to constraints". In this case, the constraint is that we have to send three Mens e-mail versions to customers, yet we want to maximize the total ROI for the campaign.
A good tool to solve this problem is "linear programming". I don't have enough room to go into all the math here, but if you use software from Unica (the folks who brought catalogers the "Affinium" campaign management tool), you can purchase an add-on that does this for you. The add-on isn't cheap, but you're likely to recoup your profit in short order.
Of course, there are many, MANY ways to solve this problem. Here's an opportunity for you to share your thoughts. How would you attack this challenge?
Could we add purchase frequency and timing to the customer records? Knowing more about the purchase patterns would help the decision.
ReplyDeleteFor example, I (as a man) might buy men's stuff all year and women's stuff in December (Xmas gifts). An email campaign in March would then know to classify me as a "man" for that email. Not my field, so not sure if databases are up to this kind of granularity...just musing!
Absolutely, purchase frequency and timing of purchase are important.
ReplyDeleteEventually, there are too many dimensions for the human mind to balance. That's where a model comes into play ... the model looks at all the factors simultaneously, and weights them appropriately.
Databases are very much up to this kind of granularity.
Good ideas, Mark!!!
What about adding recency to frequency? Creating an RF score and then creating mailings based on these segments would be a way that I would approach it.
ReplyDeleteYour mailing database could then include "If" statements to display content based on previous purchases categories i.e. men vs. women. Recent behaviour is a better predictor of future behaviour than the Monetary model alone.
What do you think?
What do I think? I think you're right!! Adding information to break ties and improve assigment of customers to versions is a good idea!
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