E-Mail Marketing Challenge: Assigning Customers To Versions

Deciding who receives a version of an e-mail campaign is a challenge.

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.

Had the Womens version been prioritized first, the assignment would have looked like this:

• Mens Version = Customer Number One, Four and Seven
  
• Womens Version = Customer Number Two, Three, Five and Six

Notice the challenge with this method. While this is the easiest method to execute, the method results in the version with the highest priority receiving the best customers. This means that the version with the highest priority will generally perform best. The version with lower priority will generally not perform as well.

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.

The database marketer could randomly decide which of the three customers with ties receives the Mens version, and then allocate the other customers to the Womens version.

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?

Three Ways To Increase E-Mail Sales

Businesses with customers who purchase fewer than three times a year seldom benefit from trigger-based e-mail marketing campaigns (with the notable exception of shopping cart prompts, which often work well).

There are at least three key factors that can be managed, to grow e-mail sales.

Factor #1 = Incremental List Size, Managed By Contact Frequency

Factor #2 = Incremental Demand Per Contact, Managed By Contact Frequency

Factor #3 = Demand Per E-Mail, Managed By Number Of Targeted Versions


Incremental list size is ultimately determined by the number of e-mail campaigns sent per week. When a customer is contacted too often, too many customers unsubscribe, driving down the total size of the e-mail list. Strategically, management may choose to execute "x" campaigns per week. Mathematically, the number of e-mail contacts per week can be determined by the number that still cause a healthy increase in the number of valid names available to be e-mailed. In the table below, you'll see that two e-mails per week are optimal, as the e-mail list continues to grow.

Incremental demand per contact is also important. As you increase e-mail frequency, you will decrease the performance of any one e-mail contact. Increased frequency will probably cause cannibalization between e-mail campaigns. The table below shows that the combination of unsubs and performance dictate two e-mail campaigns per week.

Targeted versions of an e-mail are important as well. Few retailers have the ability to dynamically create unique e-mail campaigns for each customer. As a result, management creates "x" versions of an e-mail campaign, offering different merchandise in each version. The analytics team decide which version of an e-mail campaign the customer receives, on the basis of past purchase behavior, stated customer preferences, clickstream history, and other factors. From a staffing standpoint, it could be a challenge to produce numerous versions.

In the table below, I assume that a company managed one version of an e-mail, one time per week, to the entire e-mail file. This strategy yielded $20,700 of demand per week.

Going from one campaign a week to two campaigns per week kept the file size increasing, reduced volume per e-mail, but resulted in $30,030 of demand per week. Clearly, this is a better strategy than sending just one e-mail campaign per week.

Going from one version per campaign to nine versions per campaign drove $40,040 of demand per week. Assuming this strategy can be managed with existing staff at minimal cost, this strategy could work.

Notice that the combination of list size (dictated by frequency), demand per contact (dictated by frequency), and version contribution cause a doubling in e-mail volume, on a weekly basis.

Catalogers have long mastered this type of analysis, assigning profitability to each strategy. With e-mail, profitability is not as big an issue, so if one can avoid the fixed costs associated with incremental staffing, a move to moderate frequency with increased versions can yield a significant increase in e-mail sales.

Obviously, there are many ways to increase e-mail volume. These three basic strategies almost guarantee a positive return on investment.

No Targeting Strategy
Contacts	List	New	Unsubs	Net	$ per	Weekly	Total
per Week	Size	Subs	& Invalids	Names	E-Mail	Demand	Demand
1	100000	1000	650	100350	$0.20	$0.20	$20,070
2	100000	1000	900	100100	$0.15	$0.30	$30,030
3	100000	1000	1150	99850	$0.12	$0.36	$35,946
With Targeting Strategy: 2 Contacts Per Week
Targeted	List	New	Unsubs	Net	$ per	Weekly	Total
Versions	Size	Subs	& Invalids	Names	E-Mail	Demand	Demand
1	100000	1000	900	100100	$0.15	$0.30	$30,030
5	100000	1000	900	100100	$0.18	$0.36	$36,036
9	100000	1000	900	100100	$0.20	$0.40	$40,040