Akin Arikan's Multichannel Marketing Metrics Blog

Akin recently initiated a blog called Multichannel Marketing Metrics. He works at Unica Corporation. Unica supports the "Affinium" suite of products used for campaign management during my time at Nordstrom.

If you have a chance, check out his blog!

Results From The E-Mail Marketing Budget Post

Each week, I am pitched by e-mail vendors, folks asking for access to my client base, or asking me to blog about their services to you, the loyal reader.

These e-mail marketers, researchers, and vendors are hopeful that corporations will increase their e-mail marketing budgets, so that vendor products and services might be considered.

A little over three days ago, after another week of pitches, I gave the vendor community an opportunity to discuss how a hypothetical corporation might increase their marketing budget, and to quantify the sales impact of various strategies.

Here is a link to the challenge:

How many comments did I receive from the vendor community?

Zero.

One way to impress the client-side of the vendor/client relationship is to offer useful and actionable thought leadership. Given the number of pitches I receive, vendors, researchers and marketers missed an opportunity.


In this example, the e-mail marketer generated $10,400,000 demand per year by blasting a million e-mails per week, 52,000,000 per year. I asked folks to offer strategies that might increase demand, profit and ROI, quantifying the impact for my readers.

Let's talk about a few topics.


E-Mail Frequency: In this example, the corporation chose to send one campaign per week. Here is a table that illustrates the expected demand, profit and ROI based on number of contacts:

Annual E-Mail Return On Investment By Frequency (in 000s)
	Demand	Cost	Profit	ROI	$/E-Mail
1 Contact Per Month	$5,200	$250	$1,570	628.0%	$0.43
1 Contact Per Week	$10,400	$1,000	$2,640	264.0%	$0.20
2 Contacts Per Week	$14,708	$2,000	$3,148	157.4%	$0.14
3 Contacts Per Week	$18,013	$3,000	$3,305	110.2%	$0.12
4 Contacts Per Week	$20,800	$4,000	$3,280	82.0%	$0.10
5 Contacts Per Week	$23,255	$5,000	$3,139	62.8%	$0.09

Companies that do thorough e-mail contact strategy testing have learned several interesting facts. Notice that it is possible to start losing money as e-mail contact frequency increases. Increased frequency dilutes demand per e-mail --- so that even at a very minimal cost per e-mail contact, profit begins to decline after three e-mail contacts per week (in this example --- your mileage may vary).


E-Mail Targeting: Targeting strategies can effectively increase demand per e-mail. Many companies in my industry have an e-mail list where between ten percent and fifty percent of the list have no customer information appended to it. In other words, all you know about these folks is their e-mail address. You're not going to improve performance via targeting with these individuals.

With the remaining individuals, you might get a 30% increase in demand by executing e-mail targeting strategies. The weighted average of these two populations results in a conservative increase in demand of, say, 20%. The following table overlays the 20% increase in performance, adding the marginal cost required to execute the targeting strategy.

Annual E-Mail Return On Investment By Frequency (in 000s)
	Demand	Cost	Profit	ROI	$/E-Mail
1 Contact Per Month	$6,240	$269	$1,915	712.7%	$0.52
1 Contact Per Week	$12,480	$1,075	$3,293	306.3%	$0.24
2 Contacts Per Week	$17,649	$2,150	$4,027	187.3%	$0.17
3 Contacts Per Week	$21,616	$3,225	$4,341	134.6%	$0.14
4 Contacts Per Week	$24,960	$4,300	$4,436	103.2%	$0.12
5 Contacts Per Week	$27,906	$5,375	$4,392	81.7%	$0.11

Let's say your Chief Marketing Officer doesn't want to "spam" customers ... so the CMO allows you go to from one e-mail campaign per week to two targeted e-mail campaigns per week, each targeted campaign having five creative versions sent to customers based on past purchase history, past clickstream behavior, and past website preferences. Let's compare the expected results, current program vs. proposed program.


Current Program: 1x Per Week, Same Version To All Customers

• Demand = $10,400,000.
• Marketing Cost = $1,000,000.
• Profit = $2,640,000.
• ROI = 264.0%
• Demand per E-Mail = $0.20.

Proposed Program: 2x Per Week, Customer Receives One Of Five Possible Contacts

• Demand = $17,649,000.
• Marketing Cost = $2,150,000.
• Profit = $4,027,000.
• ROI = 187.3%
• Demand per E-Mail = $0.17.

Notice the difference in results between the current program and the proposed program.

Demand increases by 69%.
Marketing expense increases by 115%.
Profit increases by 52%.
ROI DECREASES.
Demand per E-Mail DECREASES.


My guess is that your CFO will be happy with you if you demonstrate that you'll double your e-mail budget, while delivering a 52% increase in profit and a 69% increase in demand.


What Did We Learn?

First, e-mail marketing is a lot like catalog marketing. There are simple ways to quantify the impact of frequency and targeting. Go ask the catalog marketer down the hall to help you, if this type of work is a challenge for your organization.

Second, once we quantify the impact of these strategies, investment in e-mail marketing is self evident. You'll quickly find the optimal contact strategy, one that yields an increase in demand and profit. The investment quickly cost-justifies itself.

Third, the outcome of the analysis points to areas where you may need help. You'll probably need help developing a targeted e-mail scoring algorithm. I've created many of these, I'm sure your e-mail vendor does a great job as well. The targeting algorithm is where the benefit occurs. I baked those costs into the example.

Fourth, you'll probably benefit by having a campaign management software tool to integrate the scoring algorithm with your selection criteria. If you're a cataloger, you are probably using Unica Affinium for catalog campaign management. Simply apply Affinium (or your campaign management tool or even use SAS/SPSS), and send the list with targeted versions by e-mail address to your e-mail vendor for blasting purposes. I baked these costs into the example.

Fifth, start demanding more of your e-mail vendors. Fluffy pitches and glowing articles mean little. In this example, ROI (as catalogers know) actually decreases! Yet, demand and profit increase. ROI doesn't pay the bills --- actual profit dollars pay the freight, keeping you employed.

Sixth, while not included in this analysis, you'll want to monitor opt-out rates as frequency increases. At one company I worked with, we noticed that if we went past "x" e-mail campaigns per week, too many people opted-out, causing us to lose all the profit we gained via the targeting strategy.

Ok, your turn. What strategies would you recommend, and what would the increase in demand and profit be after implementing these strategies?

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?