E-Mail Marketing And Geography: Multichannel Opportunities

In e-mail marketing, we obsess about simply getting a marketing message to first arrive in the inbox of a customer, then to get the image to render properly. Assuming we can accomplish these challenging issues, we focus on the offer and subject line text, trying to maximize click-through and conversion rates.

Seldom do we tailor the message based on geography.

This is a map of the greater Seattle metropolitan area. Green zip codes are those that perform best for this brand, while yellow zip codes perform worst.

The magic of e-mail marketing is that separate messages can be created, based on geography.

The customer who lives in Easton, sixty miles southeast of downtown Seattle in the Cascade Mountains, is not likely to shop in your store --- heck, in winter, this customer simply cannot get across the mountain pass. Why advertise a store message to this customer?

Conversely, the customer living in Belltown has close to a thousand shopping opportunities within ten miles of her home, and can easily walk to several hundred stores. How might the marketing message be different for this customer than for the customer living in Easton?

What about the customer living in Bremerton, just ten miles west of Seattle ... but a two hour trip due to the wait to get on to a ferry to get to Seattle? Maybe buy online / pickup in store is an option, given that this customer works in Seattle?

Zip code data is FREE! You maintain the information for every customer who purchases from your online and phone channels, and in many cases, you have this data for retail purchasers as well.

Each zip code can be classified across different dimensions.

• Urban, Suburban or Rural.
• High Spending, Average Spending, Low Spending Zip Codes.
• Distance From Closest Store.
• Store Preference.
• Channel Preference: Phone, Website, Store

E-Mail marketing strategy is crafted for each categorization. You're likely to see a ten or twenty percent increase in e-mail marketing performance based on versions of e-mail campaigns targeted to customers having different zip code characteristics.

E-Mail Marketing Gurus: Your Thoughts On Williams Sonoma

The e-mail marketing blogosphere has been buzzing lately, suggesting that we minimize campaign based blasts in favor of targeted, relevant, personalized messages that the customer eagerly anticipates. Sounds good to me!

And then a few weeks ago, Williams Sonoma mentioned that they have eighteen million opt-in e-mail addresses, across all of their brands.

So my question to all of us who share a belief in relevant, targeted e-mail marketing is this: How would we accomplish this feat for eighteen million unique customers who have multiple relationships and multiple e-mail addresses across multiple brands and multiple channels and multiple stated preferences?

And if we can answer the question effectively, how do we do this when we don't have the systems infrastructure to do what we want to do? It's really easy to blast big brands for their silly practices. How would we solve the problem when faced with real life constraints?

Discuss.

E-Mail Marketing And Customers Who Return A Lot Of Merchandise

Sometimes, our instant access to metrics cause us to screw up.

This happens to most of us.

1. We execute an e-mail campaign on a Tuesday morning at 9:00am.
2. By 10:27am, we have a forecast for how well the e-mail campaign will perform. We know open rates (or render rates as the experts now say), click-through rates, and conversion rates. We may even know $ per e-mail.

Three weeks later, twenty percent of the customers who purchased from the e-mail campaign returned their merchandise for a refund.

Did the e-mail marketing campaign work?

One of the things we can do is identify customers who are "high returners". I've done this analysis for many companies. Typically, a small subset of the audience (maybe 1% to 5% of your twelve month buyer file) are responsible for a disproportionate amount of returns.

An easy way to address this problem is to identify customers with a high return rate, and see if those customers will have a high return rate in the future. If so, you run a profit and loss statement on future sales. You talk to your folks in finance, folks who know the actual cost to process each item returned to a company.

At Eddie Bauer, we knew that if a customer had ordered at least three times in the past, and returned two-thirds or more of the merchandise she purchased, she would be unprofitable to market to in the future.

In e-mail marketing, this one is a slam dunk! You simply create a suppression list for this tiny subset of the customer file, and don't send e-mail marketing campaigns to this segment.

And then you bask in the glow of the increase in profit you obtain because of your strategy.

You are likely to see a drop in your metrics --- high returns customers are typically your most active customers --- they open e-mails, they click-through to the website, they buy stuff. And given your returns policy, you should let them buy stuff. However, there is no rule that says you must also market to the customer. So generate additional profit for your company. Stop e-mailing customers who return too much merchandise!

Emphasizing New Product In Modified RFM E-Mail Targeting

Here's another quirk. You have new product that you want to feature in your e-mail targeting strategy.

If the has never been sold, you probably cannot come up with a weighting score for this product. In this case, you make up manual rules for deciding who gets that version of the e-mail campaign.

But if the product has been available for a few months, you may get a decent weighting score. In these cases, you may need to artificially increase the weights, in order to allow enough customers to receive this version of the e-mail campaign (multiple versions on the same day, customer only receives one version on that day).

More On Modified RFM For E-Mail Targeting

Many wonder what to do when you have two potential versions of an e-mail campaign. Which version should a customer receive?

Companies loaded with analytical talent have interesting algorithms to make these decisions. Yesterday, we talked about a shortcut that gets us 80% of the benefit for about 5% of the work.

But what do you do when one version of an e-mail campaign is so much more productive than another? In other words, say you have a Mens and Womens version of an e-mail campaign, and the customer could receive either version, but the Mens version is much less productive (sales per e-mail) than the Womens version?

A shortcut is to evaluate the historical difference in productivity, and apply that to the "weighting" score from yesterday's post. In other words, if the Mens version performs at 65% the level of a Womens version, multiply your Mens weighting scheme by 65%.

Again, this is statistical blasphemy. But you don't work at a company where you have thirteen statisticians sitting around waiting for new and exciting challenges. You're lucky to have one good analyst, and the demands upon this person's time are many. So take the shortcut, and get 80% of the benefit for 5% of the work. And when you have the money and/or human resources to do e-mail targeting the right way, by all means, pursue the ideal answer.

Modified RFM For E-Mail Targeting

RFM is great for targeting one catalog to one customer. However, RFM is tough to manage in a multichannel environment.

This becomes clear in e-mail targeting. Say you have a Mens version of an e-mail campaign, and a Womens version of an e-mail campaign --- a customer could receive either version on the same date. Use this customer as an example:

• Customer spent $100 on Mens merchandise in the past three months.
• This customer also spent $200 on Womens merchandise 7-12 months ago, and spent $100 on Womens merchandise 13-24 months ago.

Which version of the e-mail campaign do you send to a customer? You could use RFM --- your customer is a 0-3 month $100 mens buyer, and is simultaneously a 7-12 month $300 womens buyer. Which "segment" carries more "weight".

This is where we apply "Modified RFM".

Have your statistician build a regression model one time --- and use the "weights" or "coefficients" for your modified RFM scheme. I realize this is statistical blasphemy, however, we aren't managing clinical trials for cancer drugs, we're deciding which version of an e-mail campaign a customer receives.

Step 1: Pick a "dependent" variable for "Mens". I like to look at the past twelve months.

Step 2: Create a series of "independent" variables:

• Dollars spent on Mens in past three months (prior to the dependent time period).
• Dollars spent on Mens 4-6 months ago (prior to the dependent time period).
• Dollars spent on Mens 7-12 months ago.
• Dollars spent on Mens 13-24 months ago.
• Dollars spent on Mens 25+ months ago.

Step 3: Regress these five variables against your dependent variable. The "coefficients" become "weights" for e-mail targeting, as you'll see soon.

Step 4: Repeat Steps 1-3 for Womens merchandise.

Now, we can evaluate which version of an e-mail campaign a customer should receive. Let's look at our example:

E-Mail Targeting Strategy: Mens Weights
	Spend	Factor	Weight
00 to 03 Months	$100.00	1.600	160.0
04 to 06 Months	$0.00	0.600	0.0
07 to 12 Months	$0.00	0.300	0.0
13 to 24 Months	$0.00	0.150	0.0
25 to 99 Months	$0.00	0.050	0.0
Total Weight			160.0

E-Mail Targeting Strategy: Womens Weights
	Spend	Factor	Weight
00 to 03 Months	$0.00	1.600	0.0
04 to 06 Months	$0.00	0.600	0.0
07 to 12 Months	$200.00	0.300	60.0
13 to 24 Months	$100.00	0.150	15.0
25 to 99 Months	$0.00	0.050	0.0
Total Weight			75.0

For the Mens version of the e-mail campaign, the customer receives a "weight" of 160.

For the Womens version of the e-mail campaign, the customer receives a "weight" of 75.

So, you should send the customer the Mens version of the e-mail.

For your next campaign, you don't have to build models again --- remember, we're not trying to cure cancer, we're just figuring out which version of an e-mail campaign will improve response a bit. Just apply the same weights built in your prior modeling process, and decide who gets which version.

The key here is to not build separate RFM schemes. Instead, you build variables in your database that summarize purchases by 0-3 month, 4-6 month, 7-12 month, 13-24 month, and 25+ month time periods. Then you "weight" those purchases based on importance. This gives you a good targeting strategy.

Statistical purists will blast me for misuse of appropriate statistical techniques. That's fine. We're just trying improve e-mail marketing performance, while minimizing use of internal resources, or minimize expense incurred when hiring consulting statisticians. This gets you 80% of the benefit for about 5% of the work.