July 23, 2012

Email Marketers Miss This Fact, Often

I simply don't understand why email marketers are stuck analyzing opens/clicks/conversions.  An entire industry follows best practices that miss much of the value of an email marketing program.


Go spend a few minutes with your geeky statistician, and if you don't have one, hire me (click here) or ask your co-op or database provider to run an analysis for you.

  1. Run a logistic regression.  Freeze your file as of one year ago.  Create independent variables (recency, frequency, average order value), and include email subscriber status as a 1/0 variable (subscriber = 1, non-subscriber = 0).
  2. With your file frozen as of a year ago, run a ordinary least squares regression model ... similar variables as above, predicting how much a customer will spend if the customer purchases in the next year.
By performing this analysis, you control for customer quality, allowing you to isolate the impact of email marketing on the overall business.  This is not an analysis where you segment by presence of an email flag (you'll obtain terribly biased results if you do that, because email subscribers are either your best buyers or they never buy).

I recently performed this analysis for an email marketer.
  • Non-Email Subscribers (after controlling for other factors) had a 35% chance of buying again in the next year (across all channels).
  • Email Subscribers (after controlling for other factors) had a 42% chance of buying again in the next year (across all channels).
  • Non-Email Subscribers (after controlling for other factors) spent $200 in the next year, if they bought again (across all channels).
  • Email Subscribers (after controlling for other factors) spent $230 in the next year, if they bought again (across all channels).
What does this mean?  Well, we can identify the value of an email address, on an annual basis.
  • Non-Email Subscribers = 0.35 * 200 = $70.00 of future demand.
  • Email Subscribers = 0.42 * 230 = $96.60 of future demand.
An email subscriber is therefore worth $96.60 - $70.00 = $26.60 more per year, after controlling for all other factors.

Now, this business typically measures email via opens/clicks/conversions.  They have 100 email campaigns per year, and typically observe performance of $0.12 per email delivered.    On an annual basis, this yields 100 * 0.12 = $12.00 of value.

But this only accounts for opens/clicks/conversions.  It does not count the fact that an email campaign inspired a customer to drive to a store and buy something, or caused the customer to buy via an email campaign, become more loyal, and then buy from a subsequent display ad or catalog.

In other words, the email marketing program is more than twice as valuable as measured via opens/clicks/conversions.

If you were an email marketer, wouldn't you want to know that?

18 comments:

  1. This comment has been removed by a blog administrator.

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  2. Anonymous3:52 PM

    This comment has been removed by a blog administrator.

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  3. Hi Kevin:

    I agree that email marketing is more valuable than opens/clicks/conversions.

    I agree with a hypothesis that states customers who opt-in for email marketing would / could be more valuable than those who don't, likely for a wide range of reasons.

    I can't fully believe that the difference in value between email opt-ins and the non-email list is solely attributable to the email campaigns. Too much complexity in the mix here.

    For me, your article demonstrates the value difference between these two groups in a unique way that I hadn't considered. You nailed it with your last statement "if you were an email marketer, wouldn't you want to know that?" Yes!

    ReplyDelete
  4. Kevin, this was great. I'm not a statistician, but find your approach cleanly logical and [perhaps most importantly] VERY difficult to refute in the C-suite.

    And, while Steve alludes to the fact that opt-ins are already favorably predisposed to buying behavior (as demonstrated by the fact that they voluntarily opted in), your analysis assigns value to the group and the program.

    I like it!

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  5. In the example I gave, the regression methodology controls for the fact that email subscribers are pre-disposed to spend more. There are historical spending variables in the model that account for the fact that the email subscriber might already be worth twice as much as the non-email subscriber. What is left is due to the email variable.

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  6. Hey Kevin -

    This is wonderful analysis.

    I was wondering if you could measure the impact of an unsubscribe - that is, what happens when a customer pushes the "Ubsub" button? Do they purchase less than those who are currently not email subscribers? How likely are they to stop purchasing, given their unsub activity?

    Put the two analyses together and you've got something very powerful.

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  7. It's the same concept, so yes, that works.

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