Now this isn't some sort of qualitative "Brand 'X' uses great subject lines" or "E-Mail is a wonderful one-to-one relationship vehicle" analysis. Nope, we're actually controlling for key factors, understanding when all factors are equal if e-mail can carry the freight.
And it turns out that the fact a customer is an e-mail subscriber is important.
You'll see something like this:
- Logistic Regression Of 12-Month Repurchase Rates = -2.000 - 0.5*(months since last purchase ^ 0.5) + 0.8*(number of orders) + 0.3*(email subscriber).
- Spend Model = 120 + 0.30*(historical spend) + 4*(email subscriber).
- Customer #1: Response = 32.9%, Spend = $214, Value = $70.34.
- Customer #2: Response = 26.6%, Spend = $210, Value = $55.89.
Your job is to compare this metric to the data you get via open rates and click through rates and conversion rates. Do you see a difference?
For instance, your forecasts might suggest that Customer #1 will generate $0.15 per e-mail, across 52 annual e-mail campaigns. If true, then you're forecasting this customer to generate $7.80 over the next twelve months.
In other words, the modeling procedure shows you that e-mail marketing is nearly twice as valuable as your traditional marketing metrics suggest. Complement this data with actual mail/holdout results, and you might really have something!
The important thing, of course, is to put your e-mail program through a full Multichannel Forensics analysis, thoroughly understanding the role e-mail plays in growing each channel.