Multichannel Forensics & E-Mail Marketing

I stumbled across this article about two months too late ... "E-Mail's Big Demographic Split".

The brief history of the internet illustrates a common trend among channels.

• Newness.
• Hype.
• Monetization.
• Outflow.
• Maturity.

Somebody invents something (E-Mail, AOL, MySpace, Facebook, Twitter, Foresquare, Quora), the early adopters hype the living daylights out of it to attract an audience, the audience hypes the living daylights out of it to attract the mainstream, the mainstream attracts monetization, monetization drives the early adopters out, resulting in a mature channel.

In catalog marketing, this happened over more than a century.

In e-mail marketing, this is happening over the course of two decades.

For MySpace, this happened over the course of maybe eight years.

Facebook?  Twitter?  Get ready.

When I run my Multichannel Forensics methodology against company data, the trends are often similar to what we observe in the article illustrated at the start of this post, from the NY Times.  Mature channels exhibit outflow among a younger audience, while exhibiting increased participation among an older audience.

This is the reason you can read two articles with completely opposite points of view ... e-mail is dead ... and "don't ask is e-mail dead".  Both parties can be "right", with outflow happening among one audience and adoption among another ... age isn't the differentiator here, rather, lifestyle is.

If I were working at a brand like Nordstrom or Lands' End today, I'd score every customer on a technological continuum ... catalog marketing and e-mail marketing on the left, search/affiliates in the center, social media right-of-center, mobile on the far right.  In my Catalog PhD projects, this is essentially the same as the "organic percentage".

If you don't have age data appended to your file, you can run a classic Multichannel Forensics project against your channels ... pay attention to what e-mail customers do next.  Customers will eventually migrate out of a channel, and you'll have plenty of warning if you run the appropriate analytics against the data.