Data Driven Email (and other) Marketing

The frequency of emails from the Obama campaign ended up being a talking point for pundits and late night talk show hosts. Jon Stewart of The Daily show even asked President Obama about email directly during his October 18th interview. (Video, email question at the 5:56 mark)

Jon Stewart: “We have been talking here for 12 – 14 minutes. I am curious. How many emails, in that time, do you think your campaign has sent me?”
President Obama: “It depends on whether you’ve maxed out!”

According to an article published this morning in Time, the Obama marketing campaign was highly data driven.

A large portion of the cash raised online came through an intricate, metric-driven e-mail campaign in which dozens of fundraising appeals went out each day. Here again, data collection and analysis were paramount. Many of the e-mails sent to supporters were just tests, with different subject lines, senders and messages.

This data and how effectively the marketers utilized it drove a billion dollars in fundraising.
While the Obama campaign sent out tons of email, they didn’t just batch and blast their marketing to their whole donor list. They were selective and targeted and tried to send the most relevant messages during their whole campaign. This targeting and focus on relevance drove their fundraising to record breaking heights.
More email is good, most marketers will tell you that. But it’s not just about sending more mail, it’s about sending more relevant mail. Marketers should look at what the Obama campaign did with data and how they managed an email campaign that sent so much mail so effectively.
 

How long is your DKIM key?
Is Google failing DKIM keys shorter than 512 bits?

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Researchers recently developed a model for email user behaviour that splits email users into two classes “e-mailaholics” that send, and presumably read, email all the time and “day labourers” that send, and presumably read, email during standard business hours. There is very little transition between groups, 75% of users stayed in the same usage group over the 2 years of the study.
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h/t arXiv blog at Technology Review

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