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Compared to social media, which is very sexy now, email is old hat. It’s downright frumpy — anything but sexy. Many pundits have even predicted the death of email, given the rise of social media. But for database marketers email is a channel and source of data with increasingly broad applications across the marketing spectrum.
This week I had the pleasure of meeting the winners of the Hearst Analytics Challenge, run by Charlie Swift, one of my former colleagues from LexisNexis. Similar to the Netflix prize for the best movie recommendation algorithm, Hearst reached out to the analytics community and challenged them with developing a model to predict that variables that drive email results. Smart. And it got me thinking of other uses of email data.
Let’s take retail buying data. Datalogix and other companies are taking offline transactional data like products purchased at retail (from providers like Nielsen) and matching it to online registration data — of which email address is a key data point. If you take the retail purchase data, match it with registration/email data and associate it all with cookies then – poof – you’ve got the ability to serve targeted online ads to people based on their offline behavior. (After doing some deals with ad networks). To make the sales of this data really scale, data companies are developing models of pre-packaged segments like organic food buyers. So the soy milk company can serve online ads to people who are known buyers of organic foods.
How else can email data be used with other data sets?
Let’s take customer service interactions. If a customer – let’s call him Joe – complains to Campbell’s that his soup is too salty he can be tagged and put into a “salt free” segment in their prospect database. Hopefully when Joe called into the call center the rep captured his mail address (or Campbell’s appended it by matching the incoming phone number to an email address). If so, then Campbell’s could then run a predictive model to determine the attributes of “salt free” buying prospects like Joe and find look alikes from outside lists. Then they would have email addresses of non-customers who may be prospects for its salt free soups. All of this is doable with the right strategy. Old hat for some marketers, new stuff to others.
Back to the online advertising piece. If the email addresses are matched to online registration data, then couldn’t Campbell’s run online ads to other prospects — potential customers who are statistically more apt to buy a salt free soup? And they could use the email addresses of known customers to build a profile over time with online behavioral data. All they have to do is set cookies when customers click on the links and then collect other online data over time — like topics of interest from online interaction on their web site. Then they can send Joe an email with helpful content like “foods that help lower blood pressure.” He clicks on the link, goes to Campbell’s site and then views a recipe for clam chowder (assuming they have one). Campbell’s could mine that data, connect the dots and then email or mail Joe a coupon for their salt free clam chowder. By doing this, Campbell’s turns Joe from a complainer to an evangelist.
And how about product development. If Campbell’s does not make a salt free clam chowder, mining the data could yield an insight: there is a market for such a product. Now that’s kind of sexy. There is huge potential to use this kind of data in product development.
Email addresses change and surely email is not the only Rosetta Stone that can help join disparate data points, but in many cases it’s darn good. Add social media and CRM data in the mix and with proper analytics you’ve got got powerful insights that can elevate the whole customer experience, with the right applications and communication mix.
The key to email’s survival is relevance. Marketers must be on target with emails that provide people with value above and beyond a transactional level. If we do that then, well, email might just have more life left in it.