Brand and Category Alerts Are Good Karma for Karmaloop

Lots of companies talk about allowing customers to choose the types of e-mails they wish to receive. Fashion e-tailer Karmaloop is actually doing it, to a highly specialized degree, and is reaping huge increases in conversion rates as a result.
 
About two and a half years ago, Karmaloop signed on with MyBuys, a provider of personalization engines, so that it could offer subscribers targeted e-mail “alerts” in addition to its traditional marketing e-mails. Website visitors can opt to receive information about new and sale items from their choice of several hundred brands (from Adidas to Zigi Shoes) and dozens of product categories (from accessories to winter coats).
 
“The alerts are hooked into our site,” explains chief operating officer Anand Shah. “You’re coming onto the site, browsing, looking at, say, the shoes. Then we ask if you want to hear anything about that brand or product category.”
 
Karmaloop has about 1.2 million subscribers to its generic e-mail campaigns and roughly 350,000 subscribers to the alerts. Open and click rates are similar for both types of e-mail, Shah says, but “conversion for the alerts is sometimes six- to eightfold those of the traditional campaigns.”
 
The company also began including user-generated product ratings from its Website (supported by PowerReviews) in its e-mail alerts. It has found that alerts featuring products with at least a four-star rating generate 16% more revenue per message than those for products without star ratings.   
 
The alert e-mails are part of what Shah calls “intelligent automation” enabled by MyBuys’ artificial intelligence engine, which Karmaloop also uses to push better-targeted product recommendations on the product pages of its site. The software “takes into account multiple data points across multiple touch points,” Shah says. “It is connecting all these data warehouses.”
 
The engine collects data from each visitor’s experience on the site, including which links he clicked and which alerts he signed up for. Then, using assorted algorithms that take into account what other customers with similar navigation patterns bought, it serves personalized recommendations in the “favorites” column that appears on the right-hand column of the product pages.
 
These targeted recommendations have increased Karmaloop’s Website conversation rate by 73%, with online sales up 11%. And because the recommendations are generated automatically, the Karmaloop staff has to do very little in terms of updating or maintaining the functionality, Shah says.
 
Shah notes that it can take three or four months before the engine has gathered enough customer behavior information to be able to provide highly targeted recommendations. “The more [visitors] interact, the more time they spend, the more intelligent it grows,” he says.
 
Karmaloop is hoping to adapt its targeted recommendations to mobile marketing and smart-phone apps. It already has about 250,000 people who have signed up to receive mobile messages, even though so far the company is sending little more than one message per quarter. Nonetheless, Shah says, “we get text messages back saying, ‘Hey, when is Karmaloop sending me more text messages?’”