Alibaba offered New Year specials but won’t deliver next week, while Amazon China keeps fulfilling orders in big cities.
It determined the best times to send messages using e-mail analytics from Retention Science.
Women’s fashion e-retailer Sway Chic, which sells online and in six West Coast stores, tripled its revenue from e-mail marketing campaigns after fine-tuning the time of day when it sends messages to individual customers, according to marketing and project manager Cheyanne Sequoyia-Mackay. It also increased average e-mail open rates by 40% and doubled its average e-mail click-through rate, she says.
Sway Chic optimizes its e-mail campaigns based on predictive analytics from vendor Retention Science. Retention Science works as an additional layer on top of a retailer’s existing e-mail services provider—MailChimp, in Sway Chic’s case—according to the vendor’s founder and CEO, Jerry Jao. It combines data on 30 to 50 attributes about an individual customer, including the times of day when she visits a web site, opens e-mails, makes purchases and engages in other activities on an e-commerce site, plus her past purchasing history and social and demographic data, he says.
Moreover, Retention Science crunches such data in aggregate among all its clients to come up with predictions of e-mail response rates for a product category or shopper segment, he says. That way, a retailer that just starts sending it data will still be able to optimize its e-mail campaigns based on the open- and click-through rates experienced by retailers with similar customers or that sell similar merchandise.
With all the data, Retention Science can tell a retailer that, for instance, 30-year-old New Yorker Jane Smith is likely to buy a party dress within the week if Sway Chic sends her an e-mail with 10% off her next purchase at 7 a.m. on Tuesday, Jao says. If a client chooses, it can connect Retention Science’s web-based platform to its e-mail provider so that its e-mail campaign settings will automatically adjust when Retention Science changes its recommendations—which the vendor is doing constantly, Sequoyia-Mackay says.
“They continually change the optimizations because people change their habits,” she says; After a month of early-morning workouts, Jane Smith might start sleeping in and thus read her e-mails in the evening instead of at 7 a.m. on Tuesday, for instance. Retention Science monitors how individual shoppers’ behaviors change over two to three e-mail campaigns, then adjusts its recommendations accordingly, Jao says. The vendor’s predictive analysis algorithms do that automatically, in a process called machine learning.
“It’s much more cost-efficient for Retention Science to monitor this than for me having to dig through the analytics and try to figure it all out,” Sequoyia-Mackay says. She used to spend many hours trying to do this analysis manually before Sway Chic started using Retention Science about six months ago. The technology is also simple to use, she says.
Retention Science costs between $4,000 and $20,000 per month, depending on a client’s size and data requests, Jao says.