Leveraging Big Data to Boost Click-Through Rates

Catch up with NBC News Clone on today's hot topic: Wbna55453481 - Breaking News | NBC News Clone. Our editorial team reformatted this story for clarity and speed.

Retention Science uses predictive algorithms to create automated marketing campaigns for companies based on customers' buying patterns.

Back in 2012 SwayChic.com, the e-commerce site for Northern California-based casual-apparel chain Sway, was struggling to get the customers in its database to buy more. No matter what it tried, its e-mail marketing efforts fell flat, averaging a dismal 11 percent open rate and 0.9 percent click-through rate--numbers that were well behind the retail industry averages of 31 percent and 3.4 percent, respectively, according to e-mail marketing provider MailChimp.

"It was really trial and error," says Cheyanne Sequoyia-Mackay, SwayChic's project and marketing manager. "We were looking for a smarter way to send e-mails without having to put so much research into it."

The Fix
SwayChic enlisted the help of Santa Monica, Calif.-based Retention Science, which leverages predictive algorithms to create automated marketing campaigns. For a fee (undisclosed) based on the size of SwayChic's customer database, Retention Science integrated its software with the retailer's e-mail blasts, then analyzed the data, evaluating more than 300 customer behaviors such as purchase history, when they opened e-mails and when they visited the site. Armed with that information, Retention Science gave SwayChic a precise schedule, down to the day and hour, when targeted segments of its database were most likely to open and act on e-mail pitches.

The Results
The e-commerce site's revenue tripled within the first two weeks of using Retention Science, according to Sequoyia-Mackay.

"And this was just from altering the way we sent our e-mails," she says. "It didn't include any stylistic changes."

The average open rate increased to 15 percent, and the average click-through rate more than doubled to 2.2 percent; some campaigns, such as a discount sale, achieved click-through rates of more than 10 percent. SwayChic's Cyber Monday promotion last November, for example, resulted in sales that were 400 percent higher than the previous year's.

Now Sequoyia-Mackay is experimenting with Retention Science's incentive-based campaigns, wherein targeted customers receive individually tailored promotions based on their previous buying patterns and other data.

A Second Opinion
Austin-based Bryan Eisenberg, a digital marketing expert at Iterate Studio, says it's easy to grasp the benefit of Retention Science. "Digital marketing is complicated; there are so many moving pieces on top of everything that's related to retail and e-commerce in general. Most retailers are way too busy for this," he explains.

However, he notes that it's a huge jump to go from essentially no data analysis to something as powerful as Retention Science. For some, a very basic approach may provide sufficient results--and at a lower cost. "You can go into your databases, and you can pull out every single e-mail and corresponding time of purchase, and you can create different customer buckets for morning, afternoon, weekend, weekday e-mails," he says. "That should improve your overall response rate."

×
AdBlock Detected!
Please disable it to support our content.

Related Articles

Donald Trump Presidency Updates - Politics and Government | NBC News Clone | Inflation Rates 2025 Analysis - Business and Economy | NBC News Clone | Latest Vaccine Developments - Health and Medicine | NBC News Clone | Ukraine Russia Conflict Updates - World News | NBC News Clone | Openai Chatgpt News - Technology and Innovation | NBC News Clone | 2024 Paris Games Highlights - Sports and Recreation | NBC News Clone | Extreme Weather Events - Weather and Climate | NBC News Clone | Hollywood Updates - Entertainment and Celebrity | NBC News Clone | Government Transparency - Investigations and Analysis | NBC News Clone | Community Stories - Local News and Communities | NBC News Clone