Every marketer wants more email click-throughs. Patagonia knows how to get them—and increase sales in the process. The outdoor clothing company emails its customers using personalized time deployment on an individual level versus a campaign level. Simply put, it emails customers when they’re most likely to click.
Its approach began with a simple observation: “Mobile opens are increasing. That’s no secret,” says Steve Wages, Patagonia’s email marketing manager. “Open rates go up slightly on mobile, but clicks and conversions go down. We were trying to figure out what we could do about that.”
But this wasn’t the only data point Patagonia considered. Early morning email sends perform better than those later in the day, Wages points out, which makes connecting with customers tricky for the retailer. “We have some operational constraints, like the call center opens at 6 a.m. Pacific Time,” he says. So, Patagonia was sending emails at 9 a.m. Eastern Time and then throttling the deployments so its emails would take five or six hours to go out, which made it tough to do a time-of-day test.
Clearly, emailing customers when they’re most likely to click made more sense. “If you send an email in the morning, people who look in the evening will have to dig; but the early morning people then don’t get it until the next morning,” Wages says.
“Greater clicks is the objective,” adds Quinn Jalli, SVP, strategic initiatives group, at Epsilon, which provides the tool and crunches the data Patagonia needs to get its email timing right. “Marketers want opens, but the thing that matters most in email is click rates. Sales, of course, is tops, but clicks are a good proxy for conversions.”
Indeed, marketers who look for new ways to personalize their email campaigns to capture customers’ attention may find timing to be especially effective. “Sixty-five percent of opens happen on the first day, so you need to be on top of [the] inbox as much as you can, because attention wanes,” Jalli asserts. “So, [marketers] need to understand how consumers engage on a personal level with email to reach them at the right time.”
Patagonia now splices its list into 24 different hourly buckets, Wages notes. “We send 4 a.m. people an email at 4 a.m. and they’re happy,” he says. “It’s a simple solution that I hadn’t seen previously.”
When launching the new approach, Wages and his team conducted a great deal of A/B testing because “inherently, it would make sense that this [timed deployment] would outperform a standard deployment,” he says. “We tested [it] about five different times over six months, and found that it did pretty consistently. A few times a couple of metrics were tied.”
The best part? As Patagonia’s data grows, so will the success of its new email approach. The more Wages’ team uses the tool and loads more data into it, the better it becomes at predicting when each customer should receive his email. “There’s always a segment of unknown, who haven’t opened since we’ve been using the tool,” he says. “But as you run with the tool you have more and more chance of having people click or open so you have more data.”
Still, one challenge Patagonia, like many other brands, faces is getting smartphone email openers to be desktop openers. “You can’t make people open emails on a PC,” Wages notes. “They have their phone with them.”
Indeed, customers are two times as likely to open an email on a desktop as they are on a smartphone, Epsilon’s Jalli says. “Up to 65 percent of consumers check their email on mobile, but they’re 35 percent less likely to open there,” he adds. “If you can move those views to desktops, you’ll drive clicks. It’s about personalization and device.”
The way to accomplish this, not surprisingly, is data. Epsilon analyzed six months of Patagonia’s customer data to determine the optimal email timings to ensure that emails would be opened on a desktop instead of a mobile device. According to Jalli, six months of data is ideal; less than that—a month, for example—isn’t long enough because there may be exceptions, such as vacations, that would skew the analyses.
“It’s amazing how predictable people are,” he says. “The most opens happen between 10 and 11 [a.m.] Eastern Time, probably because that’s when people are taking their coffee break.”
After its six-month analysis, Epsilon tracked another two months of data because Patagonia switched to using what Jalli calls “schedule intelligence” to calculate the outcome of the new approach. Unique clicks jumped 9.8% and opens increased an aggregated 3.7%, Patagonia’s Wages says. “That makes sense because the tool is optimizing on when a customer is more likely to click than to open,” he says. “Opens are nice, but clicks are what you want to drive revenue—and the click rate is going up more dramatically than opens themselves.”
Jalli asserts that Patagonia increased its number of clicks by increasing personal engagement. He also claims that the results will only get better as the system learns more about customers’ behaviors. Here’s how: The analytics technology is integrated with the email system to send at different times to different people. The technology tracks data in real time; opens and clicks go into the algorithm; and the system makes adjustments as a result. “It takes the guesswork out of deployment,” he says. “The numbers will improve as you zero in.”
In fact, Wages points out, Patagonia’s email conversion rate and revenue overall went up in line with the other increases. “Not a huge jump, but enough that I want to use this tool whenever I can,” he says.
So, although using email to drive conversions can be an uphill climb, it’s worth the journey.
“Retailers want to sell in email; not everyone does,” Jalli adds. “To accomplish that they need interest and timing. Marketers don’t need to guess anymore when people are on their desktop; data will provide that information. It doesn’t matter what experts say about ideal timing, it matters what consumers say.”
