Mobile accounted for 25% of Ulta's e-commerce revenue during Q2.
Detailed analytics help web retailers solve the value puzzle of social shoppers.
The Active Network has a simple formula for figuring out the value of its individual Facebook and Twitter followers. Take the revenue generated from consumers clicking from those social networks to its web site minus its ad spend. Divide that total by its follower base. The result is a rough idea of what each follower is worth.
That methodology led Active, which sells registration and event management software and direct event sign-ups for consumers via its online community at Active.com, to realize late last year that its Twitter followers were worth twice as much as its Facebook fans—about 15 cents versus 7 cents apiece per month. The realization led the company to divert between $5,000 and $10,000 from its monthly Facebook ad budget—spent on ads seeking to spur shoppers to Like Active's fan page—to a Twitter Promoted Account. The ad format appears on the right side of a consumer's Twitter homepage under the heading "Who to follow." The move nearly tripled Active's Twitter follower base, from 55,000 to 164,000 in six months. Because of its ad spending, those followers' average value has slightly declined in that time but the returns are still overwhelmingly positive, says Justin Ramers, Active's director of social media.
Active's formula is one example of how social media data is informing companies' business decisions. Others tweak campaigns based on referrals, shares or even the collective sentiment of comments about their brand. No one formula exists for how best to understand and measure the return on investment from social data. That's why, depending on their business goals, retailers are using web analytics software, tracking their social campaigns with specific links and tags and bringing together all the data they have about customers—from their retweets to their buying habits—to try and figure out not only social media's value, but how to optimize social campaigns to increase profits.
Over years of looking at data for the Active Network, Ramers determined there are three key metrics he needs to measure ROI. The first is impressions, the number of times a fan of the company sees Active's posts on Facebook. The second is visits to Active.com that come from social networks. The third is conversions. Ramers uses Adobe Systems Inc.'s SiteCatalyst analytics platform to get the full picture of how consumers are responding to its posts.
Adobe tags every piece of media Active puts out with a small piece of code that notes every interaction—like clicking on an article or playing a video—and reports the activity back to the tracking platform. That way the company can tell exactly which post or Tweet garnered the most click-throughs, conversions or other outcomes. "The key is you have to be tracking everything," Ramers says.
The Active Network has three full-time employees and two interns who map out weeks in advance the roughly 1,400 pieces of content the company posts each month on various social channels. They also respond to fans, keep track of policy and functional changes on all the social platforms, optimize campaigns and train staff in other departments, including marketing, on how to create content for social sharing.
By looking at the amount of traffic and page views stemming from Facebook and Twitter that led to consumers signing up for events, Ramers and his team can show that the company is making upwards of $1 per Facebook or Twitter fan annually, he says, while only spending 60 to 70 cents to acquire each new fan. The two social networks also drive 14% of the company's site traffic and 70% of new fans and followers, facts that Ramers shows executives to demonstrate how social media benefits the company's bottom line.
Ramers monitors his key metrics daily using a customized dashboard on the SiteCatalyst platform. "We can look at macro trends and see flags where we need to focus in on the micro data," he says. For example, in June he noticed that Active.com's inbound traffic from Facebook and Twitter was lower than last June. He set an intern to dive into the line-by-line data from campaigns running that month compared to the prior June to find the lag's cause. "If we don't have the analytics to understand how our social media is performing on a day-to-day basis, we can't do that fine-tuning," he says.
Data also can help retailers produce more effective ads on social networks. Motorcycle Superstore Inc., which sells gear, parts and accessories for bikers, last year grew frustrated with the 0.05% click-through rate of its Facebook ads that failed to drive home results.
Part of the reason, it quickly learned, was that its ad was static and didn't have a strong enough message, says Motorcycle Superstore's public relations coordinator Graham Hetland, who also handles the company's social campaigns. Every consumer saw the same ad, which featured the store's logo and called for shoppers to Like its brand.
Working with analytics provider Webtrends Inc., Motorcycle Superstore looked for ways to improve ad effectiveness. It tested everything from the advertisement's content to the day of the week it placed ads to see what would generate the best customer response as measured in click-throughs and conversions.
The retailer found that it needed to segment its audience into its three main user groups—enthusiasts of sport bikes, dirt bikes and cruisers. Shoppers were far more likely to click when ads contained a relevant image instead of the logo, and when text was more attuned to that segment's lingo, says Hetland, like shortening motorcycle to 'moto' for dirt bike fans who use that terminology.