Shopping for new marketing technology is like shopping for jeans: There has to be a good fit; the product has to go with what the company already owns; and, usually, it’s not the most enjoyable experience.
About 30% of Joe’s Jeans site visitors are returning customers—making the remaining 70% new customers who are unfamiliar with the brand. Robert Muzingo, the company’s director of ecommerce and online marketing, wanted to find a way to help these newcomers discover relevant products on the site and, ultimately, drive sales.
“We don’t know whether they’re male or female,… which color palette they like within their wardrobe, what shoe style for women she likes, [or] what body type they’re looking for,” he says. “We wanted to home in on that to really push forward a well personalized experience for them.”
After doing some testing among the brand’s customers, Muzingo discovered that many of the brand’s shoppers wanted to look at products that were similar to each other, like various pairs of dark wash skinny jeans. So the brand decided to implement Certona’s product recommendation engine in March 2014 to serve up relevant items based on shoppers’ online behavior.
Even though Muzingo dubbed the technology as “very successful,” it didn’t fully solve one major dilemma: virtually zero info about new customers.
The answer: Bring that personal in-store experience to the online world. Just as how a customer would talk to a stylist when entering a Joe’s Jeans store, Muzingo wanted to find a tool that would help customers identify the right apparel based on their style, lifestyle, interests, and body type.
“When you walk into the store, someone is going to help you. They’re going to ask some questions, and [they’re] going to push products that way,” he says. “Normally, on websites with apparel, you just don’t have that. You’re on your own.”
So 11 months after installing Certona’s product recommendation engine, Joe’s Jeans started using the personalization platform provider’s Product Finder technology.
Product Finder is a tool that asks consumers a series of questions to better identify their preferences and help them discover relevant products. For instance, when consumers visit the “Style Finder” page on the Joe’s Jeans site, the company asks them to identify their gender; describe their style, body type, and color palette preferences; and provide the weather condition for their style.
“One of the most important things I wanted to put in there was the weather conditions for your style because you have a wardrobe for different times of the year,” Muzingo explains, “and depending on where you might be [and] when, this wardrobe is going to have an effect on what we would recommend for you.”
Once customers answer the questions, they’re shown an array of clothing items from a number of different categories based on their indicated preferences.
Not only does Product Finder provide a more personalized experience, but it also takes off the pressure for marketers at Joe’s Jeans because the shopper spells out exactly what she’s looking for. Marketers aren’t left wondering whether a person is shopping for herself or someone else.
“It’s a really great way to have a really guided experience for that person and to help lead them on instead of just throwing them anything at that point,” Muzingo says.
But shoppers’ responses aren’t the only data points that can affect style results. Participants’ previous onsite behavior can also make an impact. Plus, if shoppers are signed into their Joe’s Jeans account, the denim brand can pull in other relevant data, such as what items they’ve purchased in the past and which ones they’ve returned. The brand can even store the shopper’s style results in a cookie so that it can provide a more tailored experience next time she visits the site. Although, Muzingo says that right now, Joe’s Jeans isn’t doing a lot of advanced retargeting.
“The more data we have about a given visitor the more accurate the predictions get,” Sheik says.
Joe’s Jeans has primarily been promoting “Style Finder” through email, and so far the denim brand has seen flattering results. For instance, Muzingo says that the average bounce rate on the “Style Finder” page is 2%, compared to 55% across the whole site, and that the average session duration is 225% longer than the brand’s average site page.
“We definitely see sales coming out of there,” he adds.
However, that’s not to say that the jeans company didn’t experience any challenges. Although Muzingo says the five-to-six-weeks implementation process was smooth, he also says having to label all of the brand’s products on the back-end (such as by color, fit, weather condition) was time consuming.
“It was grueling at first because we had a whole catalog to do,” he says. Now, it takes the ecommerce director only a few seconds to label a product.
As for future improvements, Muzingo says that Joe’s Jeans may experiment with retargeting based on Product Finder’s data moving forward. He also says that he’d like to make it easier for users to share their style results with their communities through email and social. Plus, he wants to bring the online experience into Joe’s Jeans stores, such as by giving store associates access to “Style Finder” via tablets to help them identify exactly what consumers’ are looking for.
It just goes to show that the old saying is true: If you can dream it, you can do it.
“The biggest lesson that I learned [is] if you want to start to implement things certainly on the web, as well as in the store, to really help drive this predictive experience or any kind of interactive experience, you can do it,” Muzingo says.