O'Neill Gets Personal Online
Before you can offer customers what they’re looking for, you have to figure out what they want. Traditionally, this meant you had to look at the customer. Jack O’Neill noticed surfers looked cold, so in 1952 he was the first to glue together pieces of neoprene to make the first wetsuits. This led to the launch of O’Neill, a successful company that has become the leading innovator in beach lifestyle clothing. Now a thriving international company, O’Neill hasn’t forgotten that the roots of its success lie in observing customers and then offering exactly what they’re looking for, even if they don’t yet know what that is.
In its brick-and-mortar stores, O’Neill delivered the personalization that was key to its corporate value proposition. It trained staff to treat each customer as a unique individual, and to pull together product assortments that catered to their needs. Salespeople sized up surf enthusiasts looking for the latest performance board shorts and steered them towards a different set of products than lifestyle shoppers looking for swimwear for a pool party. They looked at a young woman in an airy summer dress and high-heeled sandals and showed her different products than another woman in a pair of old jeans and Birkenstocks. And when the company decided to launch its online channel, it was determined to maintain that same level of observation and personalization across the Internet — even though it could no longer see its customers.
“We wanted to duplicate that store experience and recommend each visitor a personalized product assortment — one that took into account everything from their own style to the temperature outside,” says Daniel Neukomm, CEO of O’Neill. “At first, we were limited in what we could do with the online experience, and we were dissatisfied. We set out to get the same level of personal insight into customers and product trends that we’d had in stores.”
O’Neill investigated its options for recreating its in-store shopping experience online and began working with predictive analytics provider Reflektion in July 2013. To test the waters, O’Neill started by sending just 5 percent of its traffic through the Reflektion platform, implementing personalized product recommendations on product pages.
The new web site takes data from each individual customer’s previous interactions and combines it with demographic data such as customer location, age, and prior purchases to formulate recommendations, like a personal shopping assistant would do. It evaluates millions of attributes about customers, products, and channels and then predicts shopping behavior and critical business trends. Its highly personalized, real-time recommendations are based on consumer behavior, trending products, top sellers, and moment-by-moment availability of the product line, all driven by individual user models. The site even understands user intent and creates a unique customer experience optimized to sell, cross-sell, or upsell. It displays product images as personally curated items on product and checkout pages.
And it works — even better than an intuitive salesperson because it knows more than any person possibly could. O’Neill saw an immediate uptick in revenue per visit, conversion rate, average order size, and page views compared to traffic not going through Reflektion’s platform. O’Neill quickly expanded use of Reflektion by replacing its category pages with personalized product assortments and implementing Reflektion’s visual site search, which actively engages shoppers with personalized product results while they are typing. Within months, the conversion rate had increased 50 percent, and O’Neill decided to put 100 percent of traffic through the Reflektion platform during the 2013 holiday season.
In addition to implementing targeted recommendations and dynamic discounting for online customers, O’Neill leveraged Reflektion for mobile optimization of the web site. This is a type of personalization too because O’Neill’s online customers are 14 to 24 years old — smartphone users, in other words. O’Neill makes sure that their mobile shopping experience is optimized for the size and capabilities of their devices.
“Reflektion has become one of our greatest strategic assets,” says Neukomm. As the company hoped it would, Reflektion’s algorithms and machine learning enhanced O’Neill’s in-store tradition of personalized service so that the company now has even greater insight into its customers and can leverage that to optimize immediate sales. But Reflektion did more than help O’Neill see its customers — it provided O’Neill fresh insight into its own operations. It now reveals product trends and predicts results in O’Neill’s overall business. This supports wholesalers and retailers in making better buying decisions and enables O’Neill to optimize its design and production processes for optimal profitability — and continued success in innovating to meet customers’ needs.
In its brick-and-mortar stores, O’Neill delivered the personalization that was key to its corporate value proposition. It trained staff to treat each customer as a unique individual, and to pull together product assortments that catered to their needs. Salespeople sized up surf enthusiasts looking for the latest performance board shorts and steered them towards a different set of products than lifestyle shoppers looking for swimwear for a pool party. They looked at a young woman in an airy summer dress and high-heeled sandals and showed her different products than another woman in a pair of old jeans and Birkenstocks. And when the company decided to launch its online channel, it was determined to maintain that same level of observation and personalization across the Internet — even though it could no longer see its customers.
“We wanted to duplicate that store experience and recommend each visitor a personalized product assortment — one that took into account everything from their own style to the temperature outside,” says Daniel Neukomm, CEO of O’Neill. “At first, we were limited in what we could do with the online experience, and we were dissatisfied. We set out to get the same level of personal insight into customers and product trends that we’d had in stores.”
O’Neill investigated its options for recreating its in-store shopping experience online and began working with predictive analytics provider Reflektion in July 2013. To test the waters, O’Neill started by sending just 5 percent of its traffic through the Reflektion platform, implementing personalized product recommendations on product pages.The new web site takes data from each individual customer’s previous interactions and combines it with demographic data such as customer location, age, and prior purchases to formulate recommendations, like a personal shopping assistant would do. It evaluates millions of attributes about customers, products, and channels and then predicts shopping behavior and critical business trends. Its highly personalized, real-time recommendations are based on consumer behavior, trending products, top sellers, and moment-by-moment availability of the product line, all driven by individual user models. The site even understands user intent and creates a unique customer experience optimized to sell, cross-sell, or upsell. It displays product images as personally curated items on product and checkout pages.
And it works — even better than an intuitive salesperson because it knows more than any person possibly could. O’Neill saw an immediate uptick in revenue per visit, conversion rate, average order size, and page views compared to traffic not going through Reflektion’s platform. O’Neill quickly expanded use of Reflektion by replacing its category pages with personalized product assortments and implementing Reflektion’s visual site search, which actively engages shoppers with personalized product results while they are typing. Within months, the conversion rate had increased 50 percent, and O’Neill decided to put 100 percent of traffic through the Reflektion platform during the 2013 holiday season.
In addition to implementing targeted recommendations and dynamic discounting for online customers, O’Neill leveraged Reflektion for mobile optimization of the web site. This is a type of personalization too because O’Neill’s online customers are 14 to 24 years old — smartphone users, in other words. O’Neill makes sure that their mobile shopping experience is optimized for the size and capabilities of their devices.
“Reflektion has become one of our greatest strategic assets,” says Neukomm. As the company hoped it would, Reflektion’s algorithms and machine learning enhanced O’Neill’s in-store tradition of personalized service so that the company now has even greater insight into its customers and can leverage that to optimize immediate sales. But Reflektion did more than help O’Neill see its customers — it provided O’Neill fresh insight into its own operations. It now reveals product trends and predicts results in O’Neill’s overall business. This supports wholesalers and retailers in making better buying decisions and enables O’Neill to optimize its design and production processes for optimal profitability — and continued success in innovating to meet customers’ needs.