Online shoppers increasingly expect curated experiences and great service that replicates what they can find at the best brick-and-mortar stores. Meanwhile brands are striving to drive conversion by serving up products that anticipate consumers’ needs and wants.

Online personalization is a challenging mandate that is nonetheless becoming essential to top-line growth as traffic becomes a more expensive lever. These five do’s and don’ts for brands seeking to serve up intelligent product recommendations emerged from recent research on personalization conducted by L2 in partnership with Monetate.

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DO: Use several types of data-driven recommendations
DON’T: Miss opportunities to cross-sell by failing to fully leverage analytics

Recommendation engines can leverage data to provide three types of recommendations to consumers: items similar to what a consumer is browsing, complementary items and recently viewed items. Yet nearly half of 107 brands that L2 studied offer just one type, missing the opportunity to spur product discovery and achieve distinct conversion outcomes with different cross-selling and upselling strategies. Aldo, for example, relies heavily on recommending the same product in different colors — failing to cross-sell accessories or introduce consumers to other styles based on their browsing behavior.

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Best in class: Sephora, which offers all three types of recommendations on the product page, exemplifies how multiple types of cross-sells can convert unique subsets of shoppers. A “Complete the Look” bar pulls in other products from the brand being browsed, to cater to loyalists and grow basket sizes. A “Similar Products” bar appeals to shoppers in the research phase, helping them hone in on the perfect product. And an archive of recently viewed items helps consumers revisit products of interest at the moment they are ready to buy.


DO: Couple technology with human intuition
DON’T: Over-leverage algorithms

Leaning too heavily on algorithms and machine learning can be counter-productive — without applying a critical human lens, brand recommendations can result in an e-commerce fail. Express appears to be relying on an engine that returns the same recommendations regardless of the product being viewed, leading to nonsensical experiences such as seeing recommendations for the exact item being browsed or suggestions for scarves and socks while shopping for a swimsuit.

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Best in class:
Ralph Lauren, which frequently merchandises a single item as part of a complete look, is able to override its algorithmic cross-selling engine to maintain the integrity of its look-based product recommendations.

Use data to provide superior customer service
DON’T: Assume that online shopping can’t match the in-store experience

Across digital customer service touchpoints, robust personalization is the first step toward matching the type of service offered by an in-store associate. Online fitting and finding tools that create a rich and highly personalized browsing experience can emulate — or even outperform — the experience of trying on products at retail with the help of a salesperson. One way to do this is by leveraging size profiles to personalize assortment and provide fit recommendations at the SKU level.

Best in class: DXL, which sells extra-large men’s apparel, prompts new site visitors to create a size profile that is cookied and saved regardless of log-in status. The browsing experience is then dynamically personalized, so a shopper sees only brands that fit his size profile (with an easy option to turn off the feature and see all sizes). On individual product pages, a fit predictor engine uses the consumer’s measurements to recommend a size.

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Take context into account — location, weather, time of day, etc.
DON’T: Disregard the brick-and-mortar practice of localized merchandising

The future of personalization involves considering not only individual interests and preferences but also the local context, mimicking the ideal offline shopping experience. Olay has experimented with auto-detecting time of day, location and weather in order to serve up custom copy on the site homepage that promotes a specific product. Lilly Pulitzer is testing emails that dynamically personalize based on when and where they are opened: Consumers in moderate climates might see layering items, for example, while those in warmer climates see summer accessories. David Brussin, founder and chief product officer at Monetate, advises brands to combine in-store and online data to take the customer experience to the next level — for example, understanding which colors and styles are relevant in which markets.

Best in class: Scandinavian sportswear brand Helly Hansen is known for sailing and watersport products in southern Europe, and skiing and rainwear in northern Europe. After enabling geotargeting on its e-commerce site as the brand expanded globally, Helly Hansen took personalization a step further by pairing weather forecasts with targeted recommendations. When five days of rain were forecast in Germany, for instance, the landing page showcased rain gear — a tactic that achieved a 170 percent conversion rate and a 52 percent conversion rate among new visitors, according to Monetate.

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Cross-sell through micro-targeted offers
Take a one-size-fits-all approach to promotions and discounts

For many organizations, the ultimate goal of personalization is the ability to deliver the right offer at the right time, nudging consumers over the brink to conversion. Taking a personalized approach to offers can also help brands to sacrifice less in terms of margins and/or brand equity. American Eagle, for instance, increases the likelihood of a successful cross-sell and a higher order value by serving a promotion for belts to a consumer shopping for denim shorts.

Best in class: Staples uses browsing history to deliver real-time offers on products the consumer has spent a substantial amount of time browsing. These “Exclusive Offers” are also time-sensitive — valid for 20 minutes only — creating a sense of urgency to drive the desired behavior. When consumers revisit, they see “Special Offers for You,” featuring products related to past purchases and browsing behaviors.


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For more on how organizations can unlock return on their personalization efforts across channels, see L2’s Personalization report.

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