Innovative Product Recommendations from L2's Personalization Report
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20 L2 INTELLIGENCE REPORT PERSONALIZATION June 2, 2015 Product Recommendations For many brands, the first step on their site personalization roadmap is the incorporation of intelligent product recommendation tools. In almost all cases, these recommendation engines take one of three forms: Similar Items: Consumers are given suggestions for substitutable items, with slightly different product attributes Complimentary Items: Consumers are cross-sold accessory items to purchase in addition to the product being currently browsed Recently Viewed Items: Consumers are served a collection of products they have previously viewed on the site Brands should be wary of several common traps when incorporating recommendation engines into their site experiences. Without applying the critical lens of how to effectively merchandise products, brands risk over-leveraging automated recommendation algorithms. Express appears to be relying on an engine that is returning the same recommendations regardless of the product being viewed – leading to issues such as cross-selling scarves and socks with swimsuits, and recommending the exact item currently being browsed. On the other hand, brands often fail to leverage their analytics to the fullest extent—to provide data-driven recommendations that guide the consumer towards new product discovery. For example, shoe retailer Aldo relies heavily on recommending the same product in alternate colors, missing the opportunity to cross-sell accessories or introduce the consumer to different styles based on their browsing behavior. 71% 56% 36% Express returns the same product recommendations for all bikini tops. Rather than leverage on data-driven product adjacencies, Aldo cross-sells the same item in different colors.
Transcript of Innovative Product Recommendations from L2's Personalization Report
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L2 INTELLIGENCE REPORT PERSONALIZATION
June 2, 2015
Product RecommendationsFor many brands, the first step on their site personalization roadmap is the incorporation of intelligent product recommendation tools. In almost all cases, these recommendation engines take one of three forms:
Similar Items: Consumers are given suggestions for substitutable items, with slightly different product attributes
Complimentary Items: Consumers are cross-sold accessory items to purchase in addition to the product being currently browsed Recently Viewed Items: Consumers are served a collection of products they have previously viewed on the site
Brands should be wary of several common traps when incorporating recommendation engines into their site experiences.
Without applying the critical lens of how to effectively merchandise products, brands risk over-leveraging automated recommendation algorithms. Express appears to be relying on an engine that is returning the same recommendations regardless of the product being viewed – leading to issues such as cross-selling scarves and socks with swimsuits, and recommending the exact item currently being browsed.
On the other hand, brands often fail to leverage their analytics to the fullest extent—to provide data-driven recommendations that guide the consumer towards new product discovery. For example, shoe retailer Aldo relies heavily on recommending the same product in alternate colors, missing the opportunity to cross-sell accessories or introduce the consumer to different styles based on their browsing behavior.
71%
56%
36%
Express returns the same product recommendations for all bikini tops. Rather than leverage on data-driven product adjacencies, Aldo cross-sells the same item in different colors.
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L2 INTELLIGENCE REPORT PERSONALIZATION
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Only one in five brands are testing all three types of product recommendations on their site. Nearly half of brands are offering just one type of recommendation, missing the opportunity to achieve distinct conversion outcomes with different cross-selling and upselling strategies.
Product Recommendations (Cont’d)
Personalization: Use of Cross-Selling on Product PagesBy Cross-Sell TypeApril 2015, n=107 Brands
Recently Viewed
Similar Items Complimentary Items
None
22%
7%
14%
21%
1%
14% 3%
19%
The retailer’s “Complete The Look” bar pulls in other products from the brand being browsed, to cater to loyalists and grow basket sizes.
Sephora, one of the 22 brands offering all three types of recommendations from the product page, exemplifies how having multiple types of cross-sells can convert unique subsets of shoppers.
The “Similar Products” bar appeals to a cross-brand shopper still in the research phase, helping the consumer to narrow in on the perfect product to meet their use case. By providing an archive of recently viewed products, Sephora also helps consumers revisit products they researched previously, at the moment when they are ready to buy.
TACTIC: Target different consumer segments and inspiredistinct behaviors via multiple types of cross-selling
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L2 INTELLIGENCE REPORT PERSONALIZATION
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Product Recommendations (Cont’d)
When the consumer is still in the research phase, Bed Bath & Beyond provides a wealth of product recommendations to both increase basket size and help the consumer narrow in on their desired product.
From the product page, Bed Bath & Beyond recommends alternate products as well as expensive complimentary products, including juicers priced from $119.99 - $299.99.
After the consumer adds the product to their cart, the complimentary products are repositioned as “last minute items,” all selling for a much lower pricepoint ($8.99 - $24.95). The effect is similar to the impulse buys a retailer might offer at-counter.
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TACTIC: Adjust the products being recommended based on where the consumer is in the purchase funnel
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L2 INTELLIGENCE REPORT PERSONALIZATION
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Product Recommendations (Cont’d)
Ralph Lauren frequently merchandises a single item as part of a complete look. The brand is able to override its algorithmic cross-selling engine to maintain the integrity of look-based product recommendations. Suggested SKUs can also be added to the cart directly, from the extra-large merchandising environment.
TACTIC: Facilitate the purchase of complete looks directly from the recommendation engine
When at its best, personalization is delivered with a combination of technology and human intuition. Savvy marketers use algorithms and machine learning to help scale the impact of the customer insights they possess.”“DAVID BRUSSIN Founder and Chief Product Officer, Monetate
