L2 took a deep dive into website personalization in retail ecommerce today. They "mystery shopped" 100+ brands including Target, Maybelline, Express, and Sephora. Then they analyzed the types of personalization they received from these big brands. This section of the report

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L2 INTELLIGENCE REPORT PERSONALIZATION
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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.

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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 Pages
By Cross-Sell Type
April 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 inspire
distinct 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.
21 3
TACTIC: Adjust the products being recommended based on where the consumer is in the purchase funnel

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L2 INTELLIGENCE REPORT PERSONALIZATION
June 2, 2015
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