UNDERSTANDING RECOMMENDATIONS: VALUE, FUNCTIONALITY & BEST PRACTICES

THE VALUE OF RECOMMENDATIONS

AVERAGE ORDER VALUE

Encourage the user to explore products“I’m just browsing around”

CLICK THROUGH RATE

Optimize conversions by selecting and presenting the most relevant products based on user’s mindset and stage of purchase funnel.

BROWSING SITE

PRODUCT VIEW

ADD TO CART

PURCHASE

REPURCHASE

THE VALUE OF RECOMMENDATIONS

Surface related products & drive the user to convert

Do not distract yet increase cart value

CONVERSION RATE“I’m looking for something specific”

“I want to buy now”

RECOMMENDING THE RIGHT PRODUCTSHow do I select 3-5 products to recommend out of tens of thousands of options?

WHAT DATA SHOULD I USE?

Product attributes in the feed

The user’s behavior and activity

Common aggregate trends in behavior

HOW RECOMMENDATIONS WORK

Filter ProductsRule-based selection of

the set of eligible productsReorder the eligible

products by strategy score

Score ProductsBased on your

recommendation strategy

THE 3-STEP MACHINE

Sort & Present

SCORING BY STRATEGIES

INTRODUCING RECOMMENDATION STRATEGIES

CONTEXTUAL

PERSONALIZED

GLOBAL

By Products or Categories

MOST POPULAR

TRENDING NOW

NEWEST

SIMILAR PRODUCTS

BOUGHT TOGETHER

VIEWED TOGETHER

VIEWED AND THEN BOUGHT

COLLABORATIVE FILTERING

AFFINITY BASED

MOST POPULAR PRODUCTS Global

Now6 Months Ago

Purchase

Recent

Add to cart

Product view

● Weighted sum of all product interactions by all users

● Favors recent interactions

SIMILAR PRODUCTS Contextual (by products) Categories:

Men's Tops Short Sleeve Shirts Keywords: Stay Ready Stay Cool Loose Charged Cotton HeatGear New Arrivals Microthread

● Keywords and categories value comparison between the product in context and all other products in feed

● Factors in product popularity

BOUGHT TOGETHER Contextual (by products)

● Occurrences of product(s) in context purchased in the same transaction with other products

● Demotes products bought together with many items

AFFINITY BASED Personalized (by user)

● Derive user preference from interactions with products (real time + previous sessions)

● Reorder the most popular items by user preference of product attributes

COLLABORATIVE FILTERING Personalized (by user)

● Identify the products a user is most likely to purchase

● Based on what similar users have purchased

FILTERING PRODUCTS

• Dynamic Filters Using ‘Product Dimensions’ • Targeted Merchandising Rules

FILTER PRODUCTS BY PRODUCT ATTRIBUTES

● Match the viewed product in selected attributes (PDP)

● Differ from the viewed product in selected attributes (PDP)

● Category: Current / Parent / Any (PDP or Category)

INSERT DYNAMIC FILTERS USING PRODUCT DIMENSIONS

● Only Include (whitelist)

● Exclude (blacklist)

● Pin Product to Slot

DEPLOY TARGETED MERCHANDISING RULES

PRESENTING SORTED PRODUCTS

HOMEPAGE

Most Popular ProductsNEW VISITOR RETURNING VISITOR

PersonalizedAffinity Based

Filters: Match the user’s gender if known

PRODUCT PAGES

HIGH INTENT SIGNALS LOW / NO INTENT SIGNALSSimilar + Bought Together Similar

Viewed TogetherFilters: Match theme and category of product displayed

CART PAGES

ANY VISITORBought Together

Filters: Match items up to a certain price

THE DYNAMIC YIELD DIFFERENCE

TEST & TARGET DIFFERENT LAYOUTS & STRATEGIES

50% 50%

Users Condition

Yes No

MOST POPULAR SIMILAR

PRODUCTSMOST

POPULAR COLLABORATIVE FILTERING

FUSE MULTIPLE RECOMMENDATION STRATEGIES

“I’m just browsing around”

Surface related products & drive the user to convert

Do not distract yet increase cart value

“I’m looking for something specific”

“I want to buy now”

Encourage the user to explore products

MOST POPULAR

TRENDING NOW

NEWEST

SIMILAR PRODUCTS

BOUGHT TOGETHER

VIEWED TOGETHER

VIEWED AND THEN BOUGHT

AFFINITY BASED

COLLABORATIVE FILTERING

GlobalContextualPersonalized

INSERT RECOMMENDATIONS ANYWHERE

Personalize entire layout of your site and place recommendations anywhere on the page to drive most conversions

RENDER IN REAL-TIME, EVEN FOR EMAIL

Dynamic recommendations based on omni-channel data

USER 1

USER 2

USER 3

USER 4

ITEM 1 ITEM 2 ITEM 3

COLLABORATIVE FILTERING Personalized (by user)

ITEM 1 ITEM 2 ITEM 3

USER 1 0 1 1

USER 2 1 1

USER 3 1 1 1

USER 4 0 1

COLLABORATIVE FILTERING Personalized (by user)

DEPLOY SMARTER RECOMMENDATIONS AND DRIVE MORE REVENUE

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