Hands on with bluecore:

Increase conversion with new product

recommendation strategies

77%of email subscribers are more

likely to purchase from personalized emails

terminology Look What You Forgot…

Input Products

• Typically featured in first content area• Fed into the Bluecore algorithms that power

recommendations

• Set of products a customer engaged with• Includes browsed, carted or purchased items

How They Are Used

Output Products• Set of products generated from the

recommendation engine

terminology

Product Attribute

• Product Name• Product Price• Division• Category• Sub-Category• Brand• Stock Status

A characteristic that defines a certain product

Out of box, Bluecore’s integration will pick up these product attributes:

Recommendation algorithms:

• Attribute-based

• Collaborative-based

Recommendation strategy option #1:Attribute based recommendations

Definition: This algorithm uses product attributes (not customer behavior) to drive recommendations for other products that share similar attributes

Products RecommendedUsing similar product attributes like:Brand = CoalCategory = Women’sSub-Category = Hats

Product Browsed

Attribute based recommendations: Key takeaways

1. Best Programs for Attribute Based RecommendationsAttribute driven recommendations may yield higher conversion for higher funnel programs, where customers haven’t yet decided what to buy, and where less customer behavioral data is available.

2. Test & OptimizeShopping behavior can vary greatly by brand and industry, so test into the best strategy for your programs.

3. Data IntegrityAttribute driven recommendations are only as good as the product data available on your ecommerce site. This is the best approach for sites with structured data that is consistent and prevalent across all products.

Recommendation strategy option #2:collaborative based recommendations

Definition: Collaborative based algorithms use collective wisdom of customers to identify which products tend to show up in the same session, e.g. which products tend to be viewed together or which products tend to be bought together.

Strategies We Will Walk Through Today with Use Cases:

1. Co-View

2. Co-Cart

3. Co-Purchase

4. Best Sellers

Recommendation type usage & revenue

co-view and co-purchase are the top personalized recommendation strategies for driving revenue

Best sellers

DefinitionThe Best Seller strategy shows either site-wide best sellers or category specific best sellers.

• Site-wide is great when you do not have a lot of customer browse data available.

• Category specific is great for product notification triggers that are driven by changes in the catalog.

Where To Use

Co-view

Input ProductOutput ProductsRecommendations

DefinitionAs the name indicates this algorithm recommends products that tend to be viewed in the same session with the input products. This was popularized by Amazon's "customers that viewed these items also viewed ...".

This algorithm is a good choice for product abandon emails, especially for partners with less consistent onsite data structure.Where To Use

Co-purchase

Input Product Output ProductsRecommendations

This is similar to the Co-View/Cart algorithms except that we're recommending products that tend to be bought with the input products. This data set can be enhanced by feeding offline purchase data to Bluecore

Definition

Where To UseWe typically recommend this algorithm for post-purchase emails where cross-sell is a key strategy.

Co-cart

This algorithm recommends products that tend to be carted in the same session with the input products.

Input Product Output ProductsRecommendations

Definition

This algorithm is a good choice for partners that are unable to pass Bluecore purchase data or have low sales volume. Where To Use

How to Apply These Strategies

QUESTIONS?

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