Increasing Sales through Recommendation Systems Strategy and

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Increasing Sales through Recommendation
Systems Strategy and Customization (Creating a
Personalized Online Shopping Experience) Presente
d by Dart Marketing, LLC
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Impact of Recommendations Systems

• Industry research indicates that successful
  online retailers are generating as much as 35 of
  their business from recommendations. Are you
  leaving money on the table?
• Meanwhile, the race is on because once someone
  invests the time to provide feedback to a
  website, he/she will prefer to shop there versus
  somewhere else.
• Personalization increases the amount of time
  people spend at the site and increases repeat
  visits, growing loyalty and sales.

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Strategy
Styled to conform to your site

• New Shoppers
• Scrollable, community recommendations
• Checkout Accessories, cross-sells
• Repeat Buyers (Personal Shopping Assistant)
• Scrollable, personalized recommendations
• Profile Building Collect product ratings
• Smart Search tied to user preferences so it shows
  items customers will like
• Can be linked to existing search, or
• Used on its own. Ours displays everything the
  customer needs (not just list of products). Sort
  by popularity, price, etc.

Displays Community Ratings
Collect Ratings If Logged-In
Remove Unwanted Items
Scrollable Recommendations
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Its All About the Data
The better the data, the better the
recommendations. Its that simple.

• Product Ratings Data Customer feedback improves
  quality of recommendations. Collecting this data
  builds a relationship.
• Sales Data Most useful after ratings data.
  Removing gift purchases ensures matches are based
  on customers personal tastes.
• Click Data Used as a last resort. A poor
  predictor of customer purchases. Does nothing to
  build a relationship.

Philosophy Collect the best data, derive a
custom solution, then validate initial results by
hand. Once foundation is set, profiles update in
real time.
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Consultative/Analytic Approach
The Also Bought method shows whats popular,
but not whats most relevant. Our affinity-based
approach is much more effective.

• A different approach map product affinities in
  multi-dimensional space. 
• Successfully applied to Gevalia coffees and teas,
  and re-validated with Netflix movie data.
• Extraordinary recommendations accuracy for new
  shoppers - even better for returning customers.
• Results are applicable to product and pricing
  strategies, offline merchandising, cross-
  up-selling

Affinity Maps Recommendations The bubbles
above represent products sized by sales volume.
Products close to each other are recommended to
each other.
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Customization Dashboard

• Add product details such as
• Sku, Type, Sub-type, Category, Brand, etc.
• Pricing strategy
• Dates
• Customize recommendations by
• Business rules
• Profit margin
• Inventory availability
• On-sale items and other criteria
• Reports include
• Sales stats from recommendations
• Web stats relating to recommendations
• Date periods
• Custom reports available

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Secondary Strategies

• Other uses for customer profile data
• Product/Pricing Strategies
• My-Gift Store (Recommend Gifts)
• Recommendations for when bill-to and ship-to
  dont match
• Email gift reminders with recommendations
• Encourage customers to add new gift recipients
• New Reasons to Email
• Post-sale requests for product ratings
• Personalized promotions
• Telemarketing Up-Selling
• Direct Mail Highly recommended products
• Catalog Merchandising

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Next Steps

• Collect Data
• Gather transactions data, then refine to exclude
  gifts, ship-tos, etc.
• Analyze Data
• Begin by mining and mapping affinities.
• Create demo to compare new recommendations with
  current ones.
• Further Personalize and Enhance Initial Solution
• Motivate shoppers to share ratings to further
  personalize their recommendations.

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Why Dart? Its People

You will have a business relationship with
experts in the industry Craig Tomarkin,
President (Affinity mapping, modeling, research,
analysis) Craig has spent his career converting
ideas into profit. He helped GM design and launch
the worlds first free rewards credit card,
resulting in 5 million accounts in the first
year. For Gevalia Coffee, he developed an
innovative product mapping technique that
optimized cross-selling, pricing, and new-product
strategies a precursor of his current eCommerce
recommendations strategy. Craig holds a BSM from
the A.B. Freeman School of Business at
Tulane. Paul Delano, Technology Expert (Java,
eCommerce, SEO, hosted solutions) Pauls
innovations in artificial intelligence and
collaborative search have led to his being
awarded four patents. He created the first
Internet commerce site for PC Flowers.com as well
as the infrastructure for a nationwide
interactive television system. He has taught Java
courses at companies like JPMorganChase and
Hewlett Packard. Paul received a MS in Computer
Systems Engineering from Rensselaer Polytechnic
Institute and a BS from Carnegie Mellon. Phil
Goodhart, Direct Response Marketing Expert
(Client support, eCommerce) Phil is a veteran of
the Danbury Mint, a leading direct marketer of
consumer merchandise. He was recognized as a
premiere marketing strategist, as well as an
innovator in identifying new product
opportunities. He managed the development of the
Danbury Mints first eCommerce site. Phil earned
his MBA at Harvard and BA at Princeton.
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Dart Vs. The Competition

• Other Benefits
• Easy to integrate
• Hosted solution
• Client can customize recommendations
• Search engine included
• Optional performance based pricing

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Example Movie Recommendations I
Our recommendations stand up to the worlds best.
Caddyshack Slapstick Comedy. Chevy Chase, Bill
Murray, Rodney Dangerfield. 1980. Key Findings
Darts system doesnt recommend Caddyshack II,
even though its a sequel. Since it had a
different cast, and customers did not rated it as
highly as the original, it is not as relevant a
match.
Note There are thousands of examples on our web
site. Click on dvd demo.
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Example Movie Recommendations II
Our recommendations stand up to the worlds best.
Sleeper Slapstick Romantic Comedy. Woody Allen,
Diane Keaton. 1973. Key Findings Darts system
recommended Woody Allen romantic comedies
exclusively and chose titles from the matching
time period. His movies match very closely,
indicating he is a genre unto himself (i.e. a
Woody Allen movie).
Note There are thousands of examples on our web
site. Click on dvd demo.
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Contact Info

• DART Marketing, LLC
• Craig Tomarkin, President
• CTomarkin_at_DartM.net
• 203-259-0676
• Phil Goodhart, VP Business Development
• PGoodhart_at_DartM.net
• 203-261-4731
• http//Dartm.net