1. 1 Increasing Sales through Recommendation Systems Strategy and Customization (Creating a Personalized Online Shopping Experience) Presented by Dart Marketing, LLC

2. 2 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. Impact of Recommendations Systems

3. 3 Scrollable Recommendations Strategy Displays Community Ratings Styled to conform to your site: Remove Unwanted Items Collect Ratings If Logged-In 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.

4. 4 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. Its All About the Data The better the data, the better the recommendations. Its that simple. Philosophy: Collect the best data, derive a custom solution, then validate initial results by hand. Once foundation is set, profiles update in real time.

5. 5 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 Consultative/Analytic Approach The Also Bought method shows whats popular, but not whats most relevant. Our affinity-based approach is much more effective. Affinity Maps & Recommendations: The bubbles above represent products sized by sales volume. Products close to each other are recommended to each other.

6. 6 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

7. 7 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

8. 8 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. Next Steps

9. 9 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. Why Dart? Its People

10. 10 Dart Vs. The Competition Distinguishing Benefits DartOthers Interactive, scrollable recommendations( ) Collect product ratings to enable customers to train site( ) Extraordinary accuracy through Affinity Mapping techniques( ) Solution based on sales data and customer product ratings( ) Analytical, human-based approach tailored to each client( ) Mapping, pricing strategy, and other analytic consulting services( ) Other Benefits Easy to integrate Hosted solution Client can customize recommendations Search engine included Optional performance based pricing

11. 11 Example: Movie Recommendations I 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. Our recommendations stand up to the worlds best. Note: There are thousands of examples on our web site. Click on dvd demo.

12. 12 Example: Movie Recommendations II 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). Our recommendations stand up to the worlds best. Note: There are thousands of examples on our web site. Click on dvd demo.

13. 13 Contact Info DART Marketing, LLC Craig Tomarkin, President CTomarkin@DartM.net 203-259-0676 Phil Goodhart, VP Business Development PGoodhart@DartM.net 203-261-4731 http://Dartm.net