Leveraging Data to Achieve Greater Sales
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Presented by Ken Levy for InnoTech Oregon 2011
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Leveraging Data to Achieve Greater Sales
- 1. Leveraging Data to Achieve Greater Sales Ken Levy CEO & Co-Founder
- 4. In the store… Sales Agent’s Knowledge
- 5. Train More Sales Agents…
- 6. Online
- 7. Problem
- 8. Opportunity ONLINE DATA Purchases Product Views Product Attributes User Generated Content Social Profiles A/B Testing NEW SOLUTIONS Personalized Recommendations Ratings & Reviews
- 9. Stats eMarketer Survey: Features to Add to Website
- 11. Better Shopping Experience Convert Browsers to Buyers Larger Orders More Repeat Customers
- 12. Key Types of Recommendations Cross-sell Similar Personalized Up-sell
- 13. Data How Does It Work? Recommendation Engine
- 14. Product Page: Cross-Sell Customers Also Bought
- 15. Product Page: Similar & Cross-Sell Scrolling Window
- 20. Ratings & Review Request
- 21. Abandoned Cart
- 25. Ratings & Review Request
- 26. Abandoned Cart
- 27. Use Cross-SellBased upon your buying habits, you may also like… Based upon your purchases, you may also like…
- 28. 25% More Sales
- 29. 50% larger average order value15% higher conversion
- 30. 10% more sales
- 32. Affordable Pricing $50 to $1,300 per Month Recommendations Displayed
- 33. Ratings & Review
- 34. Recommendations & Ratings /Reviews
- 35. Future: Social Recommendations Friends who bought this… Dan Boyd Chris Wicker Nash Levy
- 36. Future: Social Profiles Movies For You
- 38. In-Store Trends Price compare and leave Keep shopper in store with recommendations
- 39. In-Store Trends Price compare and leave Keep shopper in store with recommendations In-Store Kiosks with Recommendations Employee Handhelds with Recommendations
- 40. Merchandising Decisions What type on which pages? Product Pages = Similar and Cross-sell Cart = Cross-sell Shipping & User Admin Pages = Personalized Up-sell Newsletter, Order and Shipping = Personalized Up-sell Cart abandonment = Similar and Cross-sell Social / Facebook Store = Personalized Up-sell
- 41. Merchandising Decisions (2) How many recommendations? Show 3 to 5 at a time and include a slider Where on the page? To Right of product, or Below product, but above the fold
- 42. Merchandising Decisions (3) Titles – shoppers like to know why People also bought… People also viewed… Based upon your order, may we suggest… Based upon your past purchases, you may also like… Same decisions for ratings & reviews Measure Results with Analytics
- 43. Thank You Ken Levy Co-Founder & CEO Ken@4-Tell.com 971-338-9837
Notes de l'éditeur
- Agenda: Today, we are going to look how to use your online data to increase sales, specifically with product recommendations and ratings and reviews.SurveyHow many people work in ecommerce?How many people are from web agencies?How many familiar with recommendations or product reviews?
- Sales Data for Cross-SellProduct attributes for low volume sales dataView Data for Similar1.5 seconds for websiteHold data in memory