1. The Value of Building Better Product Data Ryan DouglasSingleFeed ADNSF Conference – Las VegasMarch 9, 2011

2. Quick Intro Why Build Better Data Creating a Process How To Implement Real World Examples How To Build It Recap Q&A Quick Overview

3. Over 5 years hands on ecommerce experience At SingleFeed – Customer Development and Full Service Account Management PlumberSurplus.com – Internet Retailer Hot 100 Retailer on Custom .net platform. Oversaw All SEM including data feeds for CSEs and affiliates. 100K+ skusacross 2 sites Conference Speaker – Internet Retailer & others Remember - I used to be in your shoes! Personal Bio

4. Leading data feed management tool for retailers Founded in 2006 VC backed (True Ventures) Experienced Team – Former Yahoo, Google, Shopping Engine and eCommerce Retailers Trusted Partner – To Google and other leading shopping engines Core Customer - Retailers doing $250K to $20M Pricing – Flat Rate Service plans from $99/mo ADNSF Plug-in Available from Vortx About SingleFeed

5. Stand out from your competition. Adds value to your business. Reduce confusion or concerns of shoppers (Eliminate FUDD’s). Increase sales and traffic – sometimes within days. Many Retailers overlook the value of their data. Easier to leverage good data across channels Why Build Better Product Data?

6. On Product detail pages for SEO Print and Online Catalogs Comparison Shopping Engines Google Product Search, Bing Shopping, Pricegrabber, Nextag, Become.com and more Site Search Tools (SLI, Search Spring, Certona, etc) Sitemaps for Search Engines In Email Newsletters/Campaigns How is your Product Data Used?

7. Smaller Retailers - Manually Entered Transcribed from physical catalogs? Digital formats Other websites – Stealing from competitors or manufacturers? Online catalogs Spreadsheets and PDFs Where Does Your Data Come From?

8. Set a “data standard” Give your data Integrity! Any new fields to add? Review Process Begin Requiring New Fields like UPC, Brand/Manufacturer, model number Create a plan to update existing products Set a Goal and a Target Finish Date Separate Out Attributes into New Fields Color, Model Number, Brand Extremely useful to have this data “attributable” Ask for better product data from vendors Create a Data Entry Process

9. How To Implement Changes In-house: Interns – Free, readily available. Check w/schools Hire/Build a Data Cleansing Team Have a Team Pizza Party! Not just for Little Leaguers Contract Out: oDesk Amazon Mechanical Turk Craigslist Outsourcing Firms Leverage Technology! Implementing Improvements

10. Real World Examples

11. Example 1

12. Example 2

13. Example 3

14. Example 4

15. How To Build It Better

17. Figure out what’s relevant to your products

18. Find Keywords from your analytics

19. Typically includes:

20. Brand/Manufacturer

21. Model Numbers

22. Colors and Sizes

23. Gender

24. Keyword PhrasesWhat Goes In a Title?

26. Vizio42”LCD TV E420VO [brand] [size] [keyword phrase] [model]

27. Levi’s Women’s 501 Dark Wash Denim Jeans [brand] [gender] [model][color] [keyword phrase]Mix and Match Components

28. TDK 16x 4.7gb 50 pack TDK DVD-R Storage Media 16x 4.7gb 50 pack Arturo Fuente Chateau Arturo Fuente Chateau Cigars MephistoHurrikan MephistoHurrikanMen’s Dress Shoe Use Those Keywords!

29. Capitalization Good Bad Good Good Bad

30. Try using Synonyms for colors (next slide) Include BOTH the unique color and common color Example- IKEA Stockholm Coffee Table Espresso Black When shoppers can’t see pictures, they need colors they can understand. General vs. Refined web searches Unique and Common Colors Good Better Best

31. Red = rose, rouge, crimson, scarlet, sangria, burgundy Orange = amber, tangerine, pumpkin, persimmon, rust Yellow = lemon, chartreuse, gold, saffron Pink = coral, magenta, rose, salmon, fuchsia Green= jade, lime, olive, moss, hunter Blue = cerulean, cyan, turquoise, teal, azure, periwinkle, cornflower, cobalt, sapphire Purple = amethyst, eggplant, indigo, lavender, violet, mauve Black = espresso, carbon, charcoal, ebony, onyx, obsidian Brown = auburn, bronze, burnt umber, rust, sepia, sienna, tan, taupe, chocolate Color Synonyms

32. Invest In your Product Data Don’t take shortcuts Make a Plan Use Tools & Resources to make it easier No “Magic One Size Fits All” Solution Key Take Aways

33. oDesk - Find affordable contractors http://www.odesk.com Amazon Mechanical Turk - Pay per “task” work pool https://www.mturk.com FindWatt – Optimize Product Data and Attributes http://findwatt.com Hi Tech Outsourcing - Data Entry and Cleanup Firm http://hitechexport.com Additional Links

34. Question and Answer

35. Ryan Douglas ryan@singlefeed.com 800-705-8852 ext 201 www.SingleFeed.com Contact Info