The Value of Building Better Product Data - Ryan Douglas, SingleFeed


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The Value of Building Better Product Data - Ryan Douglas, SingleFeed

  1. 1. The Value of Building Better Product Data<br />Ryan DouglasSingleFeed<br />ADNSF Conference – Las VegasMarch 9, 2011<br />
  2. 2. Quick Intro<br />Why Build Better Data<br />Creating a Process<br />How To Implement<br />Real World Examples<br />How To Build It<br />Recap<br />Q&A<br />Quick Overview<br />
  3. 3. Over 5 years hands on ecommerce experience<br />At SingleFeed – Customer Development and Full Service Account Management<br /> – Internet Retailer Hot 100 Retailer on Custom .net platform. Oversaw All SEM including data feeds for CSEs and affiliates. 100K+ skusacross 2 sites<br />Conference Speaker – Internet Retailer & others<br />Remember - I used to be in your shoes!<br />Personal Bio<br />
  4. 4. Leading data feed management tool for retailers<br />Founded in 2006<br />VC backed (True Ventures)<br />Experienced Team – Former Yahoo, Google, Shopping Engine and eCommerce Retailers<br />Trusted Partner – To Google and other leading shopping engines<br />Core Customer - Retailers doing $250K to $20M<br />Pricing – Flat Rate Service plans from $99/mo<br />ADNSF Plug-in Available from Vortx<br />About SingleFeed<br />
  5. 5. Stand out from your competition.<br />Adds value to your business.<br />Reduce confusion or concerns of shoppers (Eliminate FUDD’s).<br />Increase sales and traffic – sometimes within days.<br />Many Retailers overlook the value of their data.<br />Easier to leverage good data across channels<br />Why Build Better Product Data?<br />
  6. 6. On Product detail pages for SEO<br />Print and Online Catalogs<br />Comparison Shopping Engines<br />Google Product Search, Bing Shopping, Pricegrabber, Nextag, and more<br />Site Search Tools (SLI, Search Spring, Certona, etc)<br />Sitemaps for Search Engines<br />In Email Newsletters/Campaigns<br />How is your Product Data Used?<br />
  7. 7. Smaller Retailers - Manually Entered<br />Transcribed from physical catalogs?<br />Digital formats<br />Other websites – Stealing from competitors or manufacturers?<br />Online catalogs<br />Spreadsheets and PDFs<br />Where Does Your Data Come From?<br />
  8. 8. Set a “data standard”<br />Give your data Integrity!<br />Any new fields to add?<br />Review Process<br />Begin Requiring New Fields like UPC, Brand/Manufacturer, model number<br />Create a plan to update existing products<br />Set a Goal and a Target Finish Date<br />Separate Out Attributes into New Fields<br />Color, Model Number, Brand<br />Extremely useful to have this data “attributable”<br />Ask for better product data from vendors<br />Create a Data Entry Process<br />
  9. 9. How To Implement Changes<br />In-house: <br />Interns – Free, readily available. Check w/schools<br />Hire/Build a Data Cleansing Team<br />Have a Team Pizza Party! Not just for Little Leaguers<br />Contract Out:<br />oDesk<br />Amazon Mechanical Turk<br />Craigslist<br />Outsourcing Firms<br />Leverage Technology!<br />Implementing Improvements<br />
  10. 10. Real World Examples<br />
  11. 11. Example 1<br />
  12. 12. Example 2<br />
  13. 13. Example 3<br />
  14. 14. Example 4<br />
  15. 15. How To Build It Better<br />
  16. 16. <ul><li>There’s no one size fits all “magic formula”
  17. 17. Figure out what’s relevant to your products
  18. 18. Find Keywords from your analytics
  19. 19. Typically includes:
  20. 20. Brand/Manufacturer
  21. 21. Model Numbers
  22. 22. Colors and Sizes
  23. 23. Gender
  24. 24. Keyword Phrases</li></ul>What Goes In a Title?<br />
  25. 25. <ul><li>Logitech K350 Wireless Keyboard & Mouse [brand] [model] [feature] [keyword phrase]
  26. 26. Vizio42”LCD TV E420VO [brand] [size] [keyword phrase] [model]
  27. 27. Levi’s Women’s 501 Dark Wash Denim Jeans [brand] [gender] [model][color] [keyword phrase]</li></ul>Mix and Match Components<br />
  28. 28. TDK 16x 4.7gb 50 pack<br />TDK DVD-R Storage Media 16x 4.7gb 50 pack<br />Arturo Fuente Chateau<br />Arturo Fuente Chateau Cigars<br />MephistoHurrikan<br />MephistoHurrikanMen’s Dress Shoe<br />Use Those Keywords!<br />
  29. 29. Capitalization<br />Good<br />Bad<br />Good<br />Good<br />Bad<br />
  30. 30. Try using Synonyms for colors (next slide)<br />Include BOTH the unique color and common color<br />Example- IKEA Stockholm Coffee Table Espresso Black<br />When shoppers can’t see pictures, they need colors they can understand.<br />General vs. Refined web searches<br />Unique and Common Colors<br />Good<br />Better<br />Best<br />
  31. 31. Red = rose, rouge, crimson, scarlet, sangria, burgundy<br />Orange = amber, tangerine, pumpkin, persimmon, rust<br />Yellow = lemon, chartreuse, gold, saffron<br />Pink = coral, magenta, rose, salmon, fuchsia<br />Green= jade, lime, olive, moss, hunter<br />Blue = cerulean, cyan, turquoise, teal, azure, periwinkle, cornflower, cobalt, sapphire<br />Purple = amethyst, eggplant, indigo, lavender, violet, mauve<br />Black = espresso, carbon, charcoal, ebony, onyx, obsidian<br />Brown = auburn, bronze, burnt umber, rust, sepia, sienna, tan, taupe, chocolate<br />Color Synonyms<br />
  32. 32. Invest In your Product Data<br />Don’t take shortcuts<br />Make a Plan<br />Use Tools & Resources to make it easier<br />No “Magic One Size Fits All” Solution<br />Key Take Aways<br />
  33. 33. oDesk - Find affordable contractors<br />Amazon Mechanical Turk - Pay per “task” work pool<br />FindWatt – Optimize Product Data and Attributes<br />Hi Tech Outsourcing - Data Entry and Cleanup Firm<br />Additional Links<br />
  34. 34. Question and Answer<br />
  35. 35. Ryan Douglas<br /><br />800-705-8852 ext 201<br /><br />Contact Info<br />