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The Value of Building Better Product Data
Ryan DouglasSingleFeed
ADNSF Conference – Las VegasMarch 9, 2011
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Quick Intro Why Build Better Data Creating a Process How To Implement Real World Examples How To Build It Recap Q&A
Quick Overview
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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+ skus across 2 sites
Conference Speaker – Internet Retailer & others
Remember - I used to be in your shoes!
Personal Bio
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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
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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?
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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?
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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?
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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
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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
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Real World Examples
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Example 1
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Example 2
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Example 3
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Example 4
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How To Build It Better
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• There’s no one size fits all “magic formula”
• Figure out what’s relevant to your products
• Find Keywords from your analytics• Typically includes:• Brand/Manufacturer• Model Numbers• Colors and Sizes• Gender• Keyword Phrases
What Goes In a Title?
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• Logitech K350 Wireless Keyboard & Mouse [brand] [model] [feature] [keyword phrase]
• Vizio 42”LCD TV E420VO [brand] [size] [keyword phrase] [model]
• Levi’s Women’s 501 Dark Wash Denim Jeans [brand] [gender] [model] [color] [keyword phrase]
Mix and Match Components
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TDK 16x 4.7gb 50 pack TDK DVD-R Storage Media 16x 4.7gb 50
pack
Arturo Fuente Chateau Arturo Fuente Chateau Cigars
Mephisto Hurrikan Mephisto Hurrikan Men’s Dress Shoe
Use Those Keywords!
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Capitalization
Bad
Good
Bad
Good
Good
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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
Better
Best
Good
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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
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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
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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
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Question and Answer