the value of building better product data - ryan douglas, singlefeed
TRANSCRIPT
The Value of Building Better Product Data
Ryan DouglasSingleFeed
ADNSF Conference – Las VegasMarch 9, 2011
Quick Intro Why Build Better Data Creating a Process How To Implement Real World Examples How To Build It Recap Q&A
Quick Overview
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
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
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?
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?
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?
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
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
Real World Examples
Example 1
Example 2
Example 3
Example 4
How To Build It Better
• 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?
• 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
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!
Capitalization
Bad
Good
Bad
Good
Good
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
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
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
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
Question and Answer
