the value of building better product data - ryan douglas, singlefeed

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

Ryan Douglasryan@singlefeed.com800-705-8852 ext 201www.SingleFeed.com

Contact Info