The document discusses the impact of web data on competitive price intelligence, highlighting how transparency has changed in the supply chain. It outlines various use cases for extracting competitive pricing data from the web and emphasizes the importance of data accuracy. The presenters advocate for leveraging web data to optimize pricing strategies and enhance market intelligence across different industries.

1. Power Up Your
Competitive Price Intelligence
with Web Data
Presenters: Vincent Sgro, Chief Technical Officer, Connotate
Christian Giarretta, VP of Sales Engineering, Connotate
Moderator: Jeffrey Sacks, Chief Marketing Officer, Connotate
Date: May 22, 2013

2. Presenters
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Vincent Sgro
Chief Technology Officer
Chris Giarretta
VP of Sales Engineering

3. 3
Transform Web Data into High-Value Assets
Some of Our Many Use Cases:
Competitive intelligence
News aggregation
Background check
Price optimization
Investment research
Online ad usage reports
Market research
Regulatory updates
Sales intelligence
Business risk assessment
Data directories
Aggregate construction bids
Supply chain monitoring
Brand monitoring
Voice of the Customer
Social media monitoring

4. The Web Turned Pricing Upside Down…Exposing
Product Data at All Stages in the Product Lifecycle
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Retail sites
Manufacturers’ sites
YouTube reviews
Product review sites Social media sites
eTail sites
Auction sites
Brand/product
aggregator sites
Facebook “likes”
Distributors’ sites
Twitter

5. 5
How Does This Affect You?

6. Manufacturer Distributor Retailer
<<< pricing hidden >>>
Before: Limited Price Transparency
• Consumers had limited access to real time price
differences between competing retailers
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• Supply chain hid pricing from consumer
Retailer 1 Price Retailer 2 Price Retailer 3 Price

7. • The Web explodes the supply chain:
• The Web, smart phones and Social Media inform
consumers of competitor’s prices in real time
After: Unprecedented Price Transparency
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Manufacturer’s price
Wholesaler’s price
Distributor’s price
Retailer 1 price
Retailer 2 price
Retailer 3 price

8. 8
How Should You Respond?

9. Use the Web! Extract Competitive Price Intelligence
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Retail sites
Manufacturers’ sites
YouTube reviews
Product review sites Social media sites
eTail sites
Auction sites
Brand/product
aggregator sites
Facebook “likes”
Distributors’ sites
Twitter

10. Know at Least as Much as Your Customers!
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Retail sites
Online news sites
YouTube reviews
Product review sites Social media sites
eTail sites
Auction sites
Brand/product
aggregator sites
Facebook “likes”
Google alerts
Twitter

11. …And Turn Web Data into Price Intelligence
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Gain visibility
Fine-tune strategy
Regain control
Data Results:
Retailers:
• Competitors’ prices on high-margin items
• Increase market share 10%
Big Box Manufacturers:
• Retailers’ prices and discounts
• Retain channels repeat orders
Electronics:
• Going prices for used devices before and
after refurbishing
• Boost “foot traffic” + sales 5% by
expanding sales of software for used
devices

12. 12
Workflow of Web Data in
Competitive Price Intelligence

13. Workflow Overview
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Position Name Score Through
1t Garcia -6 18
1t Jacobson -6 18
6t Hanson -5 18
6t Stricker -5 18
10t Bradley -4 18
Option 2: You control the workflow.
Access Web page Transform Data Feed BI Apps
Option 1: Outsource the process.
Pay 3rd party to collect/analyze data You receive reports

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In Order to Use Web Data, You Need to
Find It, Filter It and Format It…

15. Accuracy is Important in Web Data Extraction
“Business intelligence projects often fail due to dirty data”
“Organizations over estimate the quality of their data and
the cost of data errors”
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16. Accuracy is Key to Actionable Insights
• Assuring quality data requires
investment up front but it is
well worth it
• Automation improves data
quality to achieve the optimum
cost tradeoff
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Cost of bad data = cost of fixing errors + cost of faulty decisions
Clean data
+ context
Information
Information
+ analysis
Actionable
insights

17. • Connotate has tackled the problem in a new way, simplifying the process and
making it resilient to change.
• Transforming Web page content into computer-friendly data is much more difficult
than it first appears.
Accuracy is Not an Easy Problem to Solve
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18. Polling Question: Web Data Collection
Are you currently collecting data from the Web?
Yes – we are doing this using an automated process
Yes – we are collecting Web data using a manual process
Yes – we are using BOTH manual and automated approaches
No – we are not collecting Web data

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Competitive Price Intelligence
Use Cases

20. Retail Auto Parts
• Challenge/Opportunity
• Obtain more timely visibility into competitors’ pricing to support
dynamic pricing – particularly on high-margin “convenience” items
• Reduce dependency on expensive pricing catalogs (updated weekly)
• Solution
• Monitor competitors’ websites daily to obtain timely pricing
intelligence at both the national and local levels
• Business Benefit
• Increased market share 10%, moving up in national rankings –
optimizing pricing by making decisions based on timely data
• Reduce cash outlay for pricing catalog subscriptions
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21. Auto Parts: Extract Data From Web Pages
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Extract:
• Product
• Item #
• Availability
• Price
• Category
Ignore:
• Ads, etc.

22. Auto Parts: Web Data Transformed
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Clean, clear, consumable data

23. Appliance Manufacturer (Supplier to Big Box
Retailer)
• Challenge/Opportunity
• Obtain a “360 view” of products through the entire distribution chain
to optimize product positioning, pricing and branding strategy
• Solution
• Use automation to extract data from competitors websites daily to gain
visibility
• Business Benefit
• Retaining channels, ensuring repeat orders with a well-informed
product enhancement strategy based on continual access to pricing
and product reviews at the retail level
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24. Appliance Manufacturer: Extract Data and
Reviews from Web Pages
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Extract:
• Product ID
• Specs
• Price
• Ratings
• Comments
Ignore:
• Ads, etc.

25. PRODUCT ID Rating Comment
EAB7900SKSK09 5 The Yankees’ Mariano Rivera, revered as
one of baseball’s gentlemen and perhaps
its greatest closer, is expected to
announce that this season will be his
last…
EA27903SKSK77 2 Marian Gaborik scored a power-play goal
against the Islanders in overtime to
extend the Rangers’ winning streak to four
games…
INT79034777009 4 It’s not enough to retire. Now players like
Mariano Rivera are announcing that they
will announce their retirements…
PRODUCT ID CATEGORY SIZE PRIC
E
EAB7900SKSK09 Refrigerator 6 cu ft 2099
EA27903SKSK77 Refrigerator 4 cu ft 289
INT7903458SK89 Gas Range 24” 499
INT79034777009 Gas Range 24” 638
IQ666903EFFFFA Gas Range 24” 310
Accuracy, Speed, Automated Delivery
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Clean data, delivered to the right place in the right format:
• Product IDs, specs prices to spreadsheets
• Product reviews to sentiment analysis applications

26. Appliance Manufacturer: Web Data
Transformed
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• Product
• Product ID
• Price
• Specs
• Product
• Product ID
• Rating
• Comments

27. Buying and Selling Refurbished Electronics
• Challenge/Opportunity
• Expand activity in the growing market for used tablets/smartphones
• Expand sales of apps and games for used devices
• Solution
• Extract prices for used devices from auction sites; extract prices from
Gazelle, and similar sites to determine prices for refurbished items
• Business Benefit
• Increase foot traffic and boost revenue by 5% by expanding
operations into the growing market for used/refurbished devices (and
sales of apps and games for those devices)
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28. Electronics: Extract Data from Web Pages
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Offer price for
un-refurbished
Selling price for
refurbished item

29. Electronics: Web Data Transformed
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Automatically merges data from two different websites
in a “mashup” in one spreadsheet to facilitate
comparison and analysis

30. Polling Question: Competitive Intelligence
and Pricing Strategy
Do you support a competitive intelligence or pricing
strategy function in-house?
Yes – our business intelligence (BI) or Pricing team uses Excel
spreadsheets to support our CI/pricing strategy.
Yes – we use BI tools in-house (Microstrategy, Oracle Endeca,
SAP, IBM Cognos, etc.) to support our CI/pricing strategy.
No – we outsource our CI/pricing function to an outside vendor.
No – we have not implemented a pricing strategy
Other ____________________________________

31. 31
Automation Options

32. Manual versus Automated Approaches
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Your Data Needs To Automate or Not?
High-volume data monitoring  Automate
Variety of sources  Automate
Frequent updates and/or monitoring  Automate
Need for data post-processing  Automate
Small amount of data required just a few
times a year from very simple sites
A manual approach may be
adequate
One-time feed of very specific data Purchase data from 3rd party
Product matching applications where
unique identifiers are not available
We can offer a solution which
incorporates crowdsourcing or
outsourcing

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Scope Your Project: 5 Steps

34. Scoping Your Project: 5 Steps to Success
1. Clarify what you want to do with the data
2. Look at what’s happening manually today
– find out how users are accessing the
Web – these are targets for automation
3. Identify the sources you need
4. Narrow your scope….you may not
need“everything”
5. Anticipate future requirements
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35. Scoping: Use Cases
Retail Auto Parts
• Customer wanted to collect “everything”
• In this case, that was needed but we worked with them to devise a
system for automated product matching
Appliance Manufacturing
• Customer wanted to collect “everything” from many, many sites
• We refined the scope of the project to collect a sample size that would
meet their needs and be faster and less expensive to implement
Used Electronics
• Customer scoped a complex database model of lookup tables; we
advised a different approach with much less overhead
• We steered them toward an efficient method for extracting metadata
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36. Polling Question: The Value of Automated
Web Data Collection
Do you believe using automated Web data extraction
to gather competitive intelligence could add value to
your business?
Yes – we are doing this now
Yes – we are planning a project in the near future
No – not at this time
I need more information before deciding

37. Here’s What Success Looks Like…
Increase market
share 10%
overtake next
competitor by
optimizing prices
Appliance
manufacturers
ensure repeat
orders from Big
Box Retailers
Retailers expand
their presence in
the lucrative
market for used
devices
Electronic game
retailers achieve
5% increase in
software sales
revenue
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… Connotate’s experts are ready to take you there

38. Q & A
Connotate will email a link to this presentation as well as a
copy of the slides to you within 2 business days.
If you have an immediate need and would like us to contact
you about a forthcoming project, please check the appropriate
box in the last polling question or call (+1) 732-296-8844.
For more information, visit
www.connotate.com or www.connotate.co.uk
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