<Jeff>Welcome to today’s presentation, “Power Up Competitive Price Intelligence with Web Data.”My name is Jeff Sacks, and I’m the Chief Marketing Officer here at Connotate. I’ll be your moderator today.This presentation will explore how companies can optimize pricing and product strategies by leveraging Web data to achieve competitive advantage.It will last approximately 30 minutes, followed by a live question and answer session. You may submit your questions anytime during the session using the Chat feature.During the presentation, we’ll be asking you several survey questions which can be answered using the Polling feature that will appear when we open each survey..
<Jeff>Our presenters today are Vince Sgro, co-founder and CTO of Connotate andChris Giarretta, Vice President of Sales Engineering at Connotate.But before I welcome them, I’d like to take a few minutes to provide some background about Connotate and context for the discussion. Connotate is an expert in this field. Since 2000, Connotate has been helping global clients like the Associated Press, Dow Jones and Shopzilla leverage Web data for strategic advantage.
<Jeff>There are many use cases for our technology…today we’ll be talking about Competitive Intelligence.We will focus on pricing intelligence as a competitive advantage, share several related case studies, as well as best practices that we’ve developed over the years. We hope this information will help you get more value out of any Web data project you may have now or in the future.
<Jeff>Retail has changed dramatically in the past several years. Less than 5 years ago, the price of consumer goods was more or less dictated by the manufacturer with the Manufacturer’s Suggested Retail Price (MSRP). That is no longer the case.This model has been turned on it’s head by technology.The proliferation of devices such as smart phones and tablets and the incredible growth of Web content, have together given consumers unlimited access to information on products and pricing throughout the product lifecycle. Now the consumer has the control over pricing and everyone in the supply chain has lost it. Today, we’ll show you how to gain it back
<Jeff>So, how does all this affect your pricing strategy, and what can you do about it?
<Jeff>It used to be that customers only had visibility into prices set by the retailer, and then only at the specific bricks-and-mortar store they were visiting. The only way to get competitive prices was to visit a variety of stores or to read a bunch of ad circulars. Not many people had the time or the patience to drive around from store to store, or collect all the local circulars -- in search of the best price.The consumer had virtually no power over price.
<Jeff>Today, it’s a completely different story. Smart phones have empowered consumers to an unprecedented extent, and one that would have seemed truly unbelievable just a couple of years ago.Customers now have a fully transparent window into pricing, and the ability to purchase online, at any point in the supply chain, -- manufacturers, distributors, wholesalers, and retailers.As an example, I work out at the gym. I like New Balance sneakers. There was a time not too long ago when I would buy my sneakers only at a sporting goods store at a mall. Now, I can order online directly from the manufacturer at newbalance.com. Or I can purchase them from a variety of online sporting goods sites or shoe stores. Or, of course, at Amazon. The point is that I will look for and find the best price on the particular day I order.
<Jeff>So what can you do about all this?
<Jeff>You can best your customers at their own game, and your competitors too, while you’re at it! The same Web that has empowered the consumer can now help you regain some of the power – and competitive edge -- you lost.How? By providing timely, comprehensive and aggregated views into the competitive pricing landscape.Whether you are B2B or B2C, and whether you pursue real-time dynamic pricing or not, the Web should be a primary sources of data for a more informed pricing strategy.
<Jeff>For Manufacturers, it is critical to know the true street price of your product to assure you’re sustaining value in the marketplace.If your product’s suggested retail price is $1,000 but Wal-Mart is selling it at less than cost to move inventory, you need to know.For retailers, you need to know what your competitors are charging at all times, no matter how often you change your prices. You must know at least as much as your customer about the competitive pricing landscape to make intelligent, competitive, informed pricing strategy decisions.
<Jeff>In the next few minutes, you’ll see that by turning web data into pricing intelligence, you can achieve real-world, bottom-line results:You can:Increase market share Ensure repeat ordersExpand into new marketsAll of which will boost your revenue.Now, I’ll hand the presentation over to Vince, who will discuss the basics of incorporating Web data into operational workflows. This will be followed by a discussion of some use cases that will help you understand what price optimization might mean for your business.
<Vince>Thanks, Jeff.Before we go into the case study examples, I’m going to talk about the process of Web data extraction, how it fits into your workflow and the impact and importance of accurate data – as well as how difficult it is to extract accurate data from the Web.
<Vince>We just talked about using Web data to fight fire with fire. I want to give you a brief overview of how it’s done. You have at least two options for doing this:You can choose to outsource the entire process of collecting pricing data from the Web to a 3rd party who will most likely cover a very narrow slice of your product catalog and periodically give you a static report or a dashboard. This is an easy road for you to take, but that will only give you a snapshot of the spectrum of information available. For more timely and complete data – and to have more control over what you see and when you see it – you can collect Web data yourself. This may be a good choice if you have your own BI or pricing team in-house or you’d like to collect data that’s not available from specialty pricing services. We’ll be talking more about the importance of timeliness and accuracy in the next few slides.
<Vince>Here’s another way to view the problem. We start with a Web site, (here) on the left, and go through a process that we call Data Extraction. The data is presented in HTML, PDF or images which are intended for use by people, but not usable (or directly analyzable) by a computer.Once we extract the data, we transform it into something usable such as XML or Excel files. At that point, it’s ready to be used by your Business Intelligence applications to produce actionable insights.We’ll come back and revisit this process in more detail after we talk about some real world case studies.
<Vince>Taking control over the data collection process gives you the option to get more timely data. But as we’ll see in a minute, it’s not always easy to obtain accurate data.As the cartoon suggests, you need data in order to manage. And the effects of bad data are hard to ignore – bad data slows down business processes, creating inefficiencies and bad business decisions, which can be disastrous. In a minute, we’ll show you how to simplify the process of getting accurate data!But first, let’s look a little closer at the cost of bad data.
<Vince>This chart is from the Journal of Industrial Engineering and Management. The article is called “the Cost of Poor Quality Data” and it talks about:The Cost of fixing errors, andThe cost of making bad decisions based on inaccurate data – which can be quite highThe chart points out that you need to invest some money to get quality data. The investment is well worth the price. If your goal is to obtain actionable insights to optimize pricing, you need good quality data. It has to be CLEAN and ACCURATE enough to achieve the optimum cost tradeoff. That’s what this graph represents.Many people write code themselves or use shareware to extract Web content in an attempt to save money.But it’s exceedingly difficult to achieve the level of accuracy you need with those cheaper approaches. If your goal is actionable insights, data accuracy is fundamental.
<Vince>Here’s why it’s a bit more difficult than you might think to get good quality data.This example shows information about clinical trials, which is an important source of competitive intelligence for the pharmaceutical industry.
<Jeff>Thanks Vince.Now that we’ve talked about collecting timely, accurate Web data. ..…let’s take another brief pause to ask our firstsurvey question: Are you currently collecting data from the Web, and if so, how? Also, again, don’t forget to submit your questions anytime using the Chat feature on your screenNow I’m going to hand the presentation back to Vince.
<Vince>Thanks, Jeff.Now we’ll take a look at use cases across three different industries: Retail Auto Parts, Appliance Manufacturing and Retail Electronics.
<Vince>In the last five years or so, the Web has completely leveled the playing field for Auto Parts. Brand loyalty is disappearing andCustomers are constantly looking for the best price.To optimize margins, auto parts retailers MUST always be aware of what their competitors are charging.In this case, we helped this retailer increase market share 10% and overtake their next largest competitor in sales rankings.They were able to optimize their pricing by making decisions based on timely data.The value proposition here is the scale we can support.You can collect this data manually but not at scale. For this retailer, we are collecting millions of prices with a high degree of accuracy.
<Vince>Here’s an example of a subset of the data that we are extracting for this customer as it appears on a competitor’s website.The data includes product name, item number, product category, price and item availability.
<Vince>Our software automatically extracts the data from the Web page and transforms it into a usable form, in this case Excel.Here, we’ve highlighted a few of the data elements we capture; in practice, we are collecting many more data elements but to protect the customer’s identity, we are not exposing all of the detail.There are a few steps in this process that we don’t show on this slide:Matching the competitor’s price ($54.99) to your own price to see if you are charging too much or too little … and,feeding this data into a pricing analytics engine which has pre-determined rules that support your pricing strategyRegarding price matching, we can work through sets of product catalogs to do this. Admittedly, we are not getting 100% accuracy in product matching.But we’re getting close enough that we’ve been able to help this customer optimize prices where it counts, enabling them to grab market share and leap-frog over their next largest competitor.
<Vince>The next case is an appliance manufacturer that supplies big box retailers. This business case is different from the previous one; the challenges are different.The manufacturer has a well-established, premium brand. But today, brand loyalty is diminishing across the board and there is no guarantee that this manufacturer will receive repeat orders next season – they can’t depend on retaining this channel just because of brand loyalty. This manufacturer needed a 3-60 degree view of pricing up and down the distribution chain, as well as visibility into the product specs of its competitorsand consumer product reviews.Here’s why. Let’s say their competitor’s appliance has a new feature that is totally unique. What are consumers saying about that feature?Are they willing to pay more for it? Do the product reviews indicate that this feature is boosting sales or not?Should the manufacturer include this type of feature in their next product model or not?By collecting Web data, we helped the manufacturer answer these questions to help them guide product enhancement, pricing, and ensure repeat orders from the Big Box channel.
<Vince>Here is an example of the data we extract for them:Feature and function breakdowns of competing products as well as how are they priced, andproduct reviews. We can deliver information to help them understand the pricing as well as all the buzz around a particular storefront. This is important especially among young buyers who base their buying decision on what the consensus says is both the best and the cheapest. It’s absolutely critical to this manufacturer to be informed of this consensus. This helps the manufacturer understand how its products are performing in the retail chain compared to its competitors. Web data also gives this manufacturer visibility into how different retail chains are pricing their products, as well as what kind of discounts are offered by different retailers at checkout. All of this information is helping this manufacturer enhance brand reputation and market share.
<Vince>This is also a good example of a use case where we are extracting very different types of data from the same website – because you may want to feed the data to very different types of applications or uses.So here, we are extracting both product reviews AND product specs (and) pricing from the same Web page, but delivering them to separate applications or databases. The comments may be analyzed by a Voice of the Customer application while the specs (and) pricing may just go into a spreadsheet for the pricing team to review.
<Vince>This slide illustrates how clean, accurate data is delivered in a structured format – in this case, a spreadsheet which can be used by the manufacturer’s pricing and BI team. The manufacturer has control over what data they collect, and how often they collect it.Previously, this manufacturerspent their market intelligence budget on a 3rd party agency that collected data on a very narrow vertical slice of appliances. This agency controlled the collection and analysis of the data, and presented their findings in a PowerPoint every 3 or 6 months. The data was vertically rich – and the approach worked well in the old days. But today, it is no longer timely enough.Now, this manufacturer is going straight to the source – the Websites of retailers – and compiling the data to present it to their designers, engineers, and financial team as fast as needed instead of waiting 3 or 6 months for an agency report.In other words, it’s possible today to do it on your own – once you have the methodology in place you can get this competitive price intelligence as often as you need it instead of waiting months.
<Vince>This next case study describes a multi-billion dollar company with a brick and mortar presence, and a fast-growing online channel. This retailer sells a variety of software and electronics.First a bit of background:Before the iPad and the iPhone many people treated mobile devices as a commodity throw away. But Apple devices are typically much higher priced, and there is a growing high-value aftermarket for used Apple devices. With built-in obsolescence, customers are less likely to feel they got full value out of these expensive devices after only a year or two. Now, a growing number of storefronts as well as online sites (such as Gazelle) will buy used devices, refurbish them and resell them. This particular Electronics retailer sought to enter this growing market, recognizing that there is not only money in selling refurbished devicesbut also in selling software and apps for the devices.By collecting Web data, we were able to help themDetermine the offer price for used devices,determine the optimal selling price for refurbished devices, anddrive more traffic into the stores and websites. Thus increasing software sales of games and apps purchased by people who bought the refurbished devicesThe reason why they had to automate this process is because it is not easy to determine the right price for used devices:There are hundreds of product combinations on Apple alone-- and then add to itthe more than 250 current Android phones plus older onesIt’s too time-consuming to manually log onto Gazelle, Amazon or Ebay and find primary data such as, “I have a First Generation 32 GB Wifi-only iPad, what is it worth?”In order to make this a profitable operation, you have to collect this data at scale – and do it accurately across websites with different formats.
<Vince>Here is an example of the data we are collecting for this vendor: Product specs, condition and bidding price for used, un-refurbished devices from one web site;And pricing for the same refurbished devices on another web site.
<Vince>As you can see in this example, we are able to do a mashup of the data from two separate websites, and present both prices in the same spreadsheet to highlight the potential profit margin before deducting the cost of refurbishing. Here we see a margin of over $250 – with the cost of refurbishing devices at scale much, much less than this amount, you can see the potential business opportunity with millions of used devices flooding the market each year.
<Jeff>Let’s take a brief pause for our second survey question: Do you support a competitive intelligence or pricing strategy function in-house? Please check the box that best describes how your company currently manages pricing strategy.Also, don’t forget to submit questions anytime for the Q&A session at the end of the presentation. Just use the Chat feature on your screen.Now I’m going to over to Chris Giarretta so he can share some best practices on Automation Options and Scoping your Web data extraction project.
<Chris>In a number of these use cases, we mentioned the use of automation. So let’s take a look at exactly what that means, when it comes into play and how it affects your use of Web data.
<Chris>All of these actual case studies that we mentioned before achieved results by following a fully automated approach. Scenarios that warrant an automated solution include situations where a lot of internal and external data needs to be aggregated and / or you need to monitor a variety of sources. If you are dealing with high volumes of data – or Web sites which change frequently, it quickly gets very expensive to have your staff continually check sites and look for changes. Automation is also required when you need frequent updates, such as news aggregation or price optimization in retail. At Connotate, we hear a lot of different data needs from all different kinds of companies and we understand that a FULLY automated solution is not always the answer. For example, when we see a company that needs to do a lot of complex product matching---let’s say, for apparel—we may OFFER A SOLUTION WHICH INCORPORATES crowd sourcing or outsourcing to compare items. We will work with you to find the best solution. In some cases, if you have a small amount of data that you need only a few times a year – you may not need automation. But this is rare today. More and more, the value comes from aggregating “Big Data” to derive actionable insights – and you’ll need automation to do this.
ChrisSo if you are thinking about using automation for a Web data extraction project, I’d like to share some best practices we’ve learned over the years to help you get started.
<Chris>One of the biggest challenges in scoping your project is determining exactly what you want to do with the data. It sounds simple, but it really isn’t. For example, if you want to produce reports for management, you may need a different type of data delivery than if you are feeding data straight into an application.Next, you may be able to find ways to save money by automating processes that are manual today.Then, identify the Web sources you want to target. If you still need to narrow down your options, it may be possible to apply automation to leverage Google and other search engines to refine the scope of your project.Once you have the list of URLs, we can help you identify the sites that are easy to access versus those that aren’t. (Chris, can you give some examples?)Next, think about how often you need to monitor and/or collect data. It’s important to be flexible here and to work with someone who will take the time to understand your needs and adjust the scope/direction of the project, if needed to deliver you the most value. Finally, you’ll want to look in the long-term and consider the maintenance costs of your project, and how to minimize them. Deploying software on-site gives you the most control, but you’re carrying the ball when it comes to maintaining the solution and expanding scope quickly if need be. A hosted deployment eliminates those headaches and can be more cost-effective in the long-run.
<Chris>Here are some examples of how we helped our customers scope their projects in the previous Use Cases.
<Jeff>Thanks Chris.Before we wrap up today’s presentation, one last survey question, this one about the value of automation. Based on your experience and based on what you’ve heard here today, do you believe using automation to collect competitive price intelligence from the Web could add value to your pricing function?And, again, don’t forget to use the chat feature to submit questions to our presenters for the Q&A session at the end.
<Jeff>Several of you have asked about obtaining a copy of today’s presentation. We will send you a link to the archived presentation within 2 business days.Now, for your questions.
Power Up Competitive Price Intelligence with Web Data
Power Up Your
with Web Data
Presenters: Vincent Sgro, Chief
Technical Officer, Connotate
Christian Giarretta, VP of Sales
Moderator: Jeffrey Sacks, Chief
Marketing Officer, Connotate
Date: May 22, 2013
VP of Sales
Transform Web Data into
Some of Our Many Use Cases:
Online ad usage reports
Business risk assessment
Aggregate construction bids
Supply chain monitoring
Voice of the Customer
Social media monitoring
The Web Turned Pricing Upside
Down…Exposing Product Data at
All Stages in the Product
<<< pricing hidden >>>
Before: Limited Price
• Consumers had limited access
to real time price
• Supply chain hid pricing from consume
Retailer 1 Price Retailer 2 Pr
• The Web explodes the supply
• The Web, smart phones and
Social Media inform consumers
of competitor’s prices in real
Retailer 1 price
Retailer 2 price
Retailer 3 price
Use the Web! Extract
Know at Least as Much as
…And Turn Web Data into
• Competitors’ prices on high-margin items
• Increase market share 10%
Big Box Manufacturers:
• Retailers’ prices and discounts
• Retain channels repeat orders
• Going prices for used devices before and
• Boost “foot traffic” + sales 5% by expanding
sales of software for used devices
Workflow of Web Data
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
You Need to
Find It, Filter It and
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”
Accuracy is Key to
• Assuring quality
front but it is
well worth it
Cost of bad data = cost of fixing
errors + cost of faulty decisions
• 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
Polling Question: Web
Are you currently collecting
data from the Web?
Yes – we are doing this using an
Yes – we are collecting Web data using
a manual process
Yes – we are using BOTH manual and
No – we are not collecting Web
Retail Auto Parts
• 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)
• 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
Auto Parts: Extract Data
From Web Pages
• Item #
• Ads, etc.
Auto Parts: Web Data
Clean, clear, consumable data
(Supplier to Big Box
• Obtain a “360 view” of products through
the entire distribution chain to
optimize product positioning, pricing
and branding strategy
• Use automation to extract data from
competitors websites daily to gain
• 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
Extract Data and Reviews
from Web Pages
• Product ID
• Ads, etc.
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
EA27903SKSK77 2 Marian Gaborik scored a power-play goal
against the Islanders in overtime to
extend the Rangers’ winning streak to four
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
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
Clean data, delivered to the right place in the right format:
• Product IDs, specs prices to spreadsheets
• Product reviews to sentiment analysis applications
Web Data Transformed
• Product ID
• Product ID
Buying and Selling
• Expand activity in the growing market
for used tablets/smartphones
• Expand sales of apps and games for
• Extract prices for used devices from
auction sites; extract prices from
Gazelle, and similar sites to
determine prices for refurbished
• Business Benefit
• Increase foot traffic and boost
revenue by 5% by expanding operations
into the growing market for
used/refurbished devices (and sales
Electronics: Extract Data
from Web Pages
Offer price for
Selling price for
Electronics: Web Data
Automatically merges data from two different websites
in a “mashup” in one spreadsheet to facilitate
comparison and analysis
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
support our CI/pricing strategy.
Yes – we use BI tools in-house
(Microstrategy, Oracle Endeca,
SAP, IBM Cognos,
etc.) to support our CI/pricing
No – we outsource our
CI/pricing function to an outside
Manual versus Automated
Your Data Needs
To Automate or
Variety of sources Automate
Frequent updates and/or
Need for data post-
Small amount of data
required just a few
times a year from very
approach may be
One-time feed of very
from 3rd party
We can offer a
Scoping Your Project: 5
Steps to Success
1.Clarify what you want
to do with the data
2.Look at what’s
today – find out how
users are accessing the
Web – these are targets
3.Identify the sources
4.Narrow your scope….you
may not need“everything” 34
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
• 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
• Customer scoped a complex database
model of lookup tables; we advised a
Polling Question: The
Value of Automated Web
Do you believe using
automated Web data
extraction to gather
could add value to your
Yes – we are doing this now
Yes – we are planning a project in the
No – not at this time
Here’s What Success Looks
… Connotate’s experts are
ready to take you there
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 38