Power Up Competitive Price Intelligence with Web Data


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Unprecedented price transparency has shifted the balance of power to the consumer, compressing margins and shattering the strongholds of premium brands.

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  • <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

    1. 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. 2. Presenters 2 Vincent Sgro Chief Technology Officer Chris Giarretta VP of Sales Engineering
    3. 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. 4. The Web Turned Pricing Upside Down…Exposing Product Data at All Stages in the Product Lifecycle 4 Retail sites Manufacturers’ sites YouTube reviews Product review sites Social media sites eTail sites Auction sites Brand/prod uct aggregator sites Facebook “likes” Distributo rs’ sitesTwi tte r
    5. 5. 5 How Does This Affect You?
    6. 6. Manufacturer Distributor <<< pricing hidden >>> Before: Limited Price Transparency • Consumers had limited access to real time price differences between competing retailers 6 • Supply chain hid pricing from consume Retailer 1 Price Retailer 2 Pr
    7. 7. • The Web explodes the supply chain: • The Web, smart phones and Social Media inform consumers of competitor’s prices in real After: Unprecedented Price Transparency 7 Manufacturer’s price Wholesaler’s price Distributor’s price Retailer 1 price Retailer 2 price Retailer 3 price
    8. 8. 8 How Should You Respond?
    9. 9. Use the Web! Extract Competitive Price Intelligence 9 Retail sites Manufacturers’ sites YouTube reviews Product review sites Social media sites eTail sites Auction sites Brand/prod uct aggregator sites Facebook “likes” Distributo rs’ sitesTwi tte r
    10. 10. Know at Least as Much as Your Customers! 10 Retail sites Online news sites YouTube reviews Product review sites Social media sites eTail sites Auction sites Brand/prod uct aggregator sites Facebook “likes” Google alerts Twi tte r
    11. 11. …And Turn Web Data into Price Intelligence 11 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. 12 Workflow of Web Data in Competitive Price Intelligence
    13. 13. Workflow Overview 13 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
    14. 14. 14 You Need to Find It, Filter It and Format It…
    15. 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” 15
    16. 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 16 Cost of bad data = cost of fixing errors + cost of faulty decisions Clean data + context Information Information + analysis Actionable insights
    17. 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 17 ?
    18. 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
    19. 19. 19 Competitive Price Intelligence Use Cases
    20. 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 20
    21. 21. Auto Parts: Extract Data From Web Pages 21 Extract: • Product • Item # • Availability • Price • Category Ignore: • Ads, etc.
    22. 22. Auto Parts: Web Data Transformed 22 Clean, clear, consumable data
    23. 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 23
    24. 24. Appliance Manufacturer: Extract Data and Reviews from Web Pages 24 Extract: • Product ID • Specs • Price • Ratings • Comments Ignore: • Ads, etc.
    25. 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 25 Clean data, delivered to the right place in the right format: • Product IDs, specs prices to spreadsheets • Product reviews to sentiment analysis applications
    26. 26. Appliance Manufacturer: Web Data Transformed 26 • Product • Product ID • Price • Specs • Product • Product ID • Rating • Comments
    27. 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 27
    28. 28. Electronics: Extract Data from Web Pages 28 Offer price for un-refurbished Selling price for refurbished item
    29. 29. Electronics: Web Data Transformed 29 Automatically merges data from two different websites in a “mashup” in one spreadsheet to facilitate comparison and analysis
    30. 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
    31. 31. 31 Automation Options
    32. 32. Manual versus Automated Approaches 32 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 We can offer a solution which incorporates
    33. 33. 33 Scope Your Project: 5 Steps
    34. 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” 34
    35. 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 35
    36. 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
    37. 37. Here’s What Success Looks Like…Increase market share 10% overtake next competitor by optimizing prices Appliance manufacture rs 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 37 … Connotate’s experts are ready to take you there
    38. 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 38