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Power Up Your Competitive Price Intelligence With Web Data
 

Power Up Your 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. If you are a manufacturer, distributor or ...

Unprecedented price transparency has shifted the balance of power to the consumer, compressing margins and shattering the strongholds of premium brands. If you are a manufacturer, distributor or retailer, how can you respond to this challenge?

This presentation will reveal strategies for regaining control and improving margins – with real-world success stories that clearly illustrate how manufacturers and retailers are gaining greater visibility into pricing and product positioning throughout the entire supply chain.

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Power Up Your Competitive Price Intelligence With Web Data Power Up Your Competitive Price Intelligence With Web Data Presentation Transcript

  • Power Up YourCompetitive Price Intelligencewith Web DataPresenters: Vincent Sgro, Chief Technical Officer, ConnotateChristian Giarretta, VP of Sales Engineering, ConnotateModerator: Jeffrey Sacks, Chief Marketing Officer, ConnotateDate: May 22, 2013
  • Presenters2Vincent SgroChief Technology OfficerChris GiarrettaVP of Sales Engineering
  • 3Transform Web Data into High-Value AssetsSome of Our Many Use Cases:Competitive intelligenceNews aggregationBackground checkPrice optimizationInvestment researchOnline ad usage reportsMarket researchRegulatory updatesSales intelligenceBusiness risk assessmentData directoriesAggregate construction bidsSupply chain monitoringBrand monitoringVoice of the CustomerSocial media monitoring
  • The Web Turned Pricing Upside Down…ExposingProduct Data at All Stages in the Product Lifecycle4Retail sitesManufacturers’ sitesYouTube reviewsProduct review sites Social media siteseTail sitesAuction sitesBrand/productaggregator sitesFacebook “likes”Distributors’ sitesTwitter
  • 5How Does This Affect You?
  • Manufacturer Distributor Retailer<<< pricing hidden >>>Before: Limited Price Transparency• Consumers had limited access to real time pricedifferences between competing retailers6• Supply chain hid pricing from consumerRetailer 1 Price Retailer 2 Price Retailer 3 Price
  • • The Web explodes the supply chain:• The Web, smart phones and Social Media informconsumers of competitor’s prices in real timeAfter: Unprecedented Price Transparency7Manufacturer’s priceWholesaler’s priceDistributor’s priceRetailer 1 priceRetailer 2 priceRetailer 3 price
  • 8How Should You Respond?
  • Use the Web! Extract Competitive Price Intelligence9Retail sitesManufacturers’ sitesYouTube reviewsProduct review sites Social media siteseTail sitesAuction sitesBrand/productaggregator sitesFacebook “likes”Distributors’ sitesTwitter
  • Know at Least as Much as Your Customers!10Retail sitesOnline news sitesYouTube reviewsProduct review sites Social media siteseTail sitesAuction sitesBrand/productaggregator sitesFacebook “likes”Google alertsTwitter
  • …And Turn Web Data into Price Intelligence11Gain visibilityFine-tune strategyRegain controlData Results:Retailers:• Competitors’ prices on high-margin items• Increase market share 10%Big Box Manufacturers:• Retailers’ prices and discounts• Retain channels repeat ordersElectronics:• Going prices for used devices before andafter refurbishing• Boost “foot traffic” + sales 5% byexpanding sales of software for useddevices
  • 12Workflow of Web Data inCompetitive Price Intelligence
  • Workflow Overview13Position Name Score Through1t Garcia -6 181t Jacobson -6 186t Hanson -5 186t Stricker -5 1810t Bradley -4 18Option 2: You control the workflow.Access Web page Transform Data Feed BI AppsOption 1: Outsource the process.Pay 3rd party to collect/analyze data You receive reports
  • 14In Order to Use Web Data, You Need toFind It, Filter It and Format It…
  • Accuracy is Important in Web Data Extraction“Business intelligence projects often fail due to dirty data”“Organizations over estimate the quality of their data andthe cost of data errors”15
  • Accuracy is Key to Actionable Insights• Assuring quality data requiresinvestment up front but it iswell worth it• Automation improves dataquality to achieve the optimumcost tradeoff16Cost of bad data = cost of fixing errors + cost of faulty decisionsClean data+ contextInformationInformation+ analysisActionableinsights
  • • Connotate has tackled the problem in a new way, simplifying the process andmaking it resilient to change.• Transforming Web page content into computer-friendly data is much more difficultthan it first appears.Accuracy is Not an Easy Problem to Solve17
  • Polling Question: Web Data CollectionAre you currently collecting data from the Web?Yes – we are doing this using an automated processYes – we are collecting Web data using a manual processYes – we are using BOTH manual and automated approachesNo – we are not collecting Web data
  • 19Competitive Price IntelligenceUse Cases
  • Retail Auto Parts• Challenge/Opportunity• Obtain more timely visibility into competitors’ pricing to supportdynamic pricing – particularly on high-margin “convenience” items• Reduce dependency on expensive pricing catalogs (updated weekly)• Solution• Monitor competitors’ websites daily to obtain timely pricingintelligence 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 subscriptions20
  • Auto Parts: Extract Data From Web Pages21Extract:• Product• Item #• Availability• Price• CategoryIgnore:• Ads, etc.
  • Auto Parts: Web Data Transformed22Clean, clear, consumable data
  • Appliance Manufacturer (Supplier to Big BoxRetailer)• Challenge/Opportunity• Obtain a “360 view” of products through the entire distribution chainto optimize product positioning, pricing and branding strategy• Solution• Use automation to extract data from competitors websites daily to gainvisibility• Business Benefit• Retaining channels, ensuring repeat orders with a well-informedproduct enhancement strategy based on continual access to pricingand product reviews at the retail level23
  • Appliance Manufacturer: Extract Data andReviews from Web Pages24Extract:• Product ID• Specs• Price• Ratings• CommentsIgnore:• Ads, etc.
  • PRODUCT ID Rating CommentEAB7900SKSK09 5 The Yankees’ Mariano Rivera, revered asone of baseball’s gentlemen and perhapsits greatest closer, is expected toannounce that this season will be hislast…EA27903SKSK77 2 Marian Gaborik scored a power-play goalagainst the Islanders in overtime toextend the Rangers’ winning streak to fourgames…INT79034777009 4 It’s not enough to retire. Now players likeMariano Rivera are announcing that theywill announce their retirements…PRODUCT ID CATEGORY SIZE PRICEEAB7900SKSK09 Refrigerator 6 cu ft 2099EA27903SKSK77 Refrigerator 4 cu ft 289INT7903458SK89 Gas Range 24” 499INT79034777009 Gas Range 24” 638IQ666903EFFFFA Gas Range 24” 310Accuracy, Speed, Automated Delivery25Clean data, delivered to the right place in the right format:• Product IDs, specs prices to spreadsheets• Product reviews to sentiment analysis applications
  • Appliance Manufacturer: Web DataTransformed26• Product• Product ID• Price• Specs• Product• Product ID• Rating• Comments
  • 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 fromGazelle, and similar sites to determine prices for refurbished items• Business Benefit• Increase foot traffic and boost revenue by 5% by expandingoperations into the growing market for used/refurbished devices (andsales of apps and games for those devices)27
  • Electronics: Extract Data from Web Pages28Offer price forun-refurbishedSelling price forrefurbished item
  • Electronics: Web Data Transformed29Automatically merges data from two different websitesin a “mashup” in one spreadsheet to facilitatecomparison and analysis
  • Polling Question: Competitive Intelligenceand Pricing StrategyDo you support a competitive intelligence or pricingstrategy function in-house?Yes – our business intelligence (BI) or Pricing team uses Excelspreadsheets 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 strategyOther ____________________________________
  • 31Automation Options
  • Manual versus Automated Approaches32Your Data Needs To Automate or Not?High-volume data monitoring  AutomateVariety of sources  AutomateFrequent updates and/or monitoring  AutomateNeed for data post-processing  AutomateSmall amount of data required just a fewtimes a year from very simple sitesA manual approach may beadequateOne-time feed of very specific data Purchase data from 3rd partyProduct matching applications whereunique identifiers are not availableWe can offer a solution whichincorporates crowdsourcing oroutsourcing
  • 33Scope Your Project: 5 Steps
  • Scoping Your Project: 5 Steps to Success1. Clarify what you want to do with the data2. Look at what’s happening manually today– find out how users are accessing theWeb – these are targets for automation3. Identify the sources you need4. Narrow your scope….you may notneed“everything”5. Anticipate future requirements34
  • Scoping: Use CasesRetail Auto Parts• Customer wanted to collect “everything”• In this case, that was needed but we worked with them to devise asystem for automated product matchingAppliance Manufacturing• Customer wanted to collect “everything” from many, many sites• We refined the scope of the project to collect a sample size that wouldmeet their needs and be faster and less expensive to implementUsed Electronics• Customer scoped a complex database model of lookup tables; weadvised a different approach with much less overhead• We steered them toward an efficient method for extracting metadata35
  • Polling Question: The Value of AutomatedWeb Data CollectionDo you believe using automated Web data extractionto gather competitive intelligence could add value toyour business?Yes – we are doing this nowYes – we are planning a project in the near futureNo – not at this timeI need more information before deciding
  • Here’s What Success Looks Like…Increase marketshare 10%overtake nextcompetitor byoptimizing pricesAppliancemanufacturersensure repeatorders from BigBox RetailersRetailers expandtheir presence inthe lucrativemarket for useddevicesElectronic gameretailers achieve5% increase insoftware salesrevenue37… Connotate’s experts are ready to take you there
  • Q & AConnotate will email a link to this presentation as well as acopy of the slides to you within 2 business days.If you have an immediate need and would like us to contactyou about a forthcoming project, please check the appropriatebox in the last polling question or call (+1) 732-296-8844.For more information, visitwww.connotate.com or www.connotate.co.uk38