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Product Variety, Consumer Preferences, and Web Technology: Can the Web of Data Reduce Price Competition and Increase Customer Satisfaction?

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Product Variety, Consumer Preferences, and Web Technology: Can the Web of Data Reduce Price Competition and Increase Customer Satisfaction?

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E-Commerce on the basis of current Web technology has created fierce competition with a strong focus on price. Despite a huge variety of offerings and diversity in the individual preferences of consumers, current Web search fosters a very early reduction of the search space to just a few commodity makes and models. As soon as this reduction has taken place, search is reduced to flat price comparison.
This is unfortunate for the manufacturers and vendors, because their individual value proposition for a particular customer may get lost in the course of communication over the Web, and it is unfortunate for the customer, because he/she may not get the most utility for the money based on her/his preference function. A key limitation is that consumers cannot search using a consolidated view on all alternative offers across the Web.

In this talk, I will (1) analyze the technical effects of products and services search on the Web that cause this mismatch between supply and demand, (2) evaluate how the GoodRelations vocabulary and the current Web of Data movement can improve the situation, (3) give a brief hands-on demonstration, and (4) sketch business models for the various market participants.

E-Commerce on the basis of current Web technology has created fierce competition with a strong focus on price. Despite a huge variety of offerings and diversity in the individual preferences of consumers, current Web search fosters a very early reduction of the search space to just a few commodity makes and models. As soon as this reduction has taken place, search is reduced to flat price comparison.
This is unfortunate for the manufacturers and vendors, because their individual value proposition for a particular customer may get lost in the course of communication over the Web, and it is unfortunate for the customer, because he/she may not get the most utility for the money based on her/his preference function. A key limitation is that consumers cannot search using a consolidated view on all alternative offers across the Web.

In this talk, I will (1) analyze the technical effects of products and services search on the Web that cause this mismatch between supply and demand, (2) evaluate how the GoodRelations vocabulary and the current Web of Data movement can improve the situation, (3) give a brief hands-on demonstration, and (4) sketch business models for the various market participants.

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Product Variety, Consumer Preferences, and Web Technology: Can the Web of Data Reduce Price Competition and Increase Customer Satisfaction?

  1. 1. Product Variety, Consumer Preferences, and Web Technology: Can the Web of Data Reduce Price Competition and Increase Customer Satisfaction? September 2, 2009, Linz, Austria Martin Hepp http://www.unibw.de/ebusiness/
  2. 2. Part I: Diversity in Markets The specificity of exchanged goods has kept on growing...
  3. 3. Specificity How much you loose when you can‘t use a good for what it was designed. Martin Hepp, 3 mhepp@computer.org
  4. 4. Growth in Specificity Reason # 1: Division of Labor Martin Hepp, 4 mhepp@computer.org
  5. 5. Range of Production on the Level of the Overall Economy Parts = N * c x Similarity of components weakens the effect. N = Number of Commodities c = Number of Components per Level of Division of Labor x = Depth of the Division of Labor Martin Hepp, 5 mhepp@computer.org
  6. 6. Growth in Specificity Reason # 2: Technical Advancement and Innovation Martin Hepp, 6 mhepp@computer.org
  7. 7. 1920: 5168 Types of Goods Martin Hepp, 7 mhepp@computer.org
  8. 8. Growth in Specificity Reason # 3: Logistics Temporal Constraints etc. Martin Hepp, 8 mhepp@computer.org
  9. 9. Growth in Specificity Reason # 4: Wealth Abraham H. Maslow (1908-1970) A Theory of Human Motivation (1943) Martin Hepp, 9 mhepp@computer.org
  10. 10. Examples Martin Hepp, 10 mhepp@computer.org
  11. 11. Examples Martin Hepp, 11 mhepp@computer.org
  12. 12. Examples Martin Hepp, 12 mhepp@computer.org
  13. 13. Specificity Increases the Search Space Martin Hepp, 13 mhepp@computer.org
  14. 14. Multi-Dimensional Trade-Off Problems • Product Features • Price • Services • Logistics • Preferences regarding business partners • Etc. Martin Hepp, 14 mhepp@computer.org
  15. 15. Part II: E-Commerce on the Web
  16. 16. History Lesson: Search for Suppliers 1992: 1 Week 2009: 1 Minute Martin Hepp, 16 mhepp@computer.org
  17. 17. But: Search for Suppliers, 2009 Martin Hepp, 17 mhepp@computer.org
  18. 18. Limitations of the Web, 2009
  19. 19. No Unified View: Jumping Back and Forth Across Data Silos Site Page Page Search Engine Results Search Engine Results 1 1 2 Search Engine Results Search Engine Results Page Page 3 4 Site Page 2 5 Site Page Page Page 3 6 7 8 Martin Hepp, 19 mhepp@computer.org
  20. 20. We know the best hits only when done. Site Page Page 1 1 2 Search Engine Results Page Page 3 4 Site Page 2 5 Site Page Page Page 3 6 7 8 Martin Hepp, 20 mhepp@computer.org
  21. 21. Specificity vs. Keyword-based Search • Synonyms • Homonyms • Multiple languages • No parametric search Martin Hepp, 21 mhepp@computer.org
  22. 22. Limited Ability to Reuse Data Martin Hepp, 22 mhepp@computer.org
  23. 23. The Web: A Bottleneck for Sharing Product Data Martin Hepp, 23 mhepp@computer.org
  24. 24. Challenge: Web-wide Product Search • Find all MP3 players that have a USB interface and a color display, and sort them by weight (lightest first). ...on a Web Scale! Martin Hepp, 24 mhepp@computer.org
  25. 25. Today: Loss of Variety and Detail Many Different Variety in Products Preferences Web Search Manufacturers & Consumers Retailers Martin Hepp, 25 mhepp@computer.org
  26. 26. What’s the Consequence? Martin Hepp, 26 mhepp@computer.org
  27. 27. Effect: Overly Price Competition Only 1 – 2 Product Models Considered Comparison Shopping on the Small Subset Martin Hepp, 27 mhepp@computer.org
  28. 28. This will change soon. Actually, very soon.
  29. 29. Deep Comparison Shopping Search Engine Results Site Site Site 3 1 2 Page Page Page 6 5 1 Page Page 7 3 Page 2 Page Page 8 4 Martin Hepp, 29 mhepp@computer.org
  30. 30. Part III: The Web of Linked Data
  31. 31. The World Wide Web, Essentially: Turn References in Documents from Road Signs into Roads Click! Martin Hepp, 31 mhepp@computer.org
  32. 32. The Web of Linked Data, Essentially: 1. Cluster Web links by what they mean 2. Use URIs to indicate the type of links 3. Use HTTP URIs so that it is quick and easy to explore what this URI means. 4. Make clear whether you are referring to something or its representation. Martin Hepp, 32 mhepp@computer.org
  33. 33. The Web of Linked Data, Essentially: 1. Cluster Web links by what they mean 2. Use URIs to indicate the type of links 3. Use HTTP URIs so that it is quick and easy to explore what this URI means. 4. Make clear whether you are referring to something or its representation. Martin Hepp, 33 mhepp@computer.org
  34. 34. The Web of Linked Data, Essentially: 1. Cluster Web links by what they mean 2. Use URIs to indicate the type of links 3. Use HTTP URIs so that it is quick and easy to explore what this URI means. 4. Make clear whether you are referring to something or its representation. Martin Hepp, 34 mhepp@computer.org
  35. 35. Technical Effects & Working Assumption • This will reduce the computational complexity of processing, combining, reusing data on a Web scale Martin Hepp, 35 mhepp@computer.org
  36. 36. Core Web of Linked Data Technology Pillars • URIs for everything • RDF: A data model for exchanging conceptual graphs based on triples – Triple: (Subject, Predicate, Object) – Exchange syntax: RDF/XML, N3, etc. • RDFS and OWL: Formal languages for that help reduce ambiguity and codify implicit facts – foo:human rdfs:subClassOf foo:mammal • SPARQL: Standardized query language and endpoint interface for RDF data • LOD Principles: Best practices for keeping the current Web and the Web of Data compatible Martin Hepp, mhepp@computer.org 36
  37. 37. Part IV: E-Commerce on the Web of Linked Data
  38. 38. E-Commerce on the Web of Linked Data Martin Hepp, 38 mhepp@computer.org
  39. 39. Discovery Effort Martin Hepp, 39 mhepp@computer.org
  40. 40. Both Sides Can Help Build a Bridge Martin Hepp, 40 mhepp@computer.org
  41. 41. What Do We Need? • Vocabularies • Tools – Product or service • Applications types – Businesses – Offerings • Data Sets – Product model data – Businesses, contact details, opening hours – Offering data Martin Hepp, 41 mhepp@computer.org
  42. 42. Part V: The GoodRelations Vocabulary and Data Space
  43. 43. GoodRelations: A Unified View on Commerce Data on the Web Extraction Arbitrary Query and Reuse Manufacturers Retailers Payment Delivery Product Model Warranty Master Data Shop Spare Parts & Offerings Auctions Consumables Martin Hepp, 43 mhepp@computer.org
  44. 44. On the Shoulders of Giants A Unified View of Commerce Data on the Web Martin Hepp, 44 mhepp@computer.org
  45. 45. The GoodRelations Vocabulary • A universal and free Web vocabulary for adding product and offering data to your Web pages. • Compatible with all relevant W3C standards and recommendations – RDF – OWL http://purl.org/goodrelations/ Martin Hepp, 45 mhepp@computer.org
  46. 46. GoodRelations Design Principles • Keep simple things Lightweight Heavyweight simple and make Web of Data Web of Data complex things possible LOD OWL DL • Cater for LOD and OWL RDF + a little bit DL worlds • Academically sound • Industry-strength engineering • Practically relevant Martin Hepp, 46 mhepp@computer.org
  47. 47. Albert Einstein on Schema Design "Make everything as simple as possible, but not simpler.“ Albert Einstein Martin Hepp, 47 mhepp@computer.org
  48. 48. Basic Structure of Offers Object or Agent 1 Promise Happening Compensation Transfer of Rights Agent 2 Martin Hepp, 48 mhepp@computer.org
  49. 49. Data, Standards, Ontologies Martin Hepp, 49 mhepp@computer.org
  50. 50. GoodRelations: License • Permanent, royalty-free access for commercial and non-commercial use. http://purl.org/goodrelations/ Martin Hepp, 50 mhepp@computer.org
  51. 51. Domain Structure and Use Cases
  52. 52. The Minimal Scenario • Scope – Business entity – Points-of-sale – Opening hours – Payment options • Suitable for – Every business – E-commerce and brick-and-mortar Martin Hepp, 52 mhepp@computer.org
  53. 53. The Simple Scenario • Scope: Minimal scenario plus – Range of products or services – Business functions – Eligible regions or customer types – Delivery options • Suitable for – Any business: E-Commerce and brick-and-mortar – Specific products or services Martin Hepp, 53 mhepp@computer.org
  54. 54. GoodRelations Annotator http://www.ebusiness-unibw.org/tools/goodrelations-annotator/ Martin Hepp, 54 mhepp@computer.org
  55. 55. The Comprehensive Scenario • Scope: Simple scenario plus – Individual products or services – Product features – Pricing, rebates, etc. – Availability • Suitable for – Any business: E-commerce and brick-and-mortar – Specific products or services – Structured product database Martin Hepp, 55 mhepp@computer.org
  56. 56. osCommerce Extension http://code.google.com/p/goodrelations-for-oscommerce/ Martin Hepp, 56 mhepp@computer.org
  57. 57. Joomla/VirtueMart Extension http://code.google.com/p/goodrelations-for-joomla/ Martin Hepp, 57 mhepp@computer.org
  58. 58. Google Product Feed Converter http://tr.im/sLcX Martin Hepp, 58 mhepp@computer.org
  59. 59. Product Model Data Scenario • Scope – Individual product models – Quantitative and qualitative features • Suitable for – Manufacturers of commodities Martin Hepp, 59 mhepp@computer.org
  60. 60. Others Do Care: Pick-up in Industry • BestBuy • Smart Information Systems • ebSemantics • Yahoo! SearchMonkey • Virtuoso Sponger Cartridges for Amazon, eBay, and • Major German mail order companies • etc. Martin Hepp, 60 mhepp@computer.org
  61. 61. Yahoo Enhanced by SearchMonkey Martin Hepp, 61 mhepp@computer.org
  62. 62. Yahoo Enhanced SearchMonkey Martin Hepp, 62 mhepp@computer.org
  63. 63. Linked Open Commerce Dataspace http://loc.openlinksw.com/sparql Martin Hepp, 63 mhepp@computer.org
  64. 64. Linked Open Commerce Dataspace http://loc.openlinksw.com/sparql Martin Hepp, 64 mhepp@computer.org
  65. 65. Conclusion
  66. 66. Today: Loss of Variety and Detail Many Different Variety in Products Preferences Web Search Manufacturers & Consumers Retailers Martin Hepp, 66 mhepp@computer.org
  67. 67. 2010: Point-to-Point Commerce Many Different Variety in Products Preferences Manufacturers & Consumers Retailers Martin Hepp, 67 mhepp@computer.org
  68. 68. Why Should I Bother? • Web Shops: Better visibility in latest generation search engines (e.g. Yahoo) – Same holds for any business that has a Web page, from A as in Amusement Park to Z as in Zoo. • Manufacturers: Allow your retailers to reuse product feature data with minimal overhead at both ends. • Software Developers: Help your customers to use and generate Semantic Web data. It’s easy! Martin Hepp, 68 mhepp@computer.org
  69. 69. What Should I Do? • Web Shops: Create a GoodRelations data dump of your range of offers (rather simple) • Vendors of Web Shop Software: Create GoodRelations import and export interfaces (we can help you with that) • Every Business: Ask your webmaster to create at least a basic description of your range of products or services • Entrepreneurs: Invent new business models based on GoodRelations data Martin Hepp, 69 mhepp@computer.org
  70. 70. Part VII: The Sky Is the Limit Semantics in Affiliate Models, Serendipity, Matchmaking
  71. 71. Thank you! http://purl.org/goodrelations/ Prof. Dr. Martin Hepp Chair of General Management and E-Business Universitaet der Bundeswehr University Muenchen Werner-Heisenberg-Weg 39 D-85579 Neubiberg, Germany Phone: +49 89 6004-4217 Fax: +49 89 6004-4620 http://www.unibw.de/ebusiness/ http://purl.org/goodrelations/ mhepp@computer.org Martin Hepp, 71 mhepp@computer.org
  72. 72. Bonus Track: Tools and Resources
  73. 73. Additional Information • Web Page – Ontology – Language Reference – Primer – Recipes – Wiki http://purl.org/goodrelations/ Martin Hepp, 73 mhepp@computer.org
  74. 74. GoodRelations User‘s Guide („Primer“) http://www.heppnetz.de/projects/goodrelations/primer/ 74
  75. 75. GoodRelations Cookbook: Recipes & Examples http://www.ebusiness-unibw.org/wiki/GoodRelations#Recipes_and_Examples Martin Hepp, 75 mhepp@computer.org
  76. 76. GoodRelations Annotator http://www.ebusiness-unibw.org/tools/goodrelations-annotator/ Martin Hepp, 76 mhepp@computer.org
  77. 77. GoodRelations Validator http://www.ebusiness-unibw.org/tools/goodrelations-validator/ Martin Hepp, 77 mhepp@computer.org
  78. 78. RDF2dataRSS Tool http://www.ebusiness-unibw.org/tools/rdf2datarss/ Martin Hepp, 78 mhepp@computer.org
  79. 79. osCommerce Extension http://code.google.com/p/goodrelations-for-oscommerce/ Martin Hepp, 79 mhepp@computer.org
  80. 80. Joomla/VirtueMart Extension http://code.google.com/p/goodrelations-for-joomla/ Martin Hepp, 80 mhepp@computer.org
  81. 81. Thank you! http://purl.org/goodrelations/ Prof. Dr. Martin Hepp Chair of General Management and E-Business Universitaet der Bundeswehr University Muenchen Werner-Heisenberg-Weg 39 D-85579 Neubiberg, Germany Phone: +49 89 6004-4217 Fax: +49 89 6004-4620 http://www.unibw.de/ebusiness/ http://purl.org/goodrelations/ mhepp@computer.org Martin Hepp, 81 mhepp@computer.org

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