1. Market Blended Insight Modeling propensity to buy with the Semantic Web ISWC 2008 Manuel Salvadores, Landong Zuo, SM Hazzaz Imtiaz, John Darlington, Nicholas Gibbins, Nigel R Shadbolt, and James Dobree

2. 2 Introduction Motivation Datasets Use cases Micro Segmentation Value chain Conclusions and future work

3. Introduction The MBI project focuses its research in marketing strategies for the B2B sector. The project is extending world class Semantic Web research from the EPSRC’s “Advanced Knowledge Technologies IRC” The project plans to aggregate a broad range of business in- formation, providing unparalleled insight into UK business activity and develop rich semantic search and navigation tools. 3

4. Introduction 4 Real data, real B2B processes to ensure real scenarios for the undertaken research

5. Introduction Context problem … 5 … to overcome the problem that traditional marketing techniques have broad push without knowing if the recipient has a propensity to buy

6. Introduction 6 Innovation To create a source of information based on the 3.7 million companies that constitute the UK economy. To create a collection of ontologies that covers not just company information but a broad range of B2B scenarios too. To identify the semantic relations and queries required to determine propensity to buy.

7. Motivation 7 Micro Segmentation to classify or to segment potential customers by clustering those with common needs

8. Motivation 8 Value chain defined as a series of value generating activities. Products pass through all activities of the chain in order, and at each activity the product gains some value Inbound Logistics Inbound Logistics Operations Outbound Logistics Outbound Logistics Marketing And Sales Marketing And Sales Service Porter’s Value Chain Framework

9. Datasets in the 1 st prototype A backbone of the UK companies within the London boroughs of Lewisham and Camden (83 500 companies / 12 million RDF triples) Ordnance Survey Address Layer from Lewisham and Camden (50 million triples) Ordnance PointX dataset with point of interest on the mentioned areas. Extracted data: –MyCamden website (93k RDF triples) –Architects Journal (105k RDF triples) –SIC(92) industrial classification, a hierarchy with 6k nodes represented in 62k RDF triples. 9

10. SIC(92) standard industrial classification does not provide finer enough description of companies economic activity. Micro Segmentation 10 Total market restaurants, but are Italians, Chinese, … ?

11. Italian or Chinese restaurant ? That piece of data is out there. Micro Segmentation 11 * Screenshots source www.mycamden.co.uk

12. Micro Segmentation 12../company/1 …/SIC92/2367 hasSIC92 “Trattoria Luca” hasName “Restaurant” rdfs:label Initial company information from the backbone../item/extracted/X …/uri/2367 hasClassification “Trattoria Luca” hasName “Italian restaurants” rdfs:label Data extracted from the Internet with GATE. owl:sameAsrdfs:subClass

13. Micro Segmentation 13

14. Micro Segmentation 14

15. Micro Segmentation 5 014 companies with added information –4 406 from PointX –608 from MyCamden 843 new micro segments –777 from PointX –66 from MyCamden Second prototype will scale this scenario from Lewisham and Camden London boroughs to the all UK. 15

16. 16 Value Chain Relative to a company there are many relationships. Company Company Director - person Company - supplier Company - customer Trade Association - member Shareholder Relationships might be involved into a Value Chain process.

17. 17 Value Chain Company Company Director - person Company - supplier Company - customer Value Chain Trade Association - member Shareholder

18. Value Chain 18 Suppliers Value Chain Manufacturer Supplier (local distributor) Many network patterns depending on business sector

19. Value Chain Finding relationships in the Building and Construction industry. 19

20. Value Chain 20

21. Value Chain Pre-inference data view 21

22. Value Chain 22

23. Value Chain 23

24. Value Chain From Architects Journal (105k RDF triples): –4 000 suppliers. –600 building and construction projects –6 000 products From the inferred data (30 038 RDF triples) we detected 24 287 relationships between companies. 24

25. Conclusions It is possible to enhance companies data portfolio by extracting and thus linking information from the Internet. Complex B2B processes can be defined by ontology modeling and therefore use reasoning to infer new concepts. Validation with the Consortium has concluded that both Segmentation and Value Chain scenarios can significantly improve their marketing analysis. There is a trade-off between reasoning and query performance. 25

26. Future Work (2 nd prototype) 26

27. Questions ? 27