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

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• Introduction

•Motivation

•Datasets

•Use cases

•Micro Segmentation

•Value chain

•Conclusions and future work

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.

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Introduction

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

Introduction

Context problem …

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

Introduction

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• 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.

Motivation

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Micro Segmentation

to classify or to segment potential customers by clustering those with common needs

Motivation

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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

InboundLogisticsInboundLogistics Operations Operations Outbound

LogisticsOutboundLogistics

Marketing And

Sales

Marketing And

SalesServiceService

Porter’s Value Chain Framework

Datasets in the 1st 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

• SIC(92) standard industrial classification does not provide finer enough description of companies economic activity.

Micro Segmentation

10Total market

restaurants, but areItalians, Chinese, … ?

Italian or Chinese restaurant ? That piece of data is out there.

Micro Segmentation

11* Screenshots source www.mycamden.co.uk

Micro Segmentation

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../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:sameAs rdfs:subClass

Micro Segmentation

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Micro Segmentation

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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.

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Value ChainRelative to a company there are many relationships.

Company

Company Director - person

Company - supplier

Company - customerTrade Association -

member

Shareholder

Relationships might be involved into a Value Chain process.

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Value Chain

Company

Company Director - person

Company - supplier

Company - customer

Value Chain

Trade Association - member

Shareholder

Value Chain

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Suppliers

Value Chain

Manufacturer

Supplier(local distributor)

Many network patterns depending on business sector

Value Chain• Finding relationships in the Building and

Construction industry.

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Value Chain

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Value ChainPre-inference data view

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Value Chain

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Value Chain

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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.

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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.

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Future Work (2nd prototype)

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Questions ?

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