Date post: | 06-Apr-2017 |
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Presentations & Public Speaking |
Upload: | international-society-of-service-innovation-professionals |
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Types: Internet e-Commerce Networks, Peer-to-Peer Service/Collaboration networks, Social Networks, Enterprise (Professionals) Networks, etc.
Examples: e-Bay, healthcare support, Facebook, intranets…
Innovations: e-marketing (customer recommendation, business chaining, etc.), group activities and special interests, on-demand business and collaboration…
Technology: data integration, network analysis, clustering and statistics, personal tasks profiling…
Smartness: Network-Based intelligence; i.e., population knowledge and personalization application
Principle One: Building the Big Data
Integration of person-centered data along the life cycle of personal tasks and growth from all pertinent sources
Principle Two: Personalizing the Big Data for services
Development of personal service-oriented massive analytics to support the conduct of the personal life cycle tasks (Motto: service is the best selling)
Smart Service Value Networks: possessing the ability to self-develop the Big Data and Massive Analytics for constantly evolving applications – the innovation
Theory One: Scaling the connections up to cover the entire population (business domain) – Big Data
Theory Two: Scaling the connections down to serve each person (individuals of the network) – service analytics
Theory Three: Scaling the connections with network transformation (hyper-networking) – business innovation
All for One and One for All: a moral proposition may be an ultimate business value proposition – this is the golden rule for building Big Data and deriving Massive Analytics
1. An ontology and metadata repository for data integration – the global information resources dictionary
2. An architecture for non-intrusive integration of massively distributed (Internet) heterogeneous data sources - the Metadatabase model
3. A core logic for predictive e-marketing analytics (e.g., the well-known customer recommendation algorithms at some e-commerce sites)
A technology platform for developing the Big Data and Massive Analytics can facilitate service innovation
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Similar Customers: 1. determine a set of defining attributes for “similarity”; 2: compute the similarity indicator, e.g., S-C(i) = ∑ w(j)a(j) for each customer i, and then group customers based on this indicator; 3: recommend the additional products that the similar customers prefer most
Similar Products: use the same logic to develop a basic algorithm for using similar products S-P(i)
Similar Behaviors: use the same logic to develop an algorithm from customers-products networking (compute e.g., rating/purchase indicators and regress them on attributes, by sub-groups)
Adaptability can be built into the logic to make it “smart”.