Customer Driven Big-Data Analytics for the Companies Servitization

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1 Customer Driven Big-Data Analytics for the Companies Servitization Eugen Molnár, Natalia Kryvinska, Michal Greguš Comenius University in Bratislava, Faculty of Management

2 Principal interactions in a PSS delivering an advanced Service Source: BAINES, J.-LIGHTFOOT, H. Made to Serve: How Manufacturers Can Compete Through Servitization and Product Service Systems. John Wiley & Sons, 2013.

3 Data an important dimension of servitization

4 Data an important dimension of servitization An excellent example joining of information with business demonstrated Fred Smith, founder of FedEx, who said that the information about the package is as important as the package itself and applied this insight to develop the real-time tracking tools that gave his company a huge advantage in the marketplace. Source: DEIMLER M.-LESSER, R.-RHODES, D.-SINHA, J. Own The Future: 50 Ways To Win From The Boston Consulting Group Tomorrow, information deriving from servitization and exploited on an ecosystem level, could represent the third source of revenue streams for the manufacturer. It could even become the manufacturer s main revenue stream. To depict the impact of the introduced concepts while positioning it in relevant literature, a third layer of added value was added to Thoben s representation of servitization levels, the information layer. Source: David Opresnik, Manuel Hirsch, Christian Zanetti, and Marco Taisch: Information The Hidden Value of Servitization Successful delivery of advanced services is enabled by information and communication technologies that are focused on informing and advancing actions of maintenance, repair and use. Source: BAINES, J.-LIGHTFOOT, H. Made to Serve: How Manufacturers Can Compete Through Servitization and Product Service Systems. John Wiley & Sons, 2013.

5 Wheel of Social Media Source: SAFKO, L The Fusion Marketing Bible: Fuse Traditional Media, Social Media, and Digital Media to Maximize Marketing. McGraw-Hill, 2013

6 Holistic customer view Source:

7 Speech Analytics Source: Hewlett-Packard

8 3 Pillars of Customer Experience Source: Soudagar, R., V. Iyer, and V.G. Hilderbrand The Customer Experience Edge: Technology and Techniques for Delivering an Enduring, Profitable, and Positive Experience to Your Customers. McGraw-Hill.

9 Big Data & Unstructured Data Analytics Customer analytics is an emerging approach to truly create meaningful, lasting, and profitable customer interactions through the systematic examination of a company s customer information (internal, syndicated or social; structured or unstructured) to attract, retain, and grow the most profitable customers. The main research directions in Business intelligence and analytics: Big Data Analytics technology oriented research questions related to Hadoop and MapReduce Text Analytics - was moved from search engines to enterprise search systems and from information exraction to Question Answering systems. Network Analytics - Link mining, community detection, social recommendation are the main areas in this kind of research. Source: LIM, E.-P.-CHEN, H.-CHEN, G Business intelligence and analytics: Research directions. In: ACM Trans.Manage. Inf. Syst. Vol.3, No.4, Article 17 (January 2013).

10 Innovation in Monitor, Analyze and Respond Monitor Respond Social dynamic Media, dashboard, corporate multichannel web pages, interaction chats, discussions, with Analyze Text Analytics blogs, customer, Call pushing center interaction relevant data for example e-commerce, FAQ Source: BAINES, J.-LIGHTFOOT, H. Made to Serve: How Manufacturers Can Compete Through Servitization and Product Service Systems. John Wiley & Sons, 2013.

11 Text Analytics Source: AGGARWAL, Ch. C., and Ch. X. Zhai, Mining Text Data. Springer.

12 What can one mine from unstructured (text) data?

13 Information processing Text Corpus Preprocessing Representation Knowledge Discovery

14 Information processing Text Corpus Preprocessing Representation Knowledge Discovery

15 Automatic Categorization Categorization Process of deriving precise categories through conceptual understanding. Rule-based training also supported. Why are Taxonomies useful? Makes unstructured information more accessible through directed navigation Intuitive visibility of knowledgebase

16 Clustering Visualizations Visualizations Clustering of data Clustering of users Time analysis Cascade clustering Node Decomposition Analysis (NDA) Cluster-on-demand Drag and click defined regions of interest

17 Architecture of an Enterprise Intelligent System Application Framework Rules Engine/BPM Analytics Intelligent Engine Connectors

18 Architecture of an Enterprise Intelligent System Application Framework Rules Engine/BPM Analytics Intelligent Engine Connectors On the top of this architecture is an Application Framework - APIs those provide IT capabilities and allow to compose flexibly business specific application. Such applications cover different business requirements. A Analytics Rules Engine/BPM tool provides capabilities increases traditional for data Core Enterprise and information of this Search architecture processing Engine is and Intelligent to allows discover Engine that covers implementing enhanced such insight capabilities certain and decision kind as: of business making. logic Conceptual that Connectors can use search Intelligent represent Engine an integration to deliver layer Advanced more and the sophisticated boundary search methods between and more data relevant sources and Categorization answers. Enterprise Intelligent System. Most of Clustering systems consist already of a set of connectors Sentiment to standard analysis system or social media. Hyperlinking Entity extraction Personalization

19 Sample of Rule High-level View of a Rule Engine Source: and

20 Real Knowledge Management Company Knowledge discovery from different sources Knowledge from KB and relevant information are used during an interaction with customer Customer Relevant information are provided Benefits Company provides higher quality of service reduces costs Customer increases his/her satisfaction

21 Corporate Web: FAQ Engine Screens and story User fill contact form User gets list of related FAQs before request is sent

22 Use Case - #4 Title Screens and story

23 Thank you

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