Customer Driven Big-Data Analytics for the Companies Servitization

Customer Driven Big-Data Analytics for

the Companies’ Servitization

Eugen Molnár, Natalia Kryvinska, Michal Greguš

Principal interactions in a PSS delivering

an advanced Service

Data – an important dimension of

servitization

Data – an important dimension 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.

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

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.

Wheel of Social Media

Holistic customer view

Speech Analytics

3 Pillars of Customer Experience

Source: Soudagar, R., V. Iyer, and V.G. Hilderbrand. 2012. The Customer Experience Edge: Technology and Techniques for Delivering an Enduring, Profitable, and Positive Experience to Your

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. 2012. Business intelligence and analytics: Research directions. In: ACM Trans.Manage. Inf. Syst. Vol.3, No.4, Article 17 (January 2013).

Innovation in Monitor, Analyze and

Respond

Source: BAINES, J.-LIGHTFOOT, H. Made to Serve: How Manufacturers Can Compete Through Servitization and Product Service Systems. John Wiley & Sons, 2013.

Monitor – Social Media, corporate web pages, chats, discussions, blogs, Call center interaction

Analyze – Text Analytics

Respond – dynamic dashboard, multichannel interaction with customer, pushing relevant data for example e-commerce, FAQ

Text Analytics

What can one mine from unstructured (text)

data?

Information processing

Information processing

 Categorization



_{Process of deriving precise categories}

through conceptual understanding.



_{Rule-based training also supported.}

Automatic Categorization

 Why are Taxonomies useful?



_{Makes unstructured information more}

accessible through directed navigation

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

Architecture of an Enterprise Intelligent

System

Rules Engine/BPM

Intelligent Engine

Connectors

Application Framework

Analytics

Rules Engine/BPM

Intelligent Engine Connectors

Application Framework Analytics

Core of this architecture is Intelligent Engine that

covers such capabilities as: Conceptual search

Advanced search methods Categorization Clustering Sentiment analysis Hyperlinking Entity extraction Personalization

A Rules Engine/BPM increases traditional Enterprise Search Engine and allows

implementing certain kind of business logic that can use Intelligent Engine to deliver more sophisticated and more relevant answers.

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.

Analytics tool provides capabilities for data and information processing to discover

enhanced insight and decision making.

Connectors represent an integration layer and the boundary between data sources and Enterprise Intelligent System. Most of

systems consist already of a set of connectors to standard system or social media.

Architecture of an Enterprise Intelligent

System

Sample of Rule

Source: http://decisions.com/platform/rules/ and http://docs.jboss.org/drools/release/5.3.0.Final/drools-expert-docs/html/ch01.html

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

Screens and story

Corporate Web: FAQ Engine

User fill contact form

User gets list of

related FAQs

before request is sent

Screens and story