Social Data Analytics

Collaboration for the Enterprise

Krish Krishnan, Shawn P. Rogers

• Publisher: Morgan Kaufmann
• Year: 2014
• Print ISBN: 9780123971869
• eBook ISBN: 9780123977809
• Topic: Business
• Subtopic: Digital Marketing
• Index: Business

Social Data Analytics is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease. You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization's next social data analytics project. - Provides foundational understanding of new and emerging technologies—social data, collaboration, big data, advanced analytics - Includes case studies and practical examples of success and failures - Will prepare you to lead projects and advance initiatives that will benefit you and your organization

Chapter 1

A New Universe of Data

Abstract

Social Data Analytics is a fast growing and critical component of enterprise analytic strategy. Understanding where the data is, how to integrate it into enterprise systems, and what types of analytics can be brought to bear on the data is very important for companies looking to differentiate themselves in the market.

Keywords

Social Platforms

maturity model

analytic platforms

analytic functions

Contents

Understanding Social Analytic Platforms 1

Platform Functions 4

Alerting and Workflow 4

Collaboration 4

Integration/Application Programming Interfaces 4

Natural Language Processing 4

Platform Analytic Functions 5

Geospatial Analysis 5

Sentiment Analysis 5

Influence Analysis 5

Machine Data Analysis 6

Demographic Analysis 6

Brand Affinity 6

Text Analytics 6

Social Data Analytics is a fast-growing and critical component of enterprise analytic strategy. Understanding where the data is, how to integrate it into enterprise systems, and what types of analytics can be brought to bear on the data is very important for companies looking to differentiate themselves in the market.

Understanding social analytic platforms

The biggest transformation that we are seeing in the last decade (2002 onwards) is the interest of enterprises large and small to compete for the wallet share of the customer with new and updated products and services that are targeted to satisfy the customer or prospect satisfying their need at the time they are looking at the enterprise set of products and solutions. To accomplish this with result-oriented outcomes, enterprises have strategized the process of understanding the customer by following their behaviors utilizing the social media platforms and machine learning algorithms.

In the new world where globalization and commoditization are the key terms to compete in the market, enterprises need to create and integrate an internal social analytics strategy that will mirror what they do in the outside world, thereby increasing the collaboration between teams, aligning success and quality of insights. Whether it is implementing an internal Twitter-like channel or a Facebook-like community or a Sharepoint-driven collaboration, each of these strategies have specific inputs and outputs, measurements and reports, or dashboards to monitor the same. A good set of examples of goals that organizations today are working in a collaborative fashion to strive toward include the following:

Create targeted messaging to prospects and customer leads, enabling to convert them to customers. Collaborating on competitive research and campaign marketing to achieve better confidence and trust in forming the customer relationship enables this process and produces better results.

Improve internal communications and morale of global staff in call-centers and support roles for the organization. This is enabled by implementing a series of collaboration strategies to share data and process information across teams and helps create a transparent information ecosystem. This type of internal data sharing helps in managing customer and prospect situations effectively and efficiently, resulting in satisfied end customers and more happy employees.

Build brand awareness and track the effectiveness of the current brand campaigns and marketing efforts. This type of exercise was done previously too but did not include competitive intelligence, research articles, online crawlers and content gathered by them, and collaborative comments and gamification-based results from different teams within the enterprise. The availability of the data and the collaboration between different teams have created new ideations and improvisation of existing in-process ideations to leverage the best possible results for the enterprise. This is a big benefit of social analytics in the enterprise and we will be discussing this in multiple case studies and chapters in the book.

Improved and satisfied customer service and product support. A weak area for all organizations globally, this is a real touchy subject of discussion at C-levels anytime. With the inclusion of social media analytic platforms within the organization, the customer service and support teams have got a transparent customer-focused platform that allows them to share relevant information with the customer and their teams simultaneously to reach decisions and ensure a satisfied customer and utilize the platform to also create a cross-sell and up-sell opportunity that results in more wallet share.

Improve your ability to recruit top-notch talent to your team with inclusion of social media analytic platforms. The current and future generations of employees are the generation that is referred to as the “Google” generation. These employees have grown up in a digital world where the boundaries of geographies and social demographics are absent, and the sharing of knowledge and collaboration on solutions is more of the norm. These employees feel more satisfied and are more innovative and productive when the social media platforms used within the enterprise make them improvise the skills they grow up with and utilize through school and college years into the job culture.

Increase market intelligence by collecting customer feedback, encouraging customer community and collecting information about competitors. These are major influences in the business intelligence decisions and the foundation for the growth of the business and its customers. By including the customer inside your innovation, you will get better market results and more satisfied customers.

Implement crowdsourcing platforms as a means for completing tasks faster and less expensively than at present. These platforms create and promote better orientation of problem solving, as the situation is open and available to all employees to participate or help solve. The platform drives multiple benefits to the organization: one is the measurement of time reduction and efficiency of problem solving; second is the availability of a number of resources across the organization that was never known before; third is the availability of resources globally if there are regions of operation and their ability to provide a 24/7 model of support to the problem and its solution; and last but not the least the cost savings in the entire solution and its implementation plus the availability of the same for the organization in a knowledge base.

There are many vendors in the social data analytic platform space. Each brings a somewhat unique set of features and functions to the game. Early entrants have been focused primarily on the needs of marketing and public relations teams and often focused on only monitoring the social landscape. As an organization’s social data analytics strategy evolves from “watching” to a more widely integrated approach, a new set of capabilities is required to meet the needs of enterprise users. Platform functionality will enable a wide range of analytic and functional capabilities, so it is important to make sure the solution you choose for your organization has the platform functionality needed to support action, collaboration, and integration with enterprise data and applications and can support advanced analytic functions.

Platform Functions

What should the key focus areas for platform functionality and implementation be when the organization chooses one or more social analytic platforms. The following features have emerged as some of the key features, and the list can be expanded or modified for any organization’s requirements and comfort.

Alerting and Workflow

Social data analytic platforms are gaining traction within enterprise companies and it is vital as more stakeholders become involved that the platform help these professionals to understand the insights that are critical to their organization and take faster action. Alerting and key performance indicator (KPI)–driven metrics have long been included in traditional business intelligence solutions, and they are now finding their way into Socia...

Table of contents

• Cover
• Title page
• Table of Contents
• Copyright
• Dedication
• Acknowledgments
• Preface
• Chapter 1: A New Universe of Data
• Chapter 2: Social Analytics in the Enterprise
• Chapter 3: Social Business Intelligence
• Chapter 4: Four Steps to Social Business Intelligence
• Chapter 5: Valuable Data for the Enterprise
• Chapter 6: Accessing the Data
• Chapter 7: Social Platforms
• Chapter 8: Social Business Intelligence and Collaboration
• Chapter 9: Social Media and Network Monitoring
• Chapter 10: Your First Project
• Appendix
• Index