Customer and business analytics pdf
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- What is business intelligence? Your guide to BI and why it matters
- CUSTOMER RELATIONSHIP MANAGEMENT AND BUSINESS ANALYTICS: A LEAD NURTURING APPROACH
- Business and Consumer Analytics: New Ideas
The word analytics has come into the foreground in last decade or so. The proliferation of the internet and information technology has made analytics very relevant in the current age. Analytics is a field which combines data, information technology, statistical analysis, quantitative methods and computer-based models into one. This all are combined to provide decision makers all the possible scenarios to make a well thought and researched decision. The computer-based model ensures that decision makers are able to see performance of decision under various scenarios.
What is business intelligence? Your guide to BI and why it matters
Download PDF. A short summary of this paper. PREFACEBusiness Analytics indicates the practices and competencies for exploration and introspection of business performance to make purposive, intuitive, and expedient business decisions. Business analytics involves a plethora of analysis around business data to draw information that could be used by the managers at various levels in an organization.
Business analytics enables fact-based decision making while extending accountability in decision making. Business analytics is defi ned as the process of looking at and summarizing data with the intent of extracting hidden predictive information.
Numerous studies and much evidence exist on the benefi ts of business analytics for organizations 1. Thus, this book deals with the science and art of business analytics with special emphasis on fi nancial analytics and also provides the theoretical foundations and context for various elements of business analytics in specifi c situations.
To highlight aspects of implementation, this book will show the reader how leading companies use the power of analytics to improve their investments. Often scientifi c knowledge alone cannot make good decisions unless combined with knowledge of the business with the best information available.
Quantitative methods can help managers evaluate more strategic choices using tools. While many traditional academic texts mostly focus on quantitative methods, very few cover analytics for non-quantitative managers, which this book aims to correct. I am also thankful to all who guided me directly or indirectly to fi nish this book.
I am grateful to the revered business organizations that facilitated working on the implementation of analytics applications and provided me with the intuition to write this book. I am thankful to my current employer the National Institute of Technology in Rourkela, India, and visiting faculty assignments to multiple universities that provided me with the intensive opportunity of classroom lectures to understand the curiosity of class participants better.
I am thankful to all those industries located in US and India spanning service industries, manufacturing, technology, telecom, and media that inspired me to learn from them. I am also grateful to all the researchers who have contributed to the literature and guided the direction of this study.
I am thankful to leading professional magazines and sources that provide periodic industry reviews and highlight the impending needs of business. All errors and omissions remain the sole responsibility of the author. It has been so regarded because a variety of individual styles can be observed in the handling and successful solving of common business problems in actual businesses. However, the environment in which management must operate is more complex and fast changing.
This calls for driving the art of business processes by scientifi c and objective methods. A theoretical and methodical business approach to business processes is necessary because today's business environments are far more intricate and heterogeneous than in the past and because the cost of making errors is far too high.
Conventional tools are intuitional and judgmental so long as they rely on individual opinions. Common sense may be misleading, and snap judgments may have painful consequences. The conviction of individual opinions is strengthened by using business analytics to avoid the repercussions of costly errors across the enterprise.
Therefore, the business problem can be articulated well by collecting relevant facts, by experimenting with potentially fruitful alternatives, and by implementing disruption. Plan of the bookAs the title suggests, this book is an introduction to business analytics and a theoretical aid to business decision making. Descriptions of business problems, important analytical techniques that can be deployed by managers in business situations, and the results of alternatives are embodied in the text of this book.
There are ten chapters in this book, which are arranged in accordance with the functional depth of learning.
Chapter 2 is devoted to describing business roles in an organization and presents an overview of the variety and levels of functional consumers of analytics, their end uses, and the hierarchical relationships of their consumption and intensity. Chapter 3 focuses on the aspects of functional analytics and the nature, intensity, engagement, periodicity, and strategic benefi ts that a business derives from using each of the independent components of functional analytics.
Chapters 4 through 8 cover four major and critical functional areas in analytics that are suitable for businesses. Businesses may adopt one or all of the domains of analytics mentioned in Chapters 4 through 8. For example Chapter 4 starts with a detailed discussion on human resources analytics, which covers the "people" element of an organization, including employee life cycle, performance, and retention; talent management, rewards and compensation, and satisfaction.
Chapter 5 covers supply chains and e-fi nancial supply chains in business, with a special emphasis on inventory, distribution, working capital, stock planning, demand planning, and a plethora of inquiries around procurement cycles of a business. Chapter 6 covers customer analytics for a business, which encompasses details about customer acquisition, product holding, cross-selling, satisfaction, lifetime value, retention, and the customer resolution aspects of the business.
Chapter 7 covers business processes that are critical for the smooth functioning of the business, such as operations, branches, campaigns, sales, marketing, quality, IT services, service resolutions, and re-engineering, which are applicable across both the service and manufacturing industries. Chapter 8 elaborates on fi nancial analytics and on a host of tools around measuring the incremental fi nancial impact of business decisions, such as pricing, mergers-demergers, valuation, spread, liquidity, asset liability, interest rate analysis, etc.
Chapter 9 is especially focused on the implementation aspects of analytics, which are aimed at the skills, generating evidence and demonstrating the utility, organizing teams and handling the hurdles preventing the implementation of analytical solutions in a business.
Chapter 10 supplements the text by giving use cases with detailed reports of results and their interpretation aimed at fi ve industrial sectors, such as banking, fi nancial services, and insurance BFSI , manufacturing, services, hotel chains, retail chains, etc. The Appendixes give an overview of the formulae, derivations, measures in business and statistics, and example data. This fi rst chapter introduces the subject of business analytics by giving simple defi nitions of business analytics and by also explaining the goals, characteristics, and domains of business analytics.
Business analyticsBusiness analytics is defi ned as the process of understanding the data-driven activities of a business to draw inferences to make calculated decisions with higher certainty. Business analytics encompasses a gamut of analysis around business data to draw information that could be used by the managers at various levels in an organization.
Business analytics is defi ned as the process of exploring, experimenting, simulating, and summarizing data to extract information. With the advent of real-time warehousing and web capabilities in business systems, business analytics has evolved as a practical choice for strategic business decisions. Is there any difference between business analytics and business intelligence? Business intelligence is generic and applies to any situation of discovery using data.
Business analytics goes well beyond mere presentation of data and statistics. The essence of analytics lies in the application of logic and processes to fi nd meaning in data. Through these processes, managers create activities that defi ne intelligence, including the ability to identify, locate, predict, relate, innovate, and learn to recommend choices, which also encompass statistical analysis.
Business analytics needs drivers, leaders, or business analysts who would apply the logic and processes described here in the text. Business analytics is one portion or component of business intelligence. Conventional data warehousing and reporting ends at the stage of report delivery.
Business analytics extends through the value-added knowledge stage, which in turn supports decisions and makes life easier for businesses. Hence, business analytics measures the results that are produced and provides a feedback loop that facilitates organizational learning.
With its ever-increasing popularity, business analytics has become more important and useful to a business manager. Goals of business analyticsBusiness analytics encompasses the entire key informational and decisional attributes of any business, and it is vitally important that business analytics features in the overall strategic vision of all businesses.
The understanding derived from analysis must align with business functions fi nance, marketing, sales, etc. IntroductionCause-and-effect association is often the most valuable in business decision making. Analytics that simply confi rm the status quo or reaffi rm conventional wisdom offer no insight.
Domains of business analyticsDomains refer to the variety of activities within a business. Using the right metrics can improve policies and procedures, increase team members' satisfaction and retention, focus employee training and support, improve morale, reduce costs, and increase productivity.
The activities impacted by human capital involve recruitment, training, employee relationships, employee satisfaction, and turnover. Supply chain analytics refers to the analysis of a fi rm's delivery processes, which includes acquisition of vendors, the sourcing of factors, inventory analytics, transportation and customer delivery network effi ciency, vendor management, and sourcing effi ciency. The baseline for strategic sourcing initiatives is as an enabler for process improvement. Further, supply chain analytics is a measurement device for cost-reduction programs, providing comprehensive spend visibility of both direct and indirect expenses on commodities and services, signifi cant cost-saving opportunities through supplier and commodity consolidation and enhanced compliance through effective spend and supplier monitoring.
Customer analytics is an understanding of customers, the customer life cycle, their product needs, and customer satisfaction. Customer analytics is the systematic interpretation of a business's customer information to retain profi table customers and proactively build relationships with them. Customer behavioural analysis seeks to identify and weigh the relative importance of the factors customers use to choose one product over another.
Customer profi ling is a tool that helps business better understand customers so they can increase sales and grow their business. Customer profi les can also help develop targeted marketing plans and ensure that products meet the needs of their intended audience.
By understanding the variables that infl uence individual decisions, businesses are more able to infl uence their outcome. Customer decision making will rely heavily on considerations using individuals as the unit of analysis. Business process analytics refers to the activity fl ows of product or service deliveries within an organization to improve processes and productivity.
Much of what will follow in this chapter will reply on the systems perspectives on the decisionmaking process described in Figure 1. Financial analytics is defi ned as the analysis of the fi nancial impact of business analytics. One aspect of fi nancial analytics is the opportunity of working with net fi nal fi gures, which are derived after taxes, duties, levies and penalties, or capital charges are charged to the business.
It embodies the versatility of the risks of doing business and also translates such risks to net turnout. Financial analytics enables business to maintain cash fl ow, spread and liquidity; manage pricing value acquisitions; control investments in new products and working capital; and plan funds and directed investments.
Improving fi nancial performance and expense control, through organized monitoring of expenses, drives profi tability across business units, geographic locations, products, or channels.
Therefore, the fl avours of analytics encompass both simple buy-and-sell decisions in a treasury and larger buy-and-sell decisions, i. Therefore, business choices are driven by goals that are set by top management e. Newer choices are generated when a fi xed set of choices do not suffi ce to fulfi l the given goals, which is an iterative process, and this requires exchange transfer of information among all three.
Discovering a feasible choice will be implemented only when it is visualized by CXOs, and hence the process of decision making is well understood in the context of a system shown in Figure 1. The onus of action lies with the agents, and whether they have acted successfully or not is displayed from the results and reports.
Tables 1. The contours shown in Table 1. Each type of analytics will ensure skilled teams comprising of personnel to attain primarily the goals of analytics e. HR, marketing, fi nance, etc. This means the alignment of analytics' focus should be in tune with the target goals of business, and in no situation is the period of review or the maturity of the impact or the investment unmatched with the strategy.
CUSTOMER RELATIONSHIP MANAGEMENT AND BUSINESS ANALYTICS: A LEAD NURTURING APPROACH
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. This paper makes an exploratory combination of operative customer relationship management CRM and business analytics BA. Save to Library. Create Alert. Launch Research Feed. Share This Paper.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools. Save to Library. Create Alert. Launch Research Feed.
It seems that you're in Germany. We have a dedicated site for Germany. This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies. The chapters are contributed by leading experts in the associated fields. The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching.
from the Leeds School of Business, the University of Colorado at Boulder. Her research interests relate to machine learning and big data analytics.
Business and Consumer Analytics: New Ideas
Search this site. Chan Kim. Book by Kathryn Shay.
PFEE SESN RETM - Альфа-группы из четырех знаков, - задумчиво проговорила Сьюзан.