Customer Analytics As a Source of Competitive Advantage Master Thesis
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Role of Big Data in Decision Making
OPERATIONS AND SUPPLY CHAIN MANAGEMENT Vol. 11, No. 1, 2018, pp. 36 - 44 ISSN 1979-3561 | EISSN 2759-9363 Role of Big Data in Decision Making Shirish Jeble Faculty of Management IBS Business School, Pune, India Email: [email protected] (Corresponding Author) Sneha Kumari Symbiosis Center for Research and Innovation Symbiosis International University, Pune, India Email: [email protected] Yogesh Patil Symbiosis Center for Research and Innovation Symbiosis International University, Pune, India enabled seamless integration of information systems ABSTRACT between business partners. It enhanced speed of business to business transactions (B2B), communication and reduced Information systems coupled with internet, cloud cost of inter-company transactions. computing, mobile devices and Internet of Things have led to In the next wave in early nineties, arrival of internet massive volumes of data, commonly referred as big data. It includes mix of structured, semi-structured and unstructured further simplified integration of firms with their business real-time data, constituting of data warehouse, OLAP, ETL and partners. In the last decade, information systems coupled information. Business firms and academicians have designed with internet, cloud computing, mobile devices and Internet unique ways of tapping value from big data. There is a great of Things have led to massive volumes of data, commonly scope of using large datasets as an additional input for making referred as big data. It includes structured, semi-structured decisions. The aim of the paper is to explore the role of big data and unstructured real-time data, constituting of data in these areas for making better decisions. Here we explore how warehouse, OLAP, ETL and information. -
A Framework for Analytical CRM Assessments Challenges and Recommendations
International Journal of Business and Social Science Vol. 10 • No. 6 • June 2019 doi:10.30845/ijbss.v10n6p2 A Framework for Analytical CRM Assessments Challenges and Recommendations Maruthi Rohit Ayyagari College of Business University of Dallas, Texas USA Abstract Businesses are increasingly adopting analytical customer relationship management (CRM) solutions. The critical customer information that resides within CRM can guide the decision-making process. Therefore, CRM analysis leads to higher loyalty and customer satisfaction, as well as enhanced competitive and financial performance. Data mining techniques are used to understanding customers and discovering interesting patterns. However, data mining techniques are considered a complicated process for non-technical decision makers and administrators. Therefore, the problem increases with the technical difficulty of large-scale CRM solutions for novice administrators and decision makers. This paper proposes a framework for the process of data mining in the context of analytical CRM to enhance the decision-making process. The paper also highlights the role of data mining in analytical CRM and how it assists the businesses to manage customer information better. The framework was evaluated and accepted by two senior CRM experts. The proposed framework revealed that there are still issues of customer data privacy and issues related to collected data types. Keywords: Customer Relationship Management (CRM), Analytical CRM, CRM implementation, data mining, decision making 1. Introduction In the business world, without catering to the needs of the customers, it is not possible for a business to survive amidst high competition (Hu et al., 2018). It is essential for a company to reach out to the right customer with the right offer at the right time. -
Customer Data Analytics Best Practices from Top Performers
E-Guide Customer data analytics best practices from top performers Companies are challenged in a number of areas in effective utilization of customer data analytics. However, a few capabilities and best practices stand out among the top-performing companies. In this E-Guide Readers will get learn: Customer data analytics best practices from top performers Customer data you should collect for your customer analytics program Tips and insights into analytics and integration software Sponsored By: SearchBusinessAnalytics.com E-Guide Customer data analytics best practices from top performers E-Guide Customer data analytics best practices from top performers Table of Contents Customer data analytics best practices from top performers Customer data you should collect for your customer analytics program Analytics and integration software for collecting customer data Big Data Poses Big Challenges for Traditional Analytics Approaches Big Data Analytics Fulfilling the Promise of Predictive BI Best Practices for Big Data Analytics Rely on Familiar Disciplines Skills Shortage, Training Present Pitfalls to Big Data Analytics Resources from IBM Sponsored By: Page 2 of 23 SearchBusinessAnalytics.com E-Guide Customer data analytics best practices from top performers Customer data analytics best practices from top performers By Leslie Ament, SearchBusinessAnalytics.com Contributor Companies are challenged in a number of areas in effective utilization of customer data analytics. However, a few capabilities and best practices stand out among the top- performing companies. It was not surprising to find that top performers are more mature in their use of customer data analytics techniques when leveraging customer information. Understandably, these organizations have an easier time securing both budget and organizational buy-in as a result of the tangible business case justification and return on investment metrics they provide to executive sponsors. -
Vertica Customer Behavior Analytics
Data Sheet Analytics and Big Data Vertica Customer Behavior Analytics The Vertica Advanced Analytics Platform helps data-driven organizations manage and analyze massive volumes of customer data from Web logs, third-party analytics tools, social media, call detail records (CDR), and traditional CRM and other customer records from enterprise systems. Organizations can transform that data into actionable information, enabling them to better understand how their customers and site visitors interact with their business. With Vertica, you can completely understand your customers and improve the customer experience and your bottom line. Product Highlights need to take advantage of customer analytics Quick View to remain competitive. What Customers Want • Massively Parallel Processing (MPP) (and How You Know) • Performance at Exabyte scale Delivering Value Across Industries According to Wikipedia, “Customer Analytics is Customer Behavior Analytics is vital for just • In-database machine learning functions a process by which data from customer behav- about any industry or market segment. By • Functions for clickstream, sentiment, and ior is used to help make key business decisions understanding how your customers (or con- churn analytics via market segmentation and predictive analyt- stituents) behave and interact with your orga- ics. This information is used by businesses for nization, you can better serve, and profit, from direct marketing, site selection, and customer the interactions. The key to effective customer relationship management.”