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Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.


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TL;DR: In this article, the authors developed a 2 X 2 model to explain the role of predictive analytics in the theory development process and pointed out several research questions that need to be addressed by the research community.
Abstract: Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical, however, we develop a 2 X 2 model to explain the role of predictive analytics in the theory development process. This 2 X 2 model shows that in our discipline we have traditionally taken one path to theory development but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement (MM), changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.

128 citations

Journal ArticleDOI
TL;DR: The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives.
Abstract: The purpose of this study was to examine the role of big data and business analytics (BDBA) in agile manufacturing practices. Literature has discussed the benefits and challenges related to the deployment of big data within operations and supply chains, but there has not been a study of the facilitating roles of BDBA in achieving an enhanced level of agile manufacturing practices. As a response to this gap, and drawing upon multiple qualitative case studies undertaken among four U.K. organizations, we present and validate a framework for the role of BDBA within agile manufacturing. The findings show that market turbulence has negative universal effects and that agile manufacturing enablers are being progressively deployed and aided by BDBA to yield better competitive and business performance objectives. Further, the level of intervention was found to differ across companies depending on the extent of deployment of BDBA, which accounts for variations in outcomes.

127 citations

Book
01 Jan 2007
TL;DR: Web Analytics: A Brief History of Web Analytics as discussed by the authors is an excellent overview of the current state of the art in web analytics, as well as a discussion of some of the most important aspects of a successful web analytics strategy.
Abstract: Foreword. Introduction. Chapter 1 Web Analytics-Present and Future. A Brief History of Web Analytics. Current Landscape and Challenges. Traditional Web Analytics Is Dead. What Web Analytics Should Be. Chapter 2 Data Collection-Importance and Options. Understanding the Data Landscape. Clickstream Data. Outcomes Data. Research Data. Competitive Data. Chapter 3 Overview of Qualitative Analysis. The Essence of Customer Centricity. Lab Usability Testing. Heuristic Evaluations. Site Visits (Follow-Me-Home Studies). Surveys (Questionnaires). Summary. Chapter 4 Critical Components of a Successful Web Analytics Strategy? Focus on Customer Centricity. Solve for Business Questions. Follow the 10/90 Rule. Hire Great Web Analysts. Identify Optimal Organizational Structure and Responsibilities. Chapter 5 Web Analytics Fundamentals. Capturing Data: Web Logs or JavaScript tags? Selecting Your Optimal Web Analytics Tool. Understanding Clickstream Data Quality. Implementing Best Practices. Apply the "Three Layers of So What" Test. Chapter 6 Month 1: Diving Deep into Core Web Analytics Concepts. Week 1: Preparing to Understand the Basics. Week 2: Revisiting Foundational Metrics. Week 3: Understanding Standard Reports. Week 4: Using Website Content Quality and Navigation Reports. Chapter 7 Month 2: Jump-Start Your Web Data Analysis. Prerequisites and Framing. Week 1: Creating Foundational Reports. E-commerce Website Jump-Start Guide. Support Website Jump-Start Guide. Blog Measurement Jump-Start Guide. Week 4: Reflections and Wrap-Up. Chapter 8 Month 3: Search Analytics-Internal Search, SEO, and PPC. Week 1: Performing Internal Site Search Analytics. Week 2: Beginning Search Engine Optimization. Week 3: Measuring SEO Efforts. Week 4: Analyzing Pay per Click Effectiveness. Chapter 9 Month 4: Measuring Email and Multichannel Marketing. Week 1: Email Marketing Fundamentals and a Bit More. Week 2: Email Marketing-Advanced Tracking. Weeks 3 and 4: Multichannel Marketing, Tracking, and Analysis. Chapter 10 Month 5:Website Experimentation and Testing-Shifting the Power to Customers and Achieving Significant Outcomes. Weeks 1 and 2: Why Test and What Are Your Options? Week 3: What to Test-Specific Options and Ideas. Week 4: Build a Great Experimentation and Testing Program. Chapter 11 Month 6: Three Secrets Behind Making Web Analytics Actionable. Week 1: Leveraging Benchmarks and Goals in Driving Action. Week 2: Creating High Impact Executive Dashboards. Week 3: Using Best Practices for Creating Effective Dashboard Programs. Week 4: Applying Six Sigma or Process Excellence to Web Analytics. Chapter 12 Month 7: Competitive Intelligence and Web 2.0 Analytics. Competitive Intelligence Analytics. Web 2.0 Analytics. Chapter 13 Month 8 and Beyond: Shattering the Myths of Web Analytics. Path Analysis: What Is It Good For? Absolutely Nothing. Conversion Rate: An Unworthy Obsession. Perfection: Perfection Is Dead, Long Live Perfection. Real-Time Data: It's Not Really Relevant, and It's Expensive to Boot. Standard KPIs: Less Relevant Than You Think. Chapter 14 Advanced Analytics Concepts-Turbocharge Your Web Analytics. Unlock the Power of Statistical Significance. Use the Amazing Power of Segmentation. Make Your Analysis and Reports "Connectable". Use Conversion Rate Best Practices. Elevate Your Search Engine Marketing/Pay Per Click Analysis. Measure the Adorable Site Abandonment Rate Metric. Measure Days and Visits to Purchase. Leverage Statistical Control Limits. Measure the Real Size of Your Convertible "Opportunity Pie". Chapter 15 Creating a Data-Driven Culture-Practical Steps and Best Practices. Key Skills to Look for in a Web Analytics Manager/Leader. When and How to Hire Consultants or In-House Experts. Seven Steps to Creating a Data-Driven Decision-Making Culture. Index.

126 citations

Journal ArticleDOI
TL;DR: In this article, the authors describe techniques that can be used to set up a digital marketing optimization program, including a review of how people, process, measures and tools can be combined.
Abstract: The use of web analytics to improve online marketing dates back to the 1990s when the first web analytics systems were developed. Yet, recent research suggests that many companies are failing to utilize core web analytics best practices and are therefore not getting the potential return from web analytics that they could. This paper reviews the opportunities for companies to better apply web analytics to improve digital marketing performance. An approach is defined to create a strategy to improve the value contributed by web analytics. The paper describes techniques that can be used to set up a digital marketing optimization programme, including a review of how people, process, measures and tools can be combined.

126 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023131
2022262
2021176
2020169
2019185
2018203