Products related to Forecasting:
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Data Analytics in Marketing, Entrepreneurship, and Innovation
Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences.Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics.It features case studies that provide realistic examples of applications.This multifaceted examination of data analytics looks at: Business analyticsApplying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analyticsDeveloping new productsTechnopreneurshipDisruptive versus incremental innovationThe book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing.Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery.Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques.Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.
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Advances in Business and Management Forecasting
'Advances in Business and Management Forecasting' is a blind refereed serial publication published on an annual basis.The objective of this research annual is to present state-of-the-art studies in the application of forecasting methodologies to such areas as sales, marketing and strategic decision making.
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Future Ready : How to Master Business Forecasting
The recent crisis in the financial markets has exposed serious flaws in management methods.The failure to anticipate and deal with the consequences of the unfolding collapse has starkly illustrated what many leaders and managers in business have known for years; in most organizations, the process of forecasting is badly broken.For that reason, forecasting business performance tops the list of concerns for CFO's across the globe. It is time to rethink the way businesses organize and run forecasting processes and how they use the insights that they provide to navigate through these turbulent times.This book synthesizes and structures findings from a range of disciplines and over 60 years of the authors combined practical experience.This is presented in the form of a set of simple strategies that any organization can use to master the process of forecasting.The key message of this book is that while no mortal can predict the future, you can take the steps to be ready for it. ’Good enough’ forecasts, wise preparation and the capability to take timely action, will help your organization to create its own future. Written in an engaging and thought provoking style, Future Ready leads the reader to answers to questions such as: What makes a good forecast?What period should a forecast cover?How frequently should it be updated?What information should it contain?What is the best way to produce a forecast?How can you avoid gaming and other forms of data manipulation?How should a forecast be used?How do you ensure that your forecast is reliable?How accurate does it need to be?How should you deal with risk and uncertaintyWhat is the best way to organize a forecast process?Do you need multiple forecasts?What changes should be made to other performance management processes to facilitate good forecasting?Future Ready is an invaluable guide for practicing managers and a source of insight and inspiration to leaders looking for better ways of doing things and to students of the science and craft of management. Praise for Future Ready "Will make a difference to the way you think about forecasting going forward" —Howard Green, Group Controller Unilever PLC "Great analogies and stories are combined with rock solid theory in a language that even the most reading-averse manager will love from page one" —Bjarte Bogsnes, Vice President Performance Management Development at StatoilHydro "A timely addition to the growing research on management planning and performance measurement." —Dr. Charles T. Horngren, Edmund G. Littlefield Professor of Accounting Emeritus Stanford University and author of many standard texts including Cost Accounting: A Managerial Emphasis, Introduction to Management Accounting, and Financial Accounting "In the area of Forecasting, it is the best book in the market." —Fritz Roemer.Leader of Enterprise Performance Executive Advisory Program, the Hackett Group
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Data Analytics for Business : Lessons for Sales, Marketing, and Strategy
Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down.This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications.The foundation of this text is the author’s 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail.He provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics.The book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures. Sales and marketing executives, project managers, business and engineering professionals, and graduate students will find this clear and comprehensive book the ideal companion when navigating the complex world of big data analytics.
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What is deterministic demand forecasting?
Deterministic demand forecasting is a method of predicting future demand for a product or service based on historical data and known factors without considering any randomness or variability. It assumes that future demand can be accurately forecasted using precise data and deterministic models. This approach is often used when demand patterns are stable and predictable, making it easier to forecast future demand with a high degree of certainty. However, deterministic forecasting may not account for unexpected events or changes in market conditions that could impact demand.
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Do you have experience with Parcello forecasting?
As an AI, I do not have personal experiences. However, I can provide information on Parcello forecasting. Parcello forecasting is a method used to predict future parcel delivery volumes based on historical data and trends. It helps logistics companies optimize their operations by anticipating peak delivery periods and adjusting resources accordingly. By utilizing Parcello forecasting, companies can improve efficiency, reduce costs, and enhance customer satisfaction.
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Is there a significant difference between predicting and forecasting?
Predicting and forecasting are often used interchangeably, but there is a subtle difference between the two. Predicting typically involves making an educated guess or estimation about a future event based on current data or trends. On the other hand, forecasting involves using statistical models and analysis to make more accurate and precise predictions about future outcomes. Forecasting is generally considered to be a more systematic and rigorous process compared to predicting.
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What are the main forecast and variations in weather forecasting?
The main forecast in weather forecasting includes predicting temperature, precipitation, wind speed and direction, humidity, and atmospheric pressure. Variations in weather forecasting can include short-term forecasts (up to 3 days), medium-term forecasts (3-7 days), and long-term forecasts (beyond 7 days). Additionally, specialized forecasts for events such as hurricanes, tornadoes, and severe storms are also part of weather forecasting. These forecasts are based on a combination of historical data, current weather patterns, and advanced computer models.
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Business Models : Innovation, Digital Transformation, and Analytics
Since the beginning of time, running a business has involved using logic by which the business operates.This logic is called the business model in management science, which increasingly is focusing on issues surrounding business models.Research trends related to business models include value creation, value chain operationalization, and social and ecological aspects, as well as innovation and digital transformation.Business Models: Innovation, Digital Transformation, and Analytics examines how innovation, digital transformation, and the composition of value affect the existence and development of business models.The book starts by addressing the conceptual development of business models and by discussing the essence of innovation in those models.Chapters in the book investigate how: Business models can analyze digital transformation scenarios Individual business model elements effect selected performance measures as well as how the elements are significant for the enterprise value composition The environment effects the profitability of the high-growth enterprise business models Employer branding business models are perceived by the generation Z workforce To implement responsible business models in the enterprise Cyber risk is captured in business models Decision algorithms are important to business analyticsThis book is a compendium of knowledge about the use of business models in the context of innovative activities, digital transformation, and value composition.It attempts to combine the theory and practice and offers a look at business models currently used in companies, especially high-growth enterprises, in various countries of the world and indicates the prospects for their development.
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Extreme Weather Forecasting
Extreme Weather Forecasting reviews current knowledge about extreme weather events, including key elements and less well-known variables to accurately forecast them.The book covers multiple temporal scales as well as components of current weather forecasting systems.Sections cover case studies on successful forecasting as well as the impacts of extreme weather predictability, presenting a comprehensive and model agnostic review of best practices for atmospheric scientists and others who utilize extreme weather forecasts.
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Fashion Trend Forecasting
An understanding of trends is a fundamental skill for anyone working in the fashion industry.In this book Gwyneth Holland and Rae Jones look at how to produce a well-researched trend, from initial inspiration to concrete idea and, eventually, real product.Illustrated throughout with insights from practising trend forecasters and industry insiders, it is an invaluable guide for fashion students and practitioners alike.
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Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)
Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called “ADAM” (Augmented Dynamic Adaptive Model).The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems.At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model.ADAM can do all of that in one and the same framework.Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analysts and machine learning experts who work with time series, as well as any researchers working in the area of dynamic models. Key Features:• It covers basics of forecasting,• It discusses ETS and ARIMA models,• It has chapters on extensions of ETS and ARIMA, including how to use explanatory variables and how to capture multiple frequencies,• It discusses intermittent demand and scale models for ETS, ARIMA and regression,• It covers diagnostics tools for ADAM and how to produce forecasts with it,• It does all of that with examples in R.
Price: 97.99 £ | Shipping*: 0.00 £
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What is better: Saleswoman for Marketing Communication or E-Commerce?
The answer to this question depends on individual preferences and strengths. A saleswoman for marketing communication may be better suited for someone who enjoys building relationships, networking, and effectively communicating the value of products or services to potential clients. On the other hand, e-commerce may be a better fit for someone who is tech-savvy, enjoys analyzing data, and is interested in the digital marketplace. Both roles offer unique opportunities for success and growth, so it ultimately comes down to personal interests and skills.
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Which is more worthwhile, business administration or e-commerce?
The answer to whether business administration or e-commerce is more worthwhile depends on individual interests and career goals. Business administration provides a broad foundation in various aspects of business management, while e-commerce focuses specifically on online business operations and strategies. If someone is interested in traditional business practices and wants a versatile skill set, business administration may be more suitable. On the other hand, if someone is passionate about technology and the digital marketplace, e-commerce could be a more valuable choice. Ultimately, both fields offer unique opportunities for success and growth.
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What is the type of business for E-commerce?
E-commerce is a type of business that involves buying and selling goods or services over the internet. It can include a wide range of products and services, such as retail, wholesale, digital goods, and online marketplaces. E-commerce businesses can operate on their own websites, through online marketplaces like Amazon or eBay, or through social media platforms. This type of business allows for global reach and 24/7 accessibility for customers.
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Is the e-commerce industry really the worst for web developers?
No, the e-commerce industry is not necessarily the worst for web developers. While it does come with its own set of challenges, such as the need for robust security measures and complex payment processing systems, it also offers a wide range of opportunities for developers to showcase their skills and creativity. E-commerce websites often require high-quality user interfaces, seamless navigation, and efficient backend systems, which can be exciting and rewarding for web developers to work on. Additionally, the e-commerce industry is constantly evolving, providing developers with the chance to stay updated with the latest technologies and trends.
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