Advanced Analytics: the Hurwitz Victory Index Report
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Data Mining – Intro
Data warehouse& Data Mining UNIT-3 Syllabus • UNIT 3 • Classification: Introduction, decision tree, tree induction algorithm – split algorithm based on information theory, split algorithm based on Gini index; naïve Bayes method; estimating predictive accuracy of classification method; classification software, software for association rule mining; case study; KDD Insurance Risk Assessment What is Data Mining? • Data Mining is: (1) The efficient discovery of previously unknown, valid, potentially useful, understandable patterns in large datasets (2) Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD (2) The analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner Knowledge Discovery Examples of Large Datasets • Government: IRS, NGA, … • Large corporations • WALMART: 20M transactions per day • MOBIL: 100 TB geological databases • AT&T 300 M calls per day • Credit card companies • Scientific • NASA, EOS project: 50 GB per hour • Environmental datasets KDD The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages: (1) Selection (2) Pre-processing (3) Transformation (4) Data Mining (5) Interpretation/Evaluation Data Mining Methods 1. Decision Tree Classifiers: Used for modeling, classification 2. Association Rules: Used to find associations between sets of attributes 3. Sequential patterns: Used to find temporal associations in time series 4. Hierarchical -
Text Mining Tools
Chapter 21 Text mining tools A. Zanasi TEMIS SA, France. Modena & Reggio Emilia University, Italy. Introduction Several tools are already available in the text mining (or more generally un- structured data) quickly growing market. We recognize the text mining market as composed by pure players (companies which develop text mining software, e.g. Clearforest, Inxight, Temis), indirect players (which integrate text mining into their offering, e.g. IBM, SAS, SPSS), partial players (companies which use text mining to improve their core business, e.g. Fast, Verity). A section is dedicated to each player which provided us with their company description, and a cumulative section listing the most known players in the text mining arena. Hundreds of other companies are already working successfully in the worldwide market. For a list of them, go to www.kdnuggets.com. 1 Megaputer intelligence 1.2 Company description Megaputer Intelligence (www.megaputer.com) was founded in 1993. The company provides a family of tools for the analysis of structured data and text. 1.3 Products Megaputer Intelligence offers two products: PolyAnalyst for Text™, aimed pri- marily at the analysis of incident reports, survey responses and customer comm- unications data and TextAnalyst™ . PolyAnalyst for Text™ performs semantic text analysis, record coding, identi- fication and visualization of patterns and clusters of information, automated or manual taxonomy creation and editing, taxonomy-based forced or self-learning WIT Transactions on State of the Art in Science and Eng ineering, Vol 17, © 2005 WIT Press www.witpress.com, ISSN 1755-8336 (on-line) doi:10.2495/978-1-85312-995-7/21 316 Text Mining and its Applications to Intelligence, CRM and Knowledge Management categorization of documents, text OLAP, link analysis, and interactive visual processing of various combinations of structured and unstructured data. -
Marketing Automation
THE BUSINESS CASE FOR MARKETING AUTOMATION How to Craft a Compelling Case the Executive Team Will Approve Copyright © 2016 | Act-On Software www.Act-On.com Bottom line, you can’t realize the benefits of nurture marketing the way top performers do unless you incorporate a technology platform “that can preconfigure business rules to manage timely engagement and escalate prioritized leads to sales via integration with CRM. No amount of hired resources could manually reach out and touch prospects at just the right time with just the right message. Marketing automation forms the backbone for configuring nurture marketing campaigns across channels and managing communications based on prospect engagement. It’s also one of the only ways marketers can actually start to attribute marketing spend to closed sales. — GLEANSTER, March 2013 www.Act-On.com The Business Case for Marketing Automation | II Table of Contents 1. The Basics of Marketing Automation . 1 2. How to Make a Business Case for Marketing Automation .........................................................9 3. What the Executive Suite Needs to Know .................................. 25 4. Closing Thoughts and Resources .................................................................... 27 www.Act-On.com The Business Case for Marketing Automation | III You’re Convinced Marketing Automation Will Help Your Company Leap Forward... ...if you can convince executive management to adopt the technology. The buyer has evolved. Which means you must, too. THE SCALES HAVE SHIFTED. MARKETING AUTOMATION STRIKES Technology, digital channels, and non-stop THIS BALANCE. connectivity continue to empower today’s buyers It’s a proven method for managing and optimizing with at-the-ready information and increased choice, the entire customer experience, measuring fueling unprecedented global competition. -
Application of Polyanalyst to Flight Safety Datat Southwest Airlines
Application of PolyAnalyst to Flight Safety Data at Southwest Airlines TM r Proof-of-Concept Demonstration of Data and Text Mining Prepared by: Megaputer Intelligence 120 W. 7th Street, Suite 310 Bloomington, IN 47404 Project Manager: Sergei Ananyan [email protected] Primary Analysts: Your Knowledge Partne Richie Kasprzycki [email protected] Vijay Kollepara [email protected] January 2004 DISCLAIMER This project report and the results are based on a time-constrained proof-of-concept demonstration carried out by Megaputer Intelligence for Southwest Airlines and involve analysis of only a subset of data. Therefore, the results should not be used to form any conclusions or business decisions. Table of Contents ACKNOWLEDGEMENTS ......................................................................................................................... ii EXECUTIVE SUMMARY ......................................................................................................................... iii 1.0 INTRODUCTION ................................................................................................................................. 1 1.1. PURPOSE OF THE PROOF-OF-CONCEPT DEMONSTRATION .............................................................. 1 1.2 OVERVIEW OF PARTICIPATING ORGANIZATIONS ........................................................................... 2 1.3 OVERVIEW OF POLYANALYST TEXT AND DATA MINING SYSTEM................................................. 2 1.4 INPUT DATA: AVIATION SAFETY ACTION PROGRAM -
Jaquelina Belin Rossi.Pdf (2.890Mb)
UNIVERSIDADE FEDERAL DO PARANÁ JAQUELINA BELIN ROSSI INTELIGÊNCIA ARTIFICIAL APLICADA ÀS ROTINAS JURÍDICAS: REVISÃO SISTEMÁTICA DE LITERATURA CURITIBA 2021 JAQUELINA BELIN ROSSI INTELIGÊNCIA ARTIFICIAL APLICADA ÀS ROTINAS JURÍDICAS: REVISÃO SISTEMÁTICA DE LITERATURA Trabalho de Conclusão de Curso apresentado como requisito parcial à obtenção de grau de Bacharel no Curso de Gestão da Informação, Departamento de Ciência e Gestão da Informação do Setor de Ciências Sociais Aplicadas da Universidade Federal do Paraná. Orientadora: Prof.ª Dr.ª Denise Fukumi Tsunoda CURITIBA 2021 AGRADECIMENTOS Primeiramente, agradeço a Deus que torna tudo possível, inclusive os meus sonhos. Agradeço a minha orientadora Prof.ª Dr.ª Denise Fukumi Tsunoda, por sua tolerância e paciência nos momentos difíceis. Agradeço a Prof.ª Dr.ª Suely Ferreira da Silva e ao Prof. Dr. José Simão de Paula Pinto por fazerem parte da banca examinadora por seus insights. Agradeço ao meu marido Paulo e filhos Giovanna, Guillermo e Luigi pelo apoio, paciência e colaboração. Agradeço aos meus pais e minhas irmãs pelo suporte e incentivo. Aos amigos e colegas do curso que de uma maneira ou outra e influenciaram nesta jornada. Ao Gilson, secretário do curso. E um agradecimento especial a todos os professores do curso de Gestão da Informação, pois o conhecimento e sabedoria transmitidos são inestimáveis. RESUMO A inteligência artificial está se estabelecendo no direito para auxiliar advogados e tribunais. Apresenta uma revisão sistemática aplicada em bases de dados internacionais, relacionando a inteligência artificial e o direito. As bases utilizadas incluem Scopus, Web of Science e EbscoHost, onde os artigos que compõe a base de dados da pesquisa foram selecionados para a realização de análises bibliométricas. -
Predictive Analytics
BUTLER A N A L Y T I C S BUSINESS ANALYTICS YEARBOOK 2015 Version 2 – December 2014 Business Analytics Yearbook 2015 BUTLER A N A L Y T I C S Contents Introduction Overview Business Intelligence Enterprise BI Platforms Compared Enterprise Reporting Platforms Open Source BI Platforms Free Dashboard Platforms Free MySQL Dashboard Platforms Free Dashboards for Excel Data Open Source and Free OLAP Tools 12 Cloud Business Intelligence Platforms Compared Data Integration Platforms Predictive Analytics Predictive Analytics Economics Predictive Analytics – The Idiot's Way Why Your Predictive Models Might be Wrong Enterprise Predictive Analytics Platforms Compared Open Source and Free Time Series Analytics Tools Customer Analytics Platforms Open Source and Free Data Mining Platforms Open Source and Free Social Network Analysis Tools Text Analytics What is Text Analytics? Text Analytics Methods Unstructured Meets Structured Data Copyright Butler Analytics 2014 2 Business Analytics Yearbook 2015 BUTLER A N A L Y T I C S Business Applications Text Analytics Strategy Text Analytics Platforms Qualitative Data Analysis Tools Free Qualitative Data Analysis Tools Open Source and Free Enterprise Search Platforms Prescriptive Analytics The Business Value of Prescriptive Analytics What is Prescriptive Analytics? Prescriptive Analytics Methods Integration Business Application Strategy Optimization Technologies Business Process Management * Open Source BPMS * About Butler Analytics * Version 2 additions are Business Process Management and Open Source BPMS This Year Book is updated every month, and is freely available until November 2015. Production of the sections dealing with Text Analytics and Prescriptive Analytics was supportedFICO by . Copyright Butler Analytics 2014 3 Business Analytics Yearbook 2015 BUTLER A N A L Y T I C S Introduction This yearbook is a summary of the research published on the Butler Analytics web site during 2014. -
Polyanalyst Technical Capabilities and System Requirements
PolyAnalyst 6 Technical capabilities and system requirements © 2007 Megaputer Intelligence Inc. All rights reserved. PolyAnalyst Features Client/Server architecture Server architecture enables the implementation of PolyAnalyst 6 as an enterprise level analytical system. It facilitates the collaboration between data analysts working on the same projects and sharing various related resources such as analysis scenarios, dictionaries, taxonomies, and multi-dimensional matrices. Server architecture helps enhance the performance of the system by performing calculations on the most powerful machines, reducing data transfer over the network, scheduling execution of tasks at a given time, and generating custom reports and condition based alerts for different groups of business users. It provides the centralized management and audit of the list of system users and their actions. Security Recognizing that data is one of the most valuable and sensitive assets of a modern organization, PolyAnalyst provides solid mechanisms to ensure data security. Communications between client and server are performed in a fully encrypted manner with a new encryption key generated by the server for every communication session. PolyAnalyst supports secure user login based on user rights and passwords and keeps track of individual and group rights and sequences of actions carried out by the users. In addition, full compliance with the requirements of the HIPAA legislation facilitates PolyAnalyst implementations at healthcare and insurance organizations. Scalability and Performance PolyAnalyst provides industrial level scalability: it can handle huge amounts of data within reasonable time intervals. This scalability is ensured through a combination of several factors. PolyAnalyst utilizes hard disk instead or RAM for holding all data and supporting meta-information. -
Customer Intelligence Appliance
IBM Software Customer Intelligence Appliance An appliance-based joint solution for omni-channel customer analytics from IBM and Aginity Customer Intelligence Appliance Information Management Contents By leveraging pre-developed, proven, industry best practice solution components and appliance technology from IBM and 3 Executive Summary Aginity, the CIA can be up and running in as few as twelve weeks. Utilizing CIA, retailers are able to understand the value 4 Challenges facing retailers today of individual customers and customer segments to the business 5 An appliance-based solution for omni-channel and prioritize marketing spend to target customers based on customer analytics propensity to respond and net derived value. CIA enables retailers to dynamically score customers utilizing built-in 7 Use Cases behavioral attributes, facilitating trigger based offers that may be executed in real-time across any device or channel. CIA 15 Get Started improves campaign response rate by 10-50%, as retailers leverage omni-channel insights and predictive analytics to attract, engage and retain customers. The CIA solution consists Executive Summary of the Netezza data warehouse appliance, a flexible omni- Customer Intelligence Appliance (CIA) channel data model, data loading wizards, retail reports, ExecutiveFor retailSummary executives and analysts, who are dissatisfied with siloed, analytic modules, behavioral attributes and behavioral black-box, inflexible CRM and analytics solutions, the Customer segmentation capability. The following short stories show how Intelligence Appliance (CIA) is an appliance-based solution, that retailers use CIA. provides an integrated multi-channel view of customers, best • Data Deluge. Integration and identification of customers practice reporting, and customer behavior segmentation and across all channels, despite the mountains of data created from analytics accelerators. -
Sales Profession and Professionals in the Age of Digitization and Artificial Intelligence Technologies: Concepts, Priorities, and Questions
Journal of Personal Selling & Sales Management ISSN: 0885-3134 (Print) 1557-7813 (Online) Journal homepage: https://www.tandfonline.com/loi/rpss20 Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions Jagdip Singh, Karen Flaherty, Ravipreet S. Sohi, Dawn Deeter-Schmelz, Johannes Habel, Kenneth Le Meunier-FitzHugh, Avinash Malshe, Ryan Mullins & Vincent Onyemah To cite this article: Jagdip Singh, Karen Flaherty, Ravipreet S. Sohi, Dawn Deeter-Schmelz, Johannes Habel, Kenneth Le Meunier-FitzHugh, Avinash Malshe, Ryan Mullins & Vincent Onyemah (2019): Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions, Journal of Personal Selling & Sales Management To link to this article: https://doi.org/10.1080/08853134.2018.1557525 Published online: 24 Jan 2019. Submit your article to this journal View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rpss20 Journal of Personal Selling & Sales Management, 2019 https://doi.org/10.1080/08853134.2018.1557525 Sales profession and professionals in the age of digitization and artificial intelligence technologies: concepts, priorities, and questions à Jagdip Singha, Karen Flahertyb, Ravipreet S. Sohic , Dawn Deeter-Schmelzd, Johannes Habele, Kenneth Le Meunier-FitzHughf, Avinash Malsheg, Ryan Mullinsh and Vincent Onyemahi aAT&T Professor, Marketing, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA; bMarketing, Oklahoma State University, Stillwater, OK, USA; cUniversity of Nebraska-Lincoln, Robert D. Hays Distinguished Chair of Sales Excellence, 1400 R St, Lincoln, NE 68588, USA; dJ.J. Vanier Distinguished Chair of Relational Selling and Marketing, Kansas State University, Manhattan, KS 66506, USA; eMarketing, ESMT Berlin, Schloßpl. -
Linguamatics Text Mining Summit 2016 October 17–19: Chatham, MA Welcome to the Linguamatics Text Mining Summit 2016
Linguamatics Text Mining Summit 2016 October 17–19: Chatham, MA Welcome to the Linguamatics Text Mining Summit 2016 The Linguamatics Text Mining Summit is a place where the text mining community comes together to exchange ideas, network, learn from each other, and explore the latest NLP text mining technology from Linguamatics. There will be discussions on best practice; you will hear real user case studies, and find out how organizations from the life science and healthcare sectors are using NLP text mining every day to gain better insights from their ever-growing data. John M. Brimacombe, Linguamatics Executive Chairman Agenda Monday October 17 11:00am–12:00pm Registration in the Monomoy Foyer 12:00pm–1:00pm Lunch at the STARS restaurant 1:00pm–5:00pm Training workshops: Session 1 1A Introduction to I2E: Tony Wu, Eldridge Room 1B Introduction to I2E—Healthcare focus: Erin Tavano, Alden Room Linguamatics I2E Query Hackathon: David Milward, Monomoy Meeting House 3:00pm–3:25pm Refreshments in the Monomoy Foyer 6:15pm onward Evening social event at The Beach House Tuesday October 18 8:30am–9:00am Registration in the Monomoy Foyer 9:00am start Main day presentations in the Monomoy Meeting House 9:00am–9:05am Introduction Phil Hastings, Chief Business Development Officer, Linguamatics 9:05am–9:20am Welcome address John M. Brimacombe, Executive Chairman, Linguamatics 1 Tuesday October 18—continued 9:20am–9:45am Natural language processing validation of high-risk patient characteristics using Linguamatics I2E Yoni Dvorkis, Data Consultant, Atrius -
Assessing Customer 360⁰ Journey Readiness
Assessing customer 360⁰ journey readiness A Mindtree Whitepaper by Abhijit Chatterjee; Anand Rao customer 360⁰ Abstract Customer behaviour and trends have changed significantly due to the COVID-19 pandemic. Further, due to the lockdown measures by the governments across the globe and the necessary social distancing norms, customers have adopted the use of new digital technologies and have learnt their applications in their day to day functioning. As more customers now move to online engagements and transactions, organisations are wrestling the ‘need for speed’ to customer intelligence and insights, to understand the new patterns of customer engagement and habits. To enable such an intelligence-fuelled Customer 360⁰ (C360⁰) ecosystem requires organisations to relook into their customer success objectives, followed by a roadmap and ecosystem assessment to redesign their C360⁰ initiatives. This paper summarises an approach to analyse the C360⁰ readiness such that a more experiential and meaningful customer relationship could be established with new age technologies and processes using ML / AI – which in the long term is key to rapidly redesigning and innovating newer customer intelligence propositions. Ultimately, for brands the aim is to increase the reach, engage to develop trust and advocacy. The day-to-day life for many people, including you and me, have changed due to the COVID-19 pandemic, which is transforming customer industries in ways that are customer-centric. Between mid-March and April 2020, total sales globally, observed a sharp decline, barring essential goods, as the effects of the pandemic hit stores. In the UK, for example, according to the May 2020 report from the office of National Statistics, sales fell by 5.2% in March 2020, and by a record 18.1% in April 20201, and many ceased trading following official government guidance on March 23, 2020. -
Web-Based Text Mining of Hotel Customer Comments Using SAS® Text Miner and Megaputer Polyanalyst®
Web-Based Text Mining of Hotel Customer Comments Using SAS® Text Miner and Megaputer Polyanalyst® Richard S. Segall Arkansas State University, Department of Computer & Information Technology, College of Business, Jonesboro, AR 72467-0130, [email protected] Qingyu Zhang Arkansas State University, Department of Computer & Information Technology, College of Business, Jonesboro, AR 72467-0130, [email protected] Mei Cao University of Wisconsin – Superior, Department of Business and Economics, Superior, WI 54880, [email protected] ABSTRACT This paper presents text mining using SAS® Text Miner and Megaputer PolyAnalyst® specifically applied for hotel customer survey data, and its data management. The paper reviews current literature of text mining, and discusses features of these two text mining software packages in analyzing unstructured qualitative data in the following key steps: data preparation, data analysis, and result reporting. Some screen shots are presented for web-based hotel customer survey data as well as comparisons and conclusions. Keywords: Web-based data, Algorithms, Web data management, Text mining, SAS® Text Miner, Megaputer PolyAnalyst®, Unstructured Data, Hotel Customer Surveys INTRODUCTION The increasing use of textual knowledge applications and the growing availability of online textual sources caused a boost in text mining research. This paper reviews some current literature of text mining and presents comparisons and summaries of the two selected software of SAS® Text Miner and Megaputer PolyAnalyst® for text mining. This paper discusses features offered by these two-selected software in the following key steps in analyzing unstructured qualitative data: software composition for text mining, data preparation, data analysis and result reporting. This paper also provides insight with the data management example of application to an actual web-based dataset for hotel customer survey data.