This document provides an overview of predictive modeling and how it can benefit businesses. It discusses:
- The benefits of predictive modeling and the overall modeling process.
- Common applications like customer intimacy, optimizing capital deployment, and detecting threats.
- The modeling process including clarifying the business problem, understanding available data, developing models, and deploying results.
- Examples of predictive modeling projects including customer segmentation for marketing offers, optimizing a website for visitor segments, and predicting sewer flooding risks to prioritize maintenance.
- Techniques like cluster modeling, correlation analysis, and models incorporating multiple time periods are used to develop solutions.
The document is intended to help business people understand how predictive
The document discusses developing an analytics strategy to drive healthcare transformation. It begins by outlining signs an analytics strategy is needed, such as having dashboards but no improvement. It then discusses components of an effective analytics strategy, including understanding business context, stakeholders, processes and data, tools and techniques, team and training, and technology. The strategy ensures analytics align with goals and avoids just collecting reports. Developing the strategy involves understanding requirements, identifying gaps, and executing the plan. The strategy provides a framework to guide analytics development and ensure optimal use of resources.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Measuring and managing customer profitability in the big-data era. How to capitalize on the opportunity.
In today's era of Big Data and related technology, the benefits of "customer-centricity" are within our reach. Analysis of Big Data sources helps to better understand customer needs, preferences, attitudes, expectations, sentiments, and buying behavior. Yet to achieve this potential, organizations need to understand and apply the classic but essential concepts of customer profitability, customer lifetime value (CLV), and customer value management analytics. Join us for an event on how to approach this challenge.
When linked with customer profitability metrics, these insights enable more profitable decisions in product design, sales, marketing, customer care, loyalty management, and risk management. This session will help attendees capitalize on this opportunity. We will cover the classic high-impact basics of measuring and managing customer profitability, customer lifetime value (CLV), as well as how to use new Big Data insights to get more value from these efforts. This tutorial which cover the topic in 5 practical steps:
1. Introduction to Customer Profitability Analytics: What is customer profitability analysis, why is it so valuable, and what are the key concepts and methodologies used to measure customer profitability, customer lifetime value (CLV), and related metrics?
2. High-Impact Use-Cases of Customer Profitability Analytics: What are the key ways customer profitability analytics is used enhance results? We will describe the highest-value ways to use customer profitability metrics to improve business results, with concrete examples in each of the following categories:
o Customer Lifetime Value optimization ("CLV")
o Customer loyalty and retention
o Share of wallet maximization
o Marketing ROI
o Impact of Customer Service, Customer Experience, and Customer Satisfaction on Profit
o Product design, pricing, promotion, and positioning
o Allocation of resources (capital, budget, HR, etc)
o Risk management
3. How to Calculate Profitability at the Customer Level : We will walk through the algorithms you need to use to turn raw data into customer profitability metrics, and share tips on how to customize them depending on your business. Related applications will also be covered, such as how to use the same algorithms to measure profit per household, salesperson, distributor, or other entity relevant to how your business makes money.
4. Data & Tech Requirements
5. Using Big Data to Maximize ROI on Customer Analytics: What are the top 5 opportunities to use Big Data to increase the benefits achieved through customer profitability analytics and related initiatives?
Speakers: Jaime Fitzgerald, Founder and Managing Partner, Fitzgerald Analytics, and Konrad Kopczynscki, Director at Fitzgerald Analytics. Konrad and Jaime have applied customer profitability methodologies to dozens of clients.
Gramener is always on the lookout for talent who like to work with numbers and aspire to be the Algorithm Translators.
This deck is presented to cohorts at IIM's and other B School as part of Gramener company overview session.
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...Fitzgerald Analytics, Inc.
Data is the ultimate intangible asset: worthless is raw form, yet priceless when used well. Financial services companies depend on analytics to transform troves of data into business advantage, insight, and profits. Yet the ugly secret is that most analytics project fail to achieve their full potential, leaving millions of dollars in potential profits on the table.
The document discusses developing an analytics strategy to drive healthcare transformation. It begins by outlining signs an analytics strategy is needed, such as having dashboards but no improvement. It then discusses components of an effective analytics strategy, including understanding business context, stakeholders, processes and data, tools and techniques, team and training, and technology. The strategy ensures analytics align with goals and avoids just collecting reports. Developing the strategy involves understanding requirements, identifying gaps, and executing the plan. The strategy provides a framework to guide analytics development and ensure optimal use of resources.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Measuring and managing customer profitability in the big-data era. How to capitalize on the opportunity.
In today's era of Big Data and related technology, the benefits of "customer-centricity" are within our reach. Analysis of Big Data sources helps to better understand customer needs, preferences, attitudes, expectations, sentiments, and buying behavior. Yet to achieve this potential, organizations need to understand and apply the classic but essential concepts of customer profitability, customer lifetime value (CLV), and customer value management analytics. Join us for an event on how to approach this challenge.
When linked with customer profitability metrics, these insights enable more profitable decisions in product design, sales, marketing, customer care, loyalty management, and risk management. This session will help attendees capitalize on this opportunity. We will cover the classic high-impact basics of measuring and managing customer profitability, customer lifetime value (CLV), as well as how to use new Big Data insights to get more value from these efforts. This tutorial which cover the topic in 5 practical steps:
1. Introduction to Customer Profitability Analytics: What is customer profitability analysis, why is it so valuable, and what are the key concepts and methodologies used to measure customer profitability, customer lifetime value (CLV), and related metrics?
2. High-Impact Use-Cases of Customer Profitability Analytics: What are the key ways customer profitability analytics is used enhance results? We will describe the highest-value ways to use customer profitability metrics to improve business results, with concrete examples in each of the following categories:
o Customer Lifetime Value optimization ("CLV")
o Customer loyalty and retention
o Share of wallet maximization
o Marketing ROI
o Impact of Customer Service, Customer Experience, and Customer Satisfaction on Profit
o Product design, pricing, promotion, and positioning
o Allocation of resources (capital, budget, HR, etc)
o Risk management
3. How to Calculate Profitability at the Customer Level : We will walk through the algorithms you need to use to turn raw data into customer profitability metrics, and share tips on how to customize them depending on your business. Related applications will also be covered, such as how to use the same algorithms to measure profit per household, salesperson, distributor, or other entity relevant to how your business makes money.
4. Data & Tech Requirements
5. Using Big Data to Maximize ROI on Customer Analytics: What are the top 5 opportunities to use Big Data to increase the benefits achieved through customer profitability analytics and related initiatives?
Speakers: Jaime Fitzgerald, Founder and Managing Partner, Fitzgerald Analytics, and Konrad Kopczynscki, Director at Fitzgerald Analytics. Konrad and Jaime have applied customer profitability methodologies to dozens of clients.
Gramener is always on the lookout for talent who like to work with numbers and aspire to be the Algorithm Translators.
This deck is presented to cohorts at IIM's and other B School as part of Gramener company overview session.
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...Fitzgerald Analytics, Inc.
Data is the ultimate intangible asset: worthless is raw form, yet priceless when used well. Financial services companies depend on analytics to transform troves of data into business advantage, insight, and profits. Yet the ugly secret is that most analytics project fail to achieve their full potential, leaving millions of dollars in potential profits on the table.
Turning Big Data Analytics To Knowledge PowerPoint Presentation SlidesSlideTeam
This complete deck covers various topics and highlights important concepts. It has PPT slides which cater to your business needs. This complete deck presentation emphasizes Turning Big Data Analytics To Knowledge PowerPoint Presentation Slides and has templates with professional background images and relevant content. This deck consists of total of twenty two slides. Our designers have created customizable templates, keeping your convenience in mind. You can edit the colour, text and font size with ease. Not just this, you can also add or delete the content if needed. Get access to this fully editable complete presentation by clicking the download button below. http://bit.ly/2HHUsqf
Your smarter data analytics strategy - Social Media Strategies Summit (SMSS) ...Clark Boyd
This document provides an overview of developing an effective analytics strategy, covering key topics such as:
- Understanding why an analytics strategy is important for gaining insights from data
- Defining the right questions to ask of your data to address business objectives
- Implementing the right metrics and processes to optimize performance based on data
- Ensuring the right technology, data, people and culture are in place to execute the strategy
- Tips for reporting data to different stakeholders and developing the right analytics team
The presentation emphasizes that an analytics strategy should start by defining business goals and questions, and focus on using data insights to drive tangible improvements rather than just reporting metrics. Both qualitative and quantitative data are important to
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
This document provides an overview and agenda for building an analytics capability. It discusses key topics such as:
- The importance of big data and analytics for business decisions
- Building an analytics capability requires the right people, processes, and technology
- Companies can build capabilities internally, outsource work, or use a hybrid approach
- When outsourcing analytics work, firms need to consider issues like vendor skills, data protection, and intellectual property ownership
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Gartner Business Intelligence & Analytics Summit - MunichNadia Smith
This document provides an agenda for the Gartner Business Intelligence & Analytics Summit 2015 occurring on October 14-15 in Munich, Germany. The summit features keynote speeches, tracks on various analytics topics, workshops, and roundtable discussions. Attendees can choose from three main tracks on leading initiatives, evangelizing new technologies, and modernizing core systems. There will also be virtual tracks on jumpstarting analytics journeys and using analytics in IoT/industrial settings. The agenda provides timing, speaker names and session titles for all events across the two day summit.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
The document discusses how to turn data into actionable insights through a multi-step process. It outlines two case studies where this process was applied. For the first case of increasing low-performing store performance, the process identified cross-selling as a hypothesis, tested it with store data, and found opportunities to improve layout, staffing, and skills. For the second case of finding new fitness center locations, the process developed a model to estimate revenues in different catchment areas and identified optimal new locations based on potential revenues.
Thomas Davenport has written numerous books, articles, and delivered presentations on "Competing on Analytics". He is considered by many the leading authority on the subject. I created this presentation to articulate many of the concepts he established in his book with the same title.
Herman Jopia discusses how American Savings Bank is using predictive analytics to drive growth and profitability. This includes developing scoring models to understand customers and the market, programming tools to optimize processes like binning data, and price optimization to determine the best offers. The goal is to move beyond "business as usual" through investing in analytical talent and infrastructure to explore new opportunities and segments for increasing profits.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
The document discusses big data analysis requirements and tools. It covers where big data comes from both internally and externally. It then discusses tools for analyzing big data such as BI tools, in-database analytics, Hadoop, decision management, and discovery tools. Techniques for analyzing big data like classification tree analysis, genetic algorithms, regression analysis, machine learning, and sentiment analysis are also covered. The key benefits and a successful implementation roadmap for big data in an organization are summarized.
Carlos Navarro discusses improving analytical platforms to empower employees and better serve customers. He states that big data needs to be analyzed and presented in meaningful and actionable ways. Empowering employees requires generating and distributing actionable insights from data. Visual analytics can turn data into insights in an interactive, graphical manner. The presentation provides examples of visualizing customer satisfaction data to generate insights such as satisfaction trends, positive and negative feedback, and underperforming teams.
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.Jennifer Walker
The document discusses how Hadoop is often used primarily as a data storage system rather than an agile analytics platform. It argues that for Hadoop to enable productive analytics, companies need to transform Hadoop into a system that allows for iterative exploration of diverse data sources through intuitive interfaces that leverage machine learning. This requires addressing challenges such as a lack of data understanding, scarce expertise, and time-consuming data preparation processes. Adopting platforms that provide self-service access and leverage business context can help democratize data access and analysis.
This document discusses privacy enhancing technologies and how to become a responsible data handler. It outlines the 7 principles of "Privacy by Design" which aim to embed privacy into system design from the start. Examples are given of how these principles can be applied, such as having a privacy expert on the design team, making privacy the default setting, and ensuring transparency. Benefits discussed include increased customer trust, profits, and insights. Trends in privacy research like differential privacy and artificial data are also mentioned. The overall message is that privacy should be seen as an opportunity rather than a hindrance.
The demand for data insights to drive decisions is higher today than ever before. This isn't just because volumes of accessible data are growing, but also because people are more data literate and accustomed to engaging information experiences from consumer apps like LinkedIn, Google Maps, & Yelp.
This same thirst for intelligence is probably apparent in your user base, whether you realize it or not - and taking the time to invest in a data & analytics strategy for your product can yield significant customer & business benefits over time.
About the Speakers:
Michelle Bradbury,Director of Product Management, Pentaho
Michelle has over 18 years of experience in technology product & project management. She enjoys collaboratively creating & delivering highly compelling products and has held roles at organizations including Microsoft, Fujitsu, & CapitalOne. Michelle's areas of expertise include database and data warehouse architecture and development, project and budget management, as well as process definition and implementation for group cohesiveness.
Ben Hopkins, Product Marketing Manager, Pentaho
Ben is focused on embedded analytics & OEM partnerships. He has also held product marketing roles at Marketo and Salesforce.com. He holds an MBA from the U.C. Berkeley Haas School of Business as well as a BA in Economics from Harvard College.
Pentaho is delivering the future of analytics with a comprehensive platform for data integration & business intelligence. Learn more at www.pentaho.com.
Upcoming Events
Would you like to lead innovation efforts within your company? Attend upcoming product innovation courses. Visit: http://bit.ly/CILCourse
Looking for a coach to accelerate your product marketing & management career?
Set up a free initial 30-minute appointment for more information: http://bit.ly/1gBFdaD.
Want To Certify Your Team?
If you have a product team of 10 or more that you want to certify, contact AIPMM at certification@aipmm.com.
About AIPMM
The AIPMM is the trusted authority in product management. It is where product professionals go for answers. With members in over 75 countries, it is the worldwide certifying body of product team professionals.
It is the world's largest professional organization of product managers, brand managers, product marketing managers and other product team professionals who are responsible for guiding their organizations, or clients, through a constantly changing business landscape.
AIPMM's certification programs are internationally recognized because they allow product professionals to demonstrate their expertise and provide corporate members an assurance that their product management and marketing teams are operating at a high competency level.
Visit http://www.aipmm.com.
Call For Speakers: http://bit.ly/1b006vm
Subscribe: http://www.aipmm.com/subscribe
Articles: http://www.aipmm.com/html/newsletter/article.ph
Membership: http://www.aipmm.com/join.php
This document discusses Seagate's channel data stewardship program. It provides an overview of Seagate's data governance processes including customer onboarding, electronic ordering, data integrity processes, and continuous process optimization. The goals of the program are to ensure accurate sales and inventory data reporting from channel partners in order to calculate rebates and incentives correctly and make informed business decisions. Key metrics such as match rates and compliance scores are monitored monthly to measure the effectiveness of the program.
This webinar was hosted by Gramener's CEO/Co-Founder, Anand S, and Ganes Kesari, Head of Analytics/Co-Founder on how data can help firms recover quickly throughout the recession and recovery period.
Who should watch this webinar :
Analytics Leaders, Business Leaders, CDOs, CTOs, etc.
Few takeaways :
-Which aspects of your company could benefit the most from a data-driven response?
-A strategy for identifying use cases that will provide the most value for the money.
How to use data in creative ways to uncover new market opportunities and customers.
Objectives :
-Data's utility in COVID situation
-How data science may assist you in navigating the recession
-Gramener's industry case studies to assist businesses in responding to COVID-19
Full Webinar: https://info.gramener.com/recession-proofing-your-business-with-data
To know more from industry leaders visit our official website: https://gramener.com/
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
This presentation was given at the festival of marketing 2014. How grown up is your analytics? This slide deck will help you understand what you need to achieve optimum business benefit from your data analytics.
Turning Big Data Analytics To Knowledge PowerPoint Presentation SlidesSlideTeam
This complete deck covers various topics and highlights important concepts. It has PPT slides which cater to your business needs. This complete deck presentation emphasizes Turning Big Data Analytics To Knowledge PowerPoint Presentation Slides and has templates with professional background images and relevant content. This deck consists of total of twenty two slides. Our designers have created customizable templates, keeping your convenience in mind. You can edit the colour, text and font size with ease. Not just this, you can also add or delete the content if needed. Get access to this fully editable complete presentation by clicking the download button below. http://bit.ly/2HHUsqf
Your smarter data analytics strategy - Social Media Strategies Summit (SMSS) ...Clark Boyd
This document provides an overview of developing an effective analytics strategy, covering key topics such as:
- Understanding why an analytics strategy is important for gaining insights from data
- Defining the right questions to ask of your data to address business objectives
- Implementing the right metrics and processes to optimize performance based on data
- Ensuring the right technology, data, people and culture are in place to execute the strategy
- Tips for reporting data to different stakeholders and developing the right analytics team
The presentation emphasizes that an analytics strategy should start by defining business goals and questions, and focus on using data insights to drive tangible improvements rather than just reporting metrics. Both qualitative and quantitative data are important to
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
This document provides an overview and agenda for building an analytics capability. It discusses key topics such as:
- The importance of big data and analytics for business decisions
- Building an analytics capability requires the right people, processes, and technology
- Companies can build capabilities internally, outsource work, or use a hybrid approach
- When outsourcing analytics work, firms need to consider issues like vendor skills, data protection, and intellectual property ownership
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Gartner Business Intelligence & Analytics Summit - MunichNadia Smith
This document provides an agenda for the Gartner Business Intelligence & Analytics Summit 2015 occurring on October 14-15 in Munich, Germany. The summit features keynote speeches, tracks on various analytics topics, workshops, and roundtable discussions. Attendees can choose from three main tracks on leading initiatives, evangelizing new technologies, and modernizing core systems. There will also be virtual tracks on jumpstarting analytics journeys and using analytics in IoT/industrial settings. The agenda provides timing, speaker names and session titles for all events across the two day summit.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
The document discusses how to turn data into actionable insights through a multi-step process. It outlines two case studies where this process was applied. For the first case of increasing low-performing store performance, the process identified cross-selling as a hypothesis, tested it with store data, and found opportunities to improve layout, staffing, and skills. For the second case of finding new fitness center locations, the process developed a model to estimate revenues in different catchment areas and identified optimal new locations based on potential revenues.
Thomas Davenport has written numerous books, articles, and delivered presentations on "Competing on Analytics". He is considered by many the leading authority on the subject. I created this presentation to articulate many of the concepts he established in his book with the same title.
Herman Jopia discusses how American Savings Bank is using predictive analytics to drive growth and profitability. This includes developing scoring models to understand customers and the market, programming tools to optimize processes like binning data, and price optimization to determine the best offers. The goal is to move beyond "business as usual" through investing in analytical talent and infrastructure to explore new opportunities and segments for increasing profits.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
The document discusses big data analysis requirements and tools. It covers where big data comes from both internally and externally. It then discusses tools for analyzing big data such as BI tools, in-database analytics, Hadoop, decision management, and discovery tools. Techniques for analyzing big data like classification tree analysis, genetic algorithms, regression analysis, machine learning, and sentiment analysis are also covered. The key benefits and a successful implementation roadmap for big data in an organization are summarized.
Carlos Navarro discusses improving analytical platforms to empower employees and better serve customers. He states that big data needs to be analyzed and presented in meaningful and actionable ways. Empowering employees requires generating and distributing actionable insights from data. Visual analytics can turn data into insights in an interactive, graphical manner. The presentation provides examples of visualizing customer satisfaction data to generate insights such as satisfaction trends, positive and negative feedback, and underperforming teams.
Hadoop: Data Storage Locker or Agile Analytics Platform? It’s Up to You.Jennifer Walker
The document discusses how Hadoop is often used primarily as a data storage system rather than an agile analytics platform. It argues that for Hadoop to enable productive analytics, companies need to transform Hadoop into a system that allows for iterative exploration of diverse data sources through intuitive interfaces that leverage machine learning. This requires addressing challenges such as a lack of data understanding, scarce expertise, and time-consuming data preparation processes. Adopting platforms that provide self-service access and leverage business context can help democratize data access and analysis.
This document discusses privacy enhancing technologies and how to become a responsible data handler. It outlines the 7 principles of "Privacy by Design" which aim to embed privacy into system design from the start. Examples are given of how these principles can be applied, such as having a privacy expert on the design team, making privacy the default setting, and ensuring transparency. Benefits discussed include increased customer trust, profits, and insights. Trends in privacy research like differential privacy and artificial data are also mentioned. The overall message is that privacy should be seen as an opportunity rather than a hindrance.
The demand for data insights to drive decisions is higher today than ever before. This isn't just because volumes of accessible data are growing, but also because people are more data literate and accustomed to engaging information experiences from consumer apps like LinkedIn, Google Maps, & Yelp.
This same thirst for intelligence is probably apparent in your user base, whether you realize it or not - and taking the time to invest in a data & analytics strategy for your product can yield significant customer & business benefits over time.
About the Speakers:
Michelle Bradbury,Director of Product Management, Pentaho
Michelle has over 18 years of experience in technology product & project management. She enjoys collaboratively creating & delivering highly compelling products and has held roles at organizations including Microsoft, Fujitsu, & CapitalOne. Michelle's areas of expertise include database and data warehouse architecture and development, project and budget management, as well as process definition and implementation for group cohesiveness.
Ben Hopkins, Product Marketing Manager, Pentaho
Ben is focused on embedded analytics & OEM partnerships. He has also held product marketing roles at Marketo and Salesforce.com. He holds an MBA from the U.C. Berkeley Haas School of Business as well as a BA in Economics from Harvard College.
Pentaho is delivering the future of analytics with a comprehensive platform for data integration & business intelligence. Learn more at www.pentaho.com.
Upcoming Events
Would you like to lead innovation efforts within your company? Attend upcoming product innovation courses. Visit: http://bit.ly/CILCourse
Looking for a coach to accelerate your product marketing & management career?
Set up a free initial 30-minute appointment for more information: http://bit.ly/1gBFdaD.
Want To Certify Your Team?
If you have a product team of 10 or more that you want to certify, contact AIPMM at certification@aipmm.com.
About AIPMM
The AIPMM is the trusted authority in product management. It is where product professionals go for answers. With members in over 75 countries, it is the worldwide certifying body of product team professionals.
It is the world's largest professional organization of product managers, brand managers, product marketing managers and other product team professionals who are responsible for guiding their organizations, or clients, through a constantly changing business landscape.
AIPMM's certification programs are internationally recognized because they allow product professionals to demonstrate their expertise and provide corporate members an assurance that their product management and marketing teams are operating at a high competency level.
Visit http://www.aipmm.com.
Call For Speakers: http://bit.ly/1b006vm
Subscribe: http://www.aipmm.com/subscribe
Articles: http://www.aipmm.com/html/newsletter/article.ph
Membership: http://www.aipmm.com/join.php
This document discusses Seagate's channel data stewardship program. It provides an overview of Seagate's data governance processes including customer onboarding, electronic ordering, data integrity processes, and continuous process optimization. The goals of the program are to ensure accurate sales and inventory data reporting from channel partners in order to calculate rebates and incentives correctly and make informed business decisions. Key metrics such as match rates and compliance scores are monitored monthly to measure the effectiveness of the program.
This webinar was hosted by Gramener's CEO/Co-Founder, Anand S, and Ganes Kesari, Head of Analytics/Co-Founder on how data can help firms recover quickly throughout the recession and recovery period.
Who should watch this webinar :
Analytics Leaders, Business Leaders, CDOs, CTOs, etc.
Few takeaways :
-Which aspects of your company could benefit the most from a data-driven response?
-A strategy for identifying use cases that will provide the most value for the money.
How to use data in creative ways to uncover new market opportunities and customers.
Objectives :
-Data's utility in COVID situation
-How data science may assist you in navigating the recession
-Gramener's industry case studies to assist businesses in responding to COVID-19
Full Webinar: https://info.gramener.com/recession-proofing-your-business-with-data
To know more from industry leaders visit our official website: https://gramener.com/
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
This presentation was given at the festival of marketing 2014. How grown up is your analytics? This slide deck will help you understand what you need to achieve optimum business benefit from your data analytics.
The document discusses strategies for real-time fraud detection. It outlines best practices for turning data mining strategies into action, including involving key stakeholders from business, analytics, and IT to align incentives and goals. Specifically, it emphasizes that for fraud analysis, speed of model deployment is the most important factor, as improvements in accuracy may be lost if there are delays implementing new models. It also stresses communicating findings to business stakeholders in clear, non-technical terms focused on relevant metrics and expected results.
Ken Demma - Big Data Morality MIT 7-22 v2Ken Demma
The document discusses balancing business value and ethics in big data. It outlines how big data is transforming businesses through opportunities like personalized marketing and customer experiences. However, it also notes privacy and security risks that could undermine consumer trust. The document advocates for transparency around data collection and use, security, delivering business value to customers, and ensuring big data practices reflect a company's values. It provides tips for unlocking big data's value while maintaining ethical standards.
This newsletter provides an overview of analytics and highlights some ways organizations are using analytics. It discusses how 140 million customer interactions can be analyzed to understand customers and how analytics is beginning to be used beyond basic reporting. Examples are given of analytics being used for customer segmentation, risk mitigation, and reducing transportation costs. Predictive analytics and big data are also discussed.
Trying to figure out if embedded analytics are for you?
According to Gartner Research, more than 90% of business leaders view content information as a strategic asset, yet fewer than 10% can quantify its economic value. Read this guide to learn why you should be leveraging an asset you already own--data--to build relationships, increase retention, and drive revenue.
The Softer Skills that analysts need (beyond Data Visualisation)Paul Laughlin
A talk I gave at #DataVizLive online event in Nov 2020. Introducing the Laughlin Consultancy 9-step model for Softer Skills needed by Analysts & previewing some of those steps (beyond data visualisation & storytelling skills).
Beyond the Dashboard:Exploratory Analytics discusses how exploratory analytics allows users to go beyond traditional dashboards and reports to test hypotheses, conduct "what if" scenarios, and build predictive models. Exploratory analytics uses visualization, modeling, and interactive capabilities to analyze data in a more flexible way compared to static reports. The presentation highlights how the Quantrix platform supports exploratory analytics through capabilities like pivot and filter charts, enhanced visualization, modeling, and multidimensional analysis for forecasting, planning, and risk analysis. Real-world examples are also provided.
Balancing Business Value and Business Values with Big DataSAP Analytics
As companies accelerate their use of big data in the pursuit of business value, they must address the moral and ethical implications or risk alienating the very people they seek to serve.
TDWI Best Practices Report- Achieving Greater Agility with Business Intellige...Attivio
The document discusses how organizations are seeking to improve the agility of their business intelligence (BI) systems in order to support faster decision making. It focuses on organizations implementing agile development methods and self-service BI tools to deliver information more quickly. Technologies like data virtualization, agile data warehousing, and analytics are helping to provide diverse and relevant data to users. Adopting managed self-service BI, improving user-developer collaboration, and applying agile development practices are recommended for enhancing a BI system's flexibility and reducing the time needed to provide value.
Déjeuner Conférence - L'analyse prédictive agile avec SAP Predictive Analytic...agileDSS
Les bénéfices de l’analyse prédictive sont trop souvent sous-utilisés dans les entreprises.Que ce soit par un manque d’expertise à l’interne ou, par une lourdeur du processus de mise à disposition des modèles statistiques, cette réalité limite l’utilisation de l’analyse prédictive au sein de l’entreprise.
SAP Predictive Analytics 2.0, démultiplie les possibilités d’utiliser l’analyse prédictive dans vos processus d’affaires. Sans forcément avoir de connaissance en statistique, les lignes d’affaires pourront avoir accès à la puissance d’un outil d’analyse prédictive complet dans leurs travaux au quotidien.
De plus, cet outil permet même de satisfaire les statisticiens les plus chevronnés grâce à un tout nouveau mode ‘’expert’’ inclus dans la version 2.0
Bruno Delahaye, VP Analyse Prédictive chez SAP, vous illustrera les capacités de l’outil à travers des cas clients comme ceux de Walmart, Rogers, RBC, Equifax et HMV.
Pourquoi participer?
- Voyez, avec l'aide de démonstrations, comment vous pouvez optimiser vos processus d’affaires, et apporter de meilleurs résultats d’affaires en toute confiance avec des analyses prédictives.
- Découvrez comment plusieurs entreprises de diverses industries comme Sears, Walmart, Rogers, RBC, Equifax et HMV utilisent actuellement la puissance, la rapidité et la facilité d'utilisation de SAP Predictive Analytics.
- Informez-vous sur comment cet outil peut réduire drastiquement le temps pour déployer des solutions prédictives en automatisant la construction de modèles prédictifs.
This document provides an overview of a session on business intelligence, data science, and data mining. The goals of the class are to understand how to solve business problems using data analytics, various tools and methods for implementing solutions, and how to store and access large amounts of data. The focus areas include data warehousing, data mining, simulation, and deriving profitable business actions from databases. Popular tools mentioned include RapidMiner, R, Excel, SQL, Python, Weka, KNIME, Hadoop, SAS, and Microsoft SQL Server. Benefits of business intelligence include increased profitability, decreased costs and risks, and improved customer relationship management.
Big Data: selling the Business Case to the businessJ On The Beach
Big Data: selling the Business Case to the business by Eline Brandt & Javier de la Torre Medina
Big Data, every company loves the idea of it, but often, selling the Business Case is a challenge. So how to build a successful Business Case for your Big Data initiative for the Business Users? This presentation is based on the most common objections one gets, and how to deal with them. We'll go through one of my most surprising projects, look at the lessons learned and how can we optimize the Business Case?
Giving Organisations new Capabilities to ask the Right Business QuestionsOReillyStrata
The document discusses various approaches organizations can take to gain insights from data. It begins by noting that making data work is difficult and that value is captured through outputs and outcomes. It then describes three common approaches: the "all in" approach of fully committing resources, the experimental approach of running small trials, and the "wait and see" approach. The document advocates for an experimental approach using agile experimentation. It provides examples of areas where organizations need to improve such as asking the right questions, choosing technologies, and interpreting results. Finally, it discusses various analytic methods and structured techniques that can be used, including decomposition and visualization, hypothesis generation and testing, and challenge analysis.
Innovative approach for reporting and analysis to reduced analytical and IT resources at world's largest bank amidst chaotic, seismic change. Deploying information set (content) delivery with flexible, interactive analysis tools (in this case QlikView from QlikTech). Walk through tips of how business analyst survived and succeeded. National Center for Database Marketing Client X Client Case Study Presentation. NCDM presentation December 2008.
Are you getting the most out of your data?SAS Canada
Data is an organizations most valuable asset, but raw data by itself has little value. To drive data’s worth, it must be managed and processed to extract value and information that decision makers can leverage and turn into actionable insights. It is the ways in which a company choses to put that information to use that will determine the true value of its data.
Through business intelligence and business analytic tools, businesses are enabling themselves to make more strategic, accurate decisions, while optimizing business processes. Hear from Info-Tech Research Group and learn what you need to consider when choosing an analytics solution provider. The webinar will highlight Info-Tech Research Group’s recently published vendor landscape for selecting and implementing Business Intelligence and Business Analytics solutions. The report positions SAS as the only leader across all four categories of Enterprise BI, Mid-Market BI, Enterprise BA and Mid-Market BA.
Business analytics workshop presentation finalBrian Beveridge
This document outlines an agenda and presentation for a business analytics seminar for credit union executives and board directors. The presentation will define business analytics, explain how it can help credit unions address key issues like margin compression and regulatory compliance, and provide examples of how analytics can be applied to areas like marketing, risk management, and branch performance. Attendees will learn how predictive analytics can help credit unions retain members, optimize pricing, and streamline operations. The presentation will also cover getting started with business analytics projects.
Marketing & SalesBig Data, Analytics, and the Future of .docxalfredacavx97
Marketing & Sales
Big Data, Analytics,
and the Future of
Marketing & Sales
March 2015
3McKinseyonMarketingandSales.com @McK_MktgSales
Table of contents
Business
Opportunities
Insight and
action
How to get
organized and
get started
8 Getting big impact from big
data
16 Big Data & advanced
analytics: Success stories
from the front lines
20 Use Big Data to find
new micromarkets
24 Smart analytics: How
marketing drives short-term
and long-term growth
30 Putting Big Data and
advanced analytics to work
34 Know your customers
wherever they are
38 Using marketing analytics to
drive superior growth
48 How leading retailers turn
insights into profits
56 Five steps to squeeze more
ROI from your marketing
60 Using Big Data to make
better pricing decisions
60 Marketing’s age of relevance 72 Gilt Groupe: Using Big Data,
mobile, and social media to
reinvent shopping
76 Under the retail microscope:
Seeing your customers for
the first time
80 Name your price: The power
of Big Data and analytics
84 Getting beyond the buzz: Is
your social media working?
90 How to get the most from big
data
94 Five Roles You Need on Your
Big Data Team
98 Want big data sales programs
to work? Get emotional
102 Get started with Big Data:
Tie strategy to performance
106 What you need to make Big
Data work: The pencil
110 Need for speed: Algorithmic
marketing and customer
data overload
114 Simplify Big Data – or it’ll be
useless for sales
54 McKinseyonMarketingandSales.com @McK_MktgSales
Introduction
Big Data is the biggest hame-changing opportunity for marketing and sales
since the Internet went mainstream almost 20 years ago. The data big bang
has unleashed torrents of terabytes about everything from customer behaviors
to weather patterns to demographic consumer shifts in emerging markets.
The companies who are successful in turning data into above-market growth
will excel at three things:
ƒ Using analytics to identify valuable business opportunities from the data to
drive decisions and improve marketing return on investment (MROI)
ƒ Turning those insights into well-designed products and offers that delight
customers
ƒ Delivering those products and offers effectively to the marketplace.
This goldmine of data represents a pivot-point moment for marketing and
sales leaders. Companies that inject big data and analytics into their operation
show productivity rates and profitability that are 5 percent to 6 percent hight
than those of their peers. That’s an advantage no company can afford to
gnome.
This compendium explores the business opportunities, company examples,
and organizational implications of Big Data and advanced analytics. We hope
it provokes good and useful conversations.
Please contact us with your reactions and thoughts.
David Court
Director
David headed McKinsey’s
functional practices, and
currently leads the firm’s digital
in.
IT Managers: Goodbye Reporting, Hello InsightNumerify
As an IT Manager you probably use operational analytic solutions like Splunk and SumoLogic to go deep into log data to tell you the health of your servers and applications, yet you still rely on spreadsheet to analyze your processes and people related data.
While KPI tools may help a little, we know with some of your problems getting to the root cause can be difficult and time consuming. Numerify Analytics gives you flexible and insight in visually appealing application to understand and improve your IT services by getting to the “Why” of things.
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
Similar to 20151008 REx Predictive presentation v 1 0 - distributed (20)
Presentation to Analytics Network of the OR Society Nov 2020
20151008 REx Predictive presentation v 1 0 - distributed
1. Information and
Data Management
What can Predictive Modelling do
for your business?
Jefferson Lynch, John McConnell
8th October 2015, Royal Exchange
Analytics and
Data Management
2. Intended audience and aims
Who is this intended for?
Business people who want to understand what
practical difference predictive modelling can offer to
their organisation, and what’s involved.
By the time you leave you should…
Understand the benefits and the overall process
involved.
Be familiar with some common problems where
modelling is used, and some modelling approaches.
Be able to assess your organisational gaps and so
what help you may need.
12/10/2015 2Copyright Red Olive 2015
3. Why you should be interested:
business context
12/10/2015 3Copyright Red Olive 2015
The business interest in using data to tackle
business problems has changed:
Not just structured data, reports and dashboards
to guide solutions to defined performance
problems…
… but also discovery of new patterns in diverse
data to address much bigger questions and
problems.
4. What is predictive analytics?
12/10/2015 4Copyright Red Olive 2015
“Predictive analytics is an area of data mining that
deals with extracting information from data and
using it to predict trends and behaviour patterns.”
(Wikipedia.org)
It can be applied to any type of unknown, whether
past, present or future.
The core idea is to capture relationships between
predictor variables and known outcomes from past
occurrences in a “model”, and then use those
relationships to predict unknown outcomes.
The accuracy depends greatly on the quality of both
the assumptions made and the data available.
5. How is predictive modelling carried
out and where?
12/10/2015 5Copyright Red Olive 2015
Predictive modelling environments:
Our tools of choice are SPSS Modeler and Statistics,
another common general platform is SAS and there are
several others.
Open source modelling (e.g. R) is popular but needs more
expert knowledge, there’s a productivity gain from
modelling software.
Some areas of usage:
Customer intimacy
Optimise capital deployment
Detect and mitigate threats
Many others…
6. Red Olive’s framework for predictive
modelling
12/10/2015 6Copyright Red Olive 2015
Illustration of modelling process
Business data for
analytics
1 Clarify problem,
create multiple
solutions
2 Work out data
needed to solve
the problem
4 Prepare data for
solution modelling
5 Develop
solution models
6 Evaluate results
7 Deploy live
model
3 Source and
capture rich data
(Refine)
(Want to
re-use?)
7. Understanding the problem and the
data
Clarify the business
problem
Does the data support
the solution?
12/10/2015 7Copyright Red Olive 2015
Business data
for analytics
1 Clarify problem,
create multiple
solutions
2 Work out data
needed to solve
the problem
4 Prepare data
for solution
modelling
5 Develop
solution models
6 Evaluate
results
7 Deploy live
model
3 Source and
capture rich
data
(Refine)
(Want to
re-use?)
8. Clarify the business problem
Copyright Red Olive 2015
etc…
Loan
applications
Person
OMG Compare
Moneysupermarket
Websites
12/10/2015 8
What’s the big
idea?
More into the
funnel?
Overall volume?
An optimised
mix?
Higher
conversion of
those who are
there already?
9. Does the data support the solution?
Copyright Red Olive 2015
Loan
applications
Person
OMG Compare
Loan Application
A = Agreement
Go Compare
No behavioural
data from web
analytics was
available.
? In future may be
able to link with
other in-house data
to enable e.g. loan
consolidation?
12/10/2015 9
Level 1
search
Level 2
search
Level 3
search
Moredataavailabletouse
formodelling…
Morelikelytoapply(andbe
successful?)
10. Modelling business solutions
Where is predictive
modelling typically
applied and what are
the benefits?
What are some of the
main techniques used?
12/10/2015 10Copyright Red Olive 2015
Business data
for analytics
1 Clarify problem,
create multiple
solutions
2 Work out data
needed to solve
the problem
4 Prepare data
for solution
modelling
5 Develop
solution models
6 Evaluate
results
7 Deploy live
model
3 Source and
capture rich
data
(Refine)
(Want to
re-use?)
12. Can we profile applicants to find interesting segments (a
“segment” means a group of people with certain things in
common)?
Could we then target certain segments with specific offers for
them?
Approach: used cluster modelling to identify some potentially
interesting segments
23%
77%
Apply Don't apply
People who progress to apply
Copyright Red Olive 201512/10/2015 12
13. 23%
53%
0%
10%
20%
30%
40%
50%
60%
All visitors Profile 1
Profile – Segment 1
• 39 or younger
• In a job for over 24 months and less than 10
years
• Looking for a loan term between 12 and 35
months
When a visitor fitting this profile comes to the
site there is a 53% chance they will make an
application
Do the available lenders have products that
match them?
Example segment 1
Copyright Red Olive 201512/10/2015 13
14. What skills do you need to do this?
Platform or coding?
Copyright Red Olive 201512/10/2015 14
As we explore we
generate many models,
keep only a few: Easier
to manage on a
platform.
Platform also easier to keep track of models, data
sets, parameters…
Also valuable when have a team of people working
together, needing co-ordination.
16. Behavioral data
- Orders
- Transactions
- Payment history
- Usage history
Descriptive data
- Attributes
- Characteristics
- Self-declared info
- (Geo)demographics
Attitudinal data
- Opinions
- Preferences
- Needs & Desires
Interaction data
- E-Mail / chat transcripts
- Call center notes
- Web Click-streams
- In person dialogues
“Traditional”
High-value, dynamic
- source of competitive differentiation
Who? What?
Why?How?
People/Customer data types
12/10/2015 Copyright Red Olive 2015 16
(*Source: IBM)
17. Modelling business solutions
The client wants to
understand core visitor
segments:
Their customer journeys
Their value
So the web site (and other
channels) can be re-
architected to better service
those requirements
The framework allow us to
enrich the behavioural data
with descriptive/attitudinal
and other data
In this example e-commerce
data
12/10/2015 17Copyright Red Olive 2015
18. Why do they visit the
site and what do they
think of it?
Who visits the site? What do they do on the
site?
12/10/2015 Copyright Red Olive 2015 18
The framework in action
19. 12/10/2015 Copyright Red Olive 2015 19
Example segment: “Happy trackers”
• Happy Trackers mainly use the site
for Track and Trace and little else.
• They tend to have a stronger
business slant and be slightly older
than the average.
• They are not heavy users of the
site and individual visits are
relatively light and narrow.
• However they are happy with
what they do and they rate the site
functionality the best out of all the
segments.
21. 0
200
400
600
800
1,000
0 2 4 6 8 10 12 14 16 18
OHW
Domestic PAFfers
Regular posters
Anxious trackers
Hobbyists
Frequent finders
Cottage Industrialists
Virgin posters
Number of visits
Average time on site per visit
Size of bubble reflects size of segment
12/10/2015 Copyright Red Olive 2015 21
Different segments have different
styles of engagement
23. Optimising capital: utility
company example
Aim:
Identify from the data those business processes that most
strongly influence customer satisfaction (CSAT, Net Promoter
Score…).
Use the results to influence decisions regarding capital
investment.
Approach:
1. Are the variations in CSAT over time significant?
2. Given limited resource for investigation, assess scale of
opportunity in a number of process areas and focus
investigation.
3. For the target processes, identify key driver variables and
attempt to calculate linkage with CSAT scores.
12/10/2015 Copyright Red Olive 2015 23
24. Measuring CSAT: last 12 weeks and
95% confidence
12/10/2015 24Copyright Red Olive 2015
Message: short-
term weekly
movement is
inconclusive
Now12 weeks ago
25. Measuring CSAT: What happened
between March and April 2014?
12/10/2015 25Copyright Red Olive 2015
Message: There
has been a
notable shift in
overall CSAT since
April 2014 – was
there some
significant event?
Now40 weeks ago
26. 12/10/2015 26Copyright Red Olive 2015
Proxy variable example: using SLA
compliance when time unavailable
SLA current week SLA previous week SLA comp 2 weeks prior
Mean CSAT 0.217 0.398 -0.039
Median CSAT 0.2 0.415 -0.031
1 Scores -0.266 -0.395 -0.002
5 Scores -0.013 0.161 -0.077
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
AxisTitle
Correlations – last 40 weeks
* Indicates that the correlation is statistically significant at the 95% level
*
*
*
28. Aims:
Identify areas most at risk of sewer flooding, the
underlying factors, and changing risk over time.
Better prioritise investigations, sewer cleansing and
repairs.
Reduce the number of sewer flooding incidents in the
most cost effective way.
Increase confidence in the level of capital maintenance
expenditure required.
28
Sewer Flooding Risk Model
Copyright Red Olive 201512/10/2015
29. Variable risk increasing
over time i.e. risk is
greater as problems
remain unattended over
time
Variable risk
increasing over time
= risk is becoming
more recent
Variable risk decreasing
over time i.e. risk
reduces as problems are
fixed by the maintenance
teams
29
Risk Model based on 365 days history
Risk Model based on 90 days history
Risk Model based on 30 days history
High Risk
Low Risk
Tracking Sewer Flooding Risk
Copyright Red Olive 201512/10/2015 29
31. Fraud detection
Here we use the term “fraud” quite loosely, to include non-
compliance and payment errors as well as abuse.
Traditional detection techniques are based on a set of
business rules that fraudsters learn and adapt to; using
analytics is one way to combat that.
Detecting fraud in a high-volume transactional setting is
different from detecting fraud in a one-off, often very high
value setting (e.g. insider trading). We’ll look at the former.
12/10/2015 Copyright Red Olive 2015 31
33. Predict the expected value for a claim,
compare that with the actual value.
Those cases that fall far outside the expected
range should be evaluated more closely.
– Use decision trees:
• income < $40K
» job > 5 yrs then good risk
» job < 5 yrs then bad risk
• income > $40K
» high debt then bad risk
» low debt then good risk
– Or Rule Sets:
• Rule #1 for good risk:
» if income > $40K
» if low debt
• Rule #2 for good risk:
» if income < $40K
» if job > 5 years
Group behavior using a clustering
algorithm
Identify outliers and investigate
Build a profile of the characteristics of
fraudulent behavior.
Pull out the cases that meet the
characteristics of fraud.
33(*Source: IBM)
34. MORE ON CAPITAL DEPLOYMENT: TEXT
MINING (NATURAL LANGUAGE
PROCESSING) EXAMPLE
Copyright Red Olive 201512/10/2015 34
35. Overview of text mining
Why is text mining of interest?
Example: Imagine you are a large telecoms company with
hundreds of customer service agents and you want to classify
all inbound customer communication quickly and direct it to
the right people to deal with it best.
12/10/2015 35Copyright Red Olive 2015
Text data
sources
Text
enrichment
Subject
matching
Sentiment
classification
Information
delivery
37. Text enrichment
12/10/2015 37Copyright Red Olive 2015
Text data
sources
Text
enrichment
Subject
matching
Sentiment
classification
Information
delivery
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap signal
but yet paying FULL monthly contract! Vodafone
sort it.
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap [----]
signal but yet paying FULL monthly contract!
Vodafone sort it.
Original Facebook Message Sentiment Amplifier
Why[WRB] not[RB] sort[VBG] your[PRP]
signal[VBP] issues [VBZ] out[IN] instead[RB]
of[IN] bringing[VBG] new[JJ]
phones[NNS]!!!![SYM] Wk[NNP] 3[CD] of[IN]
crap[NN] but[CC] yet[RB] paying[VBG]
FULL[NNP] monthly[RB] contract[NN] ![SYM]
Vodafone[NNP] sort[VBG] it[PRP] .[SYM]
Penn Treebank P.O.S. Tagger (English Messages)
sort[VBG] signal[VBP] issues [VBZ] instead[RB]
bringing[VBG] phones[NNS] Wk[NNP] 3[CD]
crap[NN] paying[VBG] monthly[RB] contract[NN]
Vodafone[NNP]
Removal of stop words and punctuation
38. Subject matching
12/10/2015 38Copyright Red Olive 2015
Text data
sources
Text
enrichment
Subject
matching
Sentiment
classification
Information
delivery
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap signal
but yet paying FULL monthly contract! Vodafone
sort it.
Original Facebook Message
Subject Matching (Fuzzy Matching)
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap signal
[NETWORK]but yet paying FULL monthly
contract! Vodafone sort it. [COMPLAINT]
BUSINESS TRANSACTION: Complaint
NETWORK: No Signal
PRODUCT: Samsung Galaxy S4
39. Sentiment classification
Many further factors help determine sentiment: Emoticons,
“Likes” on social media channels, …
Further text classification using e.g. Decision Trees.
Result: a sentiment classification.
12/10/2015 39Copyright Red Olive 2015
Text data
sources
Text
enrichment
Subject
matching
Sentiment
classification
Information
delivery
40. TEXT MINING – POLITICS
Copyright Red Olive 201512/10/2015 40
41. Analysis undertaken so far
Two samples of data from Hansard (the transcriptions of
proceedings in the Houses of Parliament) have been
downloaded, relating to:
Nicholas Soames, Conservative MP and former Defence Secretary.
Dennis Skinner, longstanding Labour MP.
The various files were loaded into SPSS Modeler’s text mining
platform. The data was parsed using Natural Language
Processing (NLP) to identify prominent “concepts” and then
some basic analysis of these concepts was carried out.
12/10/2015 41Copyright Red Olive 2015
42. Findings: Nicholas Soames’ concepts
The most commonly repeating concepts identified are listed
below with “country” the most frequent, occurring 72 times.
“Immigration” occurred 40 times and was expanded further.
12/10/2015 42Copyright Red Olive 2015
43. Findings: Nicholas Soames,
immigration
A concept map was created centred on “immigration”. This
shows the strength of association between two concepts. In
the case of “immigration”, the strongest concept associations
are with “defence”, “society” and “social”.
12/10/2015 43Copyright Red Olive 2015
44. Findings: Dennis Skinner,
immigration
In stark contrast, Dennis Skinner says virtually nothing on the
issue of immigration.
12/10/2015 44Copyright Red Olive 2015
45. Findings: Dennis Skinner’s concepts
One of the top concepts in Dennis Skinner’s comments is
“pits”, occurring 54 times.
12/10/2015 45Copyright Red Olive 2015
46. Findings: Dennis Skinner, pits
Below is a concept map centred on “pits”. The strongest
associations are with “tories”, “help” and so on.
12/10/2015 46Copyright Red Olive 2015
47. Findings: Nicholas Soames concept
categories
In the “military” context, there seem to be particularly strong
links between the categories “human resources”, “finance”
and “geographical location”…
12/10/2015 47Copyright Red Olive 2015
48. Findings: Nicholas Soames concept
categories
… so if we go back to relevant original texts, linked below, we
may expect to find the cost of having people in certain
locations as a prominent theme.
12/10/2015 48Copyright Red Olive 2015
49. Findings: Dennis Skinner concept
categories
A similar analysis of Dennis Skinner’s concept categories
based on “natural resources”.
12/10/2015 49Copyright Red Olive 2015
50. Learn more…
Has this morning whet your appetite? We’d
love to talk with you further about analytics
for your own organisation. To arrange to do
that please leave your contact details on one
of the sheets near the door or just have a
word with Jefferson, John or Mark.
12/10/2015 50Copyright Red Olive 2015
51. Preparing to try it out for real?
Ready to try this out for real? We can help you
build your business case and prove the benefits
to your business on your data. Please have a chat
with us at the end.
If you’re already further along, we run more in-
depth training courses:
Solving business problems using data analytics.
Statistical thinking.
Data mining principles and techniques.
Hands-on skills in data mining and predictive
analytics.
12/10/2015 51Copyright Red Olive 2015
52. Quick recap
What we’ve covered:
Business context, modelling process, addressing the
right problem(s).
Customer intimacy: new business offerings (internet
loans), skills you’ll need; customer development and
retention (Royal Mail).
Predictive asset management: customer satisfaction
and internal processes; flood prediction.
Fraud and anomalies: process for detection.
Text mining: telecoms complaints, political analysis.
12/10/2015 52Copyright Red Olive 2015