Genesis presentation, CCMG Conference 2017
Behavioural economics, which studies human decision-making behaviour, proves to be a powerful tool in optimising contact centre performance. Behavioural economics has been used successfully in the contact centre environment to address a range of customer challenges and behaviours across a range of industries.
Pareto Analysis is a simple technique for prioritizing possible changes by identifying the problems that will be resolved by making these changes. By using this approach, you can prioritize the individual changes that will most improve the situation.
Example section on MySQL for the Oracle DBA 1 day bootcamp.
In object management we look at the key SQL objects including what differs with Oracle and what is Oracle specific functionality.
We also look at the MySQL data dictionary, the INFORMATION_SCHEMA
Genesis presentation, CCMG Conference 2017
Behavioural economics, which studies human decision-making behaviour, proves to be a powerful tool in optimising contact centre performance. Behavioural economics has been used successfully in the contact centre environment to address a range of customer challenges and behaviours across a range of industries.
Pareto Analysis is a simple technique for prioritizing possible changes by identifying the problems that will be resolved by making these changes. By using this approach, you can prioritize the individual changes that will most improve the situation.
Example section on MySQL for the Oracle DBA 1 day bootcamp.
In object management we look at the key SQL objects including what differs with Oracle and what is Oracle specific functionality.
We also look at the MySQL data dictionary, the INFORMATION_SCHEMA
Getting started with MySQL on Amazon Web ServicesRonald Bradford
Setting up MySQL on Amazon Web Services (AWS)
Ronald Bradford, Principal at 42SQL will step you though getting started with AWS.
This introduction will assume you no nothing about AWS, and have no account. With Internet access via a Browser and a valid Credit Card, you can have your own running Web Server on the Internet in under 10 minutes, just point and click.
We will step into some more detail using the supplied command line tools for more advanced usage.
Learn how to extend your existing MySQL based website to leverage the power of MySQL variants, AWS cloud based MySQL deployments and RDBMS alternatives. Evaluate how to integrate and use these different various technologies such as MySQL based variations KickFire, a column based optimization and InfoBright, a data warehousing solution. Understand the means of approach towards data synchronization between various database solutions in your business.
There has been significant movement in recent times towards less structured approaches of storing and retrieving data. No longer the realm of Relational Databases, there is a new crop of structured key/value pair stores and unstructured data offerings. This closing panel debate at Open SQL Camp 2009 discussed the SQL v NoSQL topic.
Best Practices in Migrating to MySQL - Part 1Ronald Bradford
This presentation to the Federal Government sector was a follow up on my successful "MySQL for the Oracle DBA Bootcamp". Best Practices in Migrating to MySQL was a focus on software applications running on Oracle and Microsoft SQL Server database products. Topic in this 4 hr workshop included:
1. Reasons to migrate to MySQL
2. Ideal application candidates
3. Migration process overview
4. Migration assistance tools
5. Specific migration issues
6. Ideals for minimizing future migrations
7. General MySQL Information
Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to subscription based companies allowing them to maximize the results of retention campaigns. The problem of churn predictive modeling has been widely studied by the data mining and machine learning communities. It is usually tackled by using classification algorithms in order to learn the different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art classification algorithms are not well aligned with commercial goals, in the sense that, the models miss to include the real financial costs and benefits during the training and evaluation phases. In the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive power, does not yield to the best results when measured by the actual financial cost, ie. investment per subscriber on a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this presentacion, we present a new cost-sensitive framework for customer churn predictive modeling. First we propose a new financial based measure for evaluating the effectiveness of a churn campaign taking into account the available portfolio of offers, their individual financial cost and probability of offer acceptance depending on the customer profile. Then, using a real-world churn dataset we compare different cost-insensitive and cost-sensitive classification algorithms and measure their effectiveness based on their predictive power and also the cost optimization. The results show that using a cost-sensitive approach yields to an increase in cost savings of up to 26.4 %.
Getting started with MySQL on Amazon Web ServicesRonald Bradford
Setting up MySQL on Amazon Web Services (AWS)
Ronald Bradford, Principal at 42SQL will step you though getting started with AWS.
This introduction will assume you no nothing about AWS, and have no account. With Internet access via a Browser and a valid Credit Card, you can have your own running Web Server on the Internet in under 10 minutes, just point and click.
We will step into some more detail using the supplied command line tools for more advanced usage.
Learn how to extend your existing MySQL based website to leverage the power of MySQL variants, AWS cloud based MySQL deployments and RDBMS alternatives. Evaluate how to integrate and use these different various technologies such as MySQL based variations KickFire, a column based optimization and InfoBright, a data warehousing solution. Understand the means of approach towards data synchronization between various database solutions in your business.
There has been significant movement in recent times towards less structured approaches of storing and retrieving data. No longer the realm of Relational Databases, there is a new crop of structured key/value pair stores and unstructured data offerings. This closing panel debate at Open SQL Camp 2009 discussed the SQL v NoSQL topic.
Best Practices in Migrating to MySQL - Part 1Ronald Bradford
This presentation to the Federal Government sector was a follow up on my successful "MySQL for the Oracle DBA Bootcamp". Best Practices in Migrating to MySQL was a focus on software applications running on Oracle and Microsoft SQL Server database products. Topic in this 4 hr workshop included:
1. Reasons to migrate to MySQL
2. Ideal application candidates
3. Migration process overview
4. Migration assistance tools
5. Specific migration issues
6. Ideals for minimizing future migrations
7. General MySQL Information
Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to subscription based companies allowing them to maximize the results of retention campaigns. The problem of churn predictive modeling has been widely studied by the data mining and machine learning communities. It is usually tackled by using classification algorithms in order to learn the different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art classification algorithms are not well aligned with commercial goals, in the sense that, the models miss to include the real financial costs and benefits during the training and evaluation phases. In the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive power, does not yield to the best results when measured by the actual financial cost, ie. investment per subscriber on a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this presentacion, we present a new cost-sensitive framework for customer churn predictive modeling. First we propose a new financial based measure for evaluating the effectiveness of a churn campaign taking into account the available portfolio of offers, their individual financial cost and probability of offer acceptance depending on the customer profile. Then, using a real-world churn dataset we compare different cost-insensitive and cost-sensitive classification algorithms and measure their effectiveness based on their predictive power and also the cost optimization. The results show that using a cost-sensitive approach yields to an increase in cost savings of up to 26.4 %.
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...Big-Data-Summit
Los modelos predictivos de fuga de clientes churn tratan de predecir la probabilidad de que un cliente sea desertor de la empresa analizando su comportamiento histórico y su información socio económica. Esta herramienta permite maximizar los resultados de las campañas de retención. Los actuales algoritmos de clasificación de última generación no están bien alineados con los objetivos comerciales, en el sentido de que los modelos no incluyen los costos y beneficios financieros reales durante las etapas de entrenamiento y evaluación. En esta presentación, se muestra una nueva metodología sensible al costo para el modelo predictivo de churn de clientes. Primero proponemos una nueva medida financiera para evaluar la efectividad de una campaña de churn teniendo en cuenta la cartera de ofertas disponible, su costo financiero individual y la probabilidad de aceptación de la oferta en función del perfil del cliente. Luego, usando un conjunto de datos de churn del mundo real, comparamos diferentes algoritmos de clasificación y mediremos su efectividad basándonos en su poder predictivo y también en la optimización de costos. Los resultados muestran que el uso de un enfoque sensible al costo produce un aumento en los ahorros de costos de hasta el 26,4%.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
Worldwide, billions of euros are lost every year due to credit card fraud. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative new fraud patterns emerge. Hence, it remains challenging to find effective methods of mitigating fraud. Existing solutions include simple if-then rules and classical machine learning algorithms. Credit card fraud is by definition an example-dependent and cost-sensitive classification problem, in which the costs due to is classification vary between examples and not only within classes, i.e., misclassifying a fraudulent transaction may have a financial impact ranging from a few to thousands of euros. In this paper, we propose an extension to the cost-sensitive decision trees algorithm, by creating an ensemble of such trees, and combining them using a stacking approach with a cost-sensitive logistic regression. We compare our method with standard machine learning algorithms and state-of-the-art cost-sensitive classification methods using a real credit card fraud dataset provided by a large European card processing company. The results show that our method achieves savings of up to 73.3%, more than 2 percentage points more than a single cost-sensitive decision tree.
PECB Webinar: Achieve business excellence through the power of Six SigmaPECB
We will cover:
• Why every company needs Six Sigma implementation
• How processes are improved by using Six Sigma
• Real benefits in profits, and reductions in defects
Presenter:
This webinar will be presented by M.Youssef.K, Executive Consultant & Trainer at Six Sigma Associates - SSA.
Data Science Introduction by Emerging India AnalyticsAyeshaSharma29
This is the data science basic introduction which covers Big data ,machine learning including supervised machine learning & unsupervised machine learning. This presentation also covers Hadoop tool and its landscape. This will help in deciding where to start your career in data science. It has all the skills you require to build a career in data science industry.
Buy Verified PayPal Account | Buy Google 5 Star Reviewsusawebmarket
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Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
B2B payments are rapidly changing. Find out the 5 key questions you need to be asking yourself to be sure you are mastering B2B payments today. Learn more at www.BlueSnap.com.
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
Building Your Employer Brand with Social MediaLuanWise
Presented at The Global HR Summit, 6th June 2024
In this keynote, Luan Wise will provide invaluable insights to elevate your employer brand on social media platforms including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok. You'll learn how compelling content can authentically showcase your company culture, values, and employee experiences to support your talent acquisition and retention objectives. Additionally, you'll understand the power of employee advocacy to amplify reach and engagement – helping to position your organization as an employer of choice in today's competitive talent landscape.
VAT Registration Outlined In UAE: Benefits and Requirementsuae taxgpt
Vat Registration is a legal obligation for businesses meeting the threshold requirement, helping companies avoid fines and ramifications. Contact now!
https://viralsocialtrends.com/vat-registration-outlined-in-uae/
1. Core Purpose: To Enable Organisations Become Happier
Decision Analysis
4th November 2013
2. Training | Mentoring | Data Analytics | Execution | Deployment
What is Decision Analysis?
• A quantitative framework for making decisions
• Selection of a decision from a set of possible decision alternatives
when uncertainties regarding the future exist
• Goal is to optimize the resulting payoff in terms of a decision
criterion
3. Training | Mentoring | Data Analytics | Execution | Deployment
Decision Models
• Deterministic models
• Probabilistic models
• Decision-making under pure uncertainty
• Maxmin
• Maxmax
• Minmax
• Decision-making under risk
• Expected value criterion
• Expected value of perfect information
• Bayesian analysis
4. Training | Mentoring | Data Analytics | Execution | Deployment
Case Study
States of nature
>1000
points
300-1000 +/-300
-300 to -
1000
<-1000
points
Large rise Small rise No change Small fall Large fall
Alternatives
Bonds 9% 7% 6% 0% -1%
Stocks 17% 9% 5% -3% -10%
Fixed
deposit
7% 7% 7% 7% 7%
5. Training | Mentoring | Data Analytics | Execution | Deployment
MaxMin
Pessimistic approach based on worst case scenario
1. Write min for each row
2. Choose max of the above
States of nature
>1000
points
300-
1000
+/-300
-300 to -
1000
<-1000
points
Large
rise
Small
rise
No
change
Small fall
Large
fall
Min
Alternatives
Bonds 9% 7% 6% 0% -1% -4%
Stocks 17% 9% 5% -3% -10% -10%
Fixed
deposit
7% 7% 7% 7% 7% 7%
6. Training | Mentoring | Data Analytics | Execution | Deployment
MaxMax
Pessimistic approach based on worst case scenario
1. Write max for each row
2. Choose max of the above
States of nature
>1000
points
300-
1000
+/-300
-300 to -
1000
<-1000
points
Large
rise
Small
rise
No
change
Small fall
Large
fall
Max
Alternatives
Bonds 9% 7% 6% 0% -1% 9%
Stocks 17% 9% 5% -3% -10% 17%
Fixed
deposit
7% 7% 7% 7% 7% 7%
7. Training | Mentoring | Data Analytics | Execution | Deployment
MinMax
Pessimistic approach to minimize regret or opportunity loss
1. Take the largest number in each coloumn
2. Subtract all the numbers in the coloumn from it
3. Choose maximum number for each option
4. Choose minimum number from step 3
8. Training | Mentoring | Data Analytics | Execution | Deployment
Case Study
States of nature
>1000
points
300-1000 +/-300
-300 to -
1000
<-1000
points
Large rise Small rise No change Small fall Large fall
Alternatives
Bonds 9% 7% 6% 0% -1%
Stocks 17% 9% 5% -3% -10%
Fixed
deposit
7% 7% 7% 7% 7%
9. Training | Mentoring | Data Analytics | Execution | Deployment
Regret Matrix
States of nature
>1000
points
300-1000 +/-300
-300 to -
1000
<-1000
points
Large rise Small rise No change Small fall Large fall
Alternatives
Bonds (17%-9%) (9%-7%) (7%-6%) (7%-0%) (7%+1%)
Stocks (17%-17%) (9%-9%) (7%-5%) (7%+3%) (7%+10%)
Fixed
deposit
(17%-7%) (9%-7%) (7%-7%) (7%-7%) (7%-7%)
10. Training | Mentoring | Data Analytics | Execution | Deployment
Regret Matrix
States of nature
>1000
points
300-1000 +/-300
-300 to -
1000
<-1000
points
Large rise Small rise
No
change
Small fall Large fall Max
Alternatives
Bonds 8% 2% 1% 7% 8% 8%
Stocks 0% 0% 2% 10% 17% 17%
Fixed
deposit
10% 2% 0% 0% 0% 10%
11. Training | Mentoring | Data Analytics | Execution | Deployment
Applications
• Project solution selection
• Make or buy decisions
12. Training | Mentoring | Data Analytics | Execution | Deployment
References
• University of Baltimore:
http://home.ubalt.edu/ntsbarsh/opre640a/partIX.htm
• John Wiley & Sons