The document discusses applying portfolio management techniques used for loan portfolios to customer relationship management (CRM). It argues that customers can be viewed as bonds that generate cash flows for a company through repeat purchases and visits. This view of CRM as portfolio management aims to keep customers engaged over the long term to maximize their lifetime value. Key aspects of applying this approach include measuring CRM success longitudinally using metrics like repeat purchase rates, predicting customer behavior with loyalty models, and using analytics to determine the most valuable customer states to target with marketing.
- Credit Risk Modeling
- Scorecards and Cutoff Scores
- Alternative Lending, Marketplace Lending, and Fintech
- Common predictive modeling conventions
- Regulatory Pressure
- The Demand for Creativity and Disruption
- What Makes “Alternative Lending Analytics” Different from “Banking Analytics”?
- The Continued Relevance of Logistic Regression
Overview of Data Analytics in Lending BusinessSanjay Kar
AI/ML use cases
BFSI industry overview
Lending Products
Underwriting Strategy
Customer Lifecycle Management
How to prepare for becoming a banking analyst
Materials to study for statistics
What is fintech?
What is a Credit Bureau?
Books for statistics
Tools for data science
Techniques for data science
Market Practice Series (Credit Losses Modeling)Yahya Kamel
The Central Bank of Egypt “CBE” has adopted IFRS in year 2008. In specific IAS 39 has a discussion about implementing a model that can derive the incurred credit losses for a pool of receivables/ loans, which was quite open for market development & practical initiatives.
From the part of the CBE, it has adopted same approach, which led to some wide different market practices, logic, and interpretations, which sometimes have been questionable on a wide scale basis!
So, I've thought to develop some sort of materials that can serve as a practical guidance for quantifying the credit risk, using different simple models, based on Basel II definitions of the risk components.
The intended users of this material are the credit risk professionals who conduct risk analysis, implement risk management policies, or/and are in charge of quantifying the credit risk for a loan portfolio (corporate & retail).
Also, other professionals or officers complying with IFRS, or CBE GAAP.
- Credit Risk Modeling
- Scorecards and Cutoff Scores
- Alternative Lending, Marketplace Lending, and Fintech
- Common predictive modeling conventions
- Regulatory Pressure
- The Demand for Creativity and Disruption
- What Makes “Alternative Lending Analytics” Different from “Banking Analytics”?
- The Continued Relevance of Logistic Regression
Overview of Data Analytics in Lending BusinessSanjay Kar
AI/ML use cases
BFSI industry overview
Lending Products
Underwriting Strategy
Customer Lifecycle Management
How to prepare for becoming a banking analyst
Materials to study for statistics
What is fintech?
What is a Credit Bureau?
Books for statistics
Tools for data science
Techniques for data science
Market Practice Series (Credit Losses Modeling)Yahya Kamel
The Central Bank of Egypt “CBE” has adopted IFRS in year 2008. In specific IAS 39 has a discussion about implementing a model that can derive the incurred credit losses for a pool of receivables/ loans, which was quite open for market development & practical initiatives.
From the part of the CBE, it has adopted same approach, which led to some wide different market practices, logic, and interpretations, which sometimes have been questionable on a wide scale basis!
So, I've thought to develop some sort of materials that can serve as a practical guidance for quantifying the credit risk, using different simple models, based on Basel II definitions of the risk components.
The intended users of this material are the credit risk professionals who conduct risk analysis, implement risk management policies, or/and are in charge of quantifying the credit risk for a loan portfolio (corporate & retail).
Also, other professionals or officers complying with IFRS, or CBE GAAP.
Understanding Credit Scoring for Mortgage ProfessionalsSusan McCullah
Learn more about how credit scores are formulated, what they predict, and how they can be increased. We examine the factors that make up a credit score, common credit myths, and big credit mistakes.
In this presentation Gopalkrishna Rajagopal talks about what a financial company is, with examples of who they are and what they do. And goes through the key sectors and the business model they have in place at the Williams Capital Group.
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.
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
Project Details: In this study, the concept and application of credit scoring in a German banking environment is
explained. A credit scoring model has been developed using logistic regression and random forest. Limitations of
the model are explained and possible solutions are given with an overview of LASSO.
Guide: Dr. Sibnarayan Guria, Associate Professor and Head of the Department, Department of
Statistics, West Bengal State University
Language Used: R
A simple classification problem based on credit score data which allows us to identify whether a particular loan applicant may be given or denied credit (loan). Using Rattle and R (for some boxplot snippets), we've tried to bring out some interesting insights
Business Strategy for Banks and Credit UnionsSerge Milman
Webinar presented on August 7, 2013 on WhyBusiness Strategy is Essential for Community Banks and Credit Unions. Recording of the webinar can be accessed http://bankblog.optirate.com/business-strategy-essential/
There are 100,000 applicants for loans. Who is likely to default? How to effectively offer a loan
There are 100,000 consumers who is likely to buy my product? How to effectively market my product?
There are more than 1,000,000,000 transactions in a day. How to identify the fraud transaction?
There are 1,000,000 claims every year. How to identify the fake claims
In the business of money, there can be no errors. That goes doubly so for keeping your customers. With PNA's finance data analytics, discover the hidden patterns that customers give you, and learn the language needed to retain them.
Hash tables provide a powerful methodology for leveraging bid data by formatting an n-dimensional array with a single, simple key. This advancement has empowered SAS® programmers to compile exponentially more missing data points than ever before, creating tables with hundreds of fields of all types in which the majority of data in this vast array is empty. However, the hash structure also supports analytics to calculate maximum likelihood estimates for missing values, leveraging extensive data resources available for each individual. An important application of this is in sentiment analysis, where social media text expresses likes or dislikes for particular products. Customer data, including sentiments for other products, are used to model sentiment where an individual’s preference has not been made known.
Understanding Credit Scoring for Mortgage ProfessionalsSusan McCullah
Learn more about how credit scores are formulated, what they predict, and how they can be increased. We examine the factors that make up a credit score, common credit myths, and big credit mistakes.
In this presentation Gopalkrishna Rajagopal talks about what a financial company is, with examples of who they are and what they do. And goes through the key sectors and the business model they have in place at the Williams Capital Group.
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.
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
Project Details: In this study, the concept and application of credit scoring in a German banking environment is
explained. A credit scoring model has been developed using logistic regression and random forest. Limitations of
the model are explained and possible solutions are given with an overview of LASSO.
Guide: Dr. Sibnarayan Guria, Associate Professor and Head of the Department, Department of
Statistics, West Bengal State University
Language Used: R
A simple classification problem based on credit score data which allows us to identify whether a particular loan applicant may be given or denied credit (loan). Using Rattle and R (for some boxplot snippets), we've tried to bring out some interesting insights
Business Strategy for Banks and Credit UnionsSerge Milman
Webinar presented on August 7, 2013 on WhyBusiness Strategy is Essential for Community Banks and Credit Unions. Recording of the webinar can be accessed http://bankblog.optirate.com/business-strategy-essential/
There are 100,000 applicants for loans. Who is likely to default? How to effectively offer a loan
There are 100,000 consumers who is likely to buy my product? How to effectively market my product?
There are more than 1,000,000,000 transactions in a day. How to identify the fraud transaction?
There are 1,000,000 claims every year. How to identify the fake claims
In the business of money, there can be no errors. That goes doubly so for keeping your customers. With PNA's finance data analytics, discover the hidden patterns that customers give you, and learn the language needed to retain them.
Hash tables provide a powerful methodology for leveraging bid data by formatting an n-dimensional array with a single, simple key. This advancement has empowered SAS® programmers to compile exponentially more missing data points than ever before, creating tables with hundreds of fields of all types in which the majority of data in this vast array is empty. However, the hash structure also supports analytics to calculate maximum likelihood estimates for missing values, leveraging extensive data resources available for each individual. An important application of this is in sentiment analysis, where social media text expresses likes or dislikes for particular products. Customer data, including sentiments for other products, are used to model sentiment where an individual’s preference has not been made known.
The nature of sales in retail banking has changed dramatically. While there is a renewed pressure to grow accounts, the techniques banks have traditionally used to acquire new accounts have become less effective.
As consumer preferences continue to shift and non-traditional competitors continue to disrupt the market, the ROI of acquisition techniques like batch mail and branch cross-sell will continue to decline. In order to thrive, banks need to leverage the tremendous amount of data they have on each of their customers to drive more profitable and satisfying customer interactions across all of their channels.
This presentation will:
• Identify the market trends impacting banks’ growth strategies.
• Explore the role of marketing and risk analytics in making better acquisition decisions.
• Introduce best practices for implementing a more holistic approach to account acquisition.
Factors in a time series analysis can be tested for leading / behavior by calculating the correlation coefficient for a range of time lags
The amount of time lag between two indicators can be measured by finding the time difference at the maximum correlation coefficient
Leading / lagging indicators have wide application in many areas beyond economics
Mathematical models employing an autoregressive integrated moving average (ARIMA) have found very wide applications following work by Box and Jenkins in 1970, especially in time series analysis. ARIMA models have been very successful in financial forecasting, forming the basis of such things as predicting how much gas prices will rise. However, no mathematical requirement exists requiring the data to be a time series: only the use of equally spaced intervals for the independent variable is necessary. This can be done by binning data into standard ranges, such as income by $10,000 intervals. This paper reviews the fundamental statistical concepts of ARIMA models and applications of non-temporal ARIMA models in statistical research. Examples and applications are given in biostatistics, meteorology, and econometrics as well as astrostatistics.
First presented at the MSUG Conference on June 4, 2015, this presentation discusses concepts and tools to add to your logistic regression modeling practice and also how to use these concepts and tools.
Why Your Customer HealthScore is Useless and How to Overcome ItBoaz S. Maor
Customer Health Score (CHS) is a common and helpful metric for Customer Success Managers (CSM). But, it is insufficient to address opportunities and challenges with your customers. Why? Because it focuses on the vendor-customer relationship and fails to assess the maturity of the customer in running their business.
This is why Ralf Wiggten and I recently coined the term Customer Maturity Index (CMI) and developed a methodology for its calculation. Combining CMI with CHS provides the clarity needed for effective playbooks to maximize both the customer’s success and yours from the relationship.
This presentation explores the short-comings of common Customer Health Scores, provides the case for Customer Maturity Index, details a suggested methodology for CMI development within a company and provides practical tools for such development.
Customer Success as a movement is so young even compared to other emerging tech sectors. But it's changing so fast it can be hard to justify it to your financial department. Scott Golden, Gainsight's Director of Customer Success Strategy, will deep dive into a data-driven webinar all about how you can account for Customer Success in such a rapidly-evolving industry.
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.Revegy, Inc.
Why waste precious time managing endless spreadsheets and presentation decks when you could be utilizing strategic technology to drive strategic results. This Revinar addresses how your team can collaborate efficiently to drive significant increases in revenue from your customer base.
- 1 - Ivey Business Journal NovemberDecember 2002No one SilvaGraf83
- 1 - Ivey Business Journal November/December 2002
No one company has written the book on CRM.
And rightly so, says this author, whose
examination of how companies practice this
much-talked about discipline led him to develop
comprehensive guidelines for enhancing a
company's returns from CRM.
By Ian Gordon
Ian Gordon is President of Convergence
Management Consultants Ltd., (www.converge.ca),
and the author of Competitor Targeting: Winning
the Battle for Market and Customer Share (Wiley,
2002).
That few companies are achieving the results they
expected from their investment in Customer
Relationship Management (CRM) is not news. That
most companies continue to invest in CRM without a
roadmap for increasing shareholder value or even for
forging closer customer relationships is also not
surprising, since there are few best practices in CRM
for companies to follow. In fact, based on our own
research and consulting, and a recent examination of
best practices in 35 Canadian and U.S. corporations,
we could not find one company that excels in every
dimension of CRM. However, we did find examples of
one or two specific best practices in individual
companies. This article discusses these selected best
practices, which, we believe, companies should consider
when trying to improve the performance of their CRM
initiatives. It also discuss the changing role of senior
managers that are developing a relationship-oriented
organization
A definition and a vision
There are many definitions for CRM, and best-
practice companies adopt one that is shared across
the organization. Otherwise, the very term "CRM"
will conjure up many things to different people and
lead to confusion. These companies see CRM as a
series of strategies and processes that support and
execute a relationship vision for the enterprise. In
their eyes, CRM is a series of strategies and processes
that create new and mutual value for individual
customers, builds preference for their organizations
and improves business results over a lifetime of
association with their customers.
With this definition, an organization can focus on
developing the only asset of the enterprise that matters
in the long term, progressively deeper relationships with
valuable customers. By sharing the definition, they can
put the customer first and avoid sending their staff into
cycles of interminable CRM programming.
These organizations then create a vision for how CRM
will change their companies. Some develop the vision
according to attributes that are important to both the
customer and the company. These include attributes that
affect customers' perceptions of value, how they can
bond with the organization, product and company
preference and purchase intent.
This vision sometimes changes as the firm gains
experience in CRM and as technology makes new things
possible. For example, at a major Canadian bank, the
vision has evolved. Initially the vision was associated
with the development of customer information ...
- 1 - Ivey Business Journal NovemberDecember 2002No one RayleneAndre399
- 1 - Ivey Business Journal November/December 2002
No one company has written the book on CRM.
And rightly so, says this author, whose
examination of how companies practice this
much-talked about discipline led him to develop
comprehensive guidelines for enhancing a
company's returns from CRM.
By Ian Gordon
Ian Gordon is President of Convergence
Management Consultants Ltd., (www.converge.ca),
and the author of Competitor Targeting: Winning
the Battle for Market and Customer Share (Wiley,
2002).
That few companies are achieving the results they
expected from their investment in Customer
Relationship Management (CRM) is not news. That
most companies continue to invest in CRM without a
roadmap for increasing shareholder value or even for
forging closer customer relationships is also not
surprising, since there are few best practices in CRM
for companies to follow. In fact, based on our own
research and consulting, and a recent examination of
best practices in 35 Canadian and U.S. corporations,
we could not find one company that excels in every
dimension of CRM. However, we did find examples of
one or two specific best practices in individual
companies. This article discusses these selected best
practices, which, we believe, companies should consider
when trying to improve the performance of their CRM
initiatives. It also discuss the changing role of senior
managers that are developing a relationship-oriented
organization
A definition and a vision
There are many definitions for CRM, and best-
practice companies adopt one that is shared across
the organization. Otherwise, the very term "CRM"
will conjure up many things to different people and
lead to confusion. These companies see CRM as a
series of strategies and processes that support and
execute a relationship vision for the enterprise. In
their eyes, CRM is a series of strategies and processes
that create new and mutual value for individual
customers, builds preference for their organizations
and improves business results over a lifetime of
association with their customers.
With this definition, an organization can focus on
developing the only asset of the enterprise that matters
in the long term, progressively deeper relationships with
valuable customers. By sharing the definition, they can
put the customer first and avoid sending their staff into
cycles of interminable CRM programming.
These organizations then create a vision for how CRM
will change their companies. Some develop the vision
according to attributes that are important to both the
customer and the company. These include attributes that
affect customers' perceptions of value, how they can
bond with the organization, product and company
preference and purchase intent.
This vision sometimes changes as the firm gains
experience in CRM and as technology makes new things
possible. For example, at a major Canadian bank, the
vision has evolved. Initially the vision was associated
with the development of customer information ...
Total Customer Experience Management Overview #TCE #CEM -- The Why, What and...Stephen King
This is a presentation we put together for our TCELab Sales Affiliates and Partners -- explains an overview of Total Customer Experience Management, Why your customer's CEO's will love it, your opportunity, and how TCELab's products and services fit into the CEM / Big Data / Customer Loyalty Space
• This Module discuss the topic related to Type of CRM, The Strategic Framework for CRM, Strategic CRM, Analytical CRM, Analytical CRM answers these questions, Successful analytical CRM solution, Benefits of Analytical CRM, Case on Analytical CRM, Collaborative CRM, Case on Collaborative CRM, Social CRM, Types of Social Media, Understanding Social CRM, Difference Between Traditional and Social CRM, Benefits of SCRM, Risk Associated with SCRM, Steps towards effective SCRM, Critical Success Factors for SCRM.
Marketing Operations ROI: It`s Simpler and Way Harder Than You ThinkClearAction Continuum
For an updated version of this presentation: https://www.slideshare.net/clearaction/marketing-operations-roi-its-simpler-and-way-harder-than-you-think-127189832
How Marketing Operations can help you more effectively utilize metric data to measure ROI.
See https://ClearAction.com
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. Extracting ROI From The Engaged Customer:
A Portfolio Management Approach to CRM
Magnify Analytic Solutions:
Keith Shields, Chief Analytics Officer – Magnify, Chief Credit Officer – Loan Science
Susan Arnot, Director, Decision Sciences
Laura Benard, Director, Client Services
Jen Boyer, Marketing Strategy Manager, Ford Customer Service Division