This document discusses applying a portfolio management approach to customer relationship management (CRM). It argues that CRM and consumer loan management are analytically similar, as both involve managing a portfolio of assets (customers or loans) to maximize cash flows. The document advocates adopting practices from consumer loan portfolio management, such as measuring customer lifetime value and using predictive analytics to manage the long-term health of the customer portfolio.
- 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
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.
- 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
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.
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.
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
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.
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
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...brucelb
A detailed case study of how to use Negotiation Strategies, an application of Game Theory and Decision Tree Analysis to develop an optimum strategy for negotiating a settlement in litigation. We demonstrate a process that can: identify and assess negotiation risks; know whether th current Negotiation Strategy will fail in time to change it;
and execute the most effective strategy to get the best possible outcome.
To help investors identify unsecured loans likely to be fully paid, a machine learning algorithm was developed to forecast probability of full payment and probability of default.
MSc research project report - Optimisation of Credit Rating Process via Machi...AmarnathVenkataraman
Optimization of Credit rating process via Machine Learning
The credit rating process is considered to be one of the vital processes that defenses the global economy. The majority of investments will be obtained based on these credit ratings which acts as the representation of the financial credibility of companies. As the current credit rating process found to be expensive, small and medium-sized enterprises(SMEs) which are considered to be the backbone of the global economy might find it difficult to access the funds via investment for their development which in turn affects the global economy as well. This issue might be solved with the outcome of this research in terms of the optimized credit rating system with improved accuracy and continuous credit rating transition. Support Vector Machine(SVM) managed to achieve the highest accuracy of 92.0% whereas Random Forest(RF) and C5.0 decision tree also achieved greater accuracies with different formats of the dataset. With the help of dictionary-based sentiment analysis, this research proved that a continuous credit rating transition system could track the changes in the financial status of the company which in turn helps to predict the crisis like bankruptcy and default in prior.
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.
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.
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
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.
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
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...brucelb
A detailed case study of how to use Negotiation Strategies, an application of Game Theory and Decision Tree Analysis to develop an optimum strategy for negotiating a settlement in litigation. We demonstrate a process that can: identify and assess negotiation risks; know whether th current Negotiation Strategy will fail in time to change it;
and execute the most effective strategy to get the best possible outcome.
To help investors identify unsecured loans likely to be fully paid, a machine learning algorithm was developed to forecast probability of full payment and probability of default.
MSc research project report - Optimisation of Credit Rating Process via Machi...AmarnathVenkataraman
Optimization of Credit rating process via Machine Learning
The credit rating process is considered to be one of the vital processes that defenses the global economy. The majority of investments will be obtained based on these credit ratings which acts as the representation of the financial credibility of companies. As the current credit rating process found to be expensive, small and medium-sized enterprises(SMEs) which are considered to be the backbone of the global economy might find it difficult to access the funds via investment for their development which in turn affects the global economy as well. This issue might be solved with the outcome of this research in terms of the optimized credit rating system with improved accuracy and continuous credit rating transition. Support Vector Machine(SVM) managed to achieve the highest accuracy of 92.0% whereas Random Forest(RF) and C5.0 decision tree also achieved greater accuracies with different formats of the dataset. With the help of dictionary-based sentiment analysis, this research proved that a continuous credit rating transition system could track the changes in the financial status of the company which in turn helps to predict the crisis like bankruptcy and default in prior.
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.
• 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.
Vortrag von Raj Venkatesan und Kim Whitler an der HWZ-Darden Konferenz vom 8. Juni 2017 an der HWZ Hochschule für Wirtschaft Zürich.
https://fh-hwz.ch/conference
Smarter opportunity qualification and deal inspection is more important in today’s challenging B2B selling environment. Sales organizations are implementing MEDDIC and its variations (i.e., MEDDICC or MEDDPICC). But they consistently hit snags when they attempt to implement the concept. Reflecting on our experiences across different sales models and industries, we see some common root cause issues. To realize the full potential of MEDDPICC, sales leaders and sales enablement need to prioritize three things.
Driven by challenges on competition, rising customer expectation and shrinking
margins, banks have been using technology to reduce cost. Apart from competitive
environment, there has been deregulation as to rate of interest, technology intensive
delivery channel like Internet Banking, Tele Banking, Mobile banking and Automated
Teller Machines (ATMs) etc have created a multiple choice to user of the bank. The
banking business is becoming more and more complex with the changes emanating from
the liberalization and globalization. For a new bank, customer creation is important, but
an established bank it is the retention is much more efficient and cost effective
mechanism.
MBA Projects, synopsis, and synopsis of various regular as well as distance learning undergraduate and postgraduate courses for various institutions like SMU – Sikkim Manipal University, SMUDE, AIMA, AMITY, IGNOU, SCDL, JAMIA, AMU, JHU etc.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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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.
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.
1. Extracting ROI From The
Engaged Customer
A Portfolio Management Approach to CRM
Keith Shields Laura Benard Jen Boyer
Chief Analytics Officer Client Services Director Marketing Strategy Manager
Magnify Analytic Solutions Magnify Analytic Solutions Ford Motor Company
10/28/2014
2. Q. How related are the following two topics?
Consumer Loan
Management
VS. CRM Marketing
1
3. Q. How related are the following two topics?
Consumer Loan
Management
VS. CRM Marketing
a. Not At All Related
2
4. Q. How related are the following two topics?
Consumer Loan
Management
VS. CRM Marketing
b. Somewhat Related
3
5. Q. How related are the following two topics?
Consumer Loan
Management
VS. CRM Marketing
c. Very Related
4
6. Both Consumer Loan Managers and CRM Managers…
Start with a portfolio of customers
Have access to enormous amounts of customer data
Can manage their portfolio using predictive analytics
Both are responsible for the long term value of their assets
5
7. Why is this an important discussion for us?
We can apply learnings from the Consumer Loan
Industry to manage our customer portfolios…
Spending on Marketing Analytics is expected
to increase 72% over the next 3 years*
Only 32% of marketing projects use analytics
Most companies do not have the right talent
to fully leverage Marketing Analytics
“…77% of marketers surveyed believe data on
customer purchase histories can improve
marketing ROI, yet only 21% actually use it.
Likewise, 88% believe behavioral data can do
the same, but only 20% use it”
Big Data and new tools are quickly
changing this landscape
*2014 February; The CMO Survey.org Highlights and Insights
6
9. Defining “Portfolio Management”
A portfolio of consumer loans, not securities.
• Portfolio Management, loosely, is the application of a set of
analytically-driven collections and servicing techniques aimed at
forecasting and maximizing a loan portfolio’s cash flows.
• Quantifying credit risk and predicting future payment is at the heart
of portfolio management.
• Credit Risk and CRM seemingly dominate the Big Data landscape.
Next slide…
7
10. “Big Data” infects the CRM and Credit Risk
disciplines more than almost any other…
Why the pervasive interest in Big Data?
• Largely to satisfy CRM and credit risk needs…
Data: Information Week Analytics, Business Intelligence and Information Management
Survey of 417 business technology professionals at companies using or planning to deploy
data analytics, BI or statistical analysis software, October 2012
CREDIT RISK
NEEDS
CRM NEEDS
8
11. (Re)Defining CRM
A portfolio of customers, not loans.
• Portfolio Management CRM, loosely, is the application of a set of
analytically-driven collections and servicing marketing techniques aimed
at forecasting and maximizing a loan portfolio’s customers’ cash flows
purchases.
• Quantifying credit defection risk and predicting future payment
purchases is at the heart of portfolio management CRM.
• Incidentally, the Wikipedia definition of CRM is:
• CRM is a system for managing a company’s interactions with current and future
customers. It involves using technology to organize, automate and synchronize sales,
marketing, customer service, and technical support.
9
12. Portfolio Management and CRM…
From an Analytics perspective, these are the same. The
only difference lies in the target variable and predictors.
• PD = 1 / (1+e-z), where z = A + Bx1 + Cx2 + Dx3 + …
• Portfolio Management:
• PD = Probability of DEFAULT
• x1 = credit score, x2 = days past due, x3 = loan to value ratio, etc…
• CRM:
• PD = Probability of DEFECTION
• x1 = prior purchases, x2 = months since last purchase, x3 =
unfavorable tweets, etc…
10
13. Others have recognized and leveraged the
overlap…
11
• Auto Pre-Approval
• Merchant Cash Advance and Small Business Loans
• Pier-to-Pier lending
• Student loan servicing
• Business Rules Engines
14. The Portfolio Management Paradigm
1
4
Managing a loan portfolio requires that we turn impaired (high
credit risk) loans into cash-flowing bonds…
Customer’s loan is
rewritten for empirically-derived
optimal amount
CASH FLOWS
Customer makes
partial payment
$A1
$A2
Loan impaired,
collections calls
ensue
Customer pays off
rewritten balance
Time
t=0
t=1 t=3
• The value of this “bond” (loan) is $A1/(1+i)1 + $A2/(1+i)3
• This paradigm applies equally to CRM. The portfolios managed by CRM
professionals are the customer bases of the companies they serve.
12
15. Adopting the PM paradigm for CRM…
1
5
An engaged customer is a bond. The effectiveness of our CRM
strategies determines the yield of that bond.
1
5
Customer comes
in for service
Customer visits
company website
$A1 CASH FLOWS
$A2
Customer signs up
for rewards program
Customer purchases
a new vehicle
Time
t=0
t=1 t=3
• The value of this “bond” (customer) is $A1/(1+i)1 + $A2/(1+i)3
• Customer Lifetime Value (CLV) models help quantify the value of customer
behaviors and CRM tactics. The success of CRM can be measured by the
extent to which CLV increases, irrespective of test-control results.
13
16. Marketers already recognize the need to
view their customer base as a portfolio…
Types of Data that Marketers Worldwide Would
Like to Add to Their Customer Data Profile
42%
42%
14
19%
19%
14%
12%
24%
35%
45%
53%
71%
Predictive analytics around lifetime…
Online customer profile
Customer service feedback
User survey and preference data
Social media data
Third-party demographic data
Sales executive insights
Finance / customer payment data
Order history
Analyting is better than what we…
0% 10% 20% 30% 40% 50% 60% 70% 80%
In-store / agent exchanges
(1Q2013)
Source: CMO Council and SAS
% of respondents
• 71% of marketers want
“predictive analytics around
lifetime value” added to their
customer data profiles…
• Lifetime value models are
nothing more than a forecast of
cash flows at the customer
level…
• Survival analysis, vintage-level
monitoring, and other popular
PM disciplines are a must…
17. But CRM trails Credit Risk / Portfolio
Management in the adoption of Big Data…why?
“…77% of marketers surveyed believe data on customer purchase histories can
improve marketing ROI, yet only 21% actually use it. Likewise, 88% believe
behavioral data can do the same, but only 20% use it”
15
• Regulation
• Accountability is “fuzzy”
• Metrics are inexact and not directly reflective of behavior.
• Secondary markets
• What would CRM analytics look like if marketers were
forced to buy, sell, and “value” their customer
portfolios?
• Metrics are inexact and not directly reflective of behavior.
18. What PM practices will help our CRM?
• Take a longitudinal view of the customer. This is the only way to get an
accurate outlook and valuation. Implies a need for a CLV model…
16
• CLV = p(sale at time 1)*E($ profit from sale) / (1 + d)1 +
p(sale at time 2)*E($ profit from sale) / (1 + d)2 +
p(sale at time 3)*E($ profit from sale) / (1 + d)3 + …
• Engagement is measured longitudinally; enticement is measured cross-sectionally.
• Quantify the impact of “mix shift” on outcomes of interest.
• Establish “regulatory-like” rigor around model validation.
• Understand that the two share not only a brain, but also a nervous
system. Next slide…
20. Does This Change the Way We Practice CRM?
17
We think so, especially in the following areas:
Measuring Success
Metrics should be more bottom-line oriented
and exact
Shift from basing success solely on campaign
performance to understanding performance of
the portfolio
Predicting Outcomes
Predictions should go beyond the “next
transaction”
All available data should be leveraged to
proactively manage customers throughout the
lifecycle to desired business objectives
Influencing Behavior
CRM becomes our “sand box” for going beyond
understanding just correlations; to understanding
causation as a way to change customer behavior
21. Predicting Outcomes
18
Transaction vs. Portfolio Management approach to predicting outcomes….
% In-Market
Short-Term: Optimizing campaign performance to
campaign objectives
Segment Size
Opportunity
Longer-Term: Enables management of entire
portfolio to business objectives (i.e. increasing CLV)
22. Influencing Behavior
Test and learn approach will determine how we influence and change the long term
health of our customer portfolio…
Monitor drivers across
the portfolio…
Design treatments, messaging and investment
based on customer value, individual customer
drivers and predicted outcome
Understand
Drivers of
Desired
Outcomes
Every CRM treatment should be analytically driven…ensuring that every CRM
dollar spent is working to move the customer into a more valuable state
19
23. Measuring Success
Strategic
Operational
Tactical
Portfolio Health
What is the value of my customer portfolio?
What is the mix and risk of my customer portfolio?
Performance and Forecasting
Do I understand both rear-ward and forward-looking performance?
What is the aggregate impact of our CRM initiatives on improving sales?
Dashboard and Diagnostics
Which champion vs. challenger campaign performs best?
Which actions influence customer outcomes both positive and negative?
20
24. Thus Ends the Prepared Remarks…
• Understand that the job of CRM is to extract repeat sales and revenue from the portfolio
of customers. The best way to do this is make sure that customers remain engaged over
a long period of time.
• If a customer is a bond, then improving engagement, in effect, increases the life of the bond.
21
• CRM groups should measure themselves with this standard in mind.
• Keeping customers in their “most valuable state” is a matter of advanced analytics and
strong marketing tactics…both of which are done with an eye towards engagement.
• The disciplines applied routinely to the management of loan portfolios are equally
applied to CRM. Champion / Challenger tests are simply one tool in a larger toolbox.
• Thank you for your time and attention.
25. “JUDGE A MAN BY HIS QUESTIONS
RATHER THAN HIS ANSWERS.” --
VOLTAIRE