This document discusses data mining techniques for customer relationship management (CRM). It defines data mining as the extraction of implicit and novel knowledge from large datasets. The document outlines common data mining applications in retail, banking, telecommunications and other industries. It then discusses how data mining can be used across the customer lifecycle in CRM, such as for up-selling, cross-selling and customer retention. Finally, it briefly introduces common data mining techniques like clustering, association rule mining, classification, regression, and decision trees.
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
There are two basic types of decision tree analysis: Classification and Regression, Classification Trees are used when the target variable is categorical and used to classify/divide data into these predefined categories. Regression Trees are used when the target variable is numeric. Decision Tree analysis is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.
This presentation was given on October 12, 2013 at the Marketing EDGE Jacobs and Clevenger Casewriter's competition, where it received a Silver Award. The case outlines how to teach descriptive analytics, profiling and clustering for a fictional company.
Project for System Analysis and Design (IS-6410).
By performing customer segmentation following are the three objectives which can be achieved
with the implementation of this new analytics system:
1. We can track the difference between loyal customers vs visitors, perform heat map
analysis of their browsing patterns.
2. Understanding customer demographics and to focus on high profitable segments.
3. Finally empowering our Marketing department to make better strategic decisions in
terms of online Ads/campaigns.
Sales Performance Deep Dive and Forecast: A ML Driven Analytics SolutionPranov Mishra
Problem Statement
One of Unilever’s brands is going through a steep decline in revenues and is requiring major changes in business execution plans. The management is expecting a thorough analysis of historical performances culminating in identification of key factors driving sales.
Data Summary and Product Life Cycle Overview
The data provided constituted more than 30 years of information of sales and related variables.
The training data suggested that the product has gone through a life-cycle of launch, growth and maturity. There were indications of a decline phase in the last few periods of training data.
The test data corroborated the indications as we could notice sharp decline (more than 25%) since 2016.
Key Insights & Driver Analysis
The factors having a significant positive impact on sales volumes were identified to be promotion expenditure, volumes produced or in stock, inflation, rainfall and visibility through social search impressions.
The factors having a significant negative impact on sales volumes were identified to be brand equity, competitor prices, fuel price and digital impressions
Forecasting
Multiple approaches were attempted including ARIMA, Holt Winter’s Double Exponential Smoothing, Bayesian approach(BSTS) and LSTM
The best results were achieved when training data was combined with 2 years of test data to capture the decline phases. MAPE of 25% achieved with Holt Winter followed by ARIMA with a mape of 33%.
For the second problem statement that required training on test data only, best results were achieved through the bsts model followed by LSTM. Mapes of 5% and 13% respectively were achieved.
Part I: Predictive models (Decision Tree and Regression) using SAS Enterprise Miner
Part II: Decision Tree using R.
Part III: Market-Basket Analysis using SAS miner.
Workshop on "Data Management - The Foundation of all Analytics" given by John Aidoo, Data Analytics Manager at Central Insurance Company, Van Wert, Ohio.
High level overview of Predictive Analytics techniques - Decision Trees, Regressions, Time Series Forecasting, Exponential Smoothing, etc.
Was put together to train friends and mentees. Based on personal learnings/research and no proprietary info, etc. and no claims on 100% accuracy. Also every institution/organization/team uses it own steps/methodologies, so please use the one relevant for you and this only for training purposes.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Reduction in customer complaints - Mortgage IndustryPranov Mishra
The project aims at analysis of Customer Complaints/Inquiries received by a US based mortgage (loan) servicing company..
The goal of the project is building a predictive model using the identified significant
contributors and coming up with recommendations for changes which will lead to
1. Reducing Re-work
2. Reducing Operational Cost
3. Improve Customer Satisfaction
4. Improve company preparedness to respond to customer.
Three models were built - Logistic Regression, Random Forest and Gradient Boosting. It was seen that the accuracy, auc (Area under the curve), sensitivity and specificity improved drastically as the model complexity increased from simple to complex.
Logistic regression was not generalizing well to a non-linear data. So the model was suffering from both bias and variance. Random Forest is an ensemble technique in itself and helps with reducing variance to a great extent. Gradient Boosting, with its sequential learning ability, helps reduce the bias. The results from both random forest and gradient boosting did not differ by much. This is confirming the bias-variance trade-off concept which states that complex models will do well on non-linear data as the inflexible simple models will have high bias and can have high variance.
Additionally, a lift chart was built which gives a Cumulative lift of 133% in the first four deciles
There are two basic types of decision tree analysis: Classification and Regression, Classification Trees are used when the target variable is categorical and used to classify/divide data into these predefined categories. Regression Trees are used when the target variable is numeric. Decision Tree analysis is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.
This presentation was given on October 12, 2013 at the Marketing EDGE Jacobs and Clevenger Casewriter's competition, where it received a Silver Award. The case outlines how to teach descriptive analytics, profiling and clustering for a fictional company.
Project for System Analysis and Design (IS-6410).
By performing customer segmentation following are the three objectives which can be achieved
with the implementation of this new analytics system:
1. We can track the difference between loyal customers vs visitors, perform heat map
analysis of their browsing patterns.
2. Understanding customer demographics and to focus on high profitable segments.
3. Finally empowering our Marketing department to make better strategic decisions in
terms of online Ads/campaigns.
Sales Performance Deep Dive and Forecast: A ML Driven Analytics SolutionPranov Mishra
Problem Statement
One of Unilever’s brands is going through a steep decline in revenues and is requiring major changes in business execution plans. The management is expecting a thorough analysis of historical performances culminating in identification of key factors driving sales.
Data Summary and Product Life Cycle Overview
The data provided constituted more than 30 years of information of sales and related variables.
The training data suggested that the product has gone through a life-cycle of launch, growth and maturity. There were indications of a decline phase in the last few periods of training data.
The test data corroborated the indications as we could notice sharp decline (more than 25%) since 2016.
Key Insights & Driver Analysis
The factors having a significant positive impact on sales volumes were identified to be promotion expenditure, volumes produced or in stock, inflation, rainfall and visibility through social search impressions.
The factors having a significant negative impact on sales volumes were identified to be brand equity, competitor prices, fuel price and digital impressions
Forecasting
Multiple approaches were attempted including ARIMA, Holt Winter’s Double Exponential Smoothing, Bayesian approach(BSTS) and LSTM
The best results were achieved when training data was combined with 2 years of test data to capture the decline phases. MAPE of 25% achieved with Holt Winter followed by ARIMA with a mape of 33%.
For the second problem statement that required training on test data only, best results were achieved through the bsts model followed by LSTM. Mapes of 5% and 13% respectively were achieved.
Part I: Predictive models (Decision Tree and Regression) using SAS Enterprise Miner
Part II: Decision Tree using R.
Part III: Market-Basket Analysis using SAS miner.
Workshop on "Data Management - The Foundation of all Analytics" given by John Aidoo, Data Analytics Manager at Central Insurance Company, Van Wert, Ohio.
High level overview of Predictive Analytics techniques - Decision Trees, Regressions, Time Series Forecasting, Exponential Smoothing, etc.
Was put together to train friends and mentees. Based on personal learnings/research and no proprietary info, etc. and no claims on 100% accuracy. Also every institution/organization/team uses it own steps/methodologies, so please use the one relevant for you and this only for training purposes.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Data Mining Concepts with Customer Relationship ManagementIJERA Editor
Data mining is important in creating a great experience at e-business. Data mining is the systematic way of extracting information from data. Many of the companies are developing an online internet presence to sell or promote their products and services. Most of the internet users are aware of on-line shopping concepts and techniques to own a product. The e-commerce landscape is the relation between customer relationship management (sales, marketing & support), internet and suppliers.
Consumer Behavior project. Examine and define best ways for Consumer Research Company (Equitec) to target and reach new customers, along with suggesting new ways for the company to market itself.
What is data mining?
Why data mining is required?
Data mining Applications
Data mining in Retail Industry
Marketing
Risk Management
Fraud Detection
Customer Acquisition and Retention
Penser Consulting answers the key questions:
- What is big data, and why does it matter?
- How can big data drive business decisions?
- How can you build data analytics capabilities in your organisation?
Developing a customer data platform to provide omnichannel customer visibility for a retailer serving +100M households.
The Global Customer Insight team for one of the world's largest retailers, serving over 100M households, wanted to create a unified customer data platform to provide complete visibility across their customer's omnichannel touchpoints. Historically, the retailer had less than 50% visibility to their customer's omnichannel engagement. As a result, their analysis and data
scientists relied on data from multiple sources and legacy technology platforms to generate customer insights for stakeholders, resulting in reduced productivity, multi-day run-times, and incomplete insights
Learn more: https://www.tredence.com/services/customer-analytics
1. Data Mining
Techniques for CRM
Seyyed Jamaleddin Pishvayi
Customer Relationship Management
Instructor : Dr. Taghiyare
Tehran University
Spring 1383
2. 2
Outlines
What is Data Mining?
Data Mining Motivation
Data Mining Applications
Applications of Data Mining in CRM
Data Mining Taxonomy
Data Mining Techniques
3. 3
Data Mining
The non-trivial extraction of novel, implicit, and actionable
knowledge from large datasets.
Extremely large datasets
Discovery of the non-obvious
Useful knowledge that can improve processes
Can not be done manually
Technology to enable data exploration, data analysis, and data
visualization of very large databases at a high level of
abstraction, without a specific hypothesis in mind.
Sophisticated data search capability that uses statistical
algorithms to discover patterns and correlations in data.
5. 5
Data Mining (cont.)
Data Mining is a step of Knowledge Discovery in
Databases (KDD) Process
Data Warehousing
Data Selection
Data Preprocessing
Data Transformation
Data Mining
Interpretation/Evaluation
Data Mining is sometimes referred to as KDD and
DM and KDD tend to be used as synonyms
7. 7
Data Mining is Not …
Data warehousing
SQL / Ad Hoc Queries / Reporting
Software Agents
Online Analytical Processing (OLAP)
Data Visualization
8. 8
Data Mining Motivation
Changes in the Business Environment
Customers becoming more demanding
Markets are saturated
Databases today are huge:
More than 1,000,000 entities/records/rows
From 10 to 10,000 fields/attributes/variables
Gigabytes and terabytes
Databases a growing at an unprecedented rate
Decisions must be made rapidly
Decisions must be made with maximum knowledge
9. 9
“The key in business is to know something that
nobody else knows.”
— Aristotle Onassis
“To understand is to perceive patterns.”
— Sir Isaiah Berlin
PHOTO:LUCINDADOUGLAS-MENZIES
PHOTO: HULTON-DEUTSCH COLL
Data Mining Motivation
11. 11
Data Mining Applications:
Retail
Performing basket analysis
Which items customers tend to purchase together. This
knowledge can improve stocking, store layout strategies, and
promotions.
Sales forecasting
Examining time-based patterns helps retailers make stocking
decisions. If a customer purchases an item today, when are they
likely to purchase a complementary item?
Database marketing
Retailers can develop profiles of customers with certain
behaviors, for example, those who purchase designer labels
clothing or those who attend sales. This information can be used
to focus cost–effective promotions.
Merchandise planning and allocation
When retailers add new stores, they can improve merchandise
planning and allocation by examining patterns in stores with
similar demographic characteristics. Retailers can also use data
mining to determine the ideal layout for a specific store.
12. 12
Data Mining Applications:
Banking
Card marketing
By identifying customer segments, card issuers and acquirers
can improve profitability with more effective acquisition and
retention programs, targeted product development, and
customized pricing.
Cardholder pricing and profitability
Card issuers can take advantage of data mining technology to
price their products so as to maximize profit and minimize loss of
customers. Includes risk-based pricing.
Fraud detection
Fraud is enormously costly. By analyzing past transactions that
were later determined to be fraudulent, banks can identify
patterns.
Predictive life-cycle management
DM helps banks predict each customer’s lifetime value and to
service each segment appropriately (for example, offering
special deals and discounts).
13. 13
Data Mining Applications:
Telecommunication
Call detail record analysis
Telecommunication companies accumulate detailed call
records. By identifying customer segments with similar use
patterns, the companies can develop attractive pricing and
feature promotions.
Customer loyalty
Some customers repeatedly switch providers, or “churn”, to
take advantage of attractive incentives by competing
companies. The companies can use DM to identify the
characteristics of customers who are likely to remain loyal
once they switch, thus enabling the companies to target
their spending on customers who will produce the most
profit.
14. 14
Data Mining Applications:
Other Applications
Customer segmentation
All industries can take advantage of DM to discover discrete
segments in their customer bases by considering additional
variables beyond traditional analysis.
Manufacturing
Through choice boards, manufacturers are beginning to
customize products for customers; therefore they must be able to
predict which features should be bundled to meet customer
demand.
Warranties
Manufacturers need to predict the number of customers who will
submit warranty claims and the average cost of those claims.
Frequent flier incentives
Airlines can identify groups of customers that can be given
incentives to fly more.
15. 15
Data Mining in CRM:
Customer Life Cycle
Customer Life Cycle
The stages in the relationship between a customer and a
business
Key stages in the customer lifecycle
Prospects: people who are not yet customers but are in
the target market
Responders: prospects who show an interest in a product
or service
Active Customers: people who are currently using the
product or service
Former Customers: may be “bad” customers who did not
pay their bills or who incurred high costs
It’s important to know life cycle events (e.g.
retirement)
16. 16
Data Mining in CRM:
Customer Life Cycle
What marketers want: Increasing customer
revenue and customer profitability
Up-sell
Cross-sell
Keeping the customers for a longer period of time
Solution: Applying data mining
17. 17
Data Mining in CRM
DM helps to
Determine the behavior surrounding a particular
lifecycle event
Find other people in similar life stages and
determine which customers are following similar
behavior patterns
18. 18
Data Mining in CRM (cont.)
Data Warehouse Data Mining
Campaign Management
Customer Profile
Customer Life Cycle Info.
19. 19
Data Mining in CRM:
More
Building Data Mining Applications for CRM
by Alex Berson, Stephen Smith, Kurt
Thearling (McGraw Hill, 2000).
20. 20
Data Mining Techniques
Data Mining Techniques
Descriptive Predictive
Clustering
Association
Classification
Regression
Sequential Analysis
Decision Tree
Rule Induction
Neural Networks
Nearest Neighbor Classification
21. 21
Two Good Algorithm Books
Intelligent Data
Analysis: An
Introduction
by Berthold and Hand
The Elements of
Statistical Learning:
Data Mining, Inference,
and Prediction
by Hastie, Tibshirani, and
Friedman
24. 24
Decision Trees
Data
height hair eyes class
short blond blue A
tall blond brown B
tall red blue A
short dark blue B
tall dark blue B
tall blond blue A
tall dark brown B
short blond brown B
25. 25
Decision Trees (cont.)
hair
dark
red
blond
short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A} short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
Completely classifies dark-haired
and red-haired people
Does not completely classify
blonde-haired people.
More work is required
26. 26
Decision Trees (cont.)
hair
dark
red
blond
short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A} short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
eye
blue brown
short = A
tall = A
tall = B
short = B
Decision tree is complete because
1. All 8 cases appear at nodes
2. At each node, all cases are in
the same class (A or B)
28. 28
Decision Trees:
Another Example
Total list
50% member
0-1 child 2-3 child
20% member
4+ children
$50-75k income
15% member
$75k+ income
70% member
$50-75k income $20-50k income
85% member
Age: 40-60
80% member
Age: 20-40
45% member
29. 29
Rule Induction
Try to find rules of the form
IF <left-hand-side> THEN <right-hand-side>
This is the reverse of a rule-based agent, where the rules are
given and the agent must act. Here the actions are given
and we have to discover the rules!
Prevalence = probability that LHS and RHS
occur together (sometimes called “support factor,”
“leverage” or “lift”)
Predictability = probability of RHS given LHS
(sometimes called “confidence” or “strength”)
31. 31
Use of Rule Associations
Coupons, discounts
Don’t give discounts on 2 items that are frequently bought
together. Use the discount on 1 to “pull” the other
Product placement
Offer correlated products to the customer at the same time.
Increases sales
Timing of cross-marketing
Send camcorder offer to VCR purchasers 2-3 months after
VCR purchase
Discovery of patterns
People who bought X, Y and Z (but not any pair) bought W
over half the time
32. 32
Finding Rule Associations
Algorithm
Example: grocery shopping
For each item, count # of occurrences (say out of 100,000)
apples 1891, caviar 3, ice cream 1088, …
Drop the ones that are below a minimum support level
apples 1891, ice cream 1088, pet food 2451, …
Make a table of each item against each other item:
Discard cells below support threshold. Now make a cube for triples,
etc. Add 1 dimension for each product on LHS.
apples ice cream pet food
apples 1891 685 24
ice cream ----- 1088 322
pet food ----- ----- 2451
33. 33
Clustering
The art of finding groups in data
Objective: gather items from a database into
sets according to (unknown) common
characteristics
Much more difficult than classification since
the classes are not known in advance (no
training)
Technique: unsupervised learning
34. 34
The K-Means Clustering
Method
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10
K=2
Arbitrarily choose K
objects as initial
cluster center
Assign
each of
the
objects
to most
similar
center
Update
the
cluster
means
Update
the
cluster
means
reassignreassign