Transcript customer relationship management

CRM and Information
Visualization
Gürdal Ertek, Ph.D.
Tuğçe Gizem Martağan
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Customer Relationship
Management (CRM)
Traditional Marketing
CRM
Goal: Expand customer base, Goal: Establish a profitable,
increase market share by
long-term, one-to-one
mass marketing
relationship with customers;
understanding their needs,
preferences, expectations
Product oriented view
Customer oriented view
Mass marketing / mass
production
Mass customization, one-to-one
marketing
Standardization of customer
needs
Customer-supplier relationship
Transactional relationship
Relational approach
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What is CRM?
“The approach of identifying, establishing,
maintaining, and enhancing lasting relationships
with customers.”
“The formation of bonds between a company
and its customers.”
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Strategies in CRM
for Mass Customization
•
•
•
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Prospecting (of first-time consumers)
Loyalty
Cross-selling / Up-selling
Win back or Save
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5
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The Marketing Perspective
CAMPAIGN MANAGEMENT
RECENCY FREQUENCY MONETARY VALUE METHOD
CUSTOMER VALUE METRICS
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Campaign Management:
The Marketing Perspective
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•
•
•
Developing effective campaigns
Effectively predicting the future
Retaining existing customers
Acquiring new customers
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Campaign Management:
The Cap Gemini Model
KNOW
TARGET
Understand market and consumers’
needs and preferences
( Offer is developed )
Define market strategies
Exploit customer intelligence,
Use channel integration
Perform segmentation
SERVICE
SELL
Retain customers by:
Acquire customers
Loyalty programs
Communication
Service forces
Use sales force effectively
Develop marketing programs 9
Campaign Management:
The Marketing Perspective
The marketing manager...
1. Defines objectives
2. Identifies customers
3. Defines communication strategies
4. Designs/improves
products/offers/services/promotions
5. Tests the impacts of her decisions
6. Revises her decisions for maximum
effectiveness
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Campaign Management
Step 1: Define Objectives
Targeting
Existing Customers
Retention Strategy
Targeting
New Customers
Acquisition Strategy
Creating Loyalty?
Increasing the satisfaction level?
Cross-selling or Up-selling?
Target customers that show
characterstics similar to
existing groups of customers
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Campaign Management
Step 2: Identify Customers
Perform SEGMENTATION
• Define the right customers
• Use information of past transactions as key
for making predicting future ones
• Define the segments and their characteristics
• Develop customized marketing strategies for
the different segments
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Campaign Management
Step 3: Communication Strategies
• Which message should be transmitted?
• Which channel should be used?
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Campaign Management
Step 4: Design the Products, Offers,
Services and Promotions
• Analyze the price, time period, risks,
marketing costs
• Define the product / offer / service / promotion
and its general structure
• Identify effective use of sales and
communication channels
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Campaign Management
Step 5: Test the Impacts
• Impacts of the decisions have to be tested and
and assessed on a sample
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Campaign Management
Step 6: Revise the Decisions
• Make revisions to the targeted offer / service /
promotions
• Finally apply the decisions to the whole
segment or population
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RFM Method
(Recency, Frequency, Monetary Value )
• Recency
– When was the last customer interaction?
• Frequency
– How frequent was the customer in its
interactions with the business?
• Monetary value of the interactions
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RFM Method
(Recency, Frequency, Monetary Value )
Marketing Problem:
A firm has sent e-mail to 30,000 of its existing
customers, announcing a promotion of $100.
458 of them responded (1.52% of the
customers)
Is there any relation between the responding
customers and their historical purchasing
behaviours?
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RFM Method:
Recency Coding
• 30,000 customers are sorted in descending
order with respect to their most recent
purchases
• Sorted data is divided into 5 equal groups,
each of them containing 6,000 people
• Recency codes are assigned: Top group has
code 5, bottom group has code 1
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RFM Method:
Recency Coding
Response %
Recency Results
• According to analysis based
on customer recency, the
group having the highest
recency group has also the
highest response rate
4.00
3.1
3.00
2
2.00
1.5
1.00
0.62
0.38
0.00
5
4
3
2
1
Recency code R
• Remark:
(3.10% + 2.00% + 1.50% +
0.62% + 0.38) / 5= 1,52% which
is the response rate
• Strict Rule: Ones who have
purchased recently are much
more willing to buy new
products than others
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purchasing in the past
RFM Method:
Frequency Coding
• Sort the 30,000 customers with respect to
frequency metrics.
– Frequency metrics: Average number of
purchases made by customer in a time period t
– Sort customers in descending order with
respect to their purchase frequency.
• Assign them to 5 groups, top %20 in the first
frequency group.
• Assign frequency codes such that the top
group has code 5 and the bottom group has
code 1.
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RFM Method:
Frequency Coding
Frequency Results
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2.8
Response %
2.5
2.1
2
1.5
1.3
1
0.8
0.9
0.5
0
5
4
3
2
Frequency code F
1
• It is observed that highest
response rate is from the
customers having highest
frequency
• Frequent people respond
better than less frequent
ones but differences
between groups are less
than the ones in the
recency
• The lowest frequency group
always contains new
customers
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• That is why it is named RFM
RFM Method:
Monetary Value Coding
• The same process as recency and frequency
coding
• Sorting is done with respect to monetary
value metric
– Monetary value metric is the average amount
purchased in a time period t
• At the end of the monetary value coding,
assign monetary value codes M = 1,...,5 to
groups according to their groups.
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RFM Method:
Monetary Value Coding
Frequency Results
2.5
Response %
2.1
2
1.8
1.4
1.5
1.2
1.1
1
0.5
0
5
4
3
2
1
• It is observed that highest
response rate is from the
customers having highest
monetary value
• Unlike the recency case,
there are not big
differences between
groups
Monetary value code M
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RFM Method:
Putting the Codes Together
• At the end of the monetary coding firm
obtain R F M metrics for customers. Each
customer belongs to one of 125 possible
combinations of the RFM values:
Database
1
2
3
4
R
5
21
22
23
24
231
232
233
234
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235
F
M
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RFM Method:
STEPS
• Create 3 digits RFM codes cells
• All cells having the same number of
customers in them
• RFM values are used to define group of
customers that marketing campaign should
target or should avoid
• Used for identifying customers having high
probability to respond to campaigns:
555’s response rate > 552’s > 543’s >541....
• Increase the response rate
• Increase profitability
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Customer Value Metrics
• Critical measures used to define customer
worth in knowledge-driven and customerfocused marketing
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Customer Value Metrics:
Size of Wallet
J
• Size of wallet =
Sj 
S
j 1
j
Sales to focal customer by firm j
• Assumption: Firms prefer customers with
large size of wallet in order to retain large
revenues and profits
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Customer Value Metrics:
Individual Share of Wallet (SW)
• A proportion expressed in terms of percentage,
calculated among buyers
• Measured at individual level
• A measure of loyalty
• Can be used in future predictions
• Different from the “market share”, which also
considers customers with no purchase
• Individual share of wallet % =
Sj 
Sales to focal customer by firm j
Sj
J
S
j 1
j
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Customer Value Metrics
• Share of wallet and size of wallet should be
analyzed together because...
Size of
Wallet
Customer 1 $500
Share of
Wallet
50%
Purchases
Customer 2 $100
50%
$50
$250
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Customer Value Metrics:
Transition Matrix
• Shows expected share of wallet from
multiple brands
• Depicts consumer’s willingness to buy over
time
• Transition probability from B to A, than from
A to C: 10%*20% = 2%
Brand A
Brand B
Brand C
Brand A
60%
30%
20%
Brand B
10%
80%
15%
Brand C
20%
15%
70%
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The Engineering Perspective
DATA MINING
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Data Mining
• Collection, storage, and analysis of –typically
huge amounts of- data
• Data readily resides in the company’s data
warehouse
• Data cleaning is almost inevitable
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Data Mining
Goals of Data Mining
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Developing deeper understanding of the data
Discovering hidden patterns
Coming up with actionable insights
Identifying relations between variables,
inputs and outputs
• Predicting future patterns
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Data Mining:
Steps
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Data selection
Data cleaning
Sampling
Dimensionality reduction
Data mining methods
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Data Mining:
Methods
• Exploratory Data Analysis
• Segmentation
– Cluster Analysis
– Decision Trees
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Market Basket Analysis
Association rules
Information Visualization
Prediction
– Regression
– Neural Network
– Time Series Analysis
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Information Visualization
Data mining algorithms...
• Can only detect certain types of
patterns and insights
• Are too complex for end users to
understand
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Information Visualization
• A field of Computer Science which has
evolved since the 1990s.
• Before 1990s: Graphical methods for data
analysis to pave the way for statistical
methods
• After 1990s:
– Computer hardware has advanced with
respect to memory, computational
power, graphics calculations
– Software has advanced with respect to
user interfaces
– Data collection systems have advanced
(barcodes, RFID, ERP)
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Information Visualization
• The analyst does not have to
understand complex
algorithms.
• Almost no training required.
• There are no limits to the
types of insights that can be
discovered.
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Case Studies
Analysis of Supermarket
Sales Data
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The Data
Field Name
Desciption
TRANSACTION_ID
Transaction ID
PRODUCT_NO
Product Number
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Frequent Itemsets
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Frequent Itemsets
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Association Rules
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Case Studies
Analysis of Spare Parts
Sales Data
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The Data
Field Name
Desciption
DEPOT
Depot ID
SKU_NO
SKU (Stock Keeping Unit) Number
VENDOR
Vendor (Customer) Number
DAY
Day of the month (1,...,31)
MONTH
Month of the year (1,...,12)
YEAR
Year (ex: 2002)
QUANTITY
Quantity required
UNIT_PRICE
Price of one unit of product in YTL*
REVENUE
Revenue from the order line
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Assumption: Each customer gives at most one order each day.
Determining Top Products:
Pivot Table for Determining REVENUE_SUM
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Determining Top Products:
Pivot Table for Determining COUNT (Frequency)
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Determining Top Products:
Scatter Plot
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Seasonality of Top Products
...
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Seasonality of Top Customers:
Pivot Table
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Cumulative % Revenue
Determining Top Customers:
Pareto Curve (ABC Analysis)
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90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
100
Cumulative % Customers
Revenue
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Seasonality of Top Customers:
Starfield Visualization
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Case Studies
Analysis of ÖSS 2004 Data
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The Data
Field Name
Desciption
HS_NAME
High School Name
HS_TYPE_TEXT
High School Type
UNIV_NAME
University Name
UNIV_DEPT
University Department
RANK_SAY
Rank According to Sayısal
Score
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Pareto Squares
L
Y(L)
H
s
T
Y5(H)
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Pareto Squares:
Model Definitions
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Pareto Squares:
Optimization Model
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General Insights
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Benchmarking Highschools
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Benchmarking Departments
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Relationship Management
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References
• Berry, M. J. A., Linoff, G. S. (2004) Data Mining
Techniques. Wiley Publishing.
• Ertek, G. Visual Data Mining with Pareto Squares for
Customer Relationship Management (CRM) (working
paper, Sabancı University, Istanbul, Turkey)
• Ertek, G., Demiriz, A. A framework for visualizing
association mining results (accepted for LNCS)
• Hughes, A. M. Quick profits with RFM analysis.
http://www.dbmarketing.com/articles/Art149.htm
• Kumar, V., Reinartz, W. J. (2006) Customer Relationship
Management, A Databased Approach. John Wiley & Sons
Inc.
• Spence, R. (2001) Information Visualization. ACM Press.
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