CRM and Information Visualization

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CRM and Information Visualization

• Gürdal Ertek, Ph.D.
• Tugçe Gizem Martagan

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Customer Relationship Management (CRM)
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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

• Prospecting (of first-time consumers)
• Loyalty
• Cross-selling / Up-selling
• Win back or Save

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The Marketing PerspectiveCAMPAIGN
MANAGEMENTRECENCY FREQUENCY MONETARY VALUE
METHODCUSTOMER VALUE METRICS
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Campaign Management The Marketing Perspective

• Developing effective campaigns
• Effectively predicting the future
• Retaining existing customers
• Acquiring new customers

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Campaign Management The Cap Gemini Model
KNOW Understand market and consumers needs and
preferences Exploit customer intelligence, Perfo
rm segmentation
TARGET ( Offer is developed ) Define market
strategies Use channel integration
SERVICE Retain customers by Loyalty
programs Communication Service forces
SELL Acquire customers Use sales force
effectively Develop marketing programs
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Campaign Management The Marketing Perspective

• The marketing manager...
• Defines objectives
• Identifies customers
• Defines communication strategies
• Designs/improves products/offers/services/promotio
  ns
• Tests the impacts of her decisions
• Revises her decisions for maximum effectiveness

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Campaign ManagementStep 1 Define Objectives
Targeting Existing Customers Retention Strategy
Creating Loyalty?
Increasing the satisfaction level?
Cross-selling or Up-selling?
Targeting New Customers Acquisition Strategy
Target customers that show characterstics
similar to existing groups of customers
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Campaign ManagementStep 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 ManagementStep 3 Communication
Strategies

• Which message should be transmitted?
• Which channel should be used?

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Campaign ManagementStep 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 ManagementStep 5 Test the Impacts

• Impacts of the decisions have to be tested and
  and assessed on a sample

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Campaign ManagementStep 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 MethodRecency 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 MethodRecency Coding

• Recency Results
• According to analysis based on customer recency,
  the group having the highest recency group has
  also the highest response rate
• 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
  purchasing in the past

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RFM MethodFrequency 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 MethodFrequency Coding

• Frequency Results
• 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
• That is why it is named RFM

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RFM MethodMonetary 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 MethodMonetary Value Coding

• Frequency Results
• 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

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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
R
1
2
3
4
5
F
21
22
23
24
25
M
231
232
233
234
235
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RFM MethodSTEPS

• 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
• 555s response rate gt 552s gt 543s gt541....
• Increase the response rate
• Increase profitability

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Customer Value Metrics

• Critical measures used to define customer worth
  in knowledge-driven and customer-focused
  marketing

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Customer Value MetricsSize of Wallet

• Size of wallet
• Assumption Firms prefer customers with large
  size of wallet in order to retain large revenues
  and profits

Sales to focal customer by firm j
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Customer Value MetricsIndividual 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

Sales to focal customer by firm j
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Customer Value Metrics

• Share of wallet and size of wallet should be
  analyzed together because...

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Customer Value MetricsTransition Matrix

• Shows expected share of wallet from multiple
  brands
• Depicts consumers willingness to buy over time
• Transition probability from B to A, than from A
  to C 1020 2

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The Engineering PerspectiveDATA MINING
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Data Mining

• Collection, storage, and analysis of typically
  huge amounts of- data
• Data readily resides in the companys data
  warehouse
• Data cleaning is almost inevitable

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Data Mining

• Goals of Data Mining
• 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

• Data selection
• Data cleaning
• Sampling
• Dimensionality reduction
• Data mining methods

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Data MiningMethods

• Exploratory Data Analysis
• Segmentation
• Cluster Analysis
• Decision Trees
• 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 StudiesAnalysis of Supermarket Sales Data
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The Data
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Frequent Itemsets
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Frequent Itemsets
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Association Rules
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Case StudiesAnalysis of Spare Parts Sales Data
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The Data
Assumption Each customer gives at most one order
each day.
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Determining Top ProductsPivot Table for
Determining REVENUE_SUM
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Determining Top ProductsPivot Table for
Determining COUNT (Frequency)
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Determining Top ProductsScatter Plot
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Seasonality of Top Products
. . .
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Seasonality of Top CustomersPivot Table
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Determining Top CustomersPareto Curve (ABC
Analysis)
Revenue
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Seasonality of Top CustomersStarfield
Visualization
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Case StudiesAnalysis of ÖSS 2004 Data
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The Data
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Pareto Squares
L
Y(L)
s
H
T
Y5(H)
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Pareto SquaresModel Definitions
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Pareto SquaresOptimization 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, Sabanci 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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