Ertek martagan imis2006 ppt

CRM and Information Visualization Gürdal Ertek, Ph.D. Tuğçe Gizem Martağan 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 long­term, one­to­one mass marketing relationship with customers; understanding their needs, preferences, expectations Product oriented view

Customer oriented view

Mass marketing / mass production

Mass customization, one­to­one marketing

Standardization of customer needs

Customer­supplier 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 • • • •

Prospecting (of first­time consumers) Loyalty Cross­selling / Up­selling Win back or Save

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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 • • • •

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 10

Campaign Management Step 1: Define Objectives Targeting Existing Customers Retention Strategy

Targeting New Customers Acquisition Strategy

Creating Loyalty?

Increasing the satisfaction level?

Cross­selling or Up­selling?

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 e­mail 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 21

RFM Method: Recency Coding Response %

Recency Results 4.00

• According to analysis based on customer recency, the group having the highest recency group has also the highest response rate

3.1

3.00 2

2.00

1.5

1.00

• Remark: (3.10% + 2.00% + 1.50% + 0.62% + 0.38) / 5= 1,52% which is the response rate 0.38

0.62

0.00 5

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3

2

1

Recency code R

• Strict Rule: Ones who have purchased recently are much more willing to buy new products than others 22 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 3

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 24 • 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

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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 26

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

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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 knowledge­driven and customer­ focused marketing

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Customer Value Metrics: Size of Wallet J

• Size of wallet = å S j j = 1

S 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 % =

S j J

å S

j

S j = Sales to focal customer by firm j

j = 1

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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 Purchases Wallet 50% $250

Customer 2 $100

50%

$50

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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 A 60% Brand B 10%

Brand B Brand C 30% 20% 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 • • • •

Developing deeper understanding of the data Discovering hidden patterns Coming up with actionable insights Identifying relations between variables, inputs and outputs • Predicting future patterns 36

Data Mining: Steps • • • • •

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

• • • •

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 Frequent Itemsets

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Frequent Itemsets Frequent Itemsets

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Association Rules 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 48

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) 100 90 80 70 60 50 40 30 20 10 0 0

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30

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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 57

Pareto Squares L Y (L)

H

s T

Y 5 (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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