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Slide 1 - 1 CRM and Information Visualization Gürdal Ertek, Ph.D. Tuğçe Gizem Martağan
Slide 2 - 2 Customer Relationship Management (CRM)
Slide 3 - 3 “The approach of identifying, establishing, maintaining, and enhancing lasting relationships with customers.” “The formation of bonds between a company and its customers.” What is CRM?
Slide 4 - 4 Prospecting (of first-time consumers) Loyalty Cross-selling / Up-selling Win back or Save Strategies in CRM for Mass Customization
Slide 5 - 5
Slide 6 - 6
Slide 7 - 7 The Marketing Perspective CAMPAIGN MANAGEMENT RECENCY FREQUENCY MONETARY VALUE METHOD CUSTOMER VALUE METRICS
Slide 8 - 8 Campaign Management: The Marketing Perspective Developing effective campaigns Effectively predicting the future Retaining existing customers Acquiring new customers
Slide 9 - 9 KNOW Understand market and consumers’ needs and preferences Exploit customer intelligence, Perform 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 Campaign Management: The Cap Gemini Model
Slide 10 - 10 The marketing manager... Defines objectives Identifies customers Defines communication strategies Designs/improves products/offers/services/promotions Tests the impacts of her decisions Revises her decisions for maximum effectiveness Campaign Management: The Marketing Perspective
Slide 11 - 11 Campaign Management Step 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
Slide 12 - 12 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 Campaign Management Step 2: Identify Customers
Slide 13 - 13 Campaign Management Step 3: Communication Strategies Which message should be transmitted? Which channel should be used?
Slide 14 - 14 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 Campaign Management Step 4: Design the Products, Offers, Services and Promotions
Slide 15 - 15 Campaign Management Step 5: Test the Impacts Impacts of the decisions have to be tested and and assessed on a sample
Slide 16 - 16 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
Slide 17 - 17
Slide 18 - 18
Slide 19 - 19 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
Slide 20 - 20 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? RFM Method (Recency, Frequency, Monetary Value )
Slide 21 - 21 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
Slide 22 - 22 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 RFM Method: Recency Coding
Slide 23 - 23 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. RFM Method: Frequency Coding
Slide 24 - 24 RFM Method: Frequency 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
Slide 25 - 25 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. RFM Method: Monetary Value Coding
Slide 26 - 26 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 RFM Method: Monetary Value Coding
Slide 27 - 27 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: R F M 1 2 3 4 5 21 22 23 24 25 231 232 233 234 235 Database
Slide 28 - 28 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 RFM Method: STEPS
Slide 29 - 29 Customer Value Metrics Critical measures used to define customer worth in knowledge-driven and customer-focused marketing
Slide 30 - 30 Customer Value Metrics: Size 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
Slide 31 - 31 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 % = Sales to focal customer by firm j
Slide 32 - 32 Customer Value Metrics Share of wallet and size of wallet should be analyzed together because...
Slide 33 - 33 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%
Slide 34 - 34 The Engineering Perspective DATA MINING
Slide 35 - 35 Collection, storage, and analysis of –typically huge amounts of- data Data readily resides in the company’s data warehouse Data cleaning is almost inevitable Data Mining
Slide 36 - 36 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 Data Mining
Slide 37 - 37 Data selection Data cleaning Sampling Dimensionality reduction Data mining methods Data Mining: Steps
Slide 38 - 38 Exploratory Data Analysis Segmentation Cluster Analysis Decision Trees Market Basket Analysis Association rules Information Visualization Prediction Regression Neural Network Time Series Analysis Data Mining: Methods
Slide 39 - 39 Information Visualization Data mining algorithms... Can only detect certain types of patterns and insights Are too complex for end users to understand
Slide 40 - 40 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)
Slide 41 - 41 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. Information Visualization
Slide 42 - 42 Case Studies Analysis of Supermarket Sales Data
Slide 43 - 43 The Data
Slide 44 - 44 Frequent Itemsets
Slide 45 - 45 Frequent Itemsets
Slide 46 - 46 Association Rules
Slide 47 - 47 Case Studies Analysis of Spare Parts Sales Data
Slide 48 - 48 The Data Assumption: Each customer gives at most one order each day.
Slide 49 - 49 Determining Top Products: Pivot Table for Determining REVENUE_SUM
Slide 50 - 50 Determining Top Products: Pivot Table for Determining COUNT (Frequency)
Slide 51 - 51 Determining Top Products: Scatter Plot
Slide 52 - 52 Seasonality of Top Products . . .
Slide 53 - 53 Seasonality of Top Customers: Pivot Table
Slide 54 - 54 Determining Top Customers: Pareto Curve (ABC Analysis) Revenue
Slide 55 - 55 Seasonality of Top Customers: Starfield Visualization
Slide 56 - 56 Case Studies Analysis of ÖSS 2004 Data
Slide 57 - 57 The Data
Slide 58 - 58 Y(L) L s H T Y5(H) Pareto Squares
Slide 59 - 59 Pareto Squares: Model Definitions
Slide 60 - 60 Pareto Squares: Optimization Model
Slide 61 - 61 General Insights
Slide 62 - 62 Benchmarking Highschools
Slide 63 - 63 Benchmarking Departments
Slide 64 - 64 Relationship Management
Slide 65 - 65 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. References
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