1. McGraw-Hill/Irwin McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Chapter 4 Marketing Intelligence and Database Research

3. 1. Understand the essential elements of market intelligence designs. 2. Explain the development of customer databases. 3. Discuss the elements of enhanced marketing databases. 4. Explain the role of data mining in marketing research. 5. Understand modeling in database analysis. Learning Objectives 4-3

4. Consumer Databases and Samsonite Market planning SalesSales CustomerSatisfactionCustomerSatisfaction 4-4

5. 1. What kind of relationships will add value to customers (e.g., loyalty programs, preferred customer status, etc.)? 2. What is the value perception of the customer segment, and how can the value be enhanced (e.g., direct communication to customers, new services, etc.)? 3. What products and services and mode of delivery have value to the customer segment (e.g., stock market alerts via Web-enabled mobile phones)? 4. What are customers’ responses to marketing and sales campaigns? Questions – Market Intelligence 4-5

6. 1.Develop meaningful communication with customers. 2.Improve efficiency of market segmentation. 3.Increase probability of repeat purchase behavior. 4.Enhance sales and media effectiveness. Customer Database – Purposes 4-6

7. How products compare with the competition? How products compare with the competition? Relationship between perceived value and price of the product? Relationship between perceived value and price of the product? How satisfied customers are with the service level and support for the product? How satisfied customers are with the service level and support for the product? What are the comparisons among lifestyles, demographics, attitudes, and media habits among heavy, medium, and light users of the product? What are the comparisons among lifestyles, demographics, attitudes, and media habits among heavy, medium, and light users of the product? Other Questions Answered from Databases 4-7

8. Database Information ProfitabilityProfitability RecencyRecency FrequencyFrequency AffinityAffinity CustomerCharacteristicsCustomerCharacteristics 4-8

9. View the total process of database development as a commitment to a long-term data acquisition plan. View the total process of database development as a commitment to a long-term data acquisition plan. View the data acquisition process in terms of the depth and width of the database. View the data acquisition process in terms of the depth and width of the database. Avoid jumping onto the database bandwagon and then failing to commit the necessary resources. Avoid jumping onto the database bandwagon and then failing to commit the necessary resources. Rules of Thumb – Database Development 4-9

10. Databases... a collection of information indicating what customers are purchasing, how often they purchase, and the amount they purchase.... a collection of information indicating what customers are purchasing, how often they purchase, and the amount they purchase. Databases are generated by... Databases are generated by... 4-10

11. Customer Database Enable users to measure, track and analyze customer behaviors Improve the efficiency of market segmentation General purpose – to develop meaningful, personal communications with customers. Specific purposes... General purpose – to develop meaningful, personal communications with customers. Specific purposes... Increase the probability of repeat purchases Enhance sales and media effectiveness 4-11

12. More knowledge of customers Increased effectiveness of marketing programs Increased effectiveness of marketing programs Predicting responses to changing marketing programs Predicting responses to changing marketing programs Data Enhancement 4-12

13. Interactive Components of a Database 4-13

14. GeodemographicGeodemographicAttributeAttribute Target Market Databases – Three Types of Data Units 4-14

15. Databases Building segment profiles Determining lifetime customer value Determining heavy users Exchanging information with customers BenefitsBenefits 4-15

16. Marketing databases answer crucial questions... Marketing databases answer crucial questions... Why do some customers buy our products or services regularly, while others do not? Why do some customers buy our products or services regularly, while others do not? How do our products compare with the competition? How do our products compare with the competition? What is the relationship between perceived value and price of the product? What is the relationship between perceived value and price of the product? How satisfied are customers with the service level and support for the product? How satisfied are customers with the service level and support for the product? What are the comparisons among lifestyles, demographics, attitudes, and media habits? What are the comparisons among lifestyles, demographics, attitudes, and media habits? 4-16

17. Typical Output of a Database Management System 4-17

18. Database Processing Systems Two Types SequentialSequentialRelationalRelational 4-18

19. Typical Output of a Database Management System 4-19

20. An Example of the Data Pattern of a Sequential Database 4-20

21. A Relational Database That Shows a Customer Profile by Industry Segment 4-21

22. oCentral repository of data. oTwo Purposes: Collect and store data Collect and store data Operational Data Operational Data Online Transactional Processing (PLTP) Online Transactional Processing (PLTP) Collect, organize and make data available Collect, organize and make data available Informational Data Informational Data Online Analytical Processing (OLAP) Online Analytical Processing (OLAP) oComparable to a library. oCentral repository of data. oTwo Purposes: Collect and store data Collect and store data Operational Data Operational Data Online Transactional Processing (PLTP) Online Transactional Processing (PLTP) Collect, organize and make data available Collect, organize and make data available Informational Data Informational Data Online Analytical Processing (OLAP) Online Analytical Processing (OLAP) oComparable to a library. Data Warehouse 4-22

23. Data Warehouses Types of Data Secondary data Primary data Real-time transactional data Customer-volunteered data 4-23

24. Marketing-Related Data and Data Warehousing Marketing-Related Data and Data Warehousing Two Unique Forms of Customer Data Two Unique Forms of Customer Data Real-time transactional data Real-time transactional data Collected at the point of sale Collected at the point of sale Customer-volunteered information Customer-volunteered information Customer comment cards or complaints Customer comment cards or complaints Customer registration information Customer registration information Customer communications via chat rooms Customer communications via chat rooms Data obtained through advisory groups Data obtained through advisory groups Database Technology and Data Warehousing 4-24

25. Data Mining Analysis procedure – identifies significant patterns of data relationship for specific customers or customer groups. Data Mining – process of finding hidden relationships among variables contained in data stored in the data warehouse. Data Mining – process of finding hidden relationships among variables contained in data stored in the data warehouse. Transforming Data Into Knowledge 4-25

26. Data Mining Framework for Marketing Decisions 4-26

27. Data Mining Process oMarketing Research Question Description Description Prediction Prediction oMarketing Research Question Description Description Prediction Prediction oData Mining Approaches Profile groups Profile groups Predict customer satisfaction Predict customer satisfaction oData Mining Approaches Profile groups Profile groups Predict customer satisfaction Predict customer satisfaction 4-27

28. Data Mining Implementation Data Mining Implementation Identifies method for storage and categorization of data Identifies method for storage and categorization of data Data mining approaches Data mining approaches Visual Data Mining Visual Data Mining Presentation of the results Presentation of the results Access and comprehension of the analysis results Access and comprehension of the analysis results Database Technology and Data Warehousing 4-28

29. Database Modeling and Analysis Database Modeling and Analysis Statistical Analysis Statistical Analysis Designed to Summarize Designed to Summarize Customer Modeling Customer Modeling Questions that should be asked Questions that should be asked Scoring Models Scoring Models Used to predict Used to predict Initial objectives Initial objectives Database Modeling 4-29

30. Example of the Data Output from a Gains Table 4-30

31. Scoring Models Enable researchers to determine which factors separate customers into purchase groups... Enable researchers to determine which factors separate customers into purchase groups... Use weights to multiply assigned values Use weights to multiply assigned values Use actual purchase behavior data Use actual purchase behavior data Key variables Key variables  Assign weights or scores depending on ability to predict purchase behavior Scoring Models Enable researchers to determine which factors separate customers into purchase groups... Enable researchers to determine which factors separate customers into purchase groups... Use weights to multiply assigned values Use weights to multiply assigned values Use actual purchase behavior data Use actual purchase behavior data Key variables Key variables  Assign weights or scores depending on ability to predict purchase behavior Database Modeling 4-31

32. Lifetime Value Models... Premise – need to determine value of customers to your company Premise – need to determine value of customers to your company Lifetime value models – examples of variables... Lifetime value models – examples of variables... Price variables Price variables Sales promotional variables Sales promotional variables Advertising expenditures Advertising expenditures Product costs Product costs Relationship-building efforts Relationship-building efforts Database Information Database Information Used to identify most profitable customers Used to identify most profitable customers 4-32

33. A Hypothetical Lifetime Value Model for a Fast-Food Restaurant 4-33