E-Marketing 4/E Judy Strauss, Adel I. El-Ansary, and Raymond Frost

1. E-Marketing 4/EJudy Strauss, Adel I. El-Ansary, and Raymond Frost Chapter 6: Marketing Knowledge 6-1

2. Question??? • Define the following • Data • Information • Knowledge • Understanding • Wisdom

3. Chapter 6 Objectives • After reading Chapter 6 you will be able to: • Identify the three main sources of data that e-marketers use to address research problems. • Discuss how and why e-marketers need to check the quality of research data gathered online. • Explain why the Internet is used as a contact method for primary research and describe the main Internet-based approaches to primary research. • Contrast client-side, server-side, and real-space approaches to data collection. • Highlight four important methods of analysis that e-marketers can apply to data warehouse information. 6-2

4. The Purina Story • Nestle Purina PetCare Company wanted to know whether their web sites and online advertising increased off-line behavior. • Nestle developed 3 research questions: • Are our buyers using our branded Web sites? • Should we invest in other Web sites? • If so, where should we place the advertising? 6-3

5. The Purina Story, cont. • They combined online and off-line shopping panel data and found that: • Banner clickthrough was low (0.06%). • 31% of subjects who were exposed to both online and off-line advertising mentioned Purina. • The high exposure group mentioned Purina more than the low exposure group. • Home/health and living sites received the most visits from their customers. • Would you also have selected petsmart.com and about.com for Purina PetCare ads? 6-4

6. Data Drives Strategy • Organizations are drowning in data. • Marketing insight occurs somewhere between information and knowledge. • Purina, for example, sorts through hundreds of millions of pieces of data about 21.5 million consumers to make decisions. 6-5

7. Data Drives Strategy • Current problem for marketing decision makers = Information overload. • Origin of data: • Survey results, product sales information, secondary data about competitors, and much more • Automated data gathering at Web sites, brick-and-mortar points of purchase, and all other customer touch points.

8. Data Drives Strategy • What to do with all the data? • Purina marketers built a roadmap for their Internet advertising strategy: • Data are collected from a myriad of sources, • Filtered into databases, • Turned into marketing knowledge, • Used to create marketing strategy.

9. From Sources to Databases to Strategy (SDS Model)

11. Terabytes of Corporate Data One Terabyte = 1,099,511,627,776 bytes The U.S. Library of Congress has claimed it contains approximately 20 terabytes of text. 6-6

12. From Data to Decision: Purina 6-7

13. Marketing Knowledge Management • Knowledge management is the process of managing the creation, use and dissemination of knowledge. • Data is the lubricant for a learning organization, and organizations are drowning in it. • This is an information technology manager’s problem, and e-marketers must determine how to glean insights from these billions of bytes. • Marketing insight occurs somewhere between information and knowledge: • Knowledge is more than a collection of information, but resides in the user, • People, not the Internet or computers, create knowledge; computers are simply learning enablers. 6-8

14. Uses of Knowledge Management 6-9

15. The Learning Organization • Uses internal and external data to: • Quickly adapt to its changing environment • Creating organizational change to improve competitive position + employee satisfaction. • Recognizes the importance of: • Employee empowerment and development, • Cross-functional teams for brainstorming • Risk-taking for breakthrough ideas.

16. The Learning Organization • Benefits from: • Improved product quality and innovation, • Better customer relations, • Shared visioning, • Process breakthrough improvements, • Stronger competitiveness through team effort. • Is a key concept in an organization because of information technology advances and the rapid growth of the Internet.

17. The Learning Organization • One of the most important area in marketing learning = the learning relationship. • The more marketers can learn about their customers, the better they can serve them with appropriate marketing mixes needs. • Example: • An American Airlines frequent flier can receive a short text message on her cell phone two hours before a flight with all flight information. • A step further = Would you like us to notify you this way for each flight you book with us? • American would be learning what the customer wants, confirming it, and then delivering it automatically.

18. The Marketing Information System • Marketers manage knowledge through a marketing information system (MIS). • Many firms store data in databases and data warehouses. • The Internet and other technologies have facilitated data collection. • Secondary data provides information about competitors, consumers, the economic environment, etc. • Marketers use the Net and other technologies to collect primary data about consumers. 6-10

19. Sources of data: Internal records • Accounting, finance, production and marketing personnel collect and analyze data. • Nonmarketing data, such as sales and advertising spending • Sales force data • Customer characteristics and behavior • Universal product codes (bar code) • Tracking of user movements through web pages 6-11

20. A hypothetical scenario for a computer company that is learning from its customers as a whole and using the information to improve products. E-Marketers Learn From Customers Source: Adaptation of ideas from Brian Caulfield (2001), “Facing up to CRM” at www.business2.com

21. Secondary data • Can be collected more quickly and less expensively than primary data. • Common sources of secondary data for social science include censuses, large surveys, and organizational records. • Secondary data may not meet e-marketer’s information needs. • Data were gathered for a different purpose. • Quality of secondary data may be unknown. • Data may be old. • Marketers continually gather business intelligence by scanning the macro-environment. 6-12

22. Public and Private Data Sources • Publicly generated data • U.S. Patent Office • American Marketing Association • Privately generated data • Forrester Research • Nielsen/NetRatings • Online databases • Secondary data help marketers understand: • Competitors, • Consumers, • The economic environment, • Political and legal factors, • Technological forces, • Other factors in the macro-environment affecting an organization. 6-13

23. Public Sources of Data in the U.S.

24. Sampling of Sources of Privately Generated Data in the U.S.

25. Public source of data in Viet Nam

26. Primary Data • Primary data = information gathered for the first time to solve a particular problem. • When secondary data are not available managers may decide to collect their own information. • They are more expensive and time-consuming to gather than secondary data. • They are current and more relevant to the marketer’s specific problem. • They are proprietary = unavailable to competitors. • Each primary data collection method can provide important information, as long as e-marketers understand the limitations. Remember that Internet research can only collect information from people who use the Internet, which leaves out a huge portion of the population.

27. Source 3: Primary Data Electronic sources of primary data collection: • The Internet: • Focus groups, observation, in-depth interviews (IDI), and survey research. • Online panels: popular survey research method _ single-source research. • Real-timeprofiling at Web sites and computer client-side or server-side automated data collection. • The real-space • Refers to technology-enabled approaches to gather information offline that is subsequently stored and used in marketing databases. • Techniques = bar code scanners and credit card terminals at brick-and-mortar retail stores, computer entry by customer service reps while talking on the telephone with customers.

28. Firms Using Online Primary Research 6-15

29. 5 Steps for Primary Research Research Problem Research Plan Data Collection Data Analysis Distribute Results Primary Research Steps

30. Primary Research Steps • Research problem. Specificity is vital. • Research plan. • Research approach. Choose from experiments, focus groups, observation techniques, in-depth interviews, and survey research, or nontraditional real-time and real-space techniques. • Sample design. Select the sample source and number of desired respondents. • Contact method. Telephone, mail, in person, via the Internet. • Instrument design. For survey = a questionnaire. For other methods = a protocol to guide the data collection.

31. Primary Research Steps • Data collection. Gather the information according to plan. • Data analysis: Analyze the results in light of the original problem. Use statistical software packages for traditional survey data analysis or data mining to find patterns and other information in databases. • Distribute finding / add to the MIS. Research data might be placed in the MIS database and be presented in written or oral form to marketing managers.

32. Some typical e-marketing research problems that electronic data can help solve. Typical Research Problems for E-Marketers

33. Online Research Advantages & Disadvantages • Advantages • Can be fast and inexpensive. • Surveys may reduce data entry errors. • Respondents may answer more honestly and openly. • Disadvantages • Sample representativeness. • Measurement validity. • Respondent authenticity. • Researchers are using online panels to combat sampling and response problems. 6-17

34. Other Technology-Enabled Approaches • Client-side Data Collection • Cookies • Use PC meter with panel of users to track the user clickstream. • Server-side Data Collection • Data log software • Real-time profiling tracks users’ movements through a web site. 6-18

35. Real-Space Data Collection, Storage, and Analysis • Offline data collection may be combined with online data. • Transaction processing databases move data from other databases to a data warehouse. • Data collected can be analyzed to help make marketing decisions. • Data Mining • Customer Profiling • Recency, Frequency, Monetary (RFM) Analysis • Report Generating 6-19

36. Marketing Databases and Data Warehouses • Regardless of whether data are collected online or offline, they are moved to various marketing databases. • Product databases = product features, prices, and inventory levels. • Customer databases = customer characteristics and behavior. • Transaction processing databases are important for moving data from other databases into a data warehouse. • Data warehouses: • Store entire organization’s historical data. • Designed specifically to support analyses necessary for decision making. • The data in a warehouse are separated into more specific subject areas (called data marts) and indexed for easy use.

37. UPC Scanner Product Database Transaction Database Data Warehouse Customer Database Real-Space Data Collection and Storage Example

38. Data Analysis and Distribution • Data collected from all customer touch points are: • Stored in the data warehouse, • Available for analysis and distribution to marketing decision makers. • Analysis for marketing decision making: • Data mining = extraction of hidden predictive information in large databases through statistical analysis. Here, marketers don’t need to approach the database with any hypotheses other than an interest in finding patterns among the data. • Patterns uncovered by marketers help them to: • Refine marketing mix strategies, • Identify new product opportunities, • Predict consumer behavior.

39. Data Analysis and Distribution • Customer profiling = uses data warehouse information to help marketers understand the characteristics and behavior of specific target groups. • Understand who buys particular products, • How customers react to promotional offers and pricing changes, • Select target groups for promotional appeals, • Find and keep customers with a higher lifetime value to the firm, • Understand the important characteristics of heavy product users, • Direct cross-selling activities to appropriate customers; • Reduce direct mailing costs by targeting high-response customers.

40. Data Analysis and Distribution • RFM analysis (recency, frequency, monetary) = scans the database for three criteria. • When did the customer last purchase (recency)? • How often has the customer purchased products (frequency)? • How much has the customer spent on product purchases (monetary value)? => Allows firms to target offers to the customers who are most responsive, saving promotional costs and increasing sales. • Report generators: • automatically create easy-to-read, high-quality reports from data warehouse information on a regular basis. • Possible to specify information that should appear in these automatic reports and the time intervals for distribution.

41. Knowledge Management Metrics • Marketing research is not cheap: • Need to weigh the cost of gaining additional information against the value of potential opportunities or the risk of possible errors from decisions made with incomplete information. • Storage cost of all those terabytes of data coming from the Web. • Two metrics are currently in widespread use: • ROI. Companies want to know: • Why they should save all those data. • How will they be used, and will the benefits in additional revenues or lowered costs return an acceptable rate on the storage space investment? • Total Cost of Ownership (TCO). Includes: • Cost of hardware, software, and labor for data storage. • Cost savings by reducing Web server downtime and reduced labor requirements.