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Industrial marketing and intelligence
This document discusses the importance of industrial marketing research and intelligence. It outlines several reasons to conduct research, including that the external environment is dynamic and knowledge becomes outdated. It also discusses limitations of research like constraints of time and money. The document then covers different types of marketing research tasks, key concerns about reliability and validity, types of data, sampling issues, designing questionnaires, and analyzing and interpreting data. It compares business to business versus business to consumer research and discusses the importance of marketing intelligence and intelligence systems.
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Industrial marketing and intelligence
- 1. Industrial Marketing Research andIntelligence Vipin Kumar
- 2. Why do research? Theexternal environment is dynamic. Knowledge becomes outdated. To gather more information Better Information Better Decisions 2
- 3. Limitations Managers NEVER haveall of the relevant information that they need. Constraints of time and money Desired information is often more costly than it’s worth. Decisions are time sensitive. Can’t wait for all of the information. 3
- 4. When NOT todo research Good research has already been done. When decisions have been made and won’t be altered by new information. When management does not understand scope necessary and won’t commit $. Don’t have talent, won’t hire. Uncertainty reduction justifies cost. 4
- 5. Marketing Research Tasks Estimatemarket potential Analyze market share/share of customer Track competitors Identify market characteristics & trends Analyze sales data Sales forecasting: Existing/new products 5
- 6. Key Concerns Reliability: measures/methodsyield consistent results Validity: research measures what it says it measures; i.e. little or no error 6
- 7. Types of Data Primary Newinformation generated for specific task. Can be expensive/time consuming. Gather by survey, tests, observation, focus groups, interviews. Secondary Existing information. May not be in useful form. Sources: government, trade/professional associations, company records 7
- 8. Sampling Issues Sample vs.Census Probability Random, equal chance Random, stratified Non-Probability Convenience Judgment 8
- 9. Questionnaires Ask what youwant to know Watch length Aesthetics Easily understood; watch vernacular Social desirability bias Non-response bias Question order effects 9
- 10. Coding-Analysis-Interpretation Data entry tedious. Mistakesare made Need to clean data Use statistical tools to analyze data. SPSS/SAS Can data mine Important to understand analysis What results means Limitations of method 10
- 11. B2B vs. B2CResearch I Technology Need to understand technical needs of customers Direct economic effect Quality/Price trade-off very important Organization, professionals Understand multiple players, in sociopolitical setting 11
- 12. B2B vs. B2CResearch II Smaller #s of buyers to study Smaller sample sizes Secondary data often exists Tough to get buyer’s attention for research Need to know which buyer(s) to study Need technical knowledge for research Surveys take longer, cost more 12
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- 14. Marketing Intelligence Continuous flowof information Strategic and Tactical Systematic and periodic Better understanding of environment over time Collect from variety of sources Customers, competitors, regulators, etc. Constant vigilance 14
- 15. Marketing Intelligence SystemI People, Procedures, Computers Acquires, Disseminates, Interprets, Stores information about internal and external environments 15
- 16. Marketing Intelligence SystemII Transform raw data to useful information Can organize information by customer, competitor, product line, territory, activity Sources Internal: sales, service, accounting External: government, trade associations, competitor literature, customers, publications Output Periodic reports Special information needs 16
- 17. Decision Support Systems Computer-aideddecision-making Involve analysis, not just retrieval Database: Repository of data Statistics: Analyze data Model: Patterns in the data; relationships Optimization: Decisions leading to best outcome given model 17
- 18. Economic/Financial Factors Competition oligopolistic Power/Dependencyrelationships Reciprocal: Doing business with companies that do business with them. Economic variables: interest rates, inflation, business cycle 18