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3. Principles of Marketing - SS2014 - University of Siegen - Paul Marx: Chapter 3. Market Research

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what is market research
the role of marketing research for managerial decisions
objects of market research
the process of market research
types of market research
information sources of market research
survey research
secondary vs. primary data
scaling and measurement
reliability and validity
sampling

3. Principles of Marketing - SS2014 - University of Siegen - Paul Marx: Chapter 3. Market Research

  1. 1. Jun.-Prof. Dr. Paul Marx | Universität Siegen WIRTSCHAFTSWISSENSCHAFTEN WIRTSCHAFTSINFORMATIK | WIRTSCHAFTSRECHT Juniorprofessur für Betriebswirtschaftslehre, insb. Marketing Jun.-Prof. Dr. Paul Marx | Universität Siegen MARKETING 1 LECTURE: THEME 3: MARKET RESEARCH SUMMER SEMESTER 2014 JUN.-PROF. DR. PAUL MARX PRINCIPLES OF
  2. 2. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 2 3.Market Research 
 as the basis of informed management decisions 
 contents - The role of market research - Sources of information for market research - Quality criteria of market research - The process of market research - Survey as the most important method of market research
  3. 3. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE BEECHCRAFT STARSHIP 3 First civilian aircraft with - carbon fiber composite airframe - canard (“duck”) design - L-shaped wings with rudders in them - Two turbo-prop engines mounted aft to pull - R&D costs est. $500Mio “For the pilot and passengers, it has really got everything... ...for the money, the performance just isn’t there... ...for $5Mio, you can buy a jet. Starship just doesn’t fit in today’s market”1 “The Starship was a $500Mio mistake because of a lack of marketing research”2 1 Dennis Murphy, a sales person at Elliot Flying Services in Des Moines, Iowa 2 Russel Munson in “The Stock Market”, 1991
  4. 4. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE ELECTROLUX 4 Electrolux - a scandinavian manufacturer of inexpensive vacuum cleaners - took its rhyming phrase “Nothing Sucks Like an Electrolux” and brought it in the early 1970s to America from English-speaking markets overseas. They didn’t know that the word “sucks” had become a derogatory word in the US.
  5. 5. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE AMERICAN AIRLINES 5 American Airlines launched a new leather first class seats ad campaign (1977-78) in the Mexican market: "Fly in Leather" (vuela encuero) meant "Fly Naked"
  6. 6. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CASE FOOD & BEVERAGES 6 In what must be one of the most bizarre brand extensions ever Colgate decided to use its name on a range of food products called Colgate's Kitchen Entrees. Needless to say, the products did not take off and never left U.S. soil. The idea must have been that consumers would eat their Colgate meal, then brush their teeth with Colgate toothpaste. The trouble was that for most people the name Colgate does not exactly get their taste buds tingling. In the 1970s and early 80s, Coke began to face stiff competition from other soft drink producers. To remain in the number one spot, Coke executives decided to cease production on the classic cola in favor of New Coke. The public was outraged, and Coca- Cola was forced to re-launch its original formula almost immediately. Lesson learned -- don't mess with success. Cocaine is a high-energy drink, containing three and a half times the amount of caffeine as Red Bull. It was pulled from U.S. shelves in 2007, after the FDA declared that its producers, Redux Beverages, were "illegally marketing their drink as an alternative to street drugs." The drink is still available, however, online, in Europe and even in select stores in the U.S. Despite the controversy, Redux Beverages does not plan to cease production any time soon. You know what they say -- there's no such thing as bad publicity.
  7. 7. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” RETURNS ON MARKETING ACTIONS 60-95% of new products fail 50% of advertising has no effect 85% of price promotions loose money 97% brands create 37% $ (Unilever) 7
  8. 8. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 8 Marketing Research is there to prevent such things from happening
  9. 9. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 9 Definition of Market Research
  10. 10. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MARKETING RESEARCH: DEFINITION BY AMA 10 Marketing research
! is the function that links the consumer, customer, and public to the marketer through information -- information used to (1) identify and define marketing opportunities and problems; (2) generate, refine, and evaluate marketing actions; (3) monitor marketing performance; and (4) improve understanding of marketing as a process. American Marketing Association (AMA), est. in 2007 Quelle: http://www.marketingpower.com/aboutama/pages/definitionofmarketing.aspx
  11. 11. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 11 MARKETING RESEARCH: A CONCISE DEFINITION ! ! Marketing Research The planning, collection, and analysis of data relevant to marketing decision making and the communication of the results of this analysis to management.
  12. 12. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” Why market research? GOALS OF MARKET RESEARCH 12 improve the quality of decision-making efficiently maintain customer relationships identify problems and opportunities detect changes in the market and understand underlying reasons
  13. 13. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 13
  14. 14. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 14 Source: Business Management Research Associates, Inc. TOP 10 MARKET RESEARCH ACTIVITIES Market measurement 18% New Product development / concept testing 14% Ad or Brand awareness monitoring / tracking 13% Customer satisfaction (incl. Mystery Shopping) 10% Usage and Attitude studies 7% Media research & evaluation 6% Advertising development and pre-testing 5% Social Surveys for central/local governments 4% Brand/corporate reputation 4% Omnibus studies 3%
  15. 15. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MONITORING AND MEASURING MARKETS 15 Source: http://holgerschmidt.tumblr.com/post/66555235834/deutscher-smartphone-markt-ist-fest-in-den-haenden-von Smartphone Manufacturers percentage of units in use Smartphone Operating Systems percentage of units in use others
  16. 16. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MONITORING AND MEASURING MARKETS 16 Source: http://holgerschmidt.tumblr.com/post/67876615759/der-medienwandel-beschleunigt-sich Advertising: Internet vs. Newspaper in billions of Euros in Germany advertising on the internet advertising in newspapers News Media of Young Professionals media used by 20-39yr. old graduates to inform themselves about current events (in percent) TV internet radio newspaper
  17. 17. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” ADS DEVELOPMENT AND PRETESTS 17
  18. 18. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” NEW PRODUCT DEVELOPMENT / CONCEPT-TESTS 18
  19. 19. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” NEW PRODUCT DEVELOPMENT / CONCEPT-TESTS 19
  20. 20. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” BASIC OBJECTS OF MARKET RESEARCH 20 market position e.g. company's position in considered market absolute and relative market share (aggregated, per product, per product group, per market segment) brand awareness and image among existing and prospective customers general market characteristics and trends e.g. market size market growth rate stage of the life cycle seasonal fluctuations development of average gains … customer segmentation e.g. general classification of customers identification of customer segments evaluation of segments monitoring segments (esp. changes) competitors e.g. identification of key competitors market position of the key competitors (e.g. market share, earnings, cost structure, customer base) monitoring competitor behavior (e.g. resources, strategies, objectives, offerings, changes of behavior) customer satisfaction and loyalty e.g. analysis of customer satisfaction with individual attributes of products and services analysis and monitoring of customer satisfaction, loyalty, trust, lifetime value, etc. … consumer behavior and needs e.g. identification and evaluation of basic customer needs and wants analysis of information seeking patterns, purchasing behavior, choice-making strategies, etc. monitoring changes of customer needs and behavior … Source: based on Homburg/Krohmer 2009, p. 58. analyze, identify, measure, evaluate, classify, monitor, report Market Research
  21. 21. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 21 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  22. 22. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 22 Types of Market Research
  23. 23. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 23 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  24. 24. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 24 TYPES OF MARKET RESEARCH By Objectives By Data Source By Methodology Exploratory
 (a.k.a. diagnostic) Descriptive Causal
 (a.k.a. predictive, experimental) Qualitative Quantitative Primary Secondary
  25. 25. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 25 Exploratory
 (a.k.a. diagnostic) Explaining data or actions to help define the problem What was the impact on sales after change 
 in the package design? Do promotions at POS influence brand awareness? MARKET RESEARCH BY OBJECTIVES Descriptive Gathering and presenting factual statements: 
 who, what, when, where, how What is historic sales trend in the industry? What are consumer attitudes toward our product? Causal
 (a.k.a. predictive, experimental) Probing cause-and-effect relationships; “What if?” Specification of how to use the research to predict 
 the results of planned marketing decisions Does level of advertising determine level of sales? small scale surveys, focus groups, interviews larger scale surveys, observation, etc. experiments, consumer panels ProblemIdentificationProblemSolving Uncertaintyinfluencesthetypeofresearch
  26. 26. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 26 UNCERTAINTY SHAPES THE TYPE OF RESEARCH Problem Identification Research Problem Solving Research Market Potential Research Market Share Research Image Research Market Characteristics Research Sales Analysis Research Forecasting Research Business Trends Research Segmentation Research Product Research Pricing Research Promotion Research Distribution Research Exploratory research Descriptive research Causal research AwareUncertain Certain degree of problem/decision certainty
  27. 27. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 27 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  28. 28. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 28 MARKET RESEARCH BY DATA SOURCE Primary Secondary Original research to collect new raw data for a specific reason. This data is then analyzed and may be published by the researcher. Research data that has been previously collected, analyzed and published in the form of books, articles, etc.
  29. 29. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 29 SECONDARY DATA: PROS-AND-CONS Secondary Data Advantages Disadvantages Saves time and money if on target Aids in determining direction for primary data collection Pinpoints the kinds of people to approach Serves as a basis for other data May not give adequate detailed information May not be on target with the research problem Quality and accuracy of data may pose a problem Information previously collected for any purpose other than the one at hand
  30. 30. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 30 PRIMARY DATA: PROS-AND-CONS Advantages Disadvantages Answers a specific research question Data are current Source of data is known Secrecy can be maintained Expensive “Piggybacking” may confuse respondents Quality declines in interviews are lengthy Reluctance to participate in lengthy interviews Primary Data Information collected for the first time to solve the particular problem under investigation Disadvantages are usually offset by the advantages of primary data
  31. 31. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 31 Exploratory research Causal research Descriptive research MARKET RESEARCH BY METHODOLOGY Qualitative Involves understanding human behavior and the reasons behind it ! Focus is on individuals and small groups Objectivity is not the goal, the aim is to understand one point of view, not all points of view. Primary Data Secondary Data Quantitative Involves collecting and measuring data ! Often requires large data sets. For example, large number of people. Uses statistical methods to analyze data Aims to achieve objective/ scientific view of the subject
  32. 32. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 32 RESEARCH METHODOLOGY research methodology The searching for and gathering of information and ideas in response to a specific question The set of methods used to address a specific research problem at hand
  33. 33. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 33 MARKET RESEARCH METHODS Primary Secondary Research Approach Society Groups Individuals Research Source Library Web Database Archive Survey Focus Group Depth Interview Projective Tech. Observation Research Method Literature review
  34. 34. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 34 Evaluating Secondary Data
  35. 35. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 35 SOURCES OF SECONDARY DATA Internal Corporate Information Government Agencies Trade and Industry Associations Business Periodicals News Media Databases Internet Sources … Secondary Data
  36. 36. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 36 Secondary Data EVALUATING SECONDARY DATA SOURCES Use the C.R.A.P. test Currency Reliability Authority Purpose
  37. 37. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 37 Secondary Data EVALUATING DATA SOURCES Currency How recent is the information? Are there more recent updates available? Is it current enough for your topic? Reliability Is content of the resource primarily opinion? Is it balanced and evidenced? Does the creator provide references or sources for the data? Authority Who is the creator or author? What are his/her credentials? Is s/he an expert? Who is the publisher os sponsor? Are they reputable? Purpose / 
 Point of View Is it promotional or educational material? Are there advertisements on the website? is this fact or opinion? Who is the intended audience?
  38. 38. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 38 Primary Data
  39. 39. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 39 Quantitative Survey Focus Groups Depth Interview Projective Techniques Observation Qualitative Primary Approaches Survey Observation Depth Interview Projective Tech.
 Focus Groups
 Survey
 Observation
  40. 40. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 40 Robson (1998), Visocky & Visocky (2009) APPARENT TRUTH Literature Review InterviewSurvey Triangulation The combination of methods in the study of the same topic
  41. 41. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 41 BUT IT IS MESSIER THAN THAT
  42. 42. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 42 Survey Research
  43. 43. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 43 SURVEY RESEARCH The most popular technique for gathering primary data in which a researcher interacts with people to obtain facts, opinions, and attitudes. Survey Research
  44. 44. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SURVEY METHODS Telephone Interviewing traditional (outdated) computer assisted (CATI) Mail Interviewing mail mail panel Personal Interviewing in-home mall intercept computer assisted (CAPI) Electronic Interviewing e-mail internet internet panel SurveyMethods panelizable
  45. 45. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” QUESTIONING TACTICS 45 direct vs. indirect questions Do you drink alcohol every day? vs. What kind of drinks do you prefer at mealtimes? open-ended vs. closed-ended questions Respondents can express themselves freely vs. Predefined response options
  46. 46. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 46 MEASUREMENT Measurement assigning numbers or other symbols to characteristics of objects according to certain pre-specified rule. one-to-one correspondence between the numbers and characteristics being measured the rules for assigning numbers should be standardized and applied uniformly rules must not change over objects or time Measurement
  47. 47. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 47 SCALING involves creating a continuum upon which measured objects are located. Scaling Extremely unfavorable Extremely favorable
  48. 48. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 48 PRIMARY SCALES OF MEASUREMENT differences between objects can be compared zero point is arbitrary numbers indicate the relative positions of objects but not the magnitude of difference between them Ordinal Interval numbers serve as labels for identifying and classifying objects not continuos Nominal zero point is fixed ratios of scale values can be computed Ratio NOT 1 2 or 1 2 1 2 3 1 2 My preference as a snack food less more 1 2 3 a.k.a. metric
  49. 49. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 49 PRIMARY SCALES OF MEASUREMENT Scale Basic Characteristics Common Examples Marketing Examples Nominal Numbers identify and classify objects Social security numbers, numbering of football players Brand numbers, store types sex, classification Ordinal Numbers indicate the relative positions of the objects but not the magnitude of differences between them Quality rankings, ranking of teams in tournament Preference rankings, market position, social class Interval Differences between objects can be compared; zero point is arbitrary Temperature (Fahrenheit, Centigrade) Attitudes, opinions, index numbers Ratio Zero point is fixed; ratios of scale values can be compared Length, weight, time, money Age, income, costs, sales, market shares
  50. 50. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” IMPORTANT SCALE TYPES: LIKERT SCALE 50 Requires respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus object within typically five to seven response categories. Listed below are different opinions about Walmart. Please indicate how strongly you agree or disagree with each by using the following scale: Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree 1 Walmart sells high-quality merchandise [1] [x] [3] [4] [5] 2 Walmart has poor in-store service [1] [x] [3] [4] [5] 3 I like to shop in Walmart [1] [2] [x] [4] [5] 4 Walmart does not offer a good mix of different brands within a product category [1] [2] [3] [x] [5] 5 The credit policies at Walmart are terrible [1] [2] [3] [x] [5] 6 Walmart is where America shops [x] [2] [3] [4] [5] 7 I do not like advertising done by Walmart [1] [2] [3] [x] [5] 8 Walmart sells a wide variety of merchandise [1] [2] [3] [x] [5] 9 Walmart charges fair prices [1] [x] [3] [4] [5] 1 = Strongly agree 2 = Disagree 3 = Neither agree nor disagree 4 = Agree 5 = Strongly agree NOTE the reversed scoring of items 2,4,5, and 7. Reverse the scale for these items prior analyzing to be consistent with the whole set of items, i.e. a higher score should denote a more favorable attitude.
  51. 51. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 51 EXAMPLES Likert
  52. 52. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SOME COMMONLY USED SCALES IN MARKETING 52 Construct Scale Descriptors Attitude Very bad Bad Neither Bad Nor Good Good Very Good Importance Not at All Important Not Important Neutral Important Very Important Satisfaction Very Dissatisfied (Somewhat) Dissatisfied Neither Dissatisfied Nor Satisfied / Neutral (Somewhat) Satisfied Very Satisfied Purchase Intention Definitely Will Not Buy Probably will Not Buy Might or Might Not Buy Probably Will Buy Definitely Will Buy Purchase Frequency Never Rarely Sometimes Often Very Often Agreement Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree Likert
  53. 53. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 53 EXAMPLES OF LABELING OF 7 AND 9 POINT SCALES  Strongly agree  Agree to a large extent  Rather agree  50/50  Rather disagree  Disagree to a large extent  Strongly disagree Like extremely Like very much Like moderately Like slightly Neither like nor dislike Dislike slightly Dislike moderately Dislike very much Dislike extremely Likert
  54. 54. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” IMPORTANT SCALE TYPES: SEMANTIC DIFFERENTIAL 54 NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. A rating scale with end point associated with bipolar labels that have semantic meaning. Respondents are to indicate how accurately or inaccurately each term describes the object. This part of the study measures what certain department stores mean to you by having you judge them on a series of descriptive scales bounded at each end by one of two bipolar adjectives. Please mark (X) the blank that best indicates how accurately one or the other adjective describes what the store means to you. Please be sure to mark every scale; do not omit any scale. NOTE: The negative adjective sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. Powerful [ ] [ ] [ ] [ ] [X] [ ] [ ] Weak Unreliable [ ] [ ] [ ] [ ] [ ] [X] [ ] Reliable Modern [ ] [ ] [ ] [ ] [ ] [ ] [X] Old fashioned Cold [ ] [ ] [ ] [ ] [ ] [X] [ ] Warm Careful [ ] [X] [ ] [ ] [ ] [ ] [ ] Careless Walmart is:
  55. 55. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” A SEMANTIC DIFFERENTIAL SCALE FOR MEASURING SELF-CONCEPTS, PERSON CONCEPTS, AND PRODUCT CONCEPTS 55 Semantic Diff. Rugged [ ] [ ] [ ] [ ] [ ] [ ] [ ] Delicate Excitable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Calm Uncomfortable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Comfortable Dominating [ ] [ ] [ ] [ ] [ ] [ ] [ ] Submissive Thrifty [ ] [ ] [ ] [ ] [ ] [ ] [ ] Indulgent Pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unpleasant Contemporary [ ] [ ] [ ] [ ] [ ] [ ] [ ] Non-contemporary Organized [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unorganized Rational [ ] [ ] [ ] [ ] [ ] [ ] [ ] Emotional Youthful [ ] [ ] [ ] [ ] [ ] [ ] [ ] Mature Formal [ ] [ ] [ ] [ ] [ ] [ ] [ ] Informal Orthodox [ ] [ ] [ ] [ ] [ ] [ ] [ ] Liberal Complex [ ] [ ] [ ] [ ] [ ] [ ] [ ] Simple Colorless [ ] [ ] [ ] [ ] [ ] [ ] [ ] Colorful Modest [ ] [ ] [ ] [ ] [ ] [ ] [ ] Vain Rating profiles of different objects / respondents / segments. Each point corresponds to a mean or median of the respective scale.
  56. 56. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SEMANTIC PROFILES 56 Semantic Diff.
  57. 57. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” EXAMPLE 57 Semantic Diff.
  58. 58. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” LATENT CONSTRUCTS & MULTI-ITEM SCALES 58 A Latent Construct is a variable that cannot be observed or measured directly but can be inferred from other observable measurable variables. ! Thus, the researcher must capture the variable through questions representing the presence/ level of the variable in question. ! ! ! ! ! ! ! A Latent Construct satisfied [ ] [ ] [ ] [ ] [ ] [ ] [ ] dissatisfied pleased [ ] [ ] [ ] [ ] [ ] [ ] [ ] displeased favorable [ ] [ ] [ ] [ ] [ ] [ ] [ ] unfavorable pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] unpleasant I like it very much [ ] [ ] [ ] [ ] [ ] [ ] [ ] I didn't like it at all contented [ ] [ ] [ ] [ ] [ ] [ ] [ ] frustrated delighted [ ] [ ] [ ] [ ] [ ] [ ] [ ] terrible Please indicate how satisfied you were with your purchase of _____ by checking the space that best gives your answer. α=.84
  59. 59. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” LATENT CONSTRUCTS & MULTI-ITEM SCALES 59 Construct Dimensions Factors Items Scale customer satisfaction satisfaction with product satisfaction with service friendli-
 ness expertise liability the salesperson 
 was appealing the salesperson 
 smiled to me the salesperson was courteous strongly agree strongly disagree largely 
 agree largely disagree
  60. 60. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” LATENT CONSTRUCTS & MULTI-ITEM SCALES 60 Advantages allow to assess abstract concepts make it easier to understand the data and phenomenon reduce dimensionality of data through aggregating a large number of observable variables in a model to represent an underlying concept link observable (“sub-symbolic”) data of the real world to symbolic data in the modeled world Satisfaction Loyalty Trust Service Quality Purchase intention Attitude Toward the Brand Involvement Price Perception Website Ease-of-Use ... Examples
  61. 61. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 61 Quality Criteria of Market Research: 
 Reliability and Validity
  62. 62. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” MULTI-ITEM SCALES: MEASUREMENT ACCURACY 62 Measurement A measurement is not the true value of the characteristic of interest but rather an observation of it. ! XO = XT + XS + XR ! where XO = the observed score of measurement XT = the true score of characteristic XS = systematic error XR = random error The True Score Model
  63. 63. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” ! extent to which a scale produces consistent results in repeated measurements RELIABILITY 63 1st Measurement (9:15h) 85kg ! 2nd Measurement (9:16h) 85kg ! 3rd Measurement (9:17h) 85kg Reliability
  64. 64. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” ! extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured VALIDITY 64 Validity 1st Measurement (9:15h) 85kg ! 2nd Measurement (9:16h) 85kg ! 3rd Measurement (9:17h) 85kg
  65. 65. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 65
  66. 66. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” RELIABILITY & VALIDITY 66 XO = XT + XS + XR Reliability extent to which a scale produces consistent results in repeated measurements absence of random error 
 (XR → 0 | XO → XR + XT) Validity extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured no measurement error 
 ( XO → XT, XS → 0, XR → 0)
  67. 67. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” RELATIONSHIP BETWEEN RELIABILITY & VALIDITY 67 XO = XT + XS + XR validity implies reliability
 ( XO = XT | XS = 0, XR = 0) unreliability implies invalidity
 ( XR ≠ 0 | XO = XT +XR ≠ XT) reliability does not imply validity
 ( XR = 0, XS ≠ 0 | XO = XT +XS ≠ XT) ! reliability is a necessary, but not sufficient, condition of validity
  68. 68. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 68 Sampling
  69. 69. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 69 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  70. 70. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 70 The world’s most famous newspaper error President Harry Truman against Thomas Dewey Chicago Tribute prepared an incorrect headline without first getting accurate information Reason? → bias → inaccurate opinion polls
  71. 71. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 71
  72. 72. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 72 Yes, dear Dilbert, it was the wrong Sample
  73. 73. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” SAMPLING 73 Population the group of people we wish to understand. Populations are often segmented by demographic or psychographic features (age, gender, interests, lifestyles, etc.) Sample a subset of population that represents the whole group Most research cannot test everyone. Instead a sample of the whole population is selected and tested. ! If this is done well, the results can be applied to the whole population. ! This selection and testing of a sample is called sampling. ! If a sample is poorly chosen, all the data may be useless.
  74. 74. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 74 SAMPLING: TWO GENERAL METHODS This relies on personal judgement of theresearcher (often on people available, e.g.,people passing in the street or walkingthrough a mall). ! This may yield good estimates of populationcharacteristics, however, doesn’t allow forobjective evaluation of the precision ofsample results. That is, the results are notprojectable to the population. Non- probability Sampling Here, sampling units are selected by chance, i.e., randomly. ! This randomness allows applying statistical techniques to determine the precision of the sample estimates and their confidence intervals. The results are generalizable and projectable to the population from which the sample is drawn. Probability Sampling
  75. 75. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” CLASSIFICATION OF SAMPLING TECHNIQUES 75 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate
  76. 76. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” QUOTA SAMPLING 76 Control Characteristic Population Composition Sample Composition Percentage Percentage Number Sex Male
 Female 
 48
 52
 ------- 100 
 48
 52
 ------- 100 
 480
 520
 ------- 1000 Age
 18-30 31-45 45-60
 Over 60 
 27
 39
 16
 18 ------- 100 
 27
 39
 16
 18 ------- 100 
 270
 390
 160
 180 ------- 1000 ! develop control categories, or quotas, of population elements (e.g., sex, age, race, income, company size, turnover, etc.) so that the proportion of the elements possessing these characteristics in the sample reflects their distribution in the population. ! The elements themselves are selected based on convenience or judgment. The only requirement, however, is that the elements selected fit the control characteristics (quota). ! Quota Sampling Often used in online surveys
  77. 77. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” 77 MARKET RESEARCH PROCESS Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  78. 78. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” DATA ANALYSIS 78 Products Blue Red Yellow Choice Respondent #1 50 40 10 Blue Respondent #2 0 65 75 Yellow Respondent #3 40 30 20 Blue Average 30 45 35 Red Given the following preferences, which product should we offer to this market? Red exhibits the highest overall preference But no one in the market prefers Red
  79. 79. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” DATA ANALYSIS 79 Given the following individual preference structures, how does the collective preference structure looks like? > > > > > > > > > respondent #1 respondent #2 respondent #3 let’s count the “votes”: vs vs vs number of votes 2 vs 1 2 vs 1 2 vs 1 ✔ ✔ ✔ Result: apple is the most and the least preferred item aggregate preferences are inconsistent!
  80. 80. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” METHODS OF DATA ANALYSIS 80 Methods of data analysis Univariate methods Bi- and multivariate methods Interdependence
 analysis Dependence analysis regression analysis … cluster analysis … average standard error / variance …
  81. 81. Jun.-Prof. Dr. Paul Marx | Universität Siegen Vorlesung “Marketing” WHEN NOT TO CONDUCT MARKET RESEARCH 81 Occasion Comments Lack of resources If quantitative research is needed, it is not worth doing unless a statistically significant sample can be used. When funds are insufficient to implement any decisions resulting from the research. Closed mindset When decision has already been made. Research is used only as a rubber stamp of a preconceived idea. Information not needed When decision-making information already exists. Vague objectives When managers cannot agree on what they need to know to make a decision. Market research cannot be helpful unless it is probing a particular issue. Results not actionable Where, e.g., psychographic data is used which will not help he company form firm decisions. Late timing When research results come too late to influence the decision. Poor timing If a product is in a “decline” phase there is little point in researching new product varieties Costs outweigh benefits The expected value of information should outweigh the costs of gathering an analyzing the data.

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