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Marketing Analytics Exam 3: Chapter 9-14

by: Carly Della Sala

Marketing Analytics Exam 3: Chapter 9-14 325

Marketplace > West Virginia University > Marketing > 325 > Marketing Analytics Exam 3 Chapter 9 14
Carly Della Sala
GPA 3.05

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About this Document

These notes cover chapter 9-14 which will be on exam 3.
Marketing Analytics
Joshua Dorsey
Study Guide
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This 12 page Study Guide was uploaded by Carly Della Sala on Tuesday March 29, 2016. The Study Guide belongs to 325 at West Virginia University taught by Joshua Dorsey in Winter 2016. Since its upload, it has received 21 views. For similar materials see Marketing Analytics in Marketing at West Virginia University.


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Date Created: 03/29/16
Chapters 9­14 Notes Chapter 9: Primary Data Collection: Experimentation and Test Markets What is an Experiment? An Experiment: ­ A research approach in which one variable is manipulated and the effect on another variable  is observed Key Variables: ­ Independent: variables one controls directly such as price, packaging, distribution, product  features, etc. ­ Dependent: variables one does not directly control such as sales or customer satisfaction­  (might control them by manipulating the independent variable) ­ Treatment: the independent variable manipulated during and experiment to measure its effect on the dependent variable ­ Extraneous: factors one does not control but has to live with, such as the weather What is Causal Research? ­ Research designed to determine whether a change in one variable likely caused an observed  change in another  ­ A causal relationships must demonstrate three things: o Concomitant Variation (Correlation) o Appropriate Time Order of Occurrence o Elimination of Other Possible Causal Factors Demonstrating Causation Concomitant Variation: ­ A statistical relationship between variables Appropriate Time  Order of Occurrence: ­ Change in an independent variable occurred before an observed change in the dependent  variable Elimination of Other  Possible Causal Factors: ­ “If you eliminate the impossible, whatever remains, however improbable, must be the truth.”  Sherlock Holmes ­ Hard to prove that something else did not cause change in B. Experimental Setting Laboratory ­ Experiments conducted in a controlled setting. Field ­ Tests conducted in an actual environment, such as a marketplace. Experimental Validity Internal Validity: ­ The extent to which competing explanations for the experimental results observed can be  ruled­out External Validity: ­ The extent to which causal relationships measured in an experiment can be generalized to  outside persons, settings, and times Extraneous Variables History: ­ Intervention, between the beginning and end of an experiment, of outside variables that  might change the dependent variable Instrument Variable: ­ Changes in measurement instruments that might affect measurements Selection Bias: ­ Systematic differences between the test group and the control group due to a biased selection  process Mortality: ­ Loss of test units or subjects during the course of an experiment which might result in a non­ representativeness Regression to the Mean: ­ Tendency of subjects with extreme behavior to move toward the average for that behavior  during the course of the experiment Controlling Extraneous Variables Randomization: the random assignment of subjects to treatment conditions to ensure equal  representation of subject characteristics Physical Control: holding constant the value or level of extraneous variables throughout the course  of an experiment Design Control: use of the experimental design to control extraneous causal factors Statistical Control: adjusting for the effects of extraneous variables by statistically adjusting the  value of the dependent variable for each treatment condition Experimental Design, Treatment, and Effects Experimental Design: a test in which the researcher has control over and manipulates one or more  independent variables Treatment variable: the independent variable that is manipulated or changed in an experiment Experimental Effect: the effect of the treatment variable on the dependent variable Limitations of Experimental Design High Cost: ­ Is the research affordable? ­ Will the research be beneficial & help solve problems? ­ Has a cost & benefit analysis been done? Security Issues: ­ Particularly critical with field experiments ­ The competition might be “tipped­off” ­ Are the data and findings secure? Implementation Problems: ­ Process contamination ­ People who unwittingly get caught into the survey ­ Outside factors unnaturally affecting the experiment ­ Participants who intentionally try to skew the results True Experimental Design ­ Research using an experimental group and a control group to which test units are randomly  assigned ­ Two Key Design Types: o Before and After with control group o After only with control group Quasi Experiments ­ Studies in which the researcher lacks complete control over the scheduling of treatments or  must assign respondents to treatments in a nonrandom manner.  ­ Interrupted Time­Series o Research in which repeated measurement of an effect “interrupts” previous patterns ­ Multiple Time­Series o Interrupted time­series design with a control group Test Markets ­ real world testing of a new product or some element of the marketing mix using an  experimental or quasi experimental design ­ Types of Test Markets o Traditional or standard o Scanner or electronic o Controlled o Stimulated Test Markets Cost Issues: ­ Advertising expenses ­ Point­of­purchase materials ­ Coupons and sampling ­ Travel and set­up expenses ­ Need for customized research ­ Possible diversion of sales from your other products ­ Potentially bad press/ public reaction if experiment fails ­ Letting competitors know what your company is doing ­ Falsely thinking the sample results are always representative of the population Steps in a Test Market Study 1. Define the Objective: a. What do you hope to learn? b. What are the characteristics of the people/products of interest? 2. Select a Basic Approach a. Simulated, controlled, or standard test? 3. Develop Detailed Test Procedures a. How will you execute the study? b. Who will be involved? c. How long will it take and how much can you spend? 4. Select the Test Market a. Market should not be over tested b. Should have little media spillover c. Demographics should be similar to your target population d. Market should be large enough to provide useful results e. Distribution and other patterns should be similar to the nation 5. Execute the Plan a. How long should the test run? b. Who should execute it? 6. Analyze the Test Results a. Purchase data? b. Awareness data? c. Competitive response? d. Source of sales? Other Types of Product Tests Rolling Rollout: ­ A product is launched in a certain region rather than in one or two cities ­ Scanner data can provide information on how the product is doing in a few days resulting in  the possible product launch in additional regions ­ General Mills has used his approach for products such as MultiGrain Cheerios Lead Country Strategy: ­ A product is tested in a foreign market before rolling it out globally Chapter 10: The Concept of Measurement Measurement Process Measurement: ­ The process of assigning numbers or labels to persons, objects, or events in accordance with  specific rules for representing quantities or qualities or attitudes Levels of Measurement 1. Nominal a. Scales that partition data into mutually exclusive and collectively exhaustive  categories 2. Ordinal a. Scales that maintain the labeling characteristics of nominal scales and have the ability to order data 3. Interval a. Scales that have the characteristics of ordinal scales, plus equal intervals between  points to show relative amounts; they may include an arbitrary zero point 4. Ratio a. Scales that have the characteristics of interval scales, plus a meaningful zero point so  that magnitudes can be compared arithmetically Reliability and Validity Reliability ­ Degree to which measures are free from random error and, therefore, provide consistent data. The extent to which the survey responses are internally consistent Validity ­ Degree to which what the researcher was trying to measure was actually measured Chapter 11: Using Measurement Scales to Build Marketing Effectiveness What are Scales for? Scaling ­ Procedures for assigning numbers (or other symbols) to properties of an object in order to impact  some numerical characteristics to the properties in question Scaling Approaches: 1. Uni­dimensional a. Measures only one attribute of a concept, respondent, or object 2. Multi­dimensional a. Measures several dimensions of a concept, respondent, or object How to Select a Scale 1. The Nature of the Construct Being Measured 2. Type of Scale and Number of Scale Categories 3. Balanced vs. Non­balanced a. Balanced: scales with equal numbers of positive & negative categories b. Non­balanced: scales weighted towards one end or the other of the scale 4. Forced vs. Non­forced a. Having an odd vs. even number of response choices Types of Questioning 1. Direct questioning 2. Indirect questioning 3. Observation Chapter 12: Questionnaire Design The Role of the Questionnaire A Questionnaire: ­ Set of questions designed to generate the data necessary to accomplish the objectives of the  research project; also called an interview schedule or survey instrument Criteria for Good Questionnaire What to Consider: ­ Does it provide decision­making information? ­ Does it consider the respondent? ­ Does it meet editing and coding requirements? Key Questionnaire Issues ­ Determine Survey Objectives, Resources, and Constraints o Objectives: outline of the decision­making information required o Resources: budget in terms of money, time, and personnel o Constratints: the budget, also and other requirements ­ Determine the Data Collection Method o The data collection method will have a major impact on the questionnaire design and  the project’s time and money budget o Exmaples:  In­person  Telephone  Mail or other self­administered  Internet ­ Determine the Question Response Format o Open­Ended  Questions to which the respondent replies in his or her own words  Probed vs. Un­probed o Closed­Ended  Questions requiring respondents to choose from a lost of answers  Dichotomous: choice is between two answers  Multiple choice: choice is among three or more options  Scaled responses: designed to capture the intensity of respondent’s  feelings o A type of closed­ended question in which the response choices  are designed to capture intensity of the respondent’s feeling ­ Decide on the Question Wording o Make sure the wording is clear o Avoid biasing the respondent o Consider the respondent’s ability to answer the questions o Consider the respondent’s willingness to answer the question ­ Establish Questionnaire Flow and Layout o Screeners­ Qualifying Questions:  Ask general questions first  Basic questions that lay the groundwork for upcoming questions o Warm­ups  Gets the respondent thinking about the topic at hand  Establishes parameters about the respondents’ attitudes, behavior, etc.  o Transitions  Questions that set the tone for the more difficult questions to come o Complicated  Use of rating scales for attributes, attitudes, beliefs, opinions, etc  Tackling controversial issues o Classification  Personal & demographic type questions ­ Evaluate the Questionnaire o Is the question necessary? o Is the questionnaire too long? o Will the questions provide the information needed to accomplish the research  objectives? ­ Obtain Approval of all Relevant Parties ­ Pretest and Revise the Questionnaire ­ Prepare the Final Copy ­ Implement the Survey Tips for Writing a Good Questionnaire 1. Avoid abbreviations, slang, or uncommon words that your audience might not understand 2. Be specific. Vague questions generate vague answers. 3. Don’t overdo it 4. Make sure your questions are easy to answer 5. Don’t assume too much 6. Watch out for double questions and double negatives 7. Check for bias The Internet Impact Pros ­ The questionnaire’s appearance consistency is easier to achieve ­ The questionnaire can be checked for typos easily ­ The survey can be created quickly ­ Skip patterns can be efficiently established ­ The survey can be distributed quickly for expert review & input Cons ­ Over reliance on electronic survey construction can lead to the researcher’s getting sloppy as  he/she might think the software will do the work and correct any errors ­ The researcher might feel less connected to the process ­ Multiple versions of the survey might get circulated/distributed Chapter 13: Basic Sampling Issues The Concept of Sampling Population ­ The entire group of people about whom information is needed; also called the universe or  population of interest Sampling ­ The process of obtaining information from a subset of a larger group; representative Developing a Sampling Plan 1. Define the Target Population a. Determine the characteristics of those you are interested in studying. Determine  which group of people or entities about which you want to learn more 2. Choose the Data Collection Method a. Determine how you collect the sample­ such as mail, Internet, telephone, mall  intercept, etc.  3. Select the Sample Frame a. A list of population elements from which units to be sampled can be selected 4. Select the Sampling Method a. Determine how you will get the sample list through probability or non­probability  methods 5. Determine the Sample Size a. What is the level of accuracy you want to achieve; the time and money you have to  do the survey, and the data collection method? 6. Determine Operational Procedures a. This is the plan of how to go about actually choosing and interviewing the  respondents 7. Execute the Sampling Plan a. Field workers must be trained to execute the sampling plan properly Sampling & Non­sampling Error Sampling Error ­ Error that occurs because the sample selected is not perfectly representative of the population Non­sampling Error ­ All error other than sampling error­ also called “measurement error” Internet Sampling Pros ­ Target respondents can complete the survey at their convenience ­ Data collection is inexpensive ­ Survey software can facilitate the data collection process ­ The survey can be completed quickly Cons ­ Sample might not be representative of the population ­ You cannot always be sure who is completing the survey ­ Maintaining respondent confidentially can be a challenge ­ Data security issues can be difficult to manage Chapter 14: Sample Size Determination Sample Size for Probability Sampling Budget Available: ­ A valid factor­ how much can we afford? Rule of Thumb: ­ Is there some convention we can apply? ­ What might make an adequate sample size? Number of Subgroups Analyzed: ­ In any sample size determination problem, consideration must be given to the number and  anticipated size of various subgroups of the total sample that must be analyzed Traditional Statistical Methods: ­ Variance, standard deviation, and confidence interval play a key role Judgment: ­ Best guess of “experts” ­ Draw on your experience to determine sample size Conventional: ­ What have others done? ­ See what the sample size has been for similar studies The Normal Distribution Central Limit Theorem: ­ The idea that a distribution of a large number of sample means or sample proportions will  approximate a normal distribution­ regardless of the distribution of the population from  which they were drawn Normal Distribution: ­ The continuous distribution that is bell shaped and symmetrical about the mean. The mean,  median, and mode are equal. Finally, about 68% of the observations are within one standard  deviation plus/minus of the mean, 96% are within two standard deviations, and 99% are  within three standard deviations of the mean respectively.  ­ Proportionate Properties of the Normal Distribution: o A feature that the number of observations falling between the mean and a given  number of standard deviations from the mean is the same for all normal distributions ­ Standard Normal Distribution: o Normal distribution with a mean of zero and a standard deviation of one ­ Standard deviation o The measure of dispersion calculated by subtracting the mean of the series of each  value in a series, squaring each result, summing the results, dividing the sum of the  number of observations minus 1 and finally taking the square root of this value.  o Square root of (x1­x)2/(N­1) Important Characteristics of a Normal Distribution 1. Bell­shaped and has only one mode 2. Symmetric about its mean 3. Uniquely defined by its mean and standard deviation 4. The total are under a normal curve is equal to one 5. The area of a region under the normal distribution curve between any two values of a  variable equals the probability of observing a value in that range when an observation is  randomly selected from the distribution 6. The area between the mean and a given number of standard deviations from the mean is the  same for all normal distributions Population and Sampling Distribution Population Distribution ­ The frequency distribution of all the elements of a population Sampling Distribution ­ The frequency distribution of all the elements of an individual sample Sampling Distribution of the Mean: ­ The theoretical frequency distribution of the means of all possible samples of a given size  drawn from a particular population; it is normally distributed ­ S= standard deviation/Square root of N Standard Error of the Mean: ­ Standard deviation of a distribution of sample means Confidence Level and Interval Confidence Level ­ the probability that a particular interval will include the true population value; also called the  confidence coefficient Confidence Interval ­ The interval that, at the specified confidence level, includes the true population value Determining the Sampling Size Problems Involving Mean: ­ Three pieces of information are needed to compute the sample size required: o 1. The acceptable or allowable level of sampling error E o 2. The acceptable level of confidence Z. In other words, how confident does the  researcher want to be that the specified confidence interval includes the population  mean? o 3. An estimate of the population standard deviation 1. Allowing Sampling Error: a. Amount of sampling error the researcher is willing to accept, E 2. The Acceptance Level of Confidence: a. How confident does the researcher want to be that an interval includes the population  mean, Z 3. Population Standard Deviation: a. Standard deviation of a variable for the entire population Sampling Issues to Consider ­ Time to generate the sample ­ Scope of the research ­ Budget available ­ Experience with sampling ­ Level of accuracy desired ­ Your knowledge of the population


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