MKTG 415 Exam 1 PDF

Topics What are the steps of the marketing research process? 1. Identification of the problem and statement of the research objective 2. Creation of the research design: the plan to be followed to answer the marketing research objectives a. A detailed blueprint to guide...

Topics What are the steps of the marketing research process? 1. Identification of the problem and statement of the research objective 2. Creation of the research design: the plan to be followed to answer the marketing research objectives a. A detailed blueprint to guide research b. Descriptive, exploratory, or causal/experimental research designs 3. Choice of the research method: observational, survey, experiments, other qualitative research 4. Selection of the sampling procedure: probability sampling, nonprobability sampling, sample size 5. Acquire the data a. survey: interviewer (in person, telephone) no interviewer (mail, internet) b. Observation (in person, machine) c. Experiments 6. Analysis of the data 7. Presentation of the report a. Research objectives b. methodology c. Key findings d. Limitations e. Conclusion f. recommendations 8. Follow up a. Will the findings/recommendations be used? b. Why will they be used or not? I Identify C Create M Method S Sample A Acquire A Analysis P Presentation F Follow up I count money so anyone anytime pays for What are the steps of the problem definition process? 1. Recognize the problem or opportunity a. Can the problem become an opportunity? b. Do we adjust the marketing mix, and how? 2. Find out why the information is being sought a. What information will be used and what decisions are made? 3. Understand the decision-making environment with exploratory research (i.e., conduct a situation analysis)  a. Examine cultural and bureaucracy issues b. What’s going on and who can we talk to? 4. Use the symptoms to help clarify the problem a. Determine cause-and-effect relationships b. Make sure we know the problem and not only the symptom R Recognize F Find out why U understand C Clarify Rylie fucks up cunts What are the differences between management decision problem, marketing research problem, and marketing research objective? NOTECARD Management decision problem (What?) - a statement specifying the type of managerial action required to solve the problem - Asks what the decision maker needs to do - Action-oriented - Focuses on symptoms - Should a new product be introduced? - Should the advertising campaign be changed? - Should the price of the brand be increased? Marketing Research Problem (Why?) - A statement specifying the type of information needed by the decision maker to help solve the management decision problem and how information can be obtained efficiently and effectively - Asks what information is needed and how it should be obtained - Information oriented - Focuses on the underlying cause - To determine consumer preferences and purchases for the proposed new product- answering the management decision problem - To determine the effectiveness of the current advertising campaign - To determine the price elasticity demand and the impact on sales and profits of various levels of price changes Marketing Research Objective (how?) - A goal statement defining the specific information needed to solve the marketing research problem - Well-formulated objectives serve as a road map in pursuing the research project - Also serve as a standard that will later enable managers to evaluate the quality and value of the work (Were the objectives met?) Descriptive vs. Exploratory vs. Causal research designs, when and how to use each type NOTECARD Descriptive Studies - Answer the who, what, where, when, and how questions. Used when one wants to gain a better understanding of the specifics or details of the research issue Purposes ○ Confirm theories ○ Describes population ○ Build customer profile ○ Gain specific information When to use: is best suited for building a customer profile and describing a population Ex: Measuring the satisfaction levels of patients using a new health supplement, Describing the demographics and purchasing habits of a supplement-consuming audience Exploratory: Preliminary research conducted to increase understanding of a concept, to clarify the exact nature of the problem to be solved, or to identify important variables to be studied. When you explore you want to find out more and clarify why your feeling the way you feel. Purposes: ○ Define terms ○ Clarify problems ○ Develop theories ○ Establish theories ○ Establish priorities ○ Gain general information Of the following, exploratory research studies are most appropriate for _____. Ex: Exploring potential reasons for low supplement usage in a particular demographic, Understanding consumer attitudes toward a new product category before launching a formal campaign. Causal Research: Research studies that examine whether the value of one variable causes or determines the value of another variable. (to give proof that a particular relationship exists) helps predict hypothetical occurrences Purposes ○ Confirm theories ○ Identify cause and effect ○ Relationships among variables Include the Dependent and independent variable ○ Dependent variable - a symbol or concept expected to be explained or influenced by the independent variable. One affects the dependent variable by manipulating the independent variable deemed to be associated with the dependent variable ○ Independent variable: A symbol or concept over which the research has some control and that is hypothesized to cause or influence the dependent variable Key requirements to demonstrate causation ○ Correlation  ○ Temporal antecedence (cause comes before effect) ○ No third factor driving both events No spurious association Elimination of alternative explanations When to use: Descriptive: Use when you need to describe characteristics, behaviors, or situations in detail, often involving large-scale data collection (e.g., surveys, observations). Exploratory: Use when you're starting to investigate a problem and need to generate insights, ideas, or hypotheses (e.g., focus groups, interviews). Causal: Use when you want to establish cause-and-effect relationships between variables, often through controlled experiments or advanced statistical analysis. Qualitative vs. Quantitative research: When do you use each type? Qualitative: Research whose findings are not subject to quantification or quantitative analysis. Its research conclusions are not based on precisely, measurable statistics but on subjective observations and analysis. Quantitative: Research that uses mathematical analysis. Typically research analysis is done using measurable and numeric standards. What are the benefits and limitations of qualitative research? Benefits: Usually cheaper than quantitative research ○ Data collection is expensive, both in dollar amount and time spent Great for understanding motivations and feelings ○ Seeing how customers truly feel while observing (focus group) can be very insightful Improve the quality, efficiency, and focus of subsequent quantitative research ○ Qualitative is usually done before quantitative research Limitations Attitudinal, perceptual, and belief differences revealed during qualitative research might not be easily measured. ○ Quantitative research will more precisely measure these differences. Qualitative research is often not statistically representative of the general population ○ A focus group of 10 students is not representative of the general population Focus Groups: a group of eight to 12 participants who are led by a moderator in an in-depth discussion on one particular topic or concept- The most common form of qualitative research- cost from 6k-8k Steps to conducting a focus group 1. Prepare for the group: select a focus group facility and recruit the participants  2. Select a moderator and create a discussion guide- (develop a moderator guide to chart the flow of the focus group) 3. Conduct the group- generally about 1.5 hours 4. Prepare the focus group report- review the videotape and analyze the results, prepare a written report for the client benefits/advantages of focus groups over other qualitative methods Interactions among respondents can stimulate new ideas and thoughts. ○ Groups will challenge participants Opportunities to observe customers or prospects through one way mirrors. ○ Helpful to get all employees to see customers sharing their opinions Quick execution compared to other research techniques. concerns/disadvantages to be aware of when conducting the focus group or interpreting Data Managers can be misled instead of Informed ○ You may feel that the immediate information in front of you is better than it truly is Solutions are often not so simple ○ Dominant participants may persuade everyone else 2. Recruiting for focus group participants can be a problem ○ Not representative of the target market Potential confounding by a moderator ○ An unskilled moderator may unintentionally bully respondents, side with some participants, let tangents go too long, fall into yes/no responses, etc What is the role of the moderator and what might make a moderator good versus Bad? Role: a person hired by the client to lead focus group- Psychology, sociology, or marketing background preferable Moderators guide includes: a timetable for each topic, clear goals/questions to be answered, a strategy for keeping the group on task, and managing the group dynamics is important What makes a good moderator? Builds a comforting environment by smiling, engaging, charming, and being authentic and empathetic ○ Characteristics: “Invisible” leadership Asks short questions and actively listens Can move things along “invisibly” and curtail tangents Handles both leaders and shy participants Works along a logical path for participants In-depth Interviews: One-on-one interviews that probe and elicit detailed answers to questions, often using non-directive techniques to uncover hidden motivations - Second most popular form of qualitative research - Usually 30-60 minutes - Begins with a general question and then probes (similar to focus groups) - Advantages  - No group pressure, respondent feels comfortable - Can probe at length about motivations and feelings underlying statements - Great for sensitive or highly personal subjects - Disadvantages - Total cost tends to be more expensive (than focus groups) - More time-consuming and exhausting for the moderators - Often doesn’t include counterpoint discussions (focus groups do) Observational Research: a systematic process of recording patterns of occurrences or behaviors without normally communicating with the people involved When to use/conditions: ○ Worried about the validity of self-reported data ○ When behavior in a natural setting (w/o interference) is critical to research questions ○ You need more exploratory research to develop hypotheses or to gather contextual information about the environment in which the behavior is occurring ○ ex. A retail store observing customer movements and product interactions to optimize store layout and merchandising different observation situations and approaches to observational research. Brand monitoring: Effectively able to track brand sentiment over time due to timestamps of public posts- Lots of historical data is also easily available as well ○ Tracking social media mentions ○ For example, brand monitoring will inform Fenty Beauty how its target audience feels about its products by looking at relevant posts and comments. Beyond the brand's social comments, it'll also inform the brand of how the market perceives its strategic marketing decisions, such as its collaboration with Target Eye tracking: technology enables researchers to observe and record detail of responses eye movements and the precise of their attentional focus ○ Screen-based eye trackers: mostly used for research where the participant interacts or is exposed to the stimuli on a screen (simulation type deal) ○ Wearable eye trackers: ideal for studying behavior in real-world situations such as browsing the aisles of a supermarket, playing sport, navigating the subway system, consuming media in the home, human interaction, or working in a factory etc. Measurement process and types of measurement (nominal, ordinal, interval, ratio) and how you can or cannot use each type Nominal (lowest): Scales that partition data into mutually exclusive and collectively exhaustive categories, implying that every bit of data will fit into one and only one category and that all data will fit somewhere on the scale. ○ Mutually exclusive and collectively exhaustive categories, non-numerical, no ranking  ○ Ex. A classic example of nominal data measurement is gender (male or female), as the categories "male" and "female" have no inherent order or ranking between them; other examples include eye color (blue, brown, green), blood type (A, B, AB, O), or favorite color (red, blue, green) - all representing distinct categories with no meaningful order ○ Classification type data: Yes or no questions Nouns in general Gender race/ethnicity Occupation Text open-ended questions ○ Do you like this movie? Yes or no ○ "What is your favorite movie genre?" Example: Action, Comedy, Drama, Horror. These are distinct categories with no inherent ranking. ○ "Which character do you like the most?" Example: Harry Potter, Iron Man, Batman. These categories represent different characters, but there’s no order or rank between them. Ordinal: scales that maintain the labeling characteristics of nominal scales and have the ability to order data ○ Ranking type data Best liked, worst liked Win, place, or show First, second, third Small medium, and large Comparison rankings: “rank these movies from best to worst” ○ "Rank these movies from best to worst." Example: A person ranks Inception, Titanic, and The Matrix as 1st, 2nd, and 3rd. There’s a clear order, but the difference between rankings isn’t precise. ○ "How would you rate this movie? (Poor, Fair, Good, Excellent)" Example: A movie rated as "Excellent" is better than one rated "Good," but the difference between each level isn't known exactly. ○ An example of ordinal data measurement is a customer satisfaction survey where respondents are asked to rate their experience as "very dissatisfied," "dissatisfied," "neutral," "satisfied," or "very satisfied"; this clearly shows a ranking order, but the exact difference between each category isn't known. Interval: Scales that have the characteristics of ordinal scales, plus equal intervals between points to show relative amounts; they may include an arbitrary zero point. ○ Comparison Type data “1-10 scale” Age, income, etc as ranges ○ Rank this movie on a scale from 1-10  Example: If a person rates one movie as a 7 and another as a 5, the difference between them (2 points) is consistent with how much better they think one is over the other Ratio (highest): Scales that have the characteristics of interval scales, plus a meaningful zero point so that magnitudes can be compared arithmetically. ○ Flat Numeric type data age= 50 (not an age range) Income= $25,000 (not an income range) Number of children: _____ ○ "How many times have you seen this movie?" Example: Someone has watched a movie 5 times versus someone else who has watched it 0 times. The zero point is meaningful here, and you can say someone watched it five times more. ○ "How long is the movie in minutes?" Example: A movie is 120 minutes long, and another is 60 minutes. The first movie is twice as long, and there is a true zero point (a movie could have 0 minutes). Why is reliability and validity important in measurement? Be able to identify potential threats to measurement reliability and validity. Two scores on a measurement scale can differ for a number of reasons. McDonald’s may score higher on one person’s survey than on another person’s because of real differences in perceptions of the service or because of a variety of random or systematic errors. M=A+E M=measurement A=complete accuracy, and E=errors Reliability: The degree to which measures are free from random error and, therefore, provide consistent data ○ Reliable instruments provide stable measurements at different times under different conditions ○ A key question regarding reliability is “if we measure some phenomenon over and over again with the same measurement device, will we get the same or highly similar results?” Movie Ratings: If you ask a group of people to rate The Dark Knight on a scale from 1 to 10, and you get consistent results (e.g., most people rate it 8 or 9 every time, regardless of when or where the survey is taken), the scale is reliable. Box Office Earnings: If you measure the box office earnings of Avatar across different sources (theaters, streaming platforms) and always get the same figure ($2.8 billion), the measurement tool (earnings tracking) is reliable because the results are consisten Validity: The degree to which what the researcher was trying to measure was actually measured Measuring Movie Enjoyment: If you want to measure how much people enjoyed a movie, and you ask, “How many times have you seen this movie?” it might not be valid, as frequency of watching doesn’t necessarily indicate enjoyment. A more valid measure might be asking, “On a scale from 1 to 10, how much did you enjoy the movie?” Character Popularity: If your goal is to measure a character's popularity and you ask people “Who is your favorite movie character?” (and they consistently choose one character), that is a valid question for measuring popularity, as it directly aligns with what you're trying to assess. To be perfectly valid means you are also reliable Potential threats: A true difference in the characteristic being measured. A perfect measurement difference is solely the result of actual differences. Differences due to stable characteristics of individual respondents, such as personality, values, and intelligence. Differences due to short-term personal factors, such as temporary mood swings, health problems, time constraints, or fatigue. Differences caused by situational factors, such as distractions or others present in the interview situation. Differences resulting from variations in administering the survey. Differences due to the sampling of items included in the questionnaire. Differences due to a lack of clarity in the measurement instrument. Differences due to mechanical or instrument factors. True Differences: When there's a real difference between people in what you're measuring (like actual knowledge or skill). Stable Traits: Differences due to someone's personality or intelligence, which don't change much over time. Temporary Conditions: Differences caused by short-term things, like being in a bad mood, feeling sick, or being tired. Situational Distractions: Differences caused by distractions or other people being around when someone is answering. Survey Administration: Differences based on how the survey is given, like the way questions are asked or explained.  Question Sampling: Differences caused by which specific questions are included in the survey. Unclear Questions: Differences due to confusing or unclear questions in the survey. Instrument Problems: Differences caused by issues with the tool or device used for measurement, like a broken scale. What are the criteria and considerations for good questionnaire design? What to consider: Necessity ○ Do we need this question? Example: If you're trying to study movie preferences, a question like "Do you like animals?" may not be necessary because it doesn't help meet the research goal. ○ Can the research objective be fulfilled without asking this question? example: If you're researching customer satisfaction at a movie theater, asking about their favorite snack might not be critical to your main objective. ○ Exactly how am I going to use the data generated by this question? Example: If you ask, “What’s your favorite movie snack?” you need to be sure you’ll actually use this information to improve snack offerings or marketing. Participant knowledge ○ Do all participants know what were asking? Example: If you ask participants to rate fast-food restaurants they’ve never been to, they can’t provide accurate answers. It’s better to first ask, "Which fast-food restaurants have you visited in the past year?" and follow up with ratings for those. ○ Please rate the following fast-food restaurants on food quality (1=very poor, 7=very good) (list of fast food places) ○ Vs. better: use filler questions “Within the last year, which of the following fast-food restaurants have u visited” ” How often do you eat there” Question-wording ○ Complex or unclear phrasing Example: "I'm always thinking about remodeling and upgrading my home" assumes participants are constantly thinking about home improvement, which might not be true for everyone. ○ Better phrasing: Break it down into clearer questions, like "Have you considered remodeling your home in the past year?" This makes it easier for participants to answer accurately. ○ “I'm always thinking about remodeling and upgrading my home” “I love using technology and entertainment but I hate shopping for them”  ○ ○ ○ Frame of reference Why are the key requirements to demonstrate causation? Be prepared to identify whether a research finding has sufficiently demonstrated causation or whether they are lacking the requirements to demonstrate causation. Key requirements Concomitant variation (measured via correlation)- There must be a consistent relationship between two things. If one changes, the other changes too. Example: People who watch more TV tend to have higher rates of obesity. Temporal antecedence (cause comes before effect): The cause must happen before the effect.  Example: If watching TV causes obesity, then TV watching must come before the weight gain. No third factor driving both events: There should be no other factor influencing both the cause and the effect. Example: If busy or neglectful parenting is causing both more TV watching and obesity, then TV isn’t the real cause. ○ No spurious association ○ Elimination of alternative explanations Examples TV and Obesity: 1. Correlation: Yes, more TV is linked to more obesity. 2. Temporal Antecedence: No, we can’t say TV watching happens before weight gain. 3. No Third Factor: No, neglectful parenting could cause both more TV and obesity. Living Together Before Marriage and Divorce: Correlation: Yes, couples who live together first tend to have higher divorce rates. Temporal Antecedence: Yes, living together comes before divorce. No Third Factor: No, being less religious or more liberal might lead to both living together and getting divorced, so cohabiting might not be the direct cause. Does watching TV cause obesity? ○ 1. Correlation - more tv/more obesity ○ 2. Temporal Antecedence - NO - one does not precede the other ○ 3. No third factor driving both - NO - neglectful or busy parents Does living together before marriage lead to divorce? ○ 1. Correlation - People who live together first are more likely to get divorced ○ 2. Temporal antecedence - Living together leads to divorce ○ 3. No third factor driving both - NO - Being less religious and more liberal could lead to both events Be able to identify key variables of an experiment: independent variable, dependent variable, treatment variable, extraneous variable. Independent: variables one controls directly such as price, packaging, distribution, product features, etc - Price of a Movie Ticket: A theater can control how much they charge for tickets. - Packaging of a DVD: A movie studio can change the design or material of DVD packaging to make it more appealing. Dependent: variables one does not directly control such as sales or customer satisfaction - (might control by manipulating the independent variable)  - Movie Ticket Sales: The theater can't directly control how many people buy tickets, but by changing the price (independent variable), they might influence it. - Customer Satisfaction: A studio can't control how satisfied customers are, but they might influence satisfaction by improving the movie quality (independent variable). Treatment: : the independent variable manipulated during an experiment to measure its effect on the dependent variable - Discount on Movie Tickets: If a theater offers a discount (the treatment), they are manipulating the price (independent variable) to see how it affects ticket sales (dependent variable). - Changing Movie Trailer Length: A studio tests different trailer lengths (treatment) to measure the effect on viewers' intent to watch the movie (dependent variable). Extraneous: factors one does not control but must live with, such as the weather - Weather: A theater can't control the weather, but bad weather could affect how many people go to the movies, even if ticket prices are low. - Holiday Season: A movie release during the holidays may get more viewers, but the timing is outside the studio's control and affects ticket sales. - Why is randomization important in an experiment? Linz shop example: group of men won't be good if we're trying to expand target market - Equal Chance for All Participants: Ensures everyone has an equal chance of being placed in any group. - Reduces Bias and Controls Unknown Factors: Reduces bias and controls unknown factors. - Increases Group Similarity: Makes groups similar, increasing accuracy and fairness of results. - Improves Internal Validity: Increases internal validity by avoiding systematic differences between groups. - Prevents Results by Chance: Helps ensure differences in outcomes aren’t just due to chance. - Controls Hidden Variables: Controls hidden variables that could affect results. - Fair and Representative Sampling: Sample represents the population fairly. - Eliminates bias ensuring that all participants have an equal chance of being assigned to different groups - Controls for unknown factors - Reduces selection bias - Increases internal validity because it ensures that there are no systematic differences between the participants in each group - Increases the likelihood that differences in the outcomes of the groups are not related to chance alone - Prevents bias and makes the results fair - All people have an equal chance of getting into the experiment  - Groups that are made for conducting experiments are as similar as possible to each other so results come out as accurate - Controls lurking variables which can affect results to be different from what they are supposed to be - Sample is meant to be representative of population and fairly selected Internal validity and external validity of an experiment. Why are they important? Internal validity means that in an experiment, you've made sure the results aren’t influenced by other factors, and you can confidently say the independent variable (IV) caused the change in the dependent variable (DV). Example: In a lab experiment testing whether a new movie trailer increases ticket sales, you control everything—like how many people see the trailer and when they see it—so the only thing affecting sales is the trailer itself. Key Question: Have we controlled all other factors that might explain the results? If outside factors (like weather or competitors’ ads) affect the outcome, it’s hard to say if the trailer alone caused the sales increase. External validity refers to how well the results of an experiment apply to the real world. It’s about whether the cause-and-effect relationship you found in the experiment can be generalized to other people, places, or times. Example: You test the movie trailer in one city, and sales go up. But can you say the same will happen in other cities, at different times, or with different groups of people? Key Question: Can the results we found in this experiment be applied to other situations or people? If the experiment conditions don’t reflect the real world, the results might not apply outside the experiment. Internal Validity is about eliminating other possible explanations in your experiment. External Validity is about whether the findings can be applied outside the experiment to other real-world situations. Internal validity- the extent to which competing explanations for the experimental results observed can't be ruled out. Lab experiments do this well-you can control all the little details and eliminate other explanations but you may lack… In an experiment:  Have we established an environment where we have eliminated most other explanations? If the effects are influenced or confounded by extraneous variables, it is difficult to draw valid inferences about a causal relationship between an IV and a DV In a perfect world, the only thing that will change will be your manipulation External validity- The extent to which casual relationships measured in an experiment can be generalized to outside persons, settings, and times. Field experiments do this well-you see results in the real world, but there's often a lot of outside noise In the experiment: Does the cause-and-effect relationships found in the experiment generalize outside of the sample we collected? If so, to what populations, settings, times, etc.? Threats to external validity arise when a specific set of experimental conditions do not realistically take other relevant “real world” variables into account You need both types of validity to draw valid conclusions about the effects of the IV’s on the test units and to make valid generalized to a large population of interest