Final Study Guide PDF

1. First Principles of Marketing Strategy 1. Know Your Customer: Understand what your customers need and want. Segment the market into different groups and focus on the most valuable one. 2. Offer Value: Make sure your product or service offers something unique and important to customers...

1. First Principles of Marketing Strategy 1. Know Your Customer: Understand what your customers need and want. Segment the market into different groups and focus on the most valuable one. 2. Offer Value: Make sure your product or service offers something unique and important to customers. Highlight the benefits, not just the features. 3. Do Your Research: Gather data and insights about your customers, competitors, and market trends to make smart decisions. 4. Brand Positioning: Position your brand to stand out and resonate with the target audience. Keep the brand message consistent. 5. Clear Messaging: Communicate clearly and persuasively to your customers. Tell a compelling story that connects emotionally. 6. The 4 Ps: Product: Create a product that meets customer needs. Price: Set a price that reflects its value. Place: Make it easy for customers to buy. Promotion: Use the right channels to reach and persuade customers. 7. Competitive Edge: Find something that sets you apart from competitors, like better service, lower costs, or unique features. 8. Measure and Improve: Track how well your marketing works using key metrics. Adjust and improve based on what you learn. 2. Type of interviews Structured Interview ○ Series of questions ○ Easy to compare answers ○ EX: “From the following five terms, tell me which coffee bean you want to try most” Unstructured Interviews  ○ Free conversation ○ Elaborate and detailed answers ○ Difficult to compare answers ○ EX: “Please tell me your story with Reviva” Semi-Structured Interviews ○ Preceded by observation, informal and unstructured interviewing to allow researchers to develop an understanding ○ Compared to unstructured interview - researcher has more control over the content ○ Compared to structured interview - researcher has more guidance over the content ○ EX: “Why do you like Reviva?” Exploratory Research ○ Allows researcher to gather insights that may not be fully understood Descriptive Research ○ Enables researcher to gain more detailed info about existing issues Explanatory Research ○ Identifies the extent and nature of cause-and-effect relationships Exploratory Research Descriptive Research Explanatory Research Nature Nature Nature 1. Unstructured 1. Structured 1. Semi-Structured 2. Semi-Structured 2. Structured 3. Typical Mistakes in Survey Design Leading questions ○ Questions that suggest or prompt a particular answer ○ EX: “shouldn’t everyone have a navigation system in their car?” Loaded questions ○ Questions that contain assumptions or emotional appeals that may influence responses ○ EX: “if navigation systems were shown to help us decrease problems, would you purchase one?” Double-Barreled questions  ○ Combining two or more questions into one, making it unclear which part the respondent is answering ○ EX: “would you consider purchasing a navigation system if it saved you time or money?” Overstated questions ○ Using exaggerated language that can mislead or pressure respondents ○ EX: “do you think a navigation system can help you avoid traffic jams that may last for hours?” Unfocused questions Complex wording Poor questionnaire requirements Lack of monitoring Failure to tailor survey modes 4. Independent Samples t-test Purpose ○ Tests for significant differences between the means of two independent groups ○ Commonly used in segmentation studies to compare groups (e.g demographics, customer preferences) Assumptions ○ Two groups must be independent ○ Data should be normally distributed ○ Variances of the two groups should be equal SPSS Implementation ○ SOSS reports only the t-value for statistical testing ○ Null Hypothesis: no difference exists between the means of the two groups ○ If the null hypothesis is rejected, it indicates a significant difference exists Advantages ○ Easy to apply for simple two-group comparisons ○ Efficient for sample sizes when compared to a z-test Example Scenarios ○ Comparing customer satisfaction scores between 2 stores ○ Evaluating the effectiveness of two marketing strategies 5. ANOVA Purpose ○ Tests for significant differences among the means of three or more groups  ○ Answers whether observed differences between group means are due to sampling error or actual population differences Assumptions ○ Groups are independent ○ Data is approximately normally distributed SPSS Implementation ○ Outputs whether a statistically significant difference exists among group means ○ Doesn’t identify which pairs of groups differ Post HOC Tests ○ Conducted when ANOVA shows significant differences Advantages ○ Simultaneously tests for differences across all groups, reducing Type I error ○ Automatically arranges group means for easier understanding of significant differences Example Scenarios ○ Analyzing differences in customer satisfaction across 3+ stores ○ Comparing the effectiveness of multiple advertising campaigns 6. Reliability, validity and their types Validity - the extent to which a tool can measure ○ Types of Validity Content - the degree to which the content of a test represents the construct EX: student satisfaction surver Construct - the degree to which a test measures the intended theoretical construct EX: questions like financial habits in a depression scale Criterion - the correlation between the test score and an external criterion Concurrent Validity: the new anxiety scale is tested by comparing its results with an already-established anxiety measure Predictive Validity: a personality test to assess candidates’ conscientiousness: if employees with higher conscientiousness scores perform better after six months of the job, the test has predictive validity Reliability - the consistency and stability of a tool  ○ Types of Reliability Internal Consistency - consistency of results across items within the same test Test-Retest Reliability - stability of results over time Inter-Rater Reliability - agreement between different observers 7. Multiple regression analysis Definition and Purpose: ○ Uses more than one independent variable to predict the dependent variable ○ Identifies which factors predict the dependent variable, the direction of their influence, and their importance Steps in Conducting Multiple Regression Analysis 1. Choose variables Identify dependent and independent 2. Check for linear relationships Use ANOVA and F-statistics to confirm significance 3. Evaluate Significance of Variables Use beta coefficients and significance levels 4. Measure Relationship Strength Use adjusted R^2 to understand the percentage of variance 5. Interpret Findings 8. Moderator and moderation effect Moderator - a variable that influences the strength or direction of the relationship between x and y ○ Purpose Changes how or when the effect of x on y occurs Moderation Effect - occurs when the relationship between x and y varies depending on the level or category of the moderator Steps to Test Moderation Effect in SPSS: 1. Prepare Your Data a. Ensure x, y and m variables b. Standardize (mean-center) x and m 2. Create the Interaction Term a. Multiply x and m to generate an interaction term 3. Conduct Hierarchical Regression a. Model 1 (main effects): add x and m to predict y b. Model 2 (interaction effect): add the x * m interaction term to the model  4. Interpret Results a. Model r^2 change b. Interaction term: if significant (p > 0.05), this confirms the moderation effect 5. Plot the Interaction 9. Mediator and mediation effect Mediator - a variable that explains the mechanism or process through which x influences y ○ Purpose Provides insight into why or how x affects y Mediation Effect - occurs when x influences y through a mediator ○ Partial Mediation The mediator explains PART of the relationship between x and y, but x still HAS a direct effect on y ○ Full Mediation The mediator COMPLETELY explains relationship between x and y, leaving NO direct effect of x on y Baron and Kenny’s Stepwise Regression Approach for Testing Mediation ○ Step 1: Test the Total Effect Conduct regression analysis with y as outcome and x as predictor ○ Step 2: Test the Direct Effect of X on M Conduct a regression analysis with m as the outcome and x as the predictor ○ Step 3: Test the Joint Effect of X and M on Y Conduct a regression analysis with y as the outcome and include both x and m as predictors If m significantly affects y and the effect of x on y diminishes completely, this indicates full mediation 10. Cluster analysis and its application Cluster Analysis - used to classify cases (e.g. individuals, products) into clusters based on a specific set of variables ○ Purpose Ensures groups are similar within themselves and different from eachother