St Edwards University Week 3 Buyer Behavior Live and Decision Making Paper

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Business Finance

St Edwards University

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please review the recording and then write a paragraph (3-5 sentences) summarizing the most salient points from the session from your perspective.

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Week 3 High and Low Effort Decision Making and Post Decision Processes and Discriminant Analysis Need Recognition Present Status Preferred State Marketing helps consumers recognize an imbalance between present status and preferred state. Information Search Internal Information Search Recall information in memory External Information Search Seek information in outside environment • Nonmarketing-controlled • Marketing-controlled External Information Searches Need Less Information Less Risk More knowledge More product experience Low level of interest Confidence in decision Need More Information More Risk Less knowledge Less product experience High level of interest Lack of confidence High-Effort Judgment Processes • Judgment: Evaluation of an object or estimation of likelihood of an outcome or event • Decision-making: Making a selection among options or activities High-Effort Consumer Decisions • Deciding which brands to consider All possible alternatives Consideration Set Inept Set Inert Set Awareness Set Brand versus Attribute Models Brand processing Attribute processing Multiattribute expectancy-value model: Type of brand-based compensatory model Additive difference model: Brands are compared by attribute, two brands at a time Conjunctive model: Sets minimum cutoffs to reject bad options Lexicographic model: Compares brands by attributes, one at a time in order of importance Disjunctive model: Sets acceptable cutoffs to find options that are good Elimination-by-aspects model: Similar to lexicographic model but adds the notion of acceptable cutoffs High-Effort Feeling-Based Decisions • Consumers tend to be more satisfied after making a feeling-based decision • Emotions aid thought-based decisions • Brands can be associated with positive or negative emotions What Affects High-Effort Decisions • Customer characteristics – Expertise, mood, time pressure, extremeness aversion, metacognitive experiences What Affects High-Effort Decisions • Decision Characteristics – Information availability, information format • Group context – Individual-alone goals, Individual-group goals Types of Heuristics Representativeness heuristic: Comparing a stimulus with the category prototype/exemplar Availability heuristic: Basing judgments on events that are easier to recall Unconscious Low-Effort Decision-Making People may make a decision without being consciously aware of how or why they are doing so • Customer decisions are affected by visual aspects • Influenced by environmental stimuli • Automatic goal-related behavior The Learning Process Cognitive Decision Making: • Performance-Related Tactics • Habit • Brand Loyalty Cognitive Decision Making: • Price • Normative Choice Tactics Low-Effort Feeling-Based Strategies Affect-related tactics: Tactics based on feelings – Affect does not necessarily result from a conscious recognition of need satisfaction – Decisions are based on affect referral Low-Effort Feeling-Based Strategies • Brand familiarity • Variety-seeking • Buying on Impulse Satisfaction and Dissatisfaction •Satisfaction A mild, positive emotional state resulting from a favorable appraisal of a consumption outcome •Dissatisfaction A mild, negative affective reaction resulting from an unfavorable appraisal of a consumption outcome Equity Theory Proposes that consumers cognitively compare their own level of inputs and outcomes to those of another party in an exchange • If outcomesA/inputsA ≈ outcomesB/inputsB, then satisfaction will be positively affected Attribution Theory Focuses on explaining why a certain event has occurred • Elements – Locus - Judgments of who is responsible for an event – Control - The extent to which an outcome was controllable or not – Stability - The likelihood that an event will occur again Cognitive Dissonance Uncomfortable feeling that occurs when a consumer has lingering doubts about a decision that has occurred Why Net Promoter Score • Measuring satisfaction is a start • But it is not a good indication of customer loyalty and purchase intention 23 NPS Driving advocacy in the enterprise Why Net Promoter Score • Plenty of satisfied customers defect from businesses every day – Increased customer retention – Lengthened customer retention – More products per customer – Cost of servicing may drop – Customer referrals Satisfaction Loyalty Advocacy Net Promoter Score 25 Three Customer Clusters • Promoters – Those customers who have the highest rates of repurchase and referral, and will be advocates for your business • Satisfied – Passively satisfied customers stay with a particular company more due to inertia than true loyalty • They would move for a better deal • Detractors – Those customers (or employees!) who spread negative word-of-mouth comments about the company • Negatively impacts company’s reputation, ability to attract new customers and employee morale On to Loyalty and Advocacy •Know what matters to your customers •Know how you stack up on these measures •Do something to fix them •Possible measures Drivers of satisfaction Perceived value Service level satisfaction Competitiveness of fees Product level satisfaction How well you listen Importance vs satisfaction The “closeness” of your relationship Likelihood to recommend Likelihood to transact Likelihood to extend the relationship Relationship strength Discriminant Analysis Discriminant Analysis • Statistical technique used to examine whether two or more mutually exclusive groups can be distinguished from each other based on a linear combination of predictor variables • Similar to Regression, except that the dependent variable is categorical rather than continuous. • Logistic regression is limited to 2 groups for the dependent variable. Discriminant Analysis • Uses of discriminant analysis – Good and bad credit risk – Respond to a marketing action – Purchase a new product – Purchase which version of a product (sedan, sports car, SUV, etc.) Discriminant Analysis 1. Determine if statistically significant differences exist between the two (or more) a priori defined groups. 2. Identify the relative importance of each of the independent variables in predicting group membership. 3. Establish the number and composition of the dimensions of discrimination between groups formed from the set of independent variables. 4. Develop procedures for classifying objects (individuals, firms, products, etc.) into groups, and then examining the predictive accuracy (hit ratio) of the discriminant function Discriminant Analysis Discriminant Analysis Example • As the marketing manager for a major airline, you like to know if business and personal travelers differ by their satisfaction and age, and, if so, can a model be built to predict passengers flying on business or personal travel. Discriminant Analysis Example Select: Variables Select: Analysis> Classify> Discriminant Select: Define Range Enter the numbers for the levels in the dependent variable Discriminant Analysis Example Selecting Statistics gives this dialog box Select the output indicated above Selecting Classify gives this dialog box Select the output indicated above Discriminant Analysis Example Selecting Statistics gives this dialog box Select the output indicated above Discriminant Analysis Example The Analysis Case Processing Summary shows how many valid cases are in the model. It also shows the number of missing cases. This is very important. Discriminant Analysis is sensitive to missing cases. There should be no missing cases. If there are missing cases, the researcher needs to determine what variables have he missing cases and remove the variables, or use the option in the analysis “Replace missing values with mean.” Discriminant Analysis Example Shows the mean, std. deviation and sample size for each group Looking to see if Wilks’ Lambda is significant for each variable p < .05. If it is, the variable discriminates between the groups. Looking to see that predictor variables are not correlated. Should be less than .8 Discriminant Analysis Example Looking to see if the log determinants are fairly similar Box’s M tests the equality of the covariance matrices. Want a non-significant result (greater than .001) If result is significant (less than .001), it is still okay to continue. Discriminant analysis is not strongly sensitive to deviations from equality of the covariance matrices. Discriminant Analysis Example Looking for high eigenvalue. Higher values indicate more variance explained by the model. Also, looking for high Canonical Correlations. Shows how well the prediction model fits. The higher the Wilks’ Lambda the better. Want this to be significant (p < .05) Discriminant Analysis Example Look at this two tables together Shows relative importance of the predictors Important to find consistency between these two tables. The coefficients in each table are relatively similar and the order is the same. No value in the structure matrix should be less than +/- .3 Discriminant Analysis Example Discriminant function coefficients for the Discriminant score equation Mean discriminant function scores for each group Discriminant Analysis Example Probabilities for group membership based on number in each group divided by the total. This is the probability prior to running the analysis These are the functions for calculating group membership based on the discriminant analysis Discriminant Analysis Example Shows the percentage of cases classified correctly Also, this gives the overall percentage that were classified correctly Discriminant Analysis Example Gives result for individual cases in the variable view Calculated group membership Discriminant score Discriminant score Discriminant score for group 2 for group 1 (Personal) (Business) Summary • Discriminant analysis uses a categorical dependent variable with continuous independent variables • Discriminant analysis can be used to predict group membership • Discriminant analysis is a powerful tool used in marketing to access credit risk, product usage, and responses to marketing actions
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Running head: BUYER BEHAVIOR

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Buyer Behavior Live
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