1 / 17

Intergrating consumer choice modeling and behavioral research

Intergrating consumer choice modeling and behavioral research.

Faraday
Download Presentation

Intergrating consumer choice modeling and behavioral research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. INTEGRATING CONSUMER CHOICE MODELING AND BEHAVIORAL RESEARCH RUSSELL S. WINER STERN SCHOOL OF BUSINESS NEW YORK UNIVERSITY

    3. AROUND 1980: BOB LUCAS’S WORK ON RATIONAL EXPECTATIONS WORK ON BEHAVIORAL PRICING (GABOR, GRANGER, MONROE)

    4. PUT ALL TOGETHER AND DEVELOPED MY OWN EMPIRICAL RESEARCH “STYLE” INTERESTED IN SECONDARY BEHAVIORAL DATA (USUALLY, PANEL DATA) ALSO INTERESTED IN THEORY TESTING BACKGROUND IN ECONOMETRICS => BEHAVIORAL MARKETING SCIENTIST

    5. LOGIT MODELS THE BASICS ARE FROM A RANDOM UTILITY MODEL: A CONSUMER WILL CHOOSE BRAND i ONLY IF Ui > Uj WHERE Ui = V(Zi, S, ß) + ľi V IS THE DETERMINISTIC COMPONENT OF UTILITY, ľ IS THE RANDOM COMPONENT

    6. ASSUMING THE ľ COMPONENTS ARE DISTRIBUTED INDEPENDENTLY AND IDENTICALLY ACCORDING TO AN EXTREME VALUE DISTRIBUTION, THE BEAUTIFUL RESULT IS Pi = eVi / SeVj THIS IS SIMPLE TO ESTIMATE USING MAXIMUM-LIKELIHOOD METHODS

    7. KEY MARKETING APPLICATION: GUADAGNI AND LITTLE (1983, MKT.SCI.) USED THE MULTINOMIAL LOGIT MODEL ON COFFEE HOUSEHOLD SCANNER PANEL DATA VARIABLES INCLUDED BRAND/SIZE LOYALTY, PROMOTION (YES/NO), SIZE OF PROMOTIONAL PRICE CUT, PRICE, BRAND CONSTANTS LATER WORK HAS EMPHASIZED TECHNICAL IMPROVEMENTS (E.G., ACCOUNTING FOR UNOBSERVED HETEROGENEITY)

    8. WHY TRY TO INTEGRATE BEHAVIORAL CONCEPTS INTO CHOICE MODELS? BRINGS MORE REALISM GIVEN THAT INDVIDUAL-LEVEL DATA ARE BEING USED FOR ESTIMATION HELPS TO “TRIANGULATE” RESULTS PREVIOUSLY FOUND IN THE LAB

    9. MCGRATH, ET.AL.: JUDGMENT CALLS IN RESEARCH

    10. MCGRATH ET.AL. TAKE A “DILEMMATIC” VIEW OF RESEARCH ALL INDIVIDUAL RESEARCH STRATEGIES AND METHODS ARE SERIOUSLY FLAWED LAB EXPERIMENTS, FIELD STUDIES, PANEL ANALYSES, ANALYTICAL WORK ARE PERFECTLY COMPLEMENTARY

    11. EXAMPLE #1: DIRECT APPLICATION OF A PSYCHOLOGICAL CONSTRUCT CONSTRUCT IS “REFERENCE PRICE” (WINER JCR 1986) MODEL: Pi=f[LOYALTY, ADVERTISING EXPOSURE, (PRr – PRo), PRo] REFERENCE PRICES CALCULATED IN SEVERAL WAYS LATER ADDED ASYMMETRY TO GAINS AND LOSSES (MAYHEW AND WINER JCR 1992)

    12. EXAMPLE #2 STIVING/WINER JCR 1997: “AN EMPIRICAL ANALYSIS OF PRICE ENDINGS WITH SCANNER DATA” RESEARCH QUESTION: HOW DO CONSUMERS PROCESS PRICE ENDINGS?

    13. HYPOTHESES H1: CONSUMERS USE A HOLISTIC PRICING PROCESS H2: CONSUMERS WEIGHT PRICE DIGITS DIFFERENTLY H3: CONSUMERS TRUNCATE H4: CONSUMERS USE LEFT-TO-RIGHT PROCESSING

    14. LOGIT MODELS MODEL 1: U = …. + ß*PRICE MODEL 2: U = …. + ß*DIME + ?*PENNY MODEL 3: U = …. + ß*DIME MODEL 4: U = …. + ß*DIME + ?*PENNY+a*d*PENNY, WHERE d=1 IF THE DIMES ARE EQUAL

    15. THIS APPROACH HAS THE BEST OF BOTH EXPERIMENTAL AND EMPIRICAL WORK: USES THE ACTUAL CHOICES OF REAL PEOPLE IN A NATURAL SETTING ALLOWS YOU TO TEST ALTERNATIVE THEORIES ABOUT HOW CONSUMERS PROCESS PRICE INFORMATION

    16. RISKS CAN’T BE USED FOR ALL PROBLEMS LOW INTERNAL VALIDITY VIEWED AS BEING “STUCK-IN-THE-MIDDLE”

    17. SUMMARY NUMEROUS EXAMPLES OF SYNERGIES BETWEEN MARKETING AND BEHAVIORAL SCIENCE/BDT DON’T BE DOGMATIC ABOUT THE PARTICULAR RESEARCH METHOD IN WHICH YOU ARE TRAINED TIP: FIND CO-AUTHORS WITH COMPLEMENTARY INTERESTS/STRENGTHS: TRIANGULATE HERE AS WELL

More Related