Money Meets Motivation: Segmentation via Reservation Prices and Perceived Value Tradeoffs

1. Money Meets Motivation: Segmentation via Reservation Pricesand Perceived Value Tradeoffs Michael Mulvey, University of Ottawa Charles Gengler, Baruch College 2008 Behavioral Pricing Conference, Drexel University

2. Goals of this Research • To build a model of perceived value based on a dollar metric • To measure the drivers of perceived value with minimal a priori assumptions • To present a methodology where consumers can be segmented based on their perceived value perspectives

3. Measuring Drivers of Perceived Value • Value is what consumers receive through consumption • Perceived value is what drives consumer choice • Perceived value is the consumers expectancy of what they will get through consumption • We need to measure the construction of these expectancies

4. Reservation Price as aMonetary Metric of Perceived Value • The maximum amount a person is willing to pay for a product is measure of the perceived value they see in that product. • We measured reservation price through a series of price trade-offs using a Visual Basic program

5. Measurement of Reservation Price • A set of competitive products was displayed at an equal low starting price, and the participant was asked to choose the most preferred. • The price of the chosen option was increased by an increment, and respondents were asked to choose again • This was repeated until the respondent choose none of the above, indicating all had reached a price one increment above the maximum they would pay.

6. Means-End Theory as an Expectation of Value • Means-End Theory proposes a mechanism of how consumers find value in products (see Zeithaml 1988) • Using laddering to interview participants, we gain elaborations of the key value dimensions they perceive in a preference decision

7. Using Laddering to Measure Value Dimensions • Each time that participants delineate a rationale of how they expect to receive value (a means-end chain), they are asked to rate the full set of products being studied on that dimension

8. Model Construction • Difference model, where Rxy= 1V1xy + 2V2xy + … NVNxy • Where • Rxy is the difference in reservation price between product X and product Y • Vnxyis the difference in performance rating on dimension n

9. Latent Cluster Regression • Latent Cluster Regression is performed to obtain a solution segmented based on value dimension salience “Ignoring heterogeneity offers an incompleteand potentially misleading view of the market”(DeSarbo, Jedidi and Sinha 2001)

10. A Study of In-Theatre Film Choice Participants Procedure • Movie theatre patronage • 74% attended on a monthly basis • 100% attended in past year • Movie rental • 78% rented every month • 97% rented in the past year • 73 undergraduate business students • 39 female and 34 male • Median age of 21 years • Watch 5 movie trailers • Viewing order randomized • Rank movie preference • Use Price Tradeoff Tool to obtain reservation prices • Conduct one-on-one “laddering” interview • Elicit preference drivers • Rate expectancies of movie performance for each self-stated criterion

11. Movie Stimuli • Trailers of films not yet released • Films epitomized different genres • Trailers were available as digital files, to allow repeated viewings of the trailers in a controlled setting • Doctor Dolittle (1998) with Eddie Murphy • The X-Files (1998) with David Duchovny, Gillian Anderson • Payback (1999) with Mel Gibson • Without Limits (1998) with Billy Crudup, Donald Sutherland • Sliding Doors (1998) with Gwyneth Paltrow

13. Results Suggest Six Value Dimensions

14. Movie MDS Map based on Preference Drivers High degree of correspondence with Frye’s Taxonomy of Myths (1957) [Stern 1995] Alignment with generic “genre” plotlines provides a theoretically-grounded validation of the findings. R2 = .975; Stress = .045

15. Model Results • A 4-segment solution was selected based on R2 contribution, classification error ratio, parsimony, and face validity

16. Reservation Prices by Gender “Guy movies?” Gender differences in reservation price “Chick flick?”

17. Reservation Prices by Latent Segment and Gender Minimum& maximum reservation prices Movie preference differences Segment-specific differences Gender differences in segment composition

18. Latent Cluster Regression Results 4-segment model fits the data well

19. Chick Flicks & Guy Movies? • Differences in mean reservation prices had a strong pattern of differentiation between gender • But, when analyzing differences in what respondents would pay (as opposed to raw amounts) the effect of gender dissipated • This indicates that the model is measuring true motivations for preference, rather than a proxy for these motivations 2008 Behavioral Pricing Conference

20. Contributions • Developed and presented a model for measuring the drivers of perceived value without preconceptions • Linked qualitative insights provided by Means-End Theory with reservation price (“brought Zeithaml 1988 to the workbench”) • Accounted for heterogeneity in reservation prices by deriving segments based on the drivers of perceived value • Concurrent validity of reservation price (give up) and preference ratings (get) measures of perceived value provide strong support for the data collected in laddering interviews • Demonstrated how first-step one-on-one interviews can deliver much more than purely qualitative results