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Multicenter Study
. 2014 Jan;133(1):52-7.
doi: 10.1097/01.prs.0000436528.78331.da.

The Best Marketing Strategy in Aesthetic Plastic Surgery: Evaluating Patients' Preferences by Conjoint Analysis

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Multicenter Study

The Best Marketing Strategy in Aesthetic Plastic Surgery: Evaluating Patients' Preferences by Conjoint Analysis

Nick Marsidi et al. Plast Reconstr Surg. .

Abstract

Background: To provide the best marketing strategy for a private clinic, knowledge of patients' preferences is essential. In marketing, conjoint analysis has been frequently used to calculate which attributes of a product are most valuable to consumers.

Methods: This study investigates the relative importance of attributes that influence the selection and decision-making process when choosing an aesthetic private clinic, using conjoint analysis. The following attributes were chosen by the senior author (R.W.L.) and a marketing and communications director after a preselection of 25 randomly selected people: relative cost of the procedure, travel time, experience of the plastic surgeon, size of the clinic, method of referral, and online presentation. The attributes were then divided into levels. Using a random factor conducted by SPSS, 18 different scenarios were created and rated online by 150 potential patients before their potential visit or consultation. The patients could rate these scenarios on a scale from 1 to 7 with respect to the likeliness of visiting the clinic.

Results: The most important attribute was experience of the surgeon (35.6 percent), followed by method of referral (21.5 percent), travel time (14.2 percent), cost of procedure (12.9 percent), online presentation (9.7 percent), and size of the clinic (6.1 percent). Six of 16 levels gave a negative influence on the decision making.

Conclusions: The authors' study shows that the two most important attributes are the experience of the surgeon and the method of referral and that conjoint analysis is effective in determining patients' preferences. It also shows which levels positively or negatively contribute per attribute.

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