In product choice models, incorporating socio-demographic characteristics is conceptually appealing and has numerous managerial benefits. The main question is how socio-demographic characteristics differentiate customers or products in order to define relevant marketing strategies. Traditionally Discrete choice models, also called the consolidated approach, have been used to understand, describe, and predict consumer choices for a particular product. In this study, a segment-oriented approach is introduced and compared with the consolidated approach. The proposed methodology uses principal component analysis to segment the population into different groups. The results indicate that principal component analysis is a robust method and outperforms the consolidated approach in terms of choice predictability and R-square index. These findings bring new insight to manufacturers as well as marketers regarding customer policies targeting.

Appropriate decision making about site selection for Residential Units by managers of cities, Housing construction companies, and recreational and educational centers, is required to understand the preferences of households in the area. The purpose of this paper is to investigate the factors influencing the choice of residential location and measuring the impacts of these factors on probability of the choice. For this purpose, the multinomial logit model was used, and different city areas were classified based on the value of real estate transactions. Then, we sampled from categories randomly based on residential density. Totally 383 households were sampled. The results showed that the household economic factors included income, price, Rent housing units, household, being House or apartment, access to recreational and educational centers, time and costs of transportation; are the main determining factors. The results of this study proved that. the behavior and preferences of families in different neighborhoods is not the same. for example, the increase in prices and rents in neighborhoods with low trading value, have a negative effect on the selections, but in neighborhoods with higher trading value, it means the improvement of the quality of housing, and increased the probability if choices. Also The tendency to apartment units in neighborhoods with high trading value, is higher than low-cost areas. Due to differences in the behavior of households in selecting housing units in different neighborhoods by stakeholders, influential and relevants, is very important.

Choosing housing is the most basic need of every household. Therefore, identification and evaluation of effective factors in choosing housing from the viewpoint of the homeowner's households will play an important role in helping urban managers to promote and improve the housing conditions of the people. Accordingly, the present study was conducted to investigate and evaluate the factors affecting the probability of choosing housing with emphasis on the demographic characteristics of homeowning households in Isfahan. The research method is applied in terms of purpose and is descriptive-survey based on the data collection method. Data gathering method, documentary method by studying and investigating previous research, and field method. Data collection was done by designing empirical questionnaire, randomly from 300 households. These data were categorized in the EXCEL software and analyzed using the Discrete multinomial logit model (MNL) in STATA software based on Random Utility Theory (RUT). The results of this study, taking into account the Z statistic of the variables, indicated that 11 variables were confirmed related to the properties of the housing, as well as the confirmation of 20 variables demographic variables defined in The model is relevant. The most influential demographic variables include: having children aged 18 and above, the number of home purchases, the reason for settling in Isfahan and the level of social relationships between the individual and his family, respectively, with 41, 37, 36, 33%, the coefficient of positive effect on the probability of choosing in the multiple logit function. The results showed that the socio-economic are important and, in urban housing studies, should be specifically considered

In product choice models, incorporating socio-demographic characteristics is conceptually appealing and has numerous managerial benefits. The main question is how socio-demographic characteristics differentiate customers or products in order to define relevant marketing mix strategies or what a disaggregated analysis reveals about the customer's profile of company products. Traditionally econometrician and marketers apply Discrete choice models into a total population or a sample of consumers (naming as consolidated approach) in order to understand, describe, and predict consumer choices for a particular product and assess socio-demographic variation on the utility functions and subsequently analyse the difference between segments behaviour in order to identify differences between target consumers. The Multinomial Logit model developed based on data obtained from new car buyers of SAIPA automotive company and final model including all significant variables and their estimated parameters on utility functions of all products presented. In this study, a segment-oriented approach is introduced and compared with the consolidated approach. The proposed methodology using principal component analysis to segment the population (or sample) into different groups so as the results indicate principal component analysis is a robust method in order to reach to differentiated segments among population as expected and finally the result is compared with consolidated approach which indicates the segmented approach outperforms the consolidated approach in terms of choice predictability and R-square index of the model. The results bring a new insight to car manufacturers as well as marketing planners concerning policy setting about customer characterizing and targeting that affects marketing mix strategies.

The present study evaluates the effect of some of the most important variables affecting the preferences of bank loan applicants, based on the Discrete choice test method. Initially, the variables and their levels were identified after consultation with banking experts and the required information was collected through a questionnaire. The results show that any increase in interest rates, intensification of bank supervision over the use of facilities, no grace period, and the existence of personal guarantees of company managers reduce the desire to obtain facilities and therefore the possibility of referring to the banking system for financing Obtaining facilities from banking resources will reduce. Given that the facility interest rate is considered as a monetary (normalizing) variable, the calculations showed that each respondent (investor) is willing to pay an average of 1. 06 percent more facility interest rate to be under the supervision of the bank or institution. In exchange for a 6-month grace period, pay 2. 17 percent more interest rate on the facility, and finally pay no risk from personal capital in exchange for paying 2. 67 percent interest rate on the facility,Therefore, the comparison of the results showed that the variable of personal guarantee of company managers has the greatest impact on the decision-making portfolio and preferences of investors so that people are willing to pay the highest cost for the protection of their personal assets and the lowest cost for exemption from bank supervision. This indicates that the type of bank supervision is not effective and serious from the point of view of the respondents.

Background: The preferences of Iranians concerning the attributes of health insurance benefit packages are not well studied. This study aimed to elicit health insurance preferences among insured people in Iran during 2016. Methods: A mixed methods study using a Discrete choice experiment (DCE) approach was conducted to elicit health insurance preferences on a total sample of 600 insured Iranians residing in Tehran. The final design of the DCE included 8 health insurance attributes. Data were analyzed using conditional logistic regression models. Results: The final model of this DCE study included 8 attributes, and the findings indicated statistically significant (P <. 001) increase in the odds ratio (OR) of choosing health insurance at all levels of cost coverage except for the rehabilitation and para-clinical benefits, where at 70% cost coverage there was insignificant (P =. 485) disutility (OR = 0. 95). With the increase in cost coverage level, the probability of choosing health insurance was significantly (P <. 001) the highest for the private hospitals’ benefits (OR = 2. 82) followed by public hospitals’ benefits (OR = 2. 02) and outpatient benefits (OR = 1. 75), and the premium revealed statistically significant (P <. 001) disutility (OR = 0. 96). Conclusion: Our findings revealed that participants would be willing to choose health insurance plans with higher cost coverage of healthcare services and with lower premiums. However, the demographic characteristics, income, and health status of the insured individuals affected their health insurance preferences. The findings can contribute to the design of better health insurance policies, improve the participation of individuals in health insurance, and increase the insured individuals’ utility from the insurance benefits packages.