Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity.
We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models. The biggest systematic difference between ratings-based (RB) and choice-based (CB) parameters is consistent with the compatibility effect, i.e., some enriched attributes like brand name tend to be more important in RB models and some comparable attributes like price are likely to be more important in CB models. Still, there were reasonably small differences between choice- and ratings-based parameters. Parameter similarity was also seen in the lack of differences both in the choice share validations when the "keep on shopping" alternative was not considered and in the profiles that were predicted to maximize choice shares. This suggests that the two approaches will produce similar estimates of the relative importance of various attributes. In spite of demonstrated success with each method, several reasons lead us to recommend the use of hierarchical Bayesian choice-based conjoint models. First, the slightly higher individual hit rate validations give us greater confidence in predictive accuracy overall as well as an increased ability to target individual customers. Additionally, the greater ease of modeling both changes in market size and competitive reactions are attractive benefits of choice-based models.
Application e.g. :
An automobile ancillary manufacturer is interested in building a high capacity tyre manufacturing plant which is going to cater to the entire Chennai and Bangalore region. The location planned for setting up the plant is near to Hosur Region . Hosur has many industries and so an attractive location to build a plant. To ensure the success of the project, a consultant is hired to conduct focus groups with current OEM automobile manufacturer.The OEM manufacturers are segmented by segment (hatchback, sedan, SUV, commercial) and the country of the origin of the manufacturer.
Study participants are given a series of index cards. Each card has 6 attributes to describe the potential building of the auto ancillary project (proximity to OEM manufacturer, cost, road connectivity, timeline to production , the layout of the plant , and supply guarantee offered). The estimated cost to construct the building described on each card is equivalent.
Participating automobile manufacturers are asked to order the cards from least to most appealing. This forced ranking exercise will indirectly reveal the participants' priorities and preferences. Conjoint Analysis may be used to determine the strength of preferences across target market segments.
This is how Conjoint analysis will help in Project Management by letting us decide in the planning phase only whether the planned project is having a market potential or not.
Posted by
Akash Sarkar(13004)
Operations Grp 1
We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models. The biggest systematic difference between ratings-based (RB) and choice-based (CB) parameters is consistent with the compatibility effect, i.e., some enriched attributes like brand name tend to be more important in RB models and some comparable attributes like price are likely to be more important in CB models. Still, there were reasonably small differences between choice- and ratings-based parameters. Parameter similarity was also seen in the lack of differences both in the choice share validations when the "keep on shopping" alternative was not considered and in the profiles that were predicted to maximize choice shares. This suggests that the two approaches will produce similar estimates of the relative importance of various attributes. In spite of demonstrated success with each method, several reasons lead us to recommend the use of hierarchical Bayesian choice-based conjoint models. First, the slightly higher individual hit rate validations give us greater confidence in predictive accuracy overall as well as an increased ability to target individual customers. Additionally, the greater ease of modeling both changes in market size and competitive reactions are attractive benefits of choice-based models.
Application e.g. :
An automobile ancillary manufacturer is interested in building a high capacity tyre manufacturing plant which is going to cater to the entire Chennai and Bangalore region. The location planned for setting up the plant is near to Hosur Region . Hosur has many industries and so an attractive location to build a plant. To ensure the success of the project, a consultant is hired to conduct focus groups with current OEM automobile manufacturer.The OEM manufacturers are segmented by segment (hatchback, sedan, SUV, commercial) and the country of the origin of the manufacturer.
Study participants are given a series of index cards. Each card has 6 attributes to describe the potential building of the auto ancillary project (proximity to OEM manufacturer, cost, road connectivity, timeline to production , the layout of the plant , and supply guarantee offered). The estimated cost to construct the building described on each card is equivalent.
Participating automobile manufacturers are asked to order the cards from least to most appealing. This forced ranking exercise will indirectly reveal the participants' priorities and preferences. Conjoint Analysis may be used to determine the strength of preferences across target market segments.
This is how Conjoint analysis will help in Project Management by letting us decide in the planning phase only whether the planned project is having a market potential or not.
Posted by
Akash Sarkar(13004)
Operations Grp 1
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