Choice Applications
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Applications of Choice-Based Conjoint Analysis
 
Choice-Based Conjoint analysis can provide information on  price sensitivity, (estimates price Elasticities and Demand Curves) and this information can be used to assess brand strength.  We can conduct "sensitivity analysis" for attributes such as price,  using a simulator to generate relative Demand curves and by also having respondent differences, then  differential cross-elasticities can be investigated. A step furthermore is to combine this kind of information, in conjunction with actual volumes and cost data, to assess the impact of various pricing strategies on revenues and profits. Depending on the  elasticity of the demand curve, one might face the case,  where you may raise the price of the product and if competitors do not respond, both share and revenues will decrease. In contrast, if you raise the price and competitors do the same, then both share and revenue will increase at a faster rate than the share. It is the case, where, if the brand equity is greater than that of any competitors', then one can achieve greater share in the face of a price increase.
Designing Products to Appeal to Unique Market Segments. What portfolio of products can I offer to appeal to different market segments and maximize overall share?  Customizing products for appealing to target segments or even individuals is a common theme in marketing. Many companies dedicate resources to develop a portfolio of products that they hope will appeal to unique segments. For line extensions, the challenge for any company is to design new products that will take share from competition, without stealing an unacceptable amount of share from products within its existing line. One common approach to designing an effective line extension is to use the conjoint data to segment the market into latent (not observed) market segments (clusters) that have similar preferences. These segments are not delineated based on an explicit variable such income, or gender. Rather, the underlying segments are revealed through a statistical segmentation  technique, such as cluster analysis or latent class modeling. Segments are formed with the goal of maximizing the differences in preference, between groups, while minimizing the differences in preference within groups. Once these latent segments have been identified, one can profile them in terms of other variables in the survey (i.e. usage, habits, demographics). In case, simulations are based on a latent class analysis  from Choice-Based Conjoint data, we can then conduct simulations "by segment".  By examining the Part-Worths and importance for each group, we gain insight into the product features that might appeal to each. Also, we should bear in mind the size of each segment, as this represents its demand potential. For line extensions simulations we conduct, we would certainly want to investigate other product configurations to make sure we were not overlooking even better opportunities to enhance share and also we would also want to consider the cost implications of different options for line extensions. It is also needed to conduct sensitivity analysis for the new product with respect to price, to determine a strategic price point. Of course, viewing the preferences and shares by segments is not required in designing an effective line extension. However, viewing the separate market segments can help us to more quickly identify patterns of preference, size the different segments of the market, and thus more easily arrive at a good solution.

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