The Key Concept Underlying Conjoint Analysis - Coursework Example

Conjoint analysis Name Professor Course Date Define and describe conjoint analysis. This is a popular marketing research technique that marketers use to determine what features a new product should have or how it should be priced. The method determines the value that consumers place on a product (Keeneya, 2006)). It helps the product designer or brand mangers to identify features or attributed that create the perceived value of a product. This in turn supports the product designs, pricing strategies, and marketing plan (Bradley, 2012). The key concept underlying conjoint analysis is that products and services can be broken to attributes. For instance, our product wine has three attributes that are; the brand, the region and the price. Attributes can be visualized such as capacity, style, efficiency, acceleration, and truck capacity. Some can be objective such as millage and other subjective such as styling (Giorgos 2009). After identifying the attribute of products, the next step is to classify the attributes in to level. For instance, fuel efficiency can be described into levels such as 6 liters per 80 kilometers or in categories such as high, medium, and low. Brand attributes can be well known; styling attributes may be described as family, and sporty (Gustafsson, 2010). In order to enhance analysis, the consumers must share an understanding of these terms. If attributes and their levels are not meaningful to the respondents, consumers the result of conjoint analysis will be disappointing. Mostly qualitative skills are an important attribute exploring the consumers understanding of the terms used to descried attributes. Qualitative studies are used to collect the consumers view on the product. Normally questionnaires are administered where consumers are asked to rate packages of attributes, usually on a scale of 0-10. Depending on their preferences, different consumers will choose different attributes packages (Train,2 004). The analysis of statics process is based on regression model. As mentioned earlier, the respondent rate each package on a scale of 0-10, this become the dependant variable. Each attribute generate one or more dummy indicators that serve as independent variable in the regression. For instance, one may code a well-known brand as one and unknown brand as zero. After coding now run the conventional regression on excel or statistical packages software such as SPSS. The regression coefficients show the incremental valuation for a change in level, which is one of the features that make the conjoint analysis effective (Thurstone, 2007). Some of the benefits of this method include; the analysis requires a relatively small sample size since respondents provide a number of observations, the method allows simultaneous rating of attributes and level, it enables the manager to provide the most preferred product. In addition, conjoint analysis enables the entrepreneur to back their claims with the real data and can calculate the product market share. This will boost their chance for getting funding since the investors are likely to believe them. The real power of conjoint analysis is the ability to predict preference for the product profiles that were not rated by the respondent. This are referred to as simulation cases. The simulation cases are required as the part of the plan, along with the profile in the holdout design. Some of the limitations of this method include the number of attributes that can be realistically be as seen is limited to 15, with some products and services, which is hard to describe in attributes and levels. Calculate the path worth’s for well-known region, well-known brand and for a $5 increase in price. Solutions From the data from choice based co joints study there are three attributes, region, brand, and price. Each has two levels Region (i) Not well known (ii) Well known Brand (i) Not well known (ii) Well known Price (i) Low (ii) High The price difference between low and high is $5. Regression analysis   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1.852022332 0.111226 16.65101 6.33E-57 1.633834542 2.07021 1.633835 2.07021 X Variable 1 -0.003935424 0.025694 -0.15317 0.87829 -0.054338249 0.046467 -0.05434 0.046467 X Variable 2 0.014039826 0.030804 0.45578 0.648619 -0.046387183 0.074467 -0.04639 0.074467 X Variable 3 0.013013947 0.020946 0.62131 0.534497 -0.028075029 0.054103 -0.02808 0.054103 From the regression analysis, it is possible for us to determine how this attribute contribute to the overall respondent rating. This are sometime called path worth or just consumers preference. Region, well known (-0.003935424) Brand, well known (0.014039826) Price, high (0.013013947) Which is the most important attribute? To determine the most important attribute, check the attribute with the highest coefficient. Brand is the most preferred attribute followed by brand and then region. From the analysis, we can determine that all the attribute has a significant influence to the consumers decision since they have p value greater than 0.05 Intercepting the product to determine the ideal product For alternative 8 (region well known; brand-well known and ; price-high) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+(-0.003935424))=1.8481 3. add the brand preference(1.8481+0.014039826)= 1.8621 4. add the price preference(1.8621+0.0130)=1.8751 For alternative 7 (region well known; brand-well known and ; price-low) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+(-0.003935424))=1.8481 3. add the brand preference(1.8481+0.014039826)= 1.8621 4. add the price preference(1.8621+0.)=1.8621 For alternative 6 (region well known; brand-0 and; price-high) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+(-0.003935424))=1.8481 3. add the brand preference(1.8481+0.0)= 1.8481 4. add the price preference(1.8481+0.0130)= 1.8611 For alternative 5 (region well known; brand-0 and; price-0) 5. begin with intercept: the base preference 1.852022332 6. add the region preference(1.852022332+(-0.003935424))=1.8481 7. add the brand preference(1.8481+0.0)= 1.8481 8. add the price preference(1.8621+0.)= 1.8481 For alternative 4 (region 0; brand-well known and; price-high) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+0.0))=1.8520 3. add the brand preference(1.8520+0.0140)= 1.8660 4. add the price preference(1.8660+0.0130)= 1.8790 For alternative 3 (region-0; brand-well known and ; price-0) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+0.0))=1.8520 3. add the brand preference(1.8520+0.0140)= 1.8660 4. add the price preference(1.8660+0.0)= 1.8660 For alternative 2 (region -0; brand-0 and ; price-high) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+0.0))=1.8520 3. add the brand preference(1.8520+0.0)= 1.8520 4. add the price preference(1.8520+0.0130)= 1.8650 For alternative 1 (region 0; brand-0 and ; price-0) 1. begin with intercept: the base preference 1.852022332 2. add the region preference(1.852022332+0.0))=1.8520 3. add the brand preference(1.8520+0.0)= 1.8520 4. add the price preference(1.8520+0.0)= 1.8520 If we were to choose among the above 8 alternatives to satisfy a customer then the best choice would be alternative four, since it has the highest customers preference How to estimate the market share To predict the market share of a product we must first determine the product design that are most likely to be on the shelves alongside each other or the new product to be launched. This will constitute a set of product that a consumer will have to compare and decide on purchasing one. The set is referred to as consumer consideration set. After determining the consumer consideration set, and then the following need to be done to the set. 1. Obtain the preference estimate 2. Estimate the product preference in consideration to the set 3. Using the maximum utility rule, determine the product, which the consumers would choose. According to the rule, the product with the maximum preference among the identified set is the one the consumer are expected to choose. Alternatively, other rile such as logit rule can be used In the current market, only three of the eight alternatives, alternatives 2, 4 and 7, are available Estimating the market share for alternative 2, 4 and 7 1. Obtaining the preference estimate for alternative 7, 4 and 2= 1.8621+1.8790+1.8650=5.6061 2. Calculating market share for alternative 7 = 33.2%, 4= 33.5% and 2=33.33% Currently there are only alternative, 2,4 and 7. The best additional alternative would be the alternative with the highest preference among alternative 1, 3,5, 6 and 8. Alternative 8 has the highest preference, thus it is the best alternative for the company to adopt when entering the market. Calculating the new market share 1. Obtain the preference estimate for alternative 2, 4, 7 and 8 1.8621+1.8790+1.8650+1.8751=7.4812 2. Calculating market share for alternative 2,4, 7 and 8 respectively2=24. 93% ; 4=25.11% ;7=24.89% and 8= 25.01 Adding the new alternative will result in reduction of the market share of the existing alternatives. Summary Conjoint analysis is a customer preference measurement and analysis technique that is widely used in several areas of marketing. The technique is particularly used in designing a new product that are likely to perfume well in the market place. In addition, the techniques can be used to determine the optimal changes that need to be done to the existing product to improve performance. References Bradley, D. (2012). Rank analysis of incomplete block designs, i. the method of paired comparisons. Biometrika, vol. 39. No 3,pp 34 -90 Giorgos Z. (2009). Generalizedrobust conjoint estimation. Marketing Science, vol. 30. No 3,pp 35 -110 Gustafsson, A (2010)., editors,Conjoint Measurement. Methods and Applications, Springer, Berlin. Keeneya, M. (2006) Decisions with Multiple Objectives: Preferences andValue Trade-O_s. Cambridge University Press, Cambridge. Thurstone, L, (2007). A law of comparative judgment. Psychological Review, vol. 9. No 3,pp 134 -290 Train Z. (2004) Discrete Choice Methods with Simulation. Cambridge University Press, Read More