Conjoint analysis advance marketing research
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The document summarizes a group project on conjoint analysis. It discusses the objectives to determine the most preferred product combination of car type, fuel type, and price where customers find maximum utility. A conjoint analysis was conducted with 20 respondents rating combinations of sedan, SUV, and MUV vehicles with CNG, petrol, or diesel fuel priced at 6, 8, or 9 lakhs. The analysis found that customers derived the most utility from a sedan powered by CNG priced at 6 lakhs.
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conjoint analysis
This document discusses conjoint analysis and provides an example using SPSS. It defines conjoint analysis as a technique used to understand how consumers develop preferences for product attributes. The key steps are identified as identifying the problem, attributes and levels, methodology, collecting responses, analysis, interpretation and application. Types include traditional, adaptive choice-based conjoint analysis. An example uses attributes of cars to identify preferred combinations through partial profile surveys and estimating utilities in SPSS. The results show price, fuel type and model have most importance in driving sales.
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Conjoint analysis
This covers the following
WHAT IS CONJOINT ANALYSIS?
WHAT DOES IT DO AND WHAT IT IS USED FOR?
SITUATION WHERE CONJOINT ANALYSIS IS APPLICABLE?
KEY CONJOINT ANALYSIS TERMS
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conjoint analysis
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2. Traditional conjoint analysis methods include full profile, partial profile, paired comparison, and self-explicated. Adaptive conjoint analysis and choice-based conjoint are also mentioned.
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The document discusses different methods for measuring consumer preferences, including direct questioning, stated preferences, and revealed preferences such as purchase data. It notes problems with directly asking consumers about their preferences and intentions to buy. The document then introduces conjoint analysis as a technique that can provide more useful insights into how consumers make tradeoffs between product attributes and their relative attribute importance.
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Conjoint analysis
The document provides definitions and explanations of key terms and concepts related to conjoint analysis. It can be summarized as follows:
1. Conjoint analysis is a multivariate tool used to understand how individuals derive utility from the different attributes of a product or brand. It breaks down the overall utility or preference into partial utilities for each attribute level.
2. Traditional conjoint analysis methods include full profile, partial profile, paired comparison, and self-explicated. Adaptive conjoint analysis and choice-based conjoint are also mentioned.
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Survey Analytics provides conjoint analysis software to help companies evaluate new products and variations of existing products. The software allows users to define product attributes and levels, conduct surveys to assess consumer preferences, and analyze results. Key features include an intuitive interface for setting up studies, previewing concepts, and reviewing utility values and relative importance of attributes. The software also includes a market segmentation simulator that allows predicting how changes to products may impact market share. Conjoint analysis provides a cost-effective way to test concepts without full product development and can help optimize offerings to meet consumer demands.
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The document discusses conjoint analysis, a market research technique used to determine how consumers value different product attributes. Conjoint analysis involves showing consumers combinations of attributes (like brand, price, features) and analyzing their preferences to determine the implicit value or "part-worth" of each attribute. This can be used to build models predicting market share and profitability of new product designs. The document provides an example conjoint analysis problem with three attributes (brand, color, price) to illustrate how a full-factorial experimental design and statistical analysis can be used to interpret consumer preferences.
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The webinar is presented by Dan Llanes, Director of Analytics at Hansa GCR. It covers the uses, benefits, and limitations of choice-based conjoint analysis (CBC) and how to structure a better approach using the technique of Adaptive CBC. Watch and learn a better way to measure what's truly important to your customers.
Don't miss our next free webinar. Register here: http://hub.am/XwTIKo
www.HansaMarketing.com
@Hansa_Tweets
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Intro to Conjoint Analysis and MaxDiff: How JetBlue Learns What Passengers Re...
Finding out what your customers are feeling is pivotal for knowing what decisions to make. Maya Angelou said, “I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.” This holds true in business. Learn how JetBlue’s Jim O’Brien has used conjoint analysis and maxdiff to help JetBlue understand how their customers are feeling and use that information to build a world-class customer experience program.
In this presentation, Qualtrics’ Craig Lutz teams up with Jim O’Brien to introduce you to conjoint analysis and its value. You’ll learn:
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- 1. GROUP PROJECT TOPIC : CONJOINT ANALYSIS PRESENTING BY: Ashish Dahiya 044 Aravind Uppala 123 Dasari Pradeep 126 Sangam Lalsivaraju 138 Rajesh Mishra 151 Neetu Gupta 155
- 2. Introduction to Conjoint analysis • Conjoint analysis attempts to determine the relative importance, consumers attach to salient attributes and the utilities they attach to the level of attributes. • This information is derived from consumer evaluations of brand profiles composed of these attributes and their levels . • The respondents are presented with stimuli that consists of attribute levels.
- 3. Contd… • They are asked to evaluate these stimuli that consist of combinations of attribute levels in terms of their desirability. • Based on the evaluations utility of each level of attribute is determined with help of Conjoint analysis.
- 4. Purpose Conjoint analysis has been used in marketing for a variety of purposes, some includes: • Determining the relative importance of attributes in the consumer choice process • Estimating market share of brands that differ in attribute levels • Determining the composition of the most preferred brand • Segmenting the market based on similarity of preferences for attribute levels
- 5. Area of application • New Product Launch
- 6. Research Objective • To determining the composition of most preferred product ( Car type, fuel Type and Price) , where the customer finds maximum utility.
- 8. Sample Design • The method which we chose to get sample frame is Convenient sampling, the sample size is 20.
- 9. Data Collection Design • Data was collected from the respondent’s order of preferences through the list of combinations that were provided by us.
- 10. Statistical Design • The combinations were generated by orthogonal method from SPSS. • The responses were tabulated manually on SPSS data file.
- 11. Operational Design • The operations were carried out with the Syntax of a pre-programmed conjoint analysis. • The results were analyzed with the help of utilities table and importance table.
- 12. Combinations SL.No Profile ID Car Type Fuel Type Price in Lakhs 1 1 Sedan CNG 6 2 2 Sedan Petrol 8 3 3 Sedan Diesel 9 4 4 SUV CNG 8 5 5 SUV Petrol 9 6 6 SUV Diesel 6 7 7 MUV CNG 9 8 8 MUV Petrol 6 9 9 MUV Diesel 8
- 13. Responses SL.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Profile 1 2 1 2 1 2 3 1 2 1 2 3 4 1 5 3 2 5 6 2 1 Profile Profile Profile Profile Profile Profile Profile Profile 2 3 4 5 6 7 8 9 1 5 6 3 9 4 7 8 3 4 2 6 5 8 9 7 4 1 3 5 7 6 9 8 3 4 2 6 5 8 7 9 4 1 5 3 9 7 8 6 1 5 2 8 7 9 6 4 9 6 4 7 2 5 3 8 3 1 5 4 7 6 9 8 4 3 2 9 8 6 7 5 5 3 1 7 4 9 8 6 1 5 7 2 9 6 4 8 1 6 2 8 3 7 9 5 5 2 4 7 9 8 6 3 1 3 7 2 4 6 9 8 2 1 5 7 6 9 4 8 3 4 1 5 9 8 7 6 1 5 3 2 4 6 8 7 2 1 3 7 4 8 5 9 3 4 1 5 9 7 8 6 2 5 4 3 9 8 6 7
- 14. Analysis
- 15. Importance Values Car Type 25.800 Fuel Type 40.456 Price 33.743
- 16. Utilities Utility estimate -.167 .343 -.567 .343 CNG 1.333 .343 -.583 .343 Diesel -.750 .343 6 Lakhs .667 .343 8 Lakhs .250 .343 9 Lakhs Constant .343 Petrol Price .833 MUV Fuel type Sedan SUV Type of car Standard error -.917 .343 5.500 .241
- 17. Conclusion • Customers perceiving maximum utility from sedan (.833) compared to SUV (-.167) & MUV (-.567) • Customers perceiving maximum utility from CNG (1.333) compared to Petrol (-.583) & Diesel (-.750) • Customers perceiving maximum utility from Price worth of 6 Lakhs (0.667) compared to 8 Lakhs (.250) & 9 Lakhs (.917) • So, from all the above figures and combination the maximum utility (Total utility=2.833) can be achieved with the combination of Sedan with CNG and Priced at 6 Lakhs
- 18. Contd… • The next alternative to get better utility (Total Utility=2.416) is from Sedan with CNG priced at 8 Lakhs • The minimum Utility (Total Utility= -2.234)is found in MUV with Diesel Priced at 9 Lakhs