Conjoint analysis webinar


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Conjoint analysis webinar

  1. 1. Lunchtime Webinar Series: Conjoint Analysis How to establish real Customer value1
  2. 2. Agenda • Common problems with understanding the customer • What is Conjoint Analysis and when to apply it • An example of Conjoint Analysis • Additional comments to 6s folks • Summary2 © BMGI. Except as may be expressly authorized by a written license agreement signed by BMGI, no portion may be altered, rewritten, edited, modified or used to create any derivative works.
  3. 3. Understanding the Customer When you ask customers “how important are each of the features of a product or service?”, many of them are not able to accurately answer. The response is often that, all of the features are important. Customers find it hard to assess the importance of each specific feature, because: a) It is difficult to imagine individual features in isolation from other features b) Features in isolation are perceived differently than in the combinations found in the product or service Let me give you one example…3
  4. 4. Understanding the Customer Imagine that you work for brewery and you want to increase market share by offering better beer. You want to improve your beer by improving some of its features. Imagine you ask Customer the following questions: 1. How important for you is transparency of beer? 2. How important is the foam? 3. How important is bitter taste? Do you see that it is difficult to answer to these questions…. So, how do you solve this problem?4
  5. 5. Conjoint Analysis Conjoint Analysis is statistical analysis tool used to determine how people value different features that make up a product or service. It is widely used in market research, product and service development and design, process improvement projects, segmentation, pricing, resource allocation etc. Instead of asking Customers what feature they value most in a product or service, or what attributes they find most important, we design certain “fake” products or services and ask Customers which of them they like more than the others. In beer example we can simply prepare a few types of beer and ask Customers how much they like each beer.5
  6. 6. Conjoint Analysis There are a few ways of evaluating different product/feature options. 1. You ask the customer how much they like each of the options on the scale i.e. from 1 to 10. 2. You ask the customer to arrange all the options in the order of preference (the most preferred option is on the top of the list). 3. You give the customer multiple times two options to compare and ask which one they like more. Each of these approaches has pros and cons (explained later).6
  7. 7. Example of Conjoint Analysis Imagine that we try to launch a new credit card and we wonder which features of the credit card customers value the most. We want to test the following: A. Payment System (Visa vs. Mastercard) B. Price (Free vs. 10 EUR) C. Grace period (14 days vs. 21 days) D. Second card for the partner (Yes vs. No) E. Discounts in selected shops (Yes vs. No) F. Insurance (Yes vs. No) G. Type of Card (Silver vs. Gold)7
  8. 8. Example of Conjoint Analysis This is how potential product options may look:8
  9. 9. Example of Conjoint Analysis Now we can ask Customers the following questions: 1. How much do you like each product on the scale 1 to 10? 2. Arrange those 8 products in the order of preference (the one that you like the most is on the top of the list). 3. Out of two products: no 1 and 2, which one you like more? What about products no 3 and 4? Etc. Now it is relatively easy to see the pros and cons of each approach.9
  10. 10. Example of Conjoint Analysis Let us imagine that we went for approach no 1 and captured the following data: What we can do with the data? How we should analyze it?10
  11. 11. Example of Conjoint Analysis11
  12. 12. Example of Conjoint Analysis In our case the results are the following:12
  13. 13. Example of Conjoint Analysis We can see that: 1. Grace period is the most important for the Customer 2. Type of card and the possibility to have second card are not important at all 3. Price is equally important as insurance So which card is the best from the customer perspective? We need to have a look at detailed results of analysis….13
  14. 14. Example of Conjoint Analysis The results are the following: We see that the best card is the free Mastercard with a grace period of 21 days, with discounts and insurance. This card should have score of 9.5. Note: We did not directly ask Customer about this card!14
  15. 15. Example of Conjoint Analysis Based on the results we can make numerous business decisions. If we want to have the card ranked by Customer at least 8, we can: 1. Charge the customer (score of 8.5), or 2. Offer no insurance (score of 8.5), or 3. Use the Visa system (score of 9.0) The impact of discounts can be compensated for by using a different system and a different price or insurance. There are more and more trade-offs that can be made…..15
  16. 16. Additional comments to 6s folks As you see Conjoint Analysis is just the application of Design of Experiments. Due to the fact that usually many features are studied with a relatively low number of product/service options prepared for Customer assessment, Conjoint Analysis is usually based on fractional factorial design, resolution III or IV. In the example above, due to the law number of degrees of freedom we ignored the risk of having Type I error. In a real life use replicates to increase the number of degrees of freedom. Whenever you do Conjoint Analysis try to ensure orthogonality.16
  17. 17. Summary Conjoint Analysis is a widely used tool in market research. It can also be used in the Define and Improve phase of DMAIC improvement projects and the Design phase of DMADV projects. It is all about features and trade-offs. If we can not or do not want to provide certain feature – which feature (or features) should we offer to have same result? It is easy to apply if you use dedicated software, but understanding of the “mechanics” of the tools helps a lot with proper application and making right conclusions.17
  18. 18. Learn more! Free Tools, Templates & Courses & Workshops eLearning Learn more statistical analysis Visit our Open Access Website tools in the following courses: for more materials and free • Lean Six Sigma Green Belt learning on Lean Six Sigma, • Lean Six Sigma Black Belt Strategy execution, Change & • DFLSS Innovation. • Tool Master (advanced statistical analysis tools) Open Access: Full Training Schedule:
  19. 19. NEW upcoming 1-Day Workshops: Applied Statistics Business Analytics Bootcamp 22nd February | London Data Driven Decision Making for Marketers 10th May | Amsterdam19
  20. 20. Questions & Answers20