1. Customer Value Prof. Markus Christen INSEAD Singapore May/June 2007 Prof. Markus Christen INSEAD Singapore May/June 2007

2. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 2 Customer Value  How can you determine what customers want to improve customer value? Attribute-level analysis Brand-level analysis Tradeoff analysis

3. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 3 Attribute-Level Analysis: Technical Specs

4. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 4 Weak Disagree Strong Agree Brand X Brand Y Importance Attribute-Level Analysis: Customer Rating

5. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 5 Rule 7: Customer Behavior People act according to their perceptions.

6. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 6 Attribute-Level Analysis A: Ease of maintenance * B: Fuel efficiency * C: Cab durability D: Roominess and comfort * E: Quality of materials F: Safety features G: Ease of steering H: Location of controls * I: Windshield design J: Instrumentation * K: Ease of entry L: Outer appearance 1234 Poor Performance Excellent Performance * Statistically significant difference (p‹0.05) Our Company Main Competitor Perceived Performance Comparison on Rating Scale Attributes: (from most (A) to least (L) important) 1 2 3 4 5 6 7 -2 012 L K J I H G F E D C B A Performance Deviation Compared to Competition Killer Weakness Key Strength Secondary Weakness Possible Overkill Example: Truck Cabin

7. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 7 Rule 8: Customer Behavior People’s choices are based on determinant attributes.

8. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 8 Attribute-Level Analysis: Summary  Input ratings of product attributes, importance and ideal values by individuals –Customers –Non-customers  Results ratings of various product attributes for different products importance rating of product attributes ideal rating of product attributes  Advantages simple, data readily available or easy to collect easy to interpret results  Assumptions & Limitations product = bundle of attributes customers can evaluate different product attributes customers are willing to answer truthfully When customers think of a “product” as a bundle of relatively well- defined attributes.

9. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 9 Brand-Level Analysis: Perceptual Maps Multidimensional Scaling (MDS) Let customers rate/rank the similarity of different items Rate each pair using a scale from 1 (very similar) to 9 (very dissimilar).

10. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 10 Brand-Level Analysis: Perceptual Maps Example: US Automobile Market

11. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 11 Brand-Level Analysis: Perceptual Maps Example: US Automobile Market Ideal

12. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 12 Brand-Level Analysis: Perceptual Maps Example: US Beer Market

13. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 13 Brand-Level Analysis: Perceptual Maps Example: Markstrat Performance Economy

14. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 14 Perceptual Maps: Summary  Input similarity among objects  Results are inferred number of dimensions used to distinguish objects relative positioning of objects along these dimensions preferred levels of these dimensions (ideal values) distance away from the ideal can be viewed as a measure of customer dissatisfaction  Advantages insights about perceptions (even customers may not know) competition from customers’ view no need to describe attributes  Assumptions & Limitations need to infer attribute level implications to take actions perceptions are influenced by many different factors no indication of attribute importance When customer perceptions of “products” are shaped by aggregated factors that cannot be easily articulated. Don’t do it yourself!

15. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 15 Conjoint Analysis (Tradeoff Analysis) Job A Location:London Salary:Average for W.E. Exposure to top-level mgmt:Minimal Crime level:Average for big W.E. city Job D Location:Eastern Europe Salary:Average for W.E. Exposure to top-level mgmt:Majority of projects Crime level:20% below average Job G Location:South Africa Salary:Average for W.E. Exposure to top-level mgmt:About 25% of proj. Crime level:50% above average Job B Location:London Salary:20% below average Exposure to top-level mgmt:About 25% of proj. Crime level:20% below average Job E Location:Eastern Europe Salary:20% below average Exposure to top-level mgmt:Minimal Crime level:50% above average Job H Location:South Africa Salary:20% below average Exposure to top-level mgmt:Majority of projects Crime level:Average for big W.E. city Job C Location:London Salary:20% above average Exposure to top-level mgmt:Majority of projects Crime level:50% above average Job F Location:Eastern Europe Salary:20% above average Exposure to top-level mgmt:About 25% of proj. Crime level:Average for big W.E. city Job I Location:South Africa Salary:20% above average Exposure to top-level mgmt:Minimal Crime level:20% below average

16. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 16 Conjoint Analysis: Utility Scores User Input Final Result RankJobUtility 1C40 2D37 3F34 4A31 5I28 6B25 7E22 8G19 9H16 LocationJobsRank ScoresMax. Diff.Utility -LondonA B C1111/13 -Eastern Eur.D E F121110/13 -South A.G H I220 Salary -BelowB E H220 -AverageA D G14138/13 -AboveC F I91 Projects -MinimalA E I166/13 -SomeB F G1755/13 -MajorityC D H1210/13 Crime -BelowB D I139/12 -AverageA F H1636/13 -AboveC E G166/13

17. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 17 Conjoint Analysis: Markstrat Example

18. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 18 Conjoint Analysis: Summary  Input judgment of ‘artificial’ attribute combinations –rankings or ratings –choice-based  Results are inferred relative importance of attributes relative utility for different levels for each attribute can create other products by combining different attributes and calculate utility  Advantages force people to make tradeoffs insights about preferences (even customers may not know) can indicate willingness to pay widely used in product design  Assumptions & Limitations utility of a product = sum of utility from attributes –no interactions between attributes difficult with some attributes (emotional, price, brand) very sensitive to research design When customers are unable or unwilling to indicate their preferences for different attributes and their willingness to pay. Don’t do it yourself!

19. Market Driving Strategies - May/June 2007 © Prof. Markus Christen Session 5 - 19 Rule 9: Customer Preferences Like the taste for Durians, customer preferences are acquired.