Customer Value

1. Customer Value Prof. Markus Christen INSEAD SingaporeMay/June 2007

2. Customer Value • How can you determine what customers want to improve customer value? • Attribute-level analysis • Brand-level analysis • Tradeoff analysis

3. Attribute-Level Analysis: Technical Specs

4. WeakDisagree StrongAgree Attribute-Level Analysis: Customer Rating Brand X Brand Y Importance

5. Rule 7: Customer Behavior People actaccording to theirperceptions.

6. Performance Deviation Compared to Competition KillerWeakness KeyStrength 7 A B 6 C D 5 F E G 4 3 H I 2 J K 1 L -2 -1 0 1 2 SecondaryWeakness PossibleOverkill Attribute-Level Analysis Example: Truck Cabin Perceived Performance Comparison on Rating Scale Attributes: (from most (A) to least (L) important) Our Company Main Competitor 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 * Statistically significant difference (p‹0.05) 1 2 3 4 Poor Performance Excellent Performance

7. Rule 8: Customer Behavior People’s choicesare based ondeterminant attributes.

8. 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 Attribute-Level Analysis: Summary When customers think of a “product” as a bundle of relatively well-defined attributes.

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. Brand-Level Analysis: Perceptual Maps Example: US Automobile Market

11. Ideal Brand-Level Analysis: Perceptual Maps Example: US Automobile Market

12. Brand-Level Analysis: Perceptual Maps Example: US Beer Market

13. Brand-Level Analysis: Perceptual Maps Example: Markstrat Performance Economy

14. 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 Perceptual Maps: Summary Don’t do it yourself! When customer perceptions of “products” are shaped by aggregated factors that cannot be easily articulated.

15. Conjoint Analysis (Tradeoff Analysis) Job A Location: London Salary: Average for W.E. Exposure totop-levelmgmt: Minimal Crime level: Average for big W.E. city Job B Location: London Salary: 20% below average Exposure totop-levelmgmt: About 25% of proj. Crime level: 20% below average Job C Location: London Salary: 20% above average Exposure totop-levelmgmt: Majority of projects Crime level: 50% above average Job D Location: Eastern Europe Salary: Average for W.E. Exposure totop-levelmgmt: Majority of projects Crime level: 20% below average Job E Location: Eastern Europe Salary: 20% below average Exposure totop-levelmgmt: Minimal Crime level: 50% above average Job F Location: Eastern Europe Salary: 20% above average Exposure totop-levelmgmt: About 25% of proj. Crime level: Average for big W.E. city Job G Location: South Africa Salary: Average for W.E. Exposure totop-levelmgmt: About 25% of proj. Crime level: 50% above average Job H Location: South Africa Salary: 20% below average Exposure totop-levelmgmt: Majority of projects Crime level: Average for big W.E. city Job I Location: South Africa Salary: 20% above average Exposure totop-levelmgmt: Minimal Crime level: 20% below average

16. Conjoint Analysis: Utility Scores

17. Conjoint Analysis: Markstrat Example

18. 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 Conjoint Analysis: Summary Don’t do it yourself! When customers are unable or unwilling to indicate their preferences for different attributes and their willingness to pay.

19. Rule 9: Customer Preferences Like the taste forDurians,customerpreferencesare acquired.