OPSM 405 Service Management

1. Koç University OPSM 405 Service Management Class 9: Service System Design Tools: Service Blueprinting Conjoint Analysis Zeynep Aksin zaksin@ku.edu.tr

2. Service blueprinting • Activities • Decision points • Precedence relations • Line of visibility • Resources

3. Low complexity, high divergence

4. High complexity, low divergence

5. High complexity, high divergence

6. Defining terminology complexity vs. divergence what is done? how is it done?

7. Understanding the link between positioning and service structureStructural change: reduce divergence • positioning: economies of scale • + : perceived increase in reliability • - : conformity, inflexibility

8. Understanding the link between positioning and service structure Structural change: increase divergence • positioning: niche • + : prestige, customization, personalization • - : difficult to manage and control

9. Understanding the link between positioning and service structure Structural change: reduce complexity • positioning: specialization • + : expert image, easy control • - : stripped down image

10. Understanding the link between positioning and service structure Structural change: increase complexity • positioning: wallet share • + : maximize revenue generation / customer • - : customer confusion, decline in service quality

11. Example: Structural Alternatives

12. Conjoint Analysis: Motivation • Objective: max profits=revenues-costs • Positioning (or repositioning) impacts both profits and costs • We said earlier: in a service concept all details matter • What do customers value? • How are trade-offs between attributes made? • Etc.

13. Conjoint Analysis • Conjoint: joined together, combined • CONsidered JOINTly

14. What’s So Good about Conjoint? • More realistic questions:Would you prefer . . .210 Horsepower or 140 Horsepower17 MPG 28 MPG • If choose left, you prefer Power. If choose right, you prefer Fuel Economy • Rather than ask directly whether you prefer Power over Fuel Economy, we present realistic tradeoff scenarios and infer preferences from your product choices

15. Conjoint Analysis • Basic idea: the service can be broken down into a set of relevant attributes • Have consumers react to a number of alternatives • Infer • Importance • Most desired level • Estimation of an individual’s value system • Overall product judgements lead to value system through some data analysis technique

16. Services broken down into attributes • Credit card Brand + Interest Rate + Annual Fee + Credit Limit • On-line brokerage Brand + Fee + Speed of Transaction + Reliability of Transaction + Research/Charting Options • Ski area for ski resort pysical setting, distance, snow base, new snow, vertical drop, type of runs, challenge, size of area, facilities, ticket price, wait for lifts, type of lift, snowboards

17. Attributes have levels • Levels are mutually exclusive • Have unambiguous meaning • Keep number of levels low (3-5) • Try to balance number of levels across attributes

18. Exampleadapted from: J. Curry • Golf balls: driving distance, ball life, price • Alternatives • 275 yards, 54 holes, $1.25 • 250 yards, 36 holes, $1.50 • 225 yards, 18 holes, $1.75 • Market’s ideal ball? • Ideal ball for manufacturing costs?

19. Rank the balls • Distance • 275 yards Rank 1 • 250 yards Rank 2 • 225 yards Rank 3 • Ball Life • 54 holes Rank 1 • 36 holes Rank 2 • 18 holes Rank 3 • Doesn’t really tell us anything we didn’t know

20. Take 2 features conjointly Note: different tradeoffs made by each buyer. Only best and worst are the same.

21. Illustration by example (source: Dolan 1999) • Fitness facility design • Towel service: yes or no • Locker service • Small storage lockers permanently assigned plus large hanging ones for daily use • Mid-size only permanently assigned • No permanently assigned locker, large hanging locker with mirror inside door

22. Rank from most to least preferred Towel Service Locker

23. Give utility points Towel Service Avg. 3 Locker 4 0.5 3.33 1.67

24. Value system

25. Question you can answer • Would this customer trade-off a storage locker on a daily basis for towel service? • Loss: 3-0.5 • Gain: 3.33-1.67

26. In sum • Collect tradeoffs • Estimate buyer value system • Make choice prediction

27. Example: Output analysis (source: Montgomery and Wittink, 1979) Geographic Area East .070 Midwest -.198 South -.321 West .449 Opportunity for Advance Rapid .216 Moderate -.216 Business Travel <= 1 night .163 2-5 nights .109 >=6 nights -.273 Range: .770 Range: .432 Range: .436 Attribute importance for business travel: .436/(.436+.770+.432) Importance analysis only relevant if attributes are in relevant ranges

28. What we can’t say about the utilities (part worths).. • >= 6 nights is unattractive to respondents • West is almost 7 times more attractive than East • <=1 night is more attractive than East • Why? • Arbitrary scaling within each attribute • Here utilities are scaled to sum to 0 within each attribute • Interval data does not support ratio operations • If count based then can say West is chosen 7 times more than East

29. Conjoint Importances • Measure of how much influence each attribute has on people’s choices • Best minus worst level of each attribute, percentaged:Vanilla - Chocolate (2.5 - 1.8) = 0.7 15.2%25¢ - 50¢ (5.3 - 1.4) = 3.9 84.8% ----- -------- Totals: 4.6 100.0% • Importances are directly affected by the range of levels you choose for each attribute

30. Output analysis: PC Example(source Dolan) Weight <= 2 lbs 1.2 2-5 lbs .9 >5lbs 0.0 BatteryLife 1 hr 0.0 2hrs 0.2 4hrs 1.5 8hrs 1.5 Resolution Below avg 0.0 Avg. .4 Above avg. .5 Price 1000 1.0 2000 0.5 3000 0.0 Product A: 2 lbs 1hr below average 2000 Product B: 5 lbs 4hrs average 3000 ProductC: >5lbs 8 hrs average 1000 Share of preference approach: Prob. of choosing A: 1.7/6.6=26% Prob of choosing B: 1.9/6.6=29% Prob. of choosing C: 3.0/6.6=45% Value of A= 1.2+0+0+0.5=1.7 Value of B = 1.9 Value of C = 3.0 Sum = 6.6 Market share: average purchase probability across all subjects

31. Output analysis • Aggregate analysis • Segmentation analysis • Scenario simulations

32. Market Simulation Example • Predict market shares for 35¢ Vanilla cone vs. 25¢ Chocolate cone for Respondent #1:Vanilla (2.5) + 35¢ (3.2) = 5.7Chocolate (1.8) + 25¢ (5.3) = 7.1 • Highest value choice (first choice rule): Respondent #1 “chooses” 25¢ Chocolate cone! • Repeat for rest of respondents. . .

33. Market Simulation Results • Predict responses for 500 respondents, and we might see “shares of preference” like: • 65% of respondents prefer the 25¢ Chocolate cone

34. Example source: sawtoothsoftware • 9 cards, ranked by 2 volunteers • Copy of Excel spreadsheet available from course web site

35. Traditional Conjoint Designs • Full profile: each service concept is defined using all attributes being studied • Full factorial: a design in which all possible product combinations are shown • Fractional Factorial: a fraction of the full factorial that permits efficient estimation of the parameters of interest) • From design catalogs • From software programs

36. Study design • Step 1: determine relevant attributes • Step 2: choose stimulus representations (how products will be described to respondents, full or partial) • Step 3: Choose response type (choice, ranking, rating) • Step 4: Choose criterion (liking, preference, likelihood of purchase) • Step 5: Choose method of data analysis

37. Summary • Blueprints for documentation • Analyze for complexity & divergence for positioning • Understand links between positioning and costs (service delivery system) • Conjoint analysis to assess customer valuations • Use output from conjoint analysis to link valuation, purchase, aggregate market share and profitability

38. Next time • Will continue Conjoint Analysis • Class will be held in the computer lab SOS Z13 • Be on-time! Counts as in-class activity. • Will practice doing conjoint analysis via regression using Excel