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Ko ç Un iversity. OPSM 405 Service Management. Class 9: Service System Design Tools: Service Blueprinting Conjoint Analysis. Zeynep Aksin zaksin @ku.edu.tr. Service blueprinting. Activities Decision points Precedence relations Line of visibility Resources.

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Opsm 405 service management

Koç University

OPSM 405 Service Management

Class 9:

Service System Design Tools:

Service Blueprinting

Conjoint Analysis

Zeynep Aksin

[email protected]


Service blueprinting
Service blueprinting

  • Activities

  • Decision points

  • Precedence relations

  • Line of visibility

  • Resources





Defining terminology
Defining terminology

complexity vs. divergence

what is done? how is it done?


Understanding the link between positioning and service structureStructural change: reduce divergence

  • positioning: economies of scale

  • + : perceived increase in reliability

  • - : conformity, inflexibility


Understanding the link between positioning and service structure

Structural change: increase divergence

  • positioning: niche

  • + : prestige, customization, personalization

  • - : difficult to manage and control


Understanding the link between positioning and service structure

Structural change: reduce complexity

  • positioning: specialization

  • + : expert image, easy control

  • - : stripped down image


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



Conjoint analysis motivation
Conjoint Analysis: Motivation structure

  • 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.


Conjoint analysis
Conjoint Analysis structure

  • Conjoint: joined together, combined

  • CONsidered JOINTly


What s so good about conjoint
What’s So Good about Conjoint? structure

  • 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


Conjoint analysis1
Conjoint Analysis structure

  • 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


Services broken down into attributes
Services broken down into attributes structure

  • 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


Attributes have levels
Attributes have levels structure

  • Levels are mutually exclusive

  • Have unambiguous meaning

  • Keep number of levels low (3-5)

  • Try to balance number of levels across attributes


Example adapted from j curry
Example structureadapted 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?


Rank the balls
Rank the balls structure

  • 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


Take 2 features conjointly
Take 2 features conjointly structure

Note: different tradeoffs made by each buyer. Only best and worst are the same.


Illustration by example source dolan 1999
Illustration by example structure(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


Rank from most to least preferred
Rank from most to least preferred structure

Towel Service

Locker


Give utility points
Give utility points structure

Towel Service

Avg.

3

Locker

4

0.5

3.33

1.67


Value system
Value system structure


Question you can answer
Question you can answer structure

  • Would this customer trade-off a storage locker on a daily basis for towel service?

  • Loss: 3-0.5

  • Gain: 3.33-1.67


In sum
In sum structure

  • Collect tradeoffs

  • Estimate buyer value system

  • Make choice prediction


Example output analysis source montgomery and wittink 1979
Example: Output analysis structure(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


What we can t say about the utilities part worths
What we structurecan’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


Conjoint importances
Conjoint Importances structure

  • 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


Output analysis pc example source dolan
Output analysis: PC Example structure(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


Output analysis
Output analysis structure

  • Aggregate analysis

  • Segmentation analysis

  • Scenario simulations


Market simulation example
Market Simulation Example structure

  • 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. . .


Market simulation results
Market Simulation Results structure

  • Predict responses for 500 respondents, and we might see “shares of preference” like:

  • 65% of respondents prefer the 25¢ Chocolate cone


Example source sawtoothsoftware
Example structuresource: sawtoothsoftware

  • 9 cards, ranked by 2 volunteers

  • Copy of Excel spreadsheet available from course web site


Traditional conjoint designs
Traditional Conjoint Designs structure

  • 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


Study design
Study design structure

  • 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


Summary
Summary structure

  • 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


Next time
Next time structure

  • 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


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