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OPSM 405 Service Management

Class 9:

Service System Design Tools:

Service Blueprinting

Conjoint Analysis

Koç University

Zeynep Aksinzaksin@ku.edu.tr

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Service blueprinting

Activities Decision points Precedence relations Line of visibility Resources

Low complexity, high divergence

High complexity, low divergence

High complexity, high divergence

Defining terminology

complexity vs. divergence

what is done? how is it done?

Understanding the link between positioning and service structure

Structural 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

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Example: Structural Alternatives

Lower Complexity/Divergence Current Process Higher Complexity/ Divergence

No reservations Take reservation Specific table selection

Self seat, menu on blackboard Seat guests, give menu Recite menu: describe choices

Eliminate Serve water and bread Assortment of meze & bread

Customer fills out form Take orders, prepare orders At table

Pre prepared-no choice Salad (4 choices) Individual prep at table

Limit to 4 choices Main dish (15 choices) Expand choices, bone fish at table etc.

Ice cream bar-self service Dessert (6 choices) Expand choices

Serve salad and main dish;

Dessert and bill together

Serve orders Separate service or orders; change plates

Cash only, pay when leaving Collect payment Choice of payment, serve karanfil & kolonyali mendil

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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.

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Conjoint Analysis

Conjoint: joined together, combined CONsidered JOINTly

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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

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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

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Services broken down into attributes

Credit cardBrand + Interest Rate + Annual Fee + Credit Limit

On-line brokerageBrand + Fee + Speed of Transaction + Reliability of Transaction + Research/Charting Options

Ski area for ski resortpysical 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

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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

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Example adapted 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?

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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

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Take 2 features conjointly

Buyer 1 54 holes 36 18

275 yards 1 2 4

250 3 5 7

225 6 8 9

Buyer 2 54 holes 36 18

275 yards 1 3 6

250 2 5 8

225 4 7 9

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

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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

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Rank from most to least preferred

Yes No

Small storage, large daily

Rank 2 Rank 4

Medium storage only

Rank 1 Rank 3

Large daily with mirror only

Rank 5 Rank 6

Towel Service

Lock

er

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Give utility points

Yes No

Small storage, large daily

4 2

Medium storage only

5 3

Large daily with mirror only

1 0

Towel Service

Lock

er

Avg.

3

4

0.5

3.33 1.67

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Value system

Product Value System Score

Value system score rank

Stated original rank

MSO+Towel 4+3.33=7.33 1 1

SSLD+Towel 3+3.33=6.33 2 2

MSO+No Towel

4+1.67=5.67 3 3

SSLD+No Towel

3+1.67=4.67 4 4

LDMO + Towel

.5+3.33=3.83 5 5

LDMO+ No Towel

.5+1.67=2.17 6 6

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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

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In sum

Collect tradeoffs Estimate buyer value system Make choice prediction

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Example: Output analysis (source: Montgomery and Wittink, 1979)

Business Travel<= 1 night .1632-5 nights .109>=6 nights -.273

Geographic AreaEast .070Midwest -.198South -.321West .449

Opportunity for AdvanceRapid .216Moderate -.216

Range: .436 Range: .770 Range: .432

Attribute importance for business travel:.436/(.436+.770+.432)

Importance analysis only relevant if attributes are in relevant ranges

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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

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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

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Output analysis: PC Example (source Dolan)

Weight<= 2 lbs 1.22-5 lbs .9>5lbs 0.0

BatteryLife1 hr 0.02hrs 0.24hrs 1.58hrs 1.5

ResolutionBelow avg 0.0Avg. .4Above avg. .5

Price1000 1.02000 0.53000 0.0

Product A: 2 lbs 1hr below average 2000Product B: 5 lbs 4hrs average 3000ProductC: >5lbs 8 hrs average 1000

Value of A= 1.2+0+0+0.5=1.7Value of B = 1.9Value of C = 3.0Sum = 6.6

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%

Market share: average purchase probability across all subjects

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Output analysis

Aggregate analysis Segmentation analysis Scenario simulations

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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. . .

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Market Simulation Results

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

65% of respondents prefer the 25¢ Chocolate cone

35%

65%

Vanilla @ 35¢

Chocolate @ 25¢

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Example source: sawtoothsoftware

9 cards, ranked by 2 volunteers Copy of Excel spreadsheet available from

course web site

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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

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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

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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

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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