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
11 Example: Structural Alternatives Lower Complexity/DivergenceCurrent ProcessHigher Complexity/ Divergence No reservationsTake reservationSpecific table selection Self seat, menu on blackboardSeat guests, give menuRecite menu: describe choices EliminateServe water and breadAssortment of meze & bread Customer fills out formTake orders, prepare ordersAt table Pre prepared-no choiceSalad (4 choices)Individual prep at table Limit to 4 choicesMain dish (15 choices)Expand choices, bone fish at table etc. Ice cream bar-self serviceDessert (6 choices)Expand choices Serve salad and main dish; Dessert and bill together Serve ordersSeparate service or orders; change plates Cash only, pay when leavingCollect paymentChoice of payment, serve karanfil & kolonyali mendil
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 Horsepower 17 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
20 Take 2 features conjointly Buyer 154 holes3618 275 yards124 250357 225689 Buyer 254 holes3618 275 yards136 250258 225479 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 YesNo Small storage, large daily Rank 2Rank 4 Medium storage only Rank 1Rank 3 Large daily with mirror only Rank 5Rank 6 Towel Service Locker
23 Give utility points YesNo Small storage, large daily 42 Medium storage only 53 Large daily with mirror only 10 Towel Service Locker Avg. 3 4 0.5 3.331.67
24 Value system ProductValue System Score Value system score rank Stated original rank MSO+Towel4+3.33=7.3311 SSLD+Towel3+3.33=6.3322 MSO+No Towel 4+1.67=5.6733 SSLD+No Towel 3+1.67=4.6744 LDMO + Towel.5+3.33=3.8355 LDMO+ No Towel.5+1.67=2.1766
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) Business Travel <= 1 night.163 2-5 nights.109 >=6 nights -.273 Geographic Area East.070 Midwest -.198 South -.321 West.449 Opportunity for Advance Rapid.216 Moderate -.216 Range:.436 Range:.770Range:.432 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) = 0.715.2% 25¢ - 50¢(5.3 - 1.4) = 3.984.8% ------------- 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 Value of A= 1.2+0+0+0.5=1.7 Value of B = 1.9 Value of C = 3.0 Sum = 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
32 Market Simulation Example Predict market shares for 35¢ Vanilla cone vs. 25¢ Chocolate cone for Respondent #1: Vanilla (2.5) + 35¢ (3.2) = 5.7 Chocolate (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
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