stefan binner advanced quant - 2011

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Advanced Quant Techniques July 14, 2011

ACBC Case Study

Stefan Binner, bms marke;ng research + strategy

Stefan Binner, bms-marketing research + strategy, Germany NewMR Advanced Quant Techniques, July 14, 2011

ACBC Case Study Stefan Binner Advanced Quant Webinar - July 14, 2011


In 2009 Sawtooth Software launched ACBC (Adaptive Choice Based Conjoint) As many researchers we expected a new “power tool” and increased value for our projects. Following presentation report our experience illustrated with base case from a commercial study…


Case Study: Background ü Chemical Product for DIY market ü 14 attributes including price, many non compensatory

attributes ü Difficult to find target group (“Moonlighters”)

=> small sample N=200 ü Objective: Product development & identification of best USP ü No focus on price


“Unacceptable” Questions “Must-‐Have” Questions

Build Your Own SectionScreening Section

(Respondent answers 6 screens like this)

Choice Tasks

Interview Progress

Design Considerations ACBC


Design Considerations ACBC Parameter Settings

Case Study:

13

8 4 3 18 6 3 3 6

mixed



Parameters Number of Attributes w/o price # of Screening Tasks # of Must Haves # of Unacceptables Max. # of Concepts in Choice T # of Calibration Concepts # of Concepts / Screening task Min. Attributes to vary from BYO Max. Attributes to vary from BYO BYO Modification Strategy

Case Study

13 8 4 3 18 6 3 3 6

mixed

Given by the nature of the project – required by customer




Case Study

13 8 4 3 18 6 3 3 6

mixed

8 x 3 = 24 concepts seen as sufficient (interview length!)




Case Study

13 8 4 3 18 6 3 3 6

mixed

Less than suggested by Sawtooth. Intention: to avoid too many MH or UA




Case Study

13 8 4 3 18 6 3 3 6

mixed

Less than suggested by Sawtooth (Interview lengths!)




Case Study

13 8 4 3 18 6 3 3 6

mixed

Used Sawtooth recommendation




Case Study

13 8 4 3 18 6 3 3 6

mixed

Screen Size Limitation Complexity



Parameters Number of Attributes # of Screening Tasks # of Must Haves # of Unacceptables Max. # of Concepts in Choice T # of Calibration Concepts # of Concepts / Screening task Min. Attributes to vary from BYO Max. Attributes to vary from BYO BYO Modification Strategy

Case Study

13 8 4 3 18 6 3 3 6

mixed

More than suggested by Sawtooth



Parameters Number of Attributes # of Screening Tasks # of Must Haves # of Unacceptables Max. # of Concepts in Choice T # of Calibration Concepts # of Concepts / Screening task Min. Attributes to vary from BYO Max. Attributes to vary from BYO BYO Modification Strategy

Case Study

13 8 4 3 18 6 3 3 6

mixed

Sawtooth recommendation


Design Considerations Prohibitions

Initial prohibitions led to huge design problems No Standard Tool to “test design efficiency” in ACBC => Be careful and avoid as possible

"  Only one prohibition was used


Design Considerations Price Attribute

v Price range +/- 50%

v Rounded to 99 cents

v Prices was used in unacceptable section

v Level Prices for selected attribute levels were used in 7 of 13 non price attributes


Design Considerations Holdout Tasks

§  No easy holdout tasks like in CBC

§  Calibration concept no substitute for holdout tasks

§  Possible Solution: Use one random tournament question as holdout


Pre Testing N = 20 Pretest

§  Internal Pre-Test indicated too long and complex interviews

§  Real Pre-Test were promising: respondents liked the questions and found good sense in them

§  => we increased the number of questions slightly


Field Work N = 200 face-to-face CAPI

§  Good response

§  Happy respondents

§  Relevant questions

§  Positive feedback

§  Average duration around 30 minutes


Results Utility Values

§  Individual level utilities calculated with HB §  All results met common sense expectations §  Some surprises and hints for segmentation

-100

-80-60

-40-20

0

2040

6080

100


Results Price

§  Linear price function result in very large price interval with only few data points at the end of the interval (base price + all possible level prices +/- 50%)

§  Use of piecewise price function with 5 data points

§  Check data! (get_acbc_prices.exe tool)

§  Rather flat price elasticity which needed calibration


Results Price

§  Due to the large interval price the IMPORTANCE of might gets over-estimated

0

5

10

15

20

25

30

35

Att

1

Att

2

Att

3

Att4

Att5

Att6

Att7

Att8

Att9

Att1

0

Att1

1

Att1

2

Att1

3

Pric

e


Results Price

§  Despite non price focus of project a calibration of PRICE ELASTICITY seem to be necessary

0

10

20

30

40

50

60

70

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 P19 P20 P21 P22 P23 P24


Results Simulations

§  Base Case scenario tuned to current market shares

§  Best attribute levels search based on Profit Contribution modeling

§  Product optimization

§  Product range optimization


Results Example for Product Optimisation

20

30

40

50

60

70

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P140,25

0,5

0,75

1

1,25

1,5

SOP BCSOP IMPPC1 BCPC1 IMP

SOP PC1


Results Benefits of the Study

§  Definition for new product development

§  Improvement of package design

§  Optimization of Product range


Lessons Learned ACBC Pro

§  Intuitive to clients

§  Realistic and relevant interview for respondents

§  Smaller sample size possible

§  Reliable results


Lessons Learned ACBC Con

§  Price range definition

§  Lack of Alternative Specific Designs

§  Danger of prohibitions

§  Measurement and simulation of price elasticity


Limitations No Alternative Specific Designs Limited Graphics Flat price elasticity Lack of experience Difficult design decisions

Model Scenarios Complex product features and options Non-Compensatory Attributes Many Attributes Small Sample Available (CPI, B2B) Deeper insights on individual level

Markets Industrial B2B Durables

Conclusions ACBC Application


A new tool in the MR Toolbox!!

CBC ACA M-D CVA

ü Rarely used ü Traditional

ü Not CJ ü M. Attributes ü Efficient

ACBC

ü Attributes ++ ü Non compensatory ü Realistic interview ü Weak on pricing ü Individual level ü Small sample

ü Mostly used ü Full Profile ü ADM ü Strong for Pricing ü Ind. level (HB) ü IIA

ü Adaptive ü Attributes++ ü Weak on Pricing ü  Ind. Level

According to the latest “Report on Conjoint Analysis Usage among Sawtooth Software Customers” 9% of all projects are done with ACBC


Ü ACBC study was great success

Ü ACBC is intuitive to clients and respondents

Ü ACBC will not replace CBC or other methods but it will become an additional tool

So what?


Questions ?

Stefan Binner bms

Sue York The Future Place

stefan binner advanced quant - 2011

Documents

bmsmarkengresearch strategy

calibration concepts

concepts screening task

screening tasks

acbc adaptive choice

base case

commercial study

choice t