CUSTOMER NEEDS ELICITATION FOR PRODUCT CUSTOMIZATION Yue Wang Advisor: Prof. Tseng Advanced Manufacturing Institute Hong Kong University of Science and

CUSTOMER NEEDS ELICITATION FOR

PRODUCT CUSTOMIZATION

Yue WangAdvisor: Prof. Tseng

Advanced Manufacturing InstituteHong Kong University of Science and Technology

Advanced ManufacturingInstitute

Background

2

Customer Needs

(CNs)

Functional Requirements

(FRs)

Design Parameters

(DPs)

Process Variables

(PVs)

Product specification

definition

Product design

Processdesign

CNs are expressed in explicit product specifications.

Axiomatic design:


Customer needs elicitation should be

Good: predictive, customer insight

Fast: for customers and for designers

Cheap: reduce market research cost

Easy: reduce drudgery and errors

Introduction

3


Can we find what people want quickly and

inexpensively?

How to avoid confusing customers with too many

products?

Research issues

4


5

Challenges

Customers are

Impatient to specify a long list of items

Unable to articulate their needs

Unaware of latent needs

Lack of information about available options

Interlocking among attributes


Research framework

Bayesian network based preferences representation

Adaptive specification definition procedure

Recommendation for customized product

Approach

6


Preferences Representation

Uncertainty of the purchasing choices

Customers are heterogeneous

Choice decisions differ under various situations

The context of purchase differs

Dependency among preferences towards different

attributes

7


Bayesian network

Preferences Representation

8


The important considerations in this phase:

Customers are not patient enough to specify a long

list of items.

The items differ a lot in terms of the amount of

information they can provide.

Specification Definition

9


Basic ideas: Present the most informative query item to

customers

The value of information: :the additional information received about

X from getting the value of Y=y.

)|( yYXf

)|()()|( YXHXHyYXfEY

i

ii ppXH log)(


10


The solution for f (Blachman, 1968#):

xx yxp

yxpxp

xpyYXHXHyYXf)|(

1log)|(

)(

1log)()|()()|(

# N. M. Blachman, “The amount of information that y gives about X,” IEEE Trans. Inform. Theory, vol. IT-14, no. 1, pp. 27-31, Jan. 1968


)|()()|( YXHXHyYXfEY

)yY|X(fEmaxarg*Y YY

11


Given: Customers preferences information

Determine: Which products should be recommended? In what order to present the recommendations if

more than one recommendations are presented?

Recommendation

12


Probability of relevance under binary independent assumption:

Probability of relevance considering first order conditional dependency:

Probabilistic relevance computation

13

mxXPxF imi |)(, mxXPxF imi |)(,

i i

i

SRaP

SRaP

SRCP

SRCPCSRP

),0|(

),1|(

),0|(

),1|(),|1(

ii ii

ii aqq

ppCSRP

)1(

)1(log),|1(

i ii

ii

SRaaP

SRaaP

SRCP

SRCPCSRP

),0,|(

),1,|(

),0|(

),1|(),|1(

)(

)(

i

iiiiii

iiiii

ii

iii

ii

ii aatqtq

rprpa

tq

rpa

qq

ppcSRP )()( )1)(1(

)1)(1(log

)1)(1(

)1)(1(log

)1(

)1(log),|1(


The idea is to rank products by their estimated

probability of relevance with respect to the information

obtained.

Probability ranking principle is optimal, in the sense that

it minimizes the expected loss.

Probability ranking principle

14


Schematic framework

15

Select suitable model

Confirm the specifications

Provide tailored product

Configuration database

Knowledge base

Update knowledge base

End product

Update configuration database

Customer Product development team

Generate the most informative query

Specify the item

Present recommendation

Satisfied with the recommendation?

Y

Start

Process flow

Information flow

Low prob. to find the feasible configuration?

N

N


Precision rate

Recall rate

Evaluation metrics

16

1

n

iip

Pn

n

pP

n

i i 1

),min(1

mn

pR

n

i i


The recommendation based on probability ranking can

guarantee the highest precision and recall rate.

If customers’ preferences to all the components are

independent and the potential preferences towards all

the alternatives of an attribute are random, the

specification definition method based on the information

gain has the highest precision and recall rate.

Evaluation results

17


Parameters setting Result (# of experiments in which the

precision and recall rate are highest/ total #

of experiments)

m ~ Uniform (3, 13) N ~ Uniform (50, 100)

|Ni|~Uniform

9,345/10,000


|Ni|~Uniform

9,325/10,000

m~Uniform (5, 15) N ~ Uniform (1000, 2000)

|Ni|~Uniform

9,344/10,000


|Ni|~Norm(1, 1)

9,560/10,000


|Ni|~Norm(1, 2)

9,603/10,000


|Ni|~Norm(1, 0.5)

9,262/10,000

Evaluation results

18


Evaluation by utility

Preliminaries:

Stochastically dominate:

If , then approach 1 stochastically

dominates approach 2.

mxXPxF imi |)(,

)()( ,2,1 xFxF mm

19


The presented method

stochastically dominates other approaches.

is optimal with respect to any nondecreasing utility

function.

Evaluation results

20


An approach to elicit customers’ preference is presented.

The model can be used to adaptively improve definition of product specification for custom product design.

Based on the model, customized query sequence can be developed to reduce redundant questions.

Product recommendation approach is adopted to further improve the efficiency of custom product design

Summary

21


22

Thank you!

Your suggestions & comments are highly appreciated!


Extension to binary independent assumption

Theorem: A probability distribution of tree dependence Pt(x) is an optimal approximation to P(x) if and only it’s maximum spanning tree. [Chow and Liu, 1968]

),(maxarg )(ijiiC

xxIMST

ji xx ji

jijiji xPxP

xxPxxPxxI

, )()(

),(log),(),(


Why customized product design

Well calibrated customized product design can integrate customers into design activities Mitigate the side effect of sticky information Better meet customers’ requirements Loyalty can be enhanced. Help identify latent needs guide future product

development


Lemma 1: Suppose approach 1 proposes n recommendations in a sequence S1=(r11,r12,…r1n). Each recommendation r1i has probability p1i to meet the customer needs. The sequence is arranged such that

. Approach 2 also proposes n recommendations in a sequence S2=(r21,r22,…r2n). These n recommendations may be different from the ones in sequence S1. Similarly, we also have corresponding probability serial and If for all , then X1 stochastically dominates X2 where Xi is an indicator of the number of satisfactory recommendations by using approach i.

nPPP 11211 ...

}1:{ 2 niP i nPPP 22221 ... ii PP 21

ni 1


Lemma 2: Suppose approach 1 proposes n recommendations in a sequence S1=(r11,r12,…r1n). Each recommendation r1i has probability p1i to meet the customer needs. The sequence is arranged such that . Approach 2 also proposes n recommendations in a sequence S2=(r21,r22,…r2n) which is a permutation of S1=(r11,r12,…r1n). Then the distribution of satisfactory product for approach 1 is identical to approach 2.

nPPP 11211 ...


Lemma 3: Let U(x) be a nondecreasing utility function where x is the number of satisfactory recommendations. Let Xi be an indicator of the number of satisfactory recommendations by using approach i. If X1 stochastically dominates X2, then the expected utility by adopting approach 1 is greater or equal to that of approach 2, i.e., .

21 XUEXUE


Evaluationm: the number of attributes

ni: the number of alternatives of the ith attribute

N: the total number of configurations

Pijk: if the jth alternative of the ith component is selected, the probability that the kth configuration is the desired one.

The entropy of the configuration space if the jth alternative of the ith component is selected:

The expected entropy of the configuration space if the ith component is proposed for a customer to specify:

m

iinN

1

N

kijkijk pp

1

log

N

kijkijk

n

jij ppp

j

11

log28


Background

Competitive and changing market

Shorter product development time

Product variety proliferation

Bigger penalty cost of failing to meet customers’ needs

or catch up customers’ needs changes

29


30


Probability of relevance (including first order conditional dependency):

Parameters setting:

Probabilistic relevance model

31

i ii

ii

SRaaP

SRaaP

SRCP

SRCPCSRP

),0,|(

),1,|(

),0|(

),1|(),|1(

)(

)(

i

iiiiii

iiiii

ii

iii

ii

ii aatqtq

rprpa

tq

rpa

qq

ppcSRP )()( )1)(1(

)1)(1(log

)1)(1(

)1)(1(log

)1(

)1(log),|1(

),0,0|1(

),0,1|1(

)(

)(

SRaaPr

SRaaPt

iii

iii

),0,1|1(

),1,1|1(

)(

)(

SRaaPq

SRaaPp

iii

iii


tailor product to different needs

how to avoid confusing customers with too many products Can we find what people want quickly and inexpensively how to find out if a customer is interested in a virtual which doesn't exist

reducing inconsistent preferences good: predictive, customer insight: what people buy or how many will people buy

it fast: for them and for us: it should be fast, doesn't cost so many time cheap: reduce market research cost: should be cheat easy reduce drudgery and errors: should be easy for both customers and

designers That's all the questions in marketing science today.

32

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CUSTOMER NEEDS ELICITATION FOR PRODUCT CUSTOMIZATION Yue Wang Advisor: Prof. Tseng Advanced Manufacturing Institute Hong Kong University of Science and