Customer Insights

Question 1

Quality Dimension according to Garvin (1984)

Answer 1

Feature
Reliability
Conformance
Durability
PQ
Serviceability
Performance

Question 2

Def Customer Insights

Answer 2

Customer Insights describe the market demands and customer expectations toward the product and the company.

Question 3

What is used to enhance decision quality in data-driven product development

Answer 3

Digital Shadows

Question 4

Dimensions of customer information

Answer 4

User
Environment
Usage

Question 5

Types of customer information

Answer 5

Latent need vs Expressed need

Problem-oriented vs Solution-oriented

Question 6

Direct customer feedback

Answer 6

 Specific question
 Collection of not yet existing data
 Less data points

Question 7

Indirect customer feedback

Answer 7

 Targeted analysis of already existing data
 Previously defined purpose
 Big Data

Question 8

Aim of Perceived Quality

Answer 8

Perceived Quality aims to transform human perception into a scalable quantity for the improvement of product development

Question 9

Name the four steps of Stimulus Processing Chain

Answer 9

1. Stimulus recording
2. Multisensory stimulus processing
3. Perception
4. Action

Question 10

Main Take-Aways from Today’s Lecture

Answer 10

The term Customer Insights covers the collection, analysis and
interpretation of customer data from heterogeneous sources. Customer
Insights are precise knowledge about the customer and his (latent) needs.
 Perceived Quality aims to combine the conventional understanding of
quality and the customers perception.
 The customer’s perception is a multidimensional process that companies try
to model and predict.
 There are basic psychophysical relations between the intensity of stimuli
and their perception. How these features are combined to a quality
assessment, is a more complex research question.

Question 11

Qualitative and Quantitative Description of Customer Data

Answer 11

• Subjectivity
• Degree of Structure
• Specificity
• Quantity
• Update Frequency
• Costs

Question 12

Def Kansei Engineering

Answer 12

Kansei Engineering is an interdisciplinary product design approach. With Kansei Engineering, product stimuli are presented and customer Kansei descriptions are captured using questionnaires.

Question 13

Steps of Kansei Engineering

Answer 13

1. Selection
2. Semantic Space
3. Property Space
4. Synthesis
5. Validity test
6. Modelling

Question 14

Why Kansei Engineering?

Answer 14

Kansei Engineering is based on subjective assessments of products and helps customers express their (implicit) requirements for future products.

Question 15

Kansei Engineering: The basic idea is to describe the product from two different perspectives

Answer 15

 Semantic Space

 Property Space

Question 16

Principal Component Analysis (PCA)

Answer 16

• Assumption: variance of the variables is fully explained by the factors
• Goal: reproduce data structure as completely as possible with as few factors as possible
• The factors are called Principal Components

Question 17

Principal Axes Factor Analysis (PFA)

Answer 17

• Assumption: variance of the variable is split between single residual variance and communalities
• Goal: discover communalities within the variables
• Estimation of the amount of communalities in advance

Question 18

Def Reliability:

Answer 18

One measurement leads to the same results when performed several times at different times.

Question 19

Def Validity

Answer 19

A measurement completely captures the underlying construct through the measured variable.

Question 20

Cronbach’s Alpha

Answer 20

• indicates how high the internal consistency reliability of a group of items used to measure a characteristic is
• Cronbach’s Alpha is defined for -unendlich to +1. Only positive values can be interpreted meaningfully.
• If Cronbach’s alpha is close to +1, it can be assumed that the items are consistent and measure the same.

Question 21

Name the modelling types of the kansei engineering

Answer 21

• Mathematical Models (Description of relationships in formulas)
• Iconic Models (Description of contexts in images)
• Verbal Models (Description of contexts in words)

Question 22

Mathematical Modelling

Modelling circle

Answer 22

real problem –> Modelling –> mathematical problem –> Analysis Simulation –> mathematical solution –> Interpretation –> real Solution –> Checking –> real Problem

Question 23

Main Take-Aways from Today’s Lecture - Exercise

Answer 23

 Kansei Engineering is a methodology to systematically improve the
Perceived Quality of a product.
 The reliability of customer clinic data can be evaluated with Cronbach’s
Alpha.
 The decision tree can be used as a grey box approach to model the human
quality perception.