Survey analysis

View all responses

Name New sports car
Num features 6
Num responses 379
Started at Fri 25th Feb 2022, 11:46am (UTC)
Status Active

Filters

Use the custom data fields on this survey to filter and find segments in your results. Only single or multi choice fields are available as filters.

Can you drive?

Favourite cars




Prioritisation

This sequence of features will maximise overall positive impact on customer satisfaction, based on the analysis of primary categories and satisfaction coefficients. Note: where features have multiple primary categories you may see them repeated in the list. You can choose which primary category you wish to go with.

Statistical significance:

Build these first:

  • Brakes (Must-have)

  • Battery level indicator (Must-have)

Then build these in order:

  1. Heated seats (Delighter)

  2. Gullwing doors (Delighter)

  3. Self driving (Delighter)

Don't build these:

  • High performance engine (Indifferent)

Do more research on these:

  • None

Standard Kano categorisation

Whichever category has the most votes is the primary category for that feature, highlighted in yellow. Multiple categories may have the same count, or a category might win only by a very small margin. We apply Fong's test [1] to see if the primary category is a statisically significant winner (90% confidence level), indicated with an asterisk *.

[1] Fong, D. (1996). Using the self-stated importance questionnaire to interpret Kano questionnaire results.The Center for Quality Management Journal, 5, 21 – 24.


  Must-havePerformanceDelighterIndifferentReverseUnknown
1
High performance engine		 61 63 106 110 14 25
2
Gullwing doors		 7 16 161 *108 74 13
3
Heated seats		 40 45 139 *108 29 18
4
Brakes		 168 *93 36 42 12 28
5
Battery level indicator		 125 *73 70 59 16 36
6
Self driving		 15 21 140 *115 60 28

Distribution between categories

To improve on the basic Kano categorisation, we show the spread of responses between the different categories as a radar chart, and compute a less strict statistical significance test using a Z-Score [2] to a level of 1.5 standard deviations. Categories meeting this threshold have a tick, otherwise they appear with a confidence % level.

[2] Z-Score on Wikipedia.

1
High performance engine

Secondary: Indifferent (86%)
Secondary: Delighter (79%)

2
Gullwing doors

Primary: Delighter ✅
Secondary: Indifferent (52%)

3
Heated seats

Primary: Delighter ✅
Secondary: Indifferent (67%)

4
Brakes

Primary: Must-have ✅

5
Battery level indicator

Primary: Must-have ✅

6
Self driving

Primary: Delighter ✅
Secondary: Indifferent (72%)

Continuous matrix analysis

This analysis plots features on a grid to show the nuance within the categorisation, a method proposed by DuMouchel [3]. You may get slightly different answers compared to other approaches - the others are 'lossy' (we lose information by reducing responses to a single category), whereas this is more sensitive and allows for the strength of each expression (eg. tolerate vs dislike vs expect) within a category.

[3] William DuMouchel, “Thoughts on Graphical and Continuous Analysis”, Center for Quality of Management Journal, Fall 1993

Positive
U
D
D
P
R
I
I
M
R
I
I
M
R
R
R
U
1
2
3
4
5
6
Negative
1

High performance engine

2

Gullwing doors

3

Heated seats

4

Brakes

5

Battery level indicator

6

Self driving

Customer satisfaction coefficients

This chart condenses all the information about each feature down to a single relative measure of how customer satisfaction could change if the feature were absent or present [4]. Red / negative numbers show the dissatisfaction if the feature was absent, green / positive numbers show satisfaction if it was present.

[4] Mike Timko, “An Experiment in Continuous Analysis”, Center for Quality of Management Journal, Fall 1993


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