Transcript No Slide Title

Choice modelling an example
Background
 Bonlac changing processed cheese and natural cheddar
offering from Bega to Perfect Cheese
 Previous research has:
 Explored an appropriate positioning for
Perfect Cheese
 Identified the optimal pack design
 Further research is required to:
 Understand market response to the new range of Perfect
Cheese in terms of:
Price sensitivity
Market share potential
Cannibalisation effects
 In addition, feedback on sensory performance of Perfect
Cheese products relative to competitors, in order to
support positioning platform ( not discussed today)
Pricing Objectives
 To understand the impact of launching of Perfect in the
Processed Cheese and Light Cheddar Block markets
 Understanding initial impact (pre-trial)
 Understand longer term impact (post-trial)
 Understand the price sensitivity of each user group
Sensory Objectives
 To evaluate the Perfect cheese slice and block products
relative to competitive offerings in terms of:
 Acceptability (unbranded vs branded)
 Sensory profiles
 Relative to consumer ideals
 Purchase intentions
 Ability to support brand positioning expectations
Method
 Central location test at Takapuna
Pre-trial
Discrete Choice Modelling
Sensory Evaluation
1. All products unbranded
2. Perfect Cheese product branded
Post-trial
Discrete Choice Modelling
Sample Population
 N=30 each of:
 Light Slice users
 Super Light Slice users
 Cheddar Slice users
 Reduced Fat Cheddar Block users
 Sample population:
 Females MHS, 20-65 years
 Mix of household types (mainly families with kids)
Pricing Methodology
 15 shelves - pre/post presented to each of 30 people in
4 user groups
 Light Slices
 Super Light Slices
 Cheddar Slices
 Light Cheddar Block
 In each shelf range of prices consumers get to choose
only one
 Imitates shopping experience
 Idealised situations (100% awareness of Perfect)
 House-brands included
Introduction…
CHEDDAR
CHEESE SLICES
PERFECT
CHESDALE
MAINLAND
FIRST CHOICE
PAMS
CHEDDAR
CHEESE SLICES
Price Scenario 1
$2.29
Please tick your
first preference only
$2.29
X
PERFECT
$2.29
V

MAINLAND
$1.99
$1.99
Y

FIRST CHOICE
Price Scenario 2
W
CHESDALE

CHEDDAR
CHEESE SLICES

Please tick your
first preference only
$2.59
Z

PAMS
$1.99
X
V

W
CHESDALE
MAINLAND
$1.99
$1.99

PERFECT
$2.59
Y

FIRST CHOICE
Z


PAMS
( 4 of the 15 scenarios)
CHEDDAR
CHEESE SLICES
Price Scenario 3
Please tick your
first preference only
$1.99
X
PERFECT

$2.59
V
$2.59

W
CHESDALE
MAINLAND
$1.99
$1.99
Y
CHEDDAR
CHEESE SLICES

FIRST CHOICE

Please tick your
first preference only
$1.99
Z
PAMS
Price Scenario 4

X
PERFECT

$1.99
V
$1.99

W
CHESDALE
MAINLAND
$1.99
$1.99
Y

FIRST CHOICE
Z
PAMS


Whoa there! - How did we get to
this conclusion?
 3 brands of interest – Mainland/Chesdale and Perfect
 The other 2, Pams and First Choice area at fixed, lower
prices, prices
 Decided to go with 3 price (low $1.99/medium $2.29 /high
$2.59) points/brand
Why?
 Therefore we have 33 =27 possible combinations
 Decided to choose a sample of 15 to reduce respondent
fatigue and to ensure we could measure all 2 order
interaction effects
eg: does a high price of Chesdale result in different pricing
response for Perfect than if it were a low price
This phenomenon is quite common so needs to be taken into
account
The design
Design
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
A
M
L
H
L
H
M
M
L
M
M
H
H
L
L
H
Discuss:
B
M
H
H
L
L
M
H
M
M
L
M
L
L
H
H
C
M
H
L
L
H
L
M
M
H
M
M
L
H
L
H
The data - raw
ID PRE1 PRE2 PRE3 PRE4 PRE5 PRE6 PRE7 PRE8 PRE9 PRE10 PRE11 PRE12 PRE13 PRE14 PRE15 POST1 POST2 POST3
61
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
62
1
1
3
1
2
3
1
1
1
2
2
2
1
1
4
1
1
3
63
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
64
4
4
4
1
4
4
4
4
4
4
4
4
4
1
4
3
4
3
65
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
66
2
1
4
2
2
4
4
2
4
2
4
2
2
1
4
5
1
5
67
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
68
4
1
3
1
2
3
4
1
4
2
4
2
1
1
4
4
1
4
69
4
1
3
2
2
3
4
1
4
2
4
2
2
1
4
2
1
4
70
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
3
71
4
1
3
2
2
3
4
1
4
2
4
2
2
3
4
4
1
3
72
2
1
2
2
2
2
2
1
2
2
2
2
2
1
2
3
1
3
73
3
1
3
3
2
3
3
3
1
2
3
3
2
3
3
3
1
3
74
2
1
4
2
2
4
1
1
2
2
4
2
2
1
4
2
1
3
75
2
2
5
5
2
2
5
1
2
4
4
2
2
5
5
3
5
3
76
4
1
4
1
4
4
4
4
4
4
4
4
4
1
4
4
1
4
77
4
1
3
2
2
3
1
1
2
2
3
3
2
3
4
2
1
4
78
2
1
3
3
2
3
3
2
3
2
3
3
2
3
5
2
2
3
79
1
1
3
3
2
1
1
1
1
2
5
2
1
1
5
1
1
3
80
5
1
5
1
2
5
5
1
5
2
5
1
1
1
5
5
1
5
81
4
1
4
2
2
4
4
1
4
2
4
2
1
1
4
4
1
3
82
4
1
2
1
2
3
4
1
4
4
4
2
4
1
4
4
2
3
83
5
1
3
1
2
3
5
1
5
2
5
2
1
1
5
5
1
5
84
1
1
3
1
2
3
1
1
1
2
2
2
1
1
1
2
1
3
85
4
1
4
1
4
4
4
1
4
2
4
2
1
1
4
3
1
3
86
4
1
3
2
2
3
5
1
4
4
5
2
1
1
4
4
1
3
87
4
1
3
2
2
3
4
1
4
2
4
2
2
1
1
4
1
3
88
1
1
3
3
3
3
3
1
1
3
3
3
1
3
3
3
1
3
89
4
1
3
2
2
3
4
1
4
2
4
2
2
3
4
3
1
3
90
2
1
3
2
2
2
4
1
2
2
2
2
2
1
2
2
1
3
The data - how it’s needed for
proc Phreg in SAS
POST OBS SET T FREQ CSD MLD PRF FC PAM PR_CSD PR_MLD PR_PRF PR_FC PR_PAM CSD_MLD CSD_PRF
0
1
1 1
8
1
0
0 0
0
2.29
0
0
0
0
0
0
0
2
1 2
22
1
0
0 0
0
2.29
0
0
0
0
0
0
0
3
1 1
7
0
1
0 0
0
0
2.29
0
0
0
2.29
0
0
4
1 2
23
0
1
0 0
0
0
2.29
0
0
0
2.29
0
0
5
1 1
1
0
0
1 0
0
0
0
2.29
0
0
0
2.29
0
6
1 2
29
0
0
1 0
0
0
0
2.29
0
0
0
2.29
0
7
1 1
12
0
0
0 1
0
0
0
0
1.99
0
0
0
0
8
1 2
18
0
0
0 1
0
0
0
0
1.99
0
0
0
0
9
1 1
2
0
0
0 0
1
0
0
0
0
1.99
0
0
0
10
1 2
28
0
0
0 0
1
0
0
0
0
1.99
0
0
0
1
2 1
28
1
0
0 0
0
1.99
0
0
0
0
0
0
0
2
2 2
2
1
0
0 0
0
1.99
0
0
0
0
0
0
0
3
2 1
1
0
1
0 0
0
0
2.59
0
0
0
1.99
0
0
4
2 2
29
0
1
0 0
0
0
2.59
0
0
0
1.99
0
0
5
2 1
0
0
0
1 0
0
0
0
2.59
0
0
0
1.99
0
6
2 2
30
0
0
1 0
0
0
0
2.59
0
0
0
1.99
0
7
2 1
1
0
0
0 1
0
0
0
0
1.99
0
0
0
0
8
2 2
29
0
0
0 1
0
0
0
0
1.99
0
0
0
0
9
2 1
0
0
0
0 0
1
0
0
0
0
1.99
0
0
0
10
2 2
30
0
0
0 0
1
0
0
0
0
1.99
0
0
0
1
3 1
4
1
0
0 0
0
2.59
0
0
0
0
0
0
0
2
3 2
26
1
0
0 0
0
2.59
0
0
0
0
0
0
0
3
3 1
3
0
1
0 0
0
0
2.59
0
0
0
2.59
0
0
4
3 2
27
0
1
0 0
0
0
2.59
0
0
0
2.59
0
0
5
3 1
15
0
0
1 0
0
0
0
1.99
0
0
0
2.59
0
6
3 2
15
0
0
1 0
0
0
0
1.99
0
0
0
2.59
0
7
3 1
6
0
0
0 1
0
0
0
0
1.99
0
0
0
0
8
3 2
24
0
0
0 1
0
0
0
0
1.99
0
0
0
0
9
3 1
2
0
0
0 0
1
0
0
0
0
1.99
0
0
0
10
3 2
28
0
0
0 0
1
0
0
0
0
1.99
0
0
0
1
4 1
13
1
0
0 0
0
1.99
0
0
0
0
0
0
0
2
4 2
17
1
0
0 0
0
1.99
0
0
0
0
0
0
0
3
4 1
12
0
1
0 0
0
0
1.99
0
0
0
1.99
0
0
4
4 2
18
0
1
0 0
0
0
1.99
0
0
0
1.99
0
Some points
 Note that we have decided to mode/post data together
 Not how the data is agrregated now
 Compare this to what we have:
Preprice 1
Cumulative
Cumulative
PRE1
Frequency
Percent
Frequency
Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1
8
26.67
8
26.67
2
7
23.33
15
50.00
3
1
3.33
16
53.33
4
12
40.00
28
93.33
5
2
6.67
30
100.00
Preprice 2
Cumulative
Cumulative
PRE2
Frequency
Percent
Frequency
Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1
28
93.33
28
93.33
2
1
3.33
29
96.67
4
1
3.33
30
100.00
Preprice 3
Cumulative
Cumulative
PRE3
Frequency
Percent
Frequency
Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1
4
13.33
4
13.33
2
3
10.00
7
23.33
3
15
50.00
22
73.33
Some Points …
 The variable T denote 1= choice, 2 = no choice
 The resulting ‘doubling up” of all rows
 The variable SET represents the appropriate scenario
 For each scenario there are 10 =5*2 rows
 Variables like CSD_MLD represents Chesdale’s effect on
Mainland and so is in the relevant rows for Mainland but
is Chesdale’s price
 Remind me to give you a Splus function called
SAS.DCM.FORMAT that helps format the appropriate
design matrix for this data
Some more code
data temp;
set hold.cslmodel;
PR_Mld2
= PR_Mld**2;
PR_Prf2
= PR_Prf**2;
PR_Anc2
= PR_Anc**2;
PR_FC2
= PR_FC**2;
PR_Pam2
= PR_Pam**2;
DMld
= POST*Mld;
DPrf
= POST*Prf;
DAnc
= POST*Anc;
Dpam
= POST*pam;
Dfc
= POST*FC;
DPR_Mld
= POST*PR_Mld;
DPR_Prf
= POST*PR_Prf;
DPR_Anc
= POST*PR_Anc;
DPR_Pam
= POST*PR_Pam;
DPR_FC
= POST*PR_FC;
.
.
.
DPam_Prf
=POST*Pam_Prf;
Note: coding up the pre/post
DPam_Anc
=POST*Pam_Anc;
and quadratic price effects
DPam_FC
=POST*Pam_FC ;
run;
effects
Analysing the data
Saving this data:
data hold.cslmodel;
set temp;
run;
Now we are ready to start finding the correct model:
** trial and error to obtain the ‘correct’ model
proc phreg data =hold.cslmodel outest =betas nosummary;
strata set;
model t*t(2) =
CsD Mld Prf FC Pam
PR_CsD PR_Mld PR_Prf PR_FC PR_pam
PR_CsD2 PR_Mld2 PR_FC2 PR_pam2
DCsD DMld DPrf Dpam Dfc
DPR_CsD DPR_Mld DPR_Prf DPR_FC DPR_Pam
DPR_CsD2 DPR_Mld2 DPR_Prf2 DPR_FC2 DPR_pam2
DCsD_Mld DCsD_Prf DCsD_FC DCsD_Pam
DMld_CsD DMld_Prf DMld_FC DMld_Pam
DPrf_CsD DPrf_Mld DPrf_FC DPrf_Pam
DFC_CsD DFC_Mld DFC_Prf DFC_Pam
DPam_CsD DPam_Mld DPam_Prf DPam_FC
/ties =breslow;
freq freq;
run;
Analysing the data…
 The final model:
proc phreg data =hold.cslmodel outest =betas nosummary;
strata set;
model t*t(2) =
CsD Mld Prf FC Pam
PR_CsD PR_Mld PR_Prf PR_FC PR_pam
PR_CsD2 PR_Mld2
DPrf
DCsD_Prf
/ties =breslow;
freq freq;
run;
PR_FC2 PR_pam2
Analysing the data…
 Output:
Analysis of Maximum Likelihood Estimates
Parameter
Standard
DF
Estimate
Error
Chi-Square
Pr > ChiSq
Ratio
CSD
1
62.03446
10.46407
35.1451
<.0001
8.734E26
MLD
1
53.51747
11.68024
20.9936
<.0001
1.747E23
PRF
1
12.41570
1.09299
129.0359
<.0001
246643.1
FC
1
1.15688
0.16214
50.9094
<.0001
3.180
PAM
0
0
.
PR_CSD
1
-48.72340
9.27818
27.5772
<.0001
0.000
PR_MLD
1
-41.00351
10.42944
15.4568
<.0001
0.000
PR_PRF
1
-5.23230
0.49984
109.5801
<.0001
0.005
PR_FC
0
0
.
.
.
.
PR_PAM
0
0
.
.
.
.
PR_CSD2
1
9.65004
2.03784
22.4241
<.0001
15522.40
PR_MLD2
1
7.85671
2.30708
11.5972
0.0007
2583.017
PR_FC2
0
0
.
PR_PAM2
0
0
.
DPRF
1
2.78607
1.13648
6.0098
0.0142
16.217
DCSD_PRF
1
-0.88705
0.48077
3.4042
0.0650
0.412
Variable
Hazard
.
.
.
.
.
.
.
.
.
Turning this into something
meaningfull
Cheddar Cheese Slices
Chesdale
Mainland
Perfect First Choice
Pams
2.29 2.29 1.99 1.99 1.99
Pre-Trial

CSD MLD PRF FC
PAM PR_CSD
PR_MLDPR_PRFPR_FC PR_PAM PR_CSD2PR_MLD2
PR_FC2 PR_PAM2DPRF DCSD_PRF
62.03 53.52 12.42 1.157
0 -48.7
-41 -5.2323
0
0 9.65004 7.8567
0
0 2.7861 -0.887
exT
2.897 2.272 7.414
3.18
1
Market Share
Mainland
Perfect Anchor First Choice
Pams
17.28 13.56 44.23 18.97 5.97
Post-Trial
exT
2.897 2.272 15.77 3.18
Market Share
Mainland
Perfect Anchor
11.53 9.05 62.78 12.66
Post
Pre
Chesdale11.53 17.28
Mainland 9.05 13.56
Perfect 62.78 44.23
First Choice
12.66 18.97
Pams
3.98 5.97
1
3.98
Presenting the data
Cheddar Cheese Slices
Price
First Choice & Pams
Chesdale
Mainland
Perfect
Low ($1.99)
Medium ($2.29)
Medium ($2.29)
High ($2.59)
10
10
Pams
33
32
First Choice
3
Perfect
Pre
Post
7
23
23
Mainland
30
29
Chesdale
0
20
40
60
80
100
Presenting the data
Sensitivity - Pre-Trial
100
80
60
40
20
0
Chesdale
Mainland
Perfect
1.
99
2.
09
2.
19
2.
29
2.
39
2.
49
2.
59
Sensitivity-Post Trial
Price
(all others at $2.29)
80
60
Chesdale
40
20
0
Mainland
Perfect
1.
99
2.
09
2.
19
2.
29
2.
39
2.
49
2.
59
Share
Share
(all others at $2.29)
Price