How to design an optimal package for your product? To answer this question, we developed a dynamic, on-the-fly optimization algorithm that combines traditional experimental research with an optimization learning process. The benefit of our method is that you can explore a vast space of alternatives, with the freedom to testing hundred of thousands of combinations of graphic designs, on-pack benefit statements, pictures etc. Eventually the method will narrow down the parameter space, acquiring observations only for a limited number of high-potential combinations while understanding the drivers of preference through "why" questions. As a result, you will be able to obtain the same levels of detail for each of them you would expect from a monadic concept test. This deck was presented at the 2013 Advanced Research Technique (ART) Forum, organized in Chicago by the American Marketing Association

1. expect great answers
Dynamically converging
to the best package designs
A simple yet effective approach
Carlo Borghi, Eline van der Gaast, Virginie Jesionka and Gerard Loosschilder
10 June 2013

2. Creating a package design
that has impact at point of sale is partly …
ART
 catch the eye
 make the brand stand out
 position the product and informs the customer
CRAFT
 optimize the mix of elements
 make the most of the potential of the creative design template
2

3. We‟re not the first to crack this nut. But we have
a remarkably simple yet effective approach
3
An immense
search space
of potential
package designs,
combining ART
and CRAFT
A convergent
procedure
involving
consumer
opinions
One „best‟ or a few
segment-specific
package designs

4. • 11-points scale purchase intent
measurements for pack designs
• Pack designs drawn from an
orthogonal design on the nth
space
• Collect set number of
observations
• Run LS regression through
PERL-embedded R code on
accumulated data
• Define (n+1)th parameter space
by dropping bottom ranking
items (attribute levels)
4
nth parameter space
Smaller, (n+1)th
parameter space
We systematically limit the parameter space
throughout the search steps

5. Nice, but does it work?
It works!
In theory, to identify the
optimal pack design using
simulated data
In practice, to improve on the
current package design and
be as good or better than
expert opinions
5

6. We test the effectiveness of our approach in an
hypothetical study in the petcare category
Set up of the study
6

7. 7

8. 8
Benefit statement: 20 levels
Design: 4 levels
Cat picture: 15 levels
Insect logo: 3 levels
Vet logo: 3 levels
Color: 5 levels
Chemicals: 2 levels

9. Parameter space for a pet anti-parasite pack
design
Attributes Levels Nature
1 Package concept 4 Visual concepts
2 Pet picture 15 Pet pictures
3 Package color 5 Integrated in pack concepts
4 Claims 20 Text statements
5 List of chemical components 2 Present / absent
6 Icons 3 Present (2 versions) / absent
7 Vet only icon 3 Present (2 versions) / absent
# of possible combinations 108,000
9

10. Respondents go through the survey to narrow
down the packages to 18 combinations
10
Levels deleted according to pre-specified order
Loop # N
# deleted
levels
Remaining
combinations after
deletion
1 200 9 51,840
2 70 8 15,552
3 50 9 2,592
4 50 7 384
5 30 7 18
…

11. On simulated data, the hit rate is surprisingly high,
even with few “respondents”
Step 1

12. The algorithm correctly eliminates the worst
levels most of the time
Design
X
-0.8
X
0.3 0.4 2.2
Cat picture X
-7.3
X
-5.4
X
-4.9
X
-4.7
X
-3.1
X
-2.9
X
-2.4
X
-1.7
X
-0.8
X
-0.5
X
0.0 0.8
X
1.0 1.4 3.3
Color X
-1.9
X
-1.8
X
0.1
X
0.9 2.3
Claim X
-3.1
X
-2.7
X
-1.4
X
-0.9
X
-0.3
X
0.5
X
0.9
X
0.9
X
1.0
X
1.2
X
1.3
X
1.6
X
1.6
X
2.12
X
2.6
X
3.6
X
3.8 3.9 5.5 5.5
Chemicals X
1.1 3.1
Insect logo X
-3.6 -1.5 2.7
Vet logo X
-1.9
X
-0.5 4.2
X
1.9
12
In the 5th and final loop, a
level is incorrectly eliminated
X if the level is eliminated
in one of the loops
Increasing utility
True average population
utility
5 iterations with 200, 70, 50, 50 and 30 respondents each, +/-20% error uniformly distributed in ratings

13. Design
X
-0.8 0.3
X
0.4 2.2
Cat picture X
-7.3
X
-5.4
X
-4.9
X
-4.7
X
-3.1
X
-2.9
X
-2.4
X
-1.7
X
-0.8
X
-0.5
X
0.0 0.8
X
1.0 1.4 3.3
Color X
-1.9
X
-1.8
X
0.1
X
0.9 2.3
Claim X
-3.1
X
-2.7
X
-1.4
X
-0.9
X
-0.3
X
0.5
X
0.9
X
0.9
X
1.0
X
1.2
X
1.3
X
1.6
X
1.6
X
2.12
X
2.6
X
3.6
X
3.8 3.9 5.5 5.5
Chemicals X
1.1 3.1
Insect logo X
-3.6 -1.5 2.7
Vet logo X
-1.9
X
-0.5 4.2
X
1.9
Even with half the respondents, the algorithm is
quite effective
13
X if the level is eliminated
in one of the loops
Increasing utility
True average population
utility
Incorrectly eliminated
in the 2nd loop
Incorrectly eliminated
in the 2nd loop
5 iterations with 100, 35, 25, 25 and 15 respondents each, +-20% error uniformly distributed in ratings

14. 14
Loop 1
108,000
combs
Rating of
polarizing
concepts
Loop 2
51,840
combs
Loop 3
15,552 comb
Loop 5
384 combs
Cluster 1
Cluster 2
Extra observations on
all remaining
packages, treated
holistically
18 combs
Loop 2
51,840
combs
Loop 3
15,552
combs
Loop 5
384 combs
The procedure identifying clusters is inserted
between loops in the screening process
Extra observations on
all remaining
packages, treated
holistically
18 combs

15. Step 2
15
In an actual consumer survey, in which we test if our
solution is better than..
• the current package
• the expert opinion

16. The experts selected their suggested winners
from the full set of pack alternatives
16
Current Optimization outcomeExperts

17. Our design process has delivered a better pack
design than the current package design
8.21
8.78
8.82
8.87
9.01
6 7 8 9 10 11
Current package (n=415)
4th best (n=96)
3rd best (n=67)
2nd best (n=91)
Best from algorithm (n=76)
17
Purchase intent rating (1-11 scale)
These
variations
would also be
“good enough”
All differences
versus current
package are
statistically
significant at a
95% CL

18. Two experts are as good as the algorithm
Although expert opinions are not always consistent
8.10
8.21
8.78
8.82
8.87
8.98
9.01
9.30
6 7 8 9 10 11
Expert A (n=61)
Current package (n=415)
4th best (n=96)
3rd best (n=67)
2nd best (n=91)
Expert C (n=61)
Best from algorithm (n=76)
Expert B (n=61)
18
Difference not
significant @
95%
Purchase intent rating (1-11 scale)

19. Conclusions and next steps
19

20. Conclusion: we have an effective approach to
identify an optimal package design
Advantages over other methods (e.g., conjoint analysis) are:
• Show one package concept per screen
• Can handle a very large parameter space with (several) attributes with many
levels
• Can target questions to the best packages such as extra scores or details
through the “why” question.
Further research steps:
• Exclude levels based on confidence tests (and create design dynamically)
• Better real-time respondent quality checks
• Test the method for complex stimuli, e.g. video advertisement
20

21. contact us or follow us online!
Carlo Borghi | c.borghi@skimgroup.com
Eline van der Gaast | e.vandergaast@skimgroup.com
Virginie Jesionka | v.jesionka@skimgroup.com
Gerard Loosschilder | g.loosschilder@skimgroup.com

22. Currentpackage
22

23. Algorithm‟sbestpackage
23

24. Experts‟bestpackage
24

25. The algorithm is effective in predicting the score of the
best concepts, but not those of the experts‟s cocepts
25
8.0
8.0
7.7
8.8
8.9
8.9
8.6
8.1
9.3
9.0
8.9
9.0
8.8
8.8
0.0 2.0 4.0 6.0 8.0 10.0
Expert A
Expert B
Expert C
Our concept1
Our concept2
Our concept3
Our concept4
Real average score
Estimated average score