Introducing Marketing Research. Showing the use of Marketing Research in business models and frameworks. Describing a typical Marketing Research process.

1. © 2021 Leap Research. All rights reserved.
Prepared for:
July 7, 2021

2. To introduce Marketing Research
To show the use of Marketing Research in business models and
frameworks
To describe a typical Marketing Research process
1
2
3

4. https://www.esomar.org/market-research-explained
Research which includes all forms of market, opinion
and social research and data analytics, is the
systematic gathering and interpretation of
information about individuals and organisations. It
uses the statistical and analytical methods and
techniques of the applied social, behavioural and
data sciences to generate insights and support
decision-making by providers of goods and services,
governments, non-profit organisations and the
general public.
ESOMAR

5. https://www.mrs.org.uk/about
Research is the collection, use, or analysis of
information about individuals or organisations
intended to establish facts, acquire knowledge or
reach conclusions. It uses techniques of the applied
social, behavioural and data sciences, statistical
principles and theory, to generate insights and
support decision-making by providers of goods and
services, governments, non-profit organisations and
the general public.
Market Research Society

6. https://www.ama.org/the-definition-of-marketing-what-is-marketing/
Marketing research is the function that links the
consumer, customer, and public to the marketer
through information—information used to identify
and define marketing opportunities and problems;
generate, refine, and evaluate marketing actions;
monitor marketing performance; and improve
understanding of marketing as a process. Marketing
research specifies the information required to address
these issues, designs the method for collecting
information, manages and implements the data
collection process, analyzes the results, and
communicates the findings and their implications.
American Marketing Association

7. Marketing research supports decision making
through collecting, analysing, and interpreting
information to identify and solve marketing
problems.
Malhotra, Nunan, & Birks (2017)

8. information
analyzing
insights
decision
making
collecting
interpreting
solving
marketing
problems
statistical &
analytical
methods
communicating
designing

10. Hague, et al. (2016)

11. Hague, et al. (2016)

12. Hague, et al. (2016)

13. Gates + =
Deliverables Criteria Output
Stages + =
Activities Analysis Deliverables
The Customers or Users
Stage 0:
Idea Screen
Stage 1:
Concept Creation
Stage 2:
Business Case
Stage 3:
Product
Development
Stage 4:
Testing &
Validation
Stage 5:
Launch &
Monitor
Gate 2:
2nd Screen
Gate 3:
Go To Development
Gate 4:
Go To test
Gate 5:
Go To Launch
Gate 1:
1st Screen
Cooper (2017)

14. Gates + =
Deliverables Criteria Output
Stages + =
Activities Analysis Deliverables
Stage 0:
Idea Screen
Usage & Attitude
Ideation
Idea Screening
Stage 2:
Business Case
Concept Test
(+ Volumetric)
Stage 3:
Product
Development
Product Test
Concept Product Test
Pack Test
Stage 4:
Testing &
Validation
Product Test
Concept Product Test
(+ Volumetric)
Pack Test
Stage 5:
Launch &
Monitor
Post Launch Test
Feedback
Gate 2:
2nd Screen
Gate 3:
Go To Development
Gate 4:
Go To test
Gate 5:
Go To Launch
Gate 1:
1st Screen
Stage 1:
Concept Creation
Concept Screening

16. Malhotra, Nunan, & Birks (2017)
An extremely
critical step!
Budget
dependent
Beware of
Quality Control
Integrity of
researchers required

17. What action will be taken when the research is completed?
What has caused this problem or led to this opportunity?
What is known about the area of research already?
Who are the (main) target groups for the research?
What specific information is needed from the research?
What is the proposed budget?
Are there any initial ideas for the research method?
Are there any (specific) reporting requirements?
When are the findings required?
Hague, et al. (2016)

18. Qualitative
E.g., Focus Group
Discussion, In-Depth
Interview
Quantitative
E.g., Face-to-Face, CATI,
Online Self-Completion
Observational
Studies
E.g., Shopper Research,
Ethnography
Social Listening
E.g., Sentiment Analysis,
Google Trends
Machine Learning
Algorithms
E.g., Attention prediction
Neuroscience-
Based Methods
E.g., Eye tracking, EEG
Passive Tracking
E.g., RealityMine

20. Base: All Respondents
By Total
63.21
64.46
66.35
66.38
66.87
67.19
67.55
67.94
68.43
68.60
70.02
70.20
71.14
71.14
72.35
72.78
74.01
74.39
75.26
75.29
75.34
75.59
I9
I7
I2
I18
I12
I8
I5
I17
I10
I20
I6
I14
I21
I3
I22
I15
I16
I1
I13
I11
I19
I4
Potential Score
Ideas
Potential Score Index
≥ 75
70 – 74
< 70
POTENTIAL
SCORE
d I4 75.59 5.77 BeGHIjLRt 4.10 eGhIqv 3.23 5.87 bGIj
s I19 75.34 5.60 bghIlr 4.10 eGhIqv 3.50 V 5.93 BGIJr
k I11 75.29 5.67 BgHILR 4.07 eGhIq 3.43 v 5.83 bGIj
m I13 75.26 5.73 BeGHILRt 4.10 eGhIqv 3.23 5.83 bGIj
a I1 74.39 5.50 Ir 4.07 eGhIq 3.47 V 6.10 BceGHIJoQR
p I16 74.01 5.53 bhIlr 3.93 G 3.60 bouV 5.83 bGIj
o I15 72.78 5.67 BgHILR 3.93 G 3.13 5.47
u ID 72.35 5.50 Ir 4.00 Gi 3.13 5.87 bGIj
c I3 71.14 5.30 I 4.07 eGhIq 3.27 5.53
v PH 71.14 5.63 bghIlR 3.77 2.97 5.70 g
n I14 70.20 5.23 I 3.93 G 3.37 5.57
f I6 70.02 5.37 I 3.80 g 3.23 5.60
t I20 68.60 5.10 i 3.83 g 3.40 v 5.57
j I10 68.43 5.17 i 3.83 g 3.33 5.23
q I17 67.94 5.20 I 3.73 3.23 5.40
e I5 67.55 5.10 i 3.73 3.30 5.53
h I8 67.19 4.93 3.73 3.60 bouV 5.43
l I12 66.87 4.93 3.90 G 3.20 5.60
r I18 66.38 4.90 3.83 g 3.33 5.37
b I2 66.35 4.93 3.93 G 3.13 5.27
g I7 64.46 5.00 3.47 3.30 5.10
i I9 63.21 4.47 3.63 3.83
BCDEFGJLMnO
QRtUV 5.17
7pt scale 5pt scale 5pt scale 7pt scale
RELEVANCY BELIEVABILITY UNIQUENESS EASE OF
UNDERSTANDING

21. Base: All Respondents
By Total
Both New and Competitor
concepts have similar
Potential Trial Scores.
Concept New performs
well on Uniqueness.
However, it performs
poorly on Price
Expensiveness, which
drags down its score a bit.
Hence, Concept New must
exploit its uniqueness in
the communication and
might consider reducing its
price a bit.
 Go
 Improve
 Rework
 Good
 OK
 Poor
LEGEND
Purchase Intention 
Overall Liking 
Relevancy 
Price Expensiveness 
Uniqueness 
Value for Money 
Believability 
Ease of Understanding 
Potential
Trial Score
78%
CONCEPT NEW
Purchase Intention 
Overall Liking 
Relevancy 
Price Expensiveness 
Uniqueness 
Value for Money 
Believability 
Ease of Understanding 
Potential
Trial Score
79%
CONCEPT COMPETITOR

22. By Total
Index
81 - 100 Excellent
61 - 80 Good
41 - 60 Average
21 - 40 Poor
1 - 20 Very Poor
Brand A
57
20 80
60
40
1 100
Brand D
64
20 80
60
40
1 100
Brand B
50
20 80
60
40
1 100
Brand E
67
20 80
60
40
1 100
Brand C
43
20 80
60
40
1 100
Brand F
68
20 80
60
40
1 100
Brand A, B, and C have
lower scores than their
respective competitors,
achieving an Average
index only. While Brand D,
E, and F manage to
achieve a Good index.
Base: All Random Respondents

23. Base: All Respondents
Top Coffee Gula Aren 30-second TVC
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
CONFUSED SURPRISED SAD DISGUSTED ANGRY HAPPY FEAR

24. Based on a machine learning algorithm
Neo Coffee Tiramisu 30-second TVC
https://youtu.be/8NoVvf5SSMg

25. Packaging Design
Royale Parfum Collection
Purple Dawn
Molto Eau De Parfum
Elegant
Downy Parfum Collection
Mystique
Based on a machine learning algorithm

26. Based on Concept & Product Test
Based on Concept & Product Test +
Volumetric
Based on Branded Product Test
Based on Unbranded Product Test
Based on Concept &
Product Test + Volumetric

28. *A term coined by Stephen Few in his book Big Data, Big Dupe (2018)
*
It’s all about the (explicit) objectives!
Data deep dive by solid data sensemaking skills
Augmented by good technologies

29. Proposal to mine value from data by Stephen Few in Big Data, Big Dupe (2018)
2 Only relevant data of good quality is useful
3 Information is only useful if it is understood
4 The steps that lead to understanding require skills that must be learned
5 The development of data-sensemaking skills involves a great deal of study and practice
6 Study and practice take time – lots of time
7 Technologies are needed to augment human data-sensemaking abilities
8 Only well-designed technologies are worth the investment of time and money
9 Skilled data sensemakers combine general analytical skills with specific domain knowledge;
one without the other is not enough
1 Useful information can be derived from data

30. Cooper, Robert G. 2017. Winning at New Products: Creating Value Through Innovation. 5th ed. New York:
Basic Books
Few, Stephen. 2018. Big Data, Big Dupe: A Little Book about a Big Bunch of Nonsense. El Dorado Hills, CA:
Analytics Press
Hague, Paul, Matthew Harrison, Julia Cupman, and Oliver Truman. 2016. Market Research in Practice: An
Introduction to Gaining Greater Market Insight. 3rd ed. London: KoganPage
Liraz, Meir. 2014. How to Conduct a Low Cost Market Research: A Step by Step Guide to Marketing
Research Strategies [Kindle Android version]. Retrieved from amazon.com
Malhotra, Naresh K., Daniel Nunan, and David F. Birks. 2017. Marketing Research: An Applied Approach.
5th ed. Harlow: Person Education Limited

31. Alur Kerja di Riset Pemasaran Kuantitatif
MARKETING RESEARCH BRIEF TEMPLATE
Considerations When Conducting An Online Quantitative Marketing Research Project
The Peril of Undermining Quality Control in Marketing Research
Prinsip "Experimentation"​ di Riset Pemasaran
Apakah "Positioning Concept"​ itu?
"Sensation Transference"​ (karya Louis Cheskin)

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