From Hype to Reality: AI in Market Research

I N S I T E S C O N S U LT I N G
03
rtificial intelligence in market research: hype, trend,
overrated or game changer? It’s time to experiment and find out.
Meet Galvin, the intelligence Insight Activation Studio assistant,
and Minority Report in research communities. These two success-
ful case studies prove that we don’t have to talk anymore about
what AI can mean for the market research industry, but that we can
actually show how it works and demonstrate real results. Build,
measure, learn … share!
A

04
To whatever conference we may go, industry magazine we may read,
business podcast we may listen to: the message is the same every-
where. Artificial Intelligence (AI) is the next big thing. Artificial intelli-
gence will disrupt every industry!
“But is this actually the case for the MR industry? Or is this perhaps
hype?” But what is hype in fact? There are two important things to say
about hype. A first characteristic of hype is that everybody is using dif-
ferent words to say the same thing. This is the case for AI! We’ve heard
all the buzz words, big data, pattern recognition, predictive analytics,
machine learning, deep learning, etc., but in a certain way, we are all
kind of saying the same thing. Bernard Marr, a leading data expert, can
help us to clarify the concept. In a recent Forbes article (Marr 2016), he
says that Artificial Intelligence must be seen as an umbrella term and
is in fact the broader concept of machines being able to carry out tasks
in a way that we would consider “smart”. A second characteristic of
hype is that, potentially, everybody is talking about it, but nobody or a
few individuals are walking the talk. This is the case for AI! In the 2016
GRIT report (Greenbook 2016), +- 77% of industry professionals say
that AI is an interesting trend or still too early to tell what it can actually
mean for us, while 23% of marketers believe that AI is a game changer.
These numbers suggest that few in business have actually implement-
ed or considered it.
“But does this hype help the MR industry any further without actual
results?” Since we have been hyping AI for too many years, we need

to stop talking. We need to start experimentation to see what it actually
means. We need to implement AI experiments within business environ-
ments and get our hands dirty. We need to go from hype to reality to
move the industry forward.
With this paper, we aim to increase understanding in the MR industry
of how to adopt AI within the MR projects by presenting a framework
of how companies should adopt AI within their business projects and
discussing two case studies that proves the value added. This paper is
the result of a top-notch collaboration between academia (IESEG
School of Management) and a research agency (InSites Consulting).
05
© GRIT 2016

06
To explore the added value of AI for MR projects, we follow the frame-
work popularized by The Lean Startup of Eric Ries (Ries 2011), whose
ideas have been worshipped within startup contexts. To effectively
explore and introduce innovation, Eric Ries states that one needs to
execute iteratively the following group of actions: 1) build, 2) measure,
3) learn. One starts by building out a Minimum Viable Product that
provides a solution for the most basic product assumptions. One tests
it with real consumers and measures the results. Using the feedback,
one identifies the strong points, weaknesses and learns from the mis-
takes. Now, one follows the group of action back again and starts from
a better product, in which the feedback of the previous iteration has
been integrated. Whenever the innovation meets the expectations or
the criteria that have been agreed upon in advance, one can stop the
loop and go from experimentation to real business adoption.
To introduce AI experimentation within the MR industry, we have
adapted the framework and introduced a fourth step, i.e. share. As AI
has both many advocates and opponents, it is crucial to share tips and
HOW CAN WE
TACKLE THIS
INDUSTRY
CHALLENGE?

results, both internally within the company and externally to industry
peers. Internal sharing is important to make other employees aware of
the project and allow them to be curious or get involved. Whenever, the
AI project is finished, adoption will be easier as employee buy-in will be
higher, as opposed to the case where AI experimentation was done in
secret. External sharing is important to move the industry forward. Only
by staying at the front-end of AI innovation, MR agencies will be able
to offer better services to clients, whereas clients can stay up-to-date.
Furthermore, it’s important to mention that one can share the results of
the loop in every iteration of the project. Only then, external knowledge
and feedback can be incorporated in the experimentation loop, increas-
ing chances of better outcomes. Figure 1 presents this new framework
and structure for AI experimentation. 07
Fig. 1
BUILD
LEARN
SHARE TEST

08
To explore the potential benefit of AI for the MR industry, we investigate
AI within two future-proof MR environments that are important for two
phases within the insight value chain, i.e. Market Research Online
Communities (insight generation) and Insight Activation Environments
(insight activation).
We adopt AI within these environments to take on critical challenges:
• The impact threat: as companies increasingly work towards the
generation of multiple consumer insights, how can they effectively
be activated within the company?
• The participation threat: as research communities increasingly
evolve towards long-term projects, how can we effectively sustain
these communities and increase the long-term value of the partic-
ipant?
This paper presents two case studies that are the result of tackling the
impact threat within insight activation environments and the participa-
tion threat within market research online communities. As AI is often
viewed as fiction, Hollywood has already explored the concept of AI in
many movies. For our case studies, many analogies can be made with
AI: FROM HYPE
TO REALITY - CASE
STUDIES

two movies “Her” and “Minority Report”. In the movie “Her”, a lonely
writer develops an unlikely relationship with an intelligent operating sy
tem designed to meet his every need, while “Minority Report” describes
a future where a special police unit is able to arrest murderers before
they commit their crimes by using oracles.
09
© Her
© Minority report

11
The market research industry is built upon consumer insights.
Companies are looking for consumer insights that they can use
for any purpose, i.e. marketing campaign, new product devel-
opment, product design, rebranding, etc. As of today, many
methodologies exist to obtain and generate these consumer
insights, both online (eye tracking, online communities, social
media listening, etc.) and offline (focus groups, in-store observa-
tion, etc.).
GALVIN,
THE CONSUMER IN
YOUR POCKET

12
As a result, companies have lots of consumer insights that can be
used. However, do they effectively monetize this flood of information?
In particular, when a customer-centric decision needs to be made, can
all consumer insights available to the company be found and when
they are actually found, do they matter? A recent study shows that
there’s still an important problem with activating consumer insights
within a company. The Market Research Impact study (Schillewaert
et Pallini 2014) shows that 45% of marketers believe MR leads to
change in attitudes and decisions, but only 50% of MR projects leads
to change. Hence, the MR industry is facing a major challenge due to
this impact threat. The problem is two-fold. First, when you know those
consumer insights exist, but when it takes too long to find them as they
are hidden in all the reports and slide decks (low efficiency). Second,
when you identify an insight, but it’s not the right insight for the intend-
ed purpose (low effectiveness). Hence, companies are under-utilizing
the consumers insights. Today, new solutions increasingly emerge that
deal with this impact gap such as insight activation environments, but
there’s also another way that this can be tackled. The MR industry can
tackle this challenge using chatbots.

Galvin is a chatbot and the personal assistant for market research.
It helps marketers to provide them the insights they are looking for.
Galvin allows companies to have direct access to all their consumer
research. It gives marketers the right answer anywhere, anytime and
inspires with new insights. Furthermore, by using predetermined con-
sumer segments, Galvin is able to impersonate the consumer; giving
the users the chance to have a simulated ‘chat’ with ‘their consumer’.
“How to use Galvin?”
We identified three ways for Galvin to be the perfect market research
assistant. Figure 2 illustrates these with different cases. 13
BUILD
Figure 2. The use cases of Galvin

14
1. Meet the consumer
As many employees want to step into the shoes of their consumer
to better understand their consumers and improve decision making
outcomes, Galvin allows employees to have a simulated chat with their
consumer. Using predetermined consumer segments, Galvin imperson-
ates different consumer persona. By choosing a persona and asking
him to tell a little bit more about his life or asking specific questions,
employees can almost feel like they have a conversation with a real
consumer.
2. Galvin as a coach
As employees always look for inspiration on new topics that inspire
them, Galvin can give them a five minute update with daily fresh
inspiration. As a consequence, Galvin coaches employees to create a
consumer-connected mindset and start the consumer habit.
3. Personal assistant
Galvin helps employees, whenever they are in a meeting and/or urgent
need of a specific insight. Galvin provides employees the right insights
anywhere, anytime. They can just ask what they need and they will be
informed on all the insights that are available on the topic.

“How does it work?”
Galvin is connected to an Insight Activation Studio, an online platform
where all sustainable consumer insights/research is collected and
shared. By starting a conversation, Galvin can be asked to bring up
specific consumer insights that are interesting for the query. Next,
Galvin will search through the Studio, create connections between the
insights and give you the answers that you need. Not by sending you
the full PowerPoint document, but by sharing them in a short, sharp
& visual way. Galvin uses LUIS AI to understand language use in the
queries and Cortana to provide smart personal assistance. Figure 3
displays the technology stack of Galvin.
15

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“Maximize the impact of your insights!”
With Galvin, we enter a new era of using consumer research within a
company. First of all, we kill the PowerPoint reports. Instead of sharing
these heavy documents via e-mail, everyone inside the company has
instant access to all consumer research via their mobile. But this type
of interaction/communication is more than just sharing. Through Galvin,
we can bring the insights to life in a new conversational way, giving
employees the chance to really know the consumer. This all will help in
effectively activating the consumer insights within the company, result-
ing in a stronger consumer connection in the hearts and minds of the
employees and leading to better market decisions and a higher ROI.
We implemented the chatbot to allow users to effectively activate con-
sumer insights using the three use cases. We did three experiments
with Galvin. We saw that it really eases the insight activation process.
We evaluated the benefit of the chatbot according to two dimensions.
• Adoption: companies have been adopting Galvin because of a
simple reason. The usage of Galvin is similar to natural human
behavior. It’s just chatting, you just chat with the chatbot like a
human.
• Satisfaction: although it’s not perfect yet, clients really use it as it is
way better than trying to finding the one insight in multiple Power-
Point decks of 100 slides.
TEST

17
The value of this new technological innovation can be evaluated ac-
cording to the framework of Mooney, et al. (Mooney et Vijay Gurbaxani
1996), which investigates the impact on business processes according
to three dimensions: process changes related to the use of ICT as a
means for directly substituting labor (automational), facilitating the use
of information (informational) and supporting process innovation (trans-
formational). We evaluate the impact of Galvin as follows:
• Automational: because it’s a digital chatbot, you can access
anytime you want & delivering what you need, it saves you time as
you don’t have to go through searching in PowerPoint reports or
emails.
• Informational: it’s mobile, right available in your pocket, so you
have access to insights anytime, anywhere you need it.
• Transformational: because of the low barriers to use it & the focus
on insights & the consumer, it improves your consumer-centric
decision making.

18
LEARN
During the process, there are three lessons that are important to share:
1. Logic: it’s straightforward that you need to implement functionality
to respond to core human behavior, but it’s more challenging to define
and allow small talk so you perceive it can deal with any question. The
chatbot must be smart, but also human.
2. Relevance: you need to first map out and define all the use cases
that you will respond to; so what you respond to is relevant and not
useless. The three core functions of the chatbot are the ones that are
relevant for people.
Instead of creating a chatbot that can do “everything”, we only looked
at those use cases that are important to the users. Sometimes it’s diffi-
cult for users to know what to do with new technology, but by focusing
on a limited number of use cases in the chatbot, it will help the users to
get along with it.
3. Adoption: by associating a personality with your chatbot, as in our
case Galvin, you humanize the chatbot, which
increases chances that users will adopt it and use it. It is in fact a ma-
chine, but the user may not experience it in
such a way.

19

21
If you were active in the market research industry in the past
ten years, you probably experienced the important evolutions
of Market Research Online Communities (MROCs), or as we
like to call them, Consumer Consulting Boards. By going online,
we could easily reach many consumers and switch to different
geographical areas. Going mobile allowed us to better immerse
in consumers’ daily life. And going structural resulted in ongoing
conversations with anyone, anytime and anywhere. Commu-
nities helped market research to step to a higher level and are
enormously popular in the industry.
A BACK-END
COMMUNITY MODER-
ATION SUPPORT
SYSTEM

22
The 2016 GRIT Report indicates that 58% of clients and 59% of sup-
pliers adopt communities within their business (Greenbook 2016). This
popularity will only continue to grow in the future. Industry watcher Ray
Poynter expects that, whereas online communities only take up 5% of
the market research budget in 2016, this will grow to 70% by 2026
(Poynter 2016).
“But are we ready to guarantee this success in the future? Are we well
prepared?”
It’s important to mention that we can rely on industry experience an
best practices to pursue community success. For example, we already
know how to use different recruitment platforms to identify those mem-
bers that are interested and interesting for our communities. Right now,
we have expert knowledge on how to manage and moderate commu-
nity dynamics to achieve favorable conditions to do market research.
Moreover, we have done extensive research on gamification and
engagement techniques to encourage participation.
“But can we use the same techniques and follow similar successful
past practices in the future also?”
The answer is “maybe not“, mainly due to two challenges, which
will only become more important as community adoption or volume
increases in the future. First, in essence, MROCs are data-loaded
environments which accumulate additional data every day. This big
data characteristic puts pressure on the moderator’s resources to deal
with and analyze community content. Second, member disengagement

23
is a fundamental problem for healthy research communities. When
members participate insufficiently in the topics which are posted in a
community (low quantity), or what they say does not contain anything
valuable (low quality), the moderator may be unable to derive useful
consumer insights from the community. Additionally, when more
communities will be organized, in the end we may all go for the same
pool of participants, putting pressure on the members’ motivations to
participate in the community. Hence, the viability of these communities
is threatened by a participation threat.
Therefore, it is important to explore new approaches on how to in-
crease the participant’s long-term value and to effectively deal with the
problem of member disengagement.
Proactive community management is a moderation practice to antic-
ipate predicted member disengagement and take proactive actions
to prevent disengagement behavior from negatively impacting the
community. It leverages the datarich environment of the community and
relies on technological innovations to support the moderator in manag-
ing the community more effectively. Proactive community management
can be considered to be the real-life realization of the movie Minority
Report in research communities and consists of a three-step approach:
detect, predict and prevent.
BUILD

24
1. Detect
In research communities, moderators are usually on their own and
have to rely on themselves and their own efforts to manage the com-
munity and combat member disengagement. But this is rather crazy.
On the one hand, communities are data-loaded environments, but we
use this data only in a limited way to derive consumer insights from it.
On the other hand, already available technologies allow exploitation of
data effectively and get more out of it, like text mining, Natural Lan-
guage Processing and behavioral analysis. So why not adopt these
techniques and use them on community data to identify community in-
sights which could support the moderator in managing the community?
We can use text mining and behavioral analysis to analyze community
behavior and detect member disengagement. Member disengagement
can be measured in terms of quantity and quality dimensions, respec-
tively by calculating the percentage of actively participated-in communi-
ty topics and the number of cognitive words a member uses per post.
Cognitive words such as because and think reflect the effort that has
been put into the post and is identified as a reliable indicator for the
posts’ quality.
Now how can the detection of member disengagement be made practi-
cal for the moderator in a community context? By using a cut-off value
to distinguish between high and low activation levels of participation
and combining quantity and quality dimensions, we can obtain with a
four-quadrant framework to classify community members and identify

25
four different community behavior profiles, i.e. the community stars, the
high-potentials, the passivists and the annoyers. Figure 4 visualizes
this four-quadrant framework.
2. Predict
But why only detect and look at the past, when it’s possible to consider
the future? Why only detect, when we can predict? We can also do this
in real life, as has been proven by many successful applications rang-
ing from Facebook to the Obama campaign. Predictive analytics and
Artificial Intelligence allow prediction of future events. We can adopt
this in a community context by creating prediction models to predict
member disengagement, low quantity and low quality behavior. The
output is a probability and reflects the risk that a member will demon-
strate disengagement behavior in the future.
Figure 4. Moderation framework for the participant profile
HIGH-
POTENTIALS
COMMUNITY
STARS
PASSIVISTS ANNOYERS
QUANTITY
LOW
LOW
HIGH
HIGH
QUALITY
1
2

26
How can we make predictions? We leverage historical data and use
machine learning techniques to identify patterns in past data that
explain future behavior. The intuitive explanation is that we try to find
habits that explain future disengagement behavior. Human behavior
is very predictable; this also goes for the community context. We can
then adopt the output of the two prediction models in our four-quadrant
framework to give insights into the future behavior of each participant.
The moderator can use the prediction models, historical data and this
framework, in order to identify what each participant’s future profile will
be. Figure 5 visualizes this framework to identify the future profile of
the participant.
Figure 5. Moderation framework for the participant future profile
QUANTITY
LOW
LOW
HIGH
HIGH
QUALITY
1
2
87%
32%
91%
94%
27%
78%
12%
91%
19%
11%
17%
14%
11%
13%
86%
93%
94%
17%
81%
20%

27
2. Prevent
Now that we can detect and predict member disengagement, why not
take actions on our predictions, so we can anticipate expected member
disengagement to prevent negative community impact? We can follow
a three-stage approach in particular, where we combine the strengths
of the first two steps with those of the moderator in the third step. Fig-
ure 6 summarizes this proactive moderation strategy.
• Identify: the prediction model predicts each member’s future pro-
file; the framework allows classifification of each member into one
of the four quadrants to identify their future participant behavior.
• Contextualize: historical community data and CRM info can be
retrieved to provide the right context for the moderator. Moreover,
actions could even be recommended to proactively correct disen-
gagement behavior; these have worked successfully in the past.
• Finalize: the moderator interprets all the information from the
previous steps and uses the intuition and creativity to finalize the
prevention campaign by deciding which action needs to be taken.
Figure 6. Proactive community management framework
Prediction model
framework
IDENTIFIY
machine machine human
IDENTIFIY
CRM info precention
suggestion
Interpretation
action
1 2 3
CONTEXTUALIZE

28
You may wonder which corrective actions we should take. To answer
that question, we can rely on industry experience on engagement
actions. The only differences are that instead of using a one-size-fits-
all approach for all the members and using it reactively when negative
impact has already been recognized, in our approach we personalize
the prevention action for the individual member and use it proactively to
prevent destructive behavior from impacting the community.
We followed the three steps and implemented this in two research
projects to evaluate the prediction and prevention step.
In a first project, we explored whether we can make these predictions.
We explored 150,000 posts from three years of data, resulting from 10
communities for seven brands; we applied text mining and behavioral
analysis techniques to construct about seven million data points to
unravel valuable community insights. We then used these variables
to detect member disengagement and identify relevant predictors. We
used logistic regression as the prediction model. This set-up allows us
to evaluate the detection ability of the model and make the comparison
with the moderator:
• Detection ability: the results show that we have reliable prediction
models. Evaluating our models on unseen data allows us to as-
sess the quality of the prediction models. We see that the accuracy
is rather good as for low quantity; we can make correction pre-
dictions in 78% of the cases, while making correct classifications
TEST

29
for low quality 78% of the time. Further clarifying these numbers,
knowing that randomly deciding between high and low activation
levels corresponds with a 50% prediction accuracy. You can see
that our models already perform better than that.
• Model versus moderator: as mentioned in the previous para-
graph, the model performs better than random choice, but it
may compete with the ability of the moderator to make accurate
predictions due to expert knowledge. However, this is the only
argument that works in favor of the moderator. Whenever you
want to effectively scale and make predictions for all community
members and make fast predictions, the moderator is never able
to be as effective as the model. Hence, in a long-term community
context, it’s a wise decision in a first step for the model to make ini-
tial predictions and a second step for the moderator to step in and
focus on the predicted members who are expected to demonstrate
unconstructive community behavior.
In a second project, using a field test in four communities, we explored
how we can prevent member disengagement. We relied on an email
campaign that used three different emails, which anticipated on differ-
ent types of benefits to participate in the community: functional (“I learn
new things”), hedonic (“I like the experience”) and social (“I make new
friends”). Using members’ predicted future profile, directly determined
by the two prediction models for the quantity and quality dimension, we
evaluated the impact of sending out motivational emails to proactively
anticipate on member disengagement. This set-up allows us to evalu-
ate the prevention capability:

30
• Prevention capability: we saw that we can actually prevent mem-
ber disengagement. The field test shows interesting results. Figure
7 summarizes these results. First, we see that we can convert
predicted high-potentials to community stars by anticipating on
their ‘functional’ benefit to participate in the community. Second,
we identified that sending out motivational emails to predicted
annoyers has a negative impact as it increases their participation
quantity in the community. Hence, we need to avoid a motiva-
tional campaign for this type of participants. Other engagement
techniques need to be explored to see how we can activate these
types of members. Third, there is no impact on predicted passiv-
ists. Thus, they just need a break. After a while, we can aim to
reactive them to continue participation in the community.
Figure 7. Proactive community management field test results
HIGH-
POTENTIALS
COMMUNITY
STARS
Increased
quantity impact
of mail with
‘functional’
participation
motivation. Go!
Increased quantity impact of
email campaign. Avoid motivation.
Human moderation needed!
No community impact.
Give them a break!
PASSIVISTS ANNOYERS
QUANTITY
LOW
LOW
HIGH
HIGH
QUALITY
1
2

31
The impact can be evaluated according to the framework of Mooney, et
al. (Mooney et Vijay Gurbaxani 1996):
• Automational: time and money is saved because the moderator
is automatically alerted of expected destructive behavior. As a
consequence, the moderator can spend more time on the research
task at hand and focus on the analyses, instead of trying to do the
identification exercise herself.
• Informational: the prediction models reveal subtleties and com-
munity patterns that the human eye may not see. Additionally, a
member may be healthy today, but not in the future! Our prediction
models detect this. The moderator may be able to predict some
behavior, but not all.
• Transformational: We move in time; it’s something we couldn’t do
before. The prediction models allow moderators to anticipate on
the expected future behavior of participants. This means, we can
go from reactive (when the damage is already done) community
management to prevention and go for proactive community man-
agement.

32
During the process, there are three lessons that are important to be
shared:
1. Adoption: to make sure that the tool will be adopted within the busi-
ness, it’s better to give up some predictive accuracy to make the model
more believable and actionable for the user. Moderators will only use
something that they understand & believe works. So it’s better, instead
of a black box model that works very well, to use a model that works
less well but is more believable. In our case, we opted for the popular
logistic regression model, instead of other black-box models, which
revealed interesting insights into the predictor variables, which can be
communicated to the moderators. For example, a narcissistic writing
style of the moderator was a signaling indicator of future member
disengagement.
2. Database: you need both sufficient volume and qualitative data
points to identify useful predictors. Therefore, it’s important to store
data: the more, the better.
3. Insights: by using a white-box prediction model, that gives insights
into the predictors, subtleties and community insights the moderator
was not aware of can be revealed. For example, we saw that a moder-
ator’s narcissistic writing style signals future disengagement behavior.
Therefore, it’s important for the moderator to take especially about
others and not about himself.
LEARN

33
CONCLUSION
This study has shown that AI shouldn’t be viewed anymore as a
hype, but that it can be actually be seen as reality. We’ve proven
that AI can be used to take on critical challenges using two
case studies. To finalize, we would like to stress the important
common denominator in the case studies and that will also be
important for future AI projects. We believe that the future of
AI involves heavy human-machine collaboration. We need to
evaluate the strengths and weaknesses of both humans and
machines to allocate those tasks to the actor that is the best
suited to execute it. Only by embracing AI can we as humans go
to the next level of augmented human intelligence.
THE FUTURE OF
AI CONSIST OF
HUMAN-MACHINE
COLLABORATION

34
Steven Debaere
Doctor of Philosophy Phd at
IESEG School of Management, France
Steven Debaere is Ph.D. candidate in marketing
analytics at IÉSEG School of Management (LEM-CNRS)
of the Catholic University of Lille in France. His ongoing
research focuses on the exploitation of social media
data to improve a company’s product strategy. Predictive
modeling methodology is the central topic within his
research projects. Steven holds a master’s degree in
Computer Science Engineering from Ghent University
and a master’s degree in General Management from
Vlerick Business School, both located in Belgium.
s.debaere@ieseg.fr
@steven_debaere
Tom De Ruyck
Managing Partner
Tom is Managing Partner and global expert in con-
sumer & employee collaboration, supporting InSites
Consulting’s efforts to make companies more consum-
er-connected. He loves leading in-depth workshops and
chairing events, and has given more than 500 speeches
all around the world. Next to that he is Adjunct Professor
at the IESEG School of Management.
Tom@insites-consulting.com
@tomderuyck

35
@kcoussement
K.Coussement@ieseg.fr
Kristof Coussement, dr.
Professor
IESEG School of Management, France
Kristof teaches several marketing related courses
including Strategic Marketing Research, Customer
Relationship Management and Database Marketing.
He also publishes in international peer-reviewed journals
like Computational Statistics & Data Analysis, Decision
Support Systems, European Journal of Operational
Research, Information & Management, Expert Systems
with Applications, among others. Moreover, his works
has been presented on various conferences around the
world.

36
REFERENCES
Greenbook. 2016. “GRIT Report.” Q3-Q4. https://www.greenbook.org/grit.
Marr, Bernard. 2016. “What Is The Difference Between Artificial Intelligence
And Machine Learning?” Forbes. December. https://www.forbes.com/sites/
bernardmarr/2016/12/06/what-is-the-difference-betweenartificial-
intelligence-and-machine-learning/.
Mooney, John G, and and Kenneth L. Kraemer Vijay Gurbaxani. 1996. “A
process oriented framework for assessing the business value of information
technology.” ACM SIGMIS Database 27 (2): 68-81.
Poynter, Ray. 2016. “Why Are Communities Taking Over The Insights
World?” December 3. https://www.linkedin.com/pulse/why-communities-tak-
ing-over-insights-world-ray-poynter.
Ries, Eric. 2011. The Lean Startup. Penguin Books Limited.
Schillewaert, Niels, and Katia Pallini. 2014. “What do clients think about the
impact of market research?” November 25. http://www.insites-consulting.
com/what-do-clients-think-about-the-impact-of-marketresearch/.

37
Marketing@insites-consulting.com
@insites

o whatever conference we may go, industry magazine we may read,
business podcast we may listen to: the message is the same everywhere.
Artificial Intelligence (AI) is the next big thing. Artificial intelligence will disrupt
every industry! But is this actually the case for the market research industry?
Is AI a hype, a trend, just overrated or a game changer? It’s time to exper-
iment and find out. Based on two successful case studies, this paper aims
to increase understanding in the MR industry of how to adopt AI within the
MR projects by presenting a framework of how companies should adopt AI
within their business projects and discussing two case studies that proves
the value added.
ABOUT THE AUTHORS
By Steven Debaere (Doctor of Philosophy Phd at IÉSEG School of
Management), Tom De Ruyck (Managing Partner at InSites Consulting)
and dr. Kristof Coussement (Professor at IÉSEG School of Management).
ABOUT INSITES CONSULTING
From the start of InSites Consulting in 1997 until
today, there has been only one constant: we are
continuously pushing the boundaries of marketing
research. With a team of academic visionaries,
passionate marketers and research innovators, we
empower people to create the future of brands. As
one of the top 5 most innovative market research
agencies in the world (GRIT), we help our clients
connect with consumers all over the world.
www.insites-consulting.com
T

From Hype to Reality: AI in Market Research

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