In this episode, we have a special guest, Ranga Somanathan, a marketing and communication veteran with over 20 years of experience in helping clients from various sectors with their growth and innovation agendas. As ex-CEO of Omnicom Media Group across Malaysia & Singapore, and now the co-founder and curator of RSquared Global Ventures, a company that connects startups, corporates and investors

About our guest:

Ranga Somanathan is a seasoned marketing and communication professional who has worked with some of the leading global and regional brands in various sectors, such as FMCG, automotive, telecom, media, and technology. He was the COO of Starcom MediaVest Group and he later became the CEO of Omnicom Media Group Singapore and Malaysia, where he was responsible for the overall growth and performance of the group. 

Currently, he is the Co-Founder and Curator of RSquared Global Ventures, a company that operates at the intersection of startups, large corporates and investors. He is also an advisor, mentor, and angel investor for several startups and organizations in the region. He is passionate about helping clients with their growth and innovation agendas, using his expertise in marketing and data analytics.

In today’s episode, we discuss :

• Ranga’s background and journey in marketing
• How to drive effective advertising strategies by starting with a business problem (not marketing problem)
• Case Study : How OMD & McDonald's solve an operational problem with a marketing solution
• Importance of using Market Mix Modelling to drive effective marketing strategies
• Why companies who seemingly successful without data-driven strategies should leverage data before it's too late
• Tips for beginner, intermediary & advanced companies who wanted to start leverage data in their marketing strategies
• Role and potential of generative AI for marketers
• How to define success metrics in marketing via Mind Measures & Operational Measures
• The importance of understanding the WHY consumer buys behind the WHAT consumer buy & how panel research can help close that gap
• Best practice of questionnaire design to get the right research data
• Ranga advises marketers to have empathy, risk-taking & financial accountability
• Recommended book for marketers
• How to connect with Ranga

Where to find Ranga Somanathan:

LinkedIn

Where to find Julie Ng:

LinkedIn

References:

• Made in Future: A Story of Marketing, Media, and Content for our Times by Prashant Kumar : https://www.amazon.com/Made-Future-Story-Marketing-Content/dp/0670096245
• Market Mix Modeling: A technique that uses statistical analysis to measure the impact of various marketing activities on sales, revenue, or profit. It helps marketers optimize their marketing mix and allocate their budget effectively. Reference link : https://www.gartner.com/en/documents/3876784
• Attribution Modeling: A technique that assigns credit to different marketing channels or touchpoints based on their contribution to a desired outcome, such as a conversion, a purchase, or a retention. It helps marketers understand the customer journey and optimize their marketing strategy. Reference link : https://www.gartner.com/en/documents/3463318/market-guide-for-attribution-and-marketing-mix-modeling.
• McDonald’s: https://www.mcdonalds.com.my/
• Generative AI: A branch of artificial intelligence that focuses on creating new content or data that mimics or enhances existing ones. It uses techniques such as deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers to generate realistic images, text, audio, video, or code. It has applications in fields such as art, entertainment, education, healthcare, and security. Reference link : https://www.forbes.com/sites/bernardmarr/2020/09/21/generative-ai-the-next-frontier-in-artificial-intelligence/?sh=1a1c9f5c6a0a.
• Brand Affinity Study: A type of market research that measures how customers feel about a brand and how loyal they are to it. It helps marketers understand the strengths and weaknesses of their brand image, the emotional connection they have with their customers, and the factors that influence their purchase decisions. Reference link : https://www.vase.ai/solutions/brand-insights.
• Usage & Attitude Study: A type of market research that examines how customers use a product or service and what attitudes they have towards it. It helps marketers identify the needs, preferences, motivations, and behaviors of their target market, as well as the opportunities and challenges they face in satisfying them. Reference link : https://www.vase.ai/resources/blogs/use-this-study-to-understand-more-about-your-customers.
  
  

Transcript:

Introduction
0:00
welcome to winning with data driven marketing podcasts this podcast is
0:05
brought to you by was Vase.ai market research I'm Julie your host in this
0:10
podcast and in every single episode we talk to Industry leaders marketers and
0:16
growth experts in Asia about how to use data to enhance the ROI in their marketing activities we bring you real
0:23
case studies while giving you background on how these leaders build their career
0:28
to where they are today joining me today is ranga sumanatat he is a marketing and
0:35
communication expert over 20 years of experience who have helped a lot of clients from the likes of PNG
0:42
Samsung with Johnson besides Citibank to grow and skill he is currently the co-founder and curator of rsquare global
0:49
Ventures is previously the CEO of Omnico Media Group across Singapore and Malaysia and before that the COO and
0:56
chairman of Starcom Media best group so you can see from his profile this is a
1:01
person who you want to listen to as he says tons of experience you to share now I would like to welcome you hi ranga
1:08
Hi how are you uh Julie good to see you good thank you so much for jumping into
1:13
our podcast today my pleasure thanks for having me so I in the past year you have started
1:22
Oscar Global Ventures can you tell our audience a little bit more about our Square sure
1:28
um our Squad Global Ventures uh was set up to operate at the intersection of
1:34
startups large corporates and investors and what we typically do is help the
1:41
startups with their growth agenda we help large corporates with their Innovation pipe and when we do this uh
1:48
we end up having a virtuous cycle of growth and Innovation which investors
1:55
are interested in and we introduce these startups to investors who ensure that they get the right startups to invest in
2:01
and have the iroy so operating at the intersection of Santa's large corporates
2:07
and investors that's what we do I uh I can see from your profile your
2:14
entire career experience has been in marketing um how what makes you pursue a career in
2:20
marketing and being so convicted in this space
2:25
I wish it was that uh much of a design involved there's an element of default
2:31
and going with the flow too um I did my graduation in statistics uh
2:37
and even the choices of subjects how I ended up doing statistics was that of a attrition rather than selection
2:45
um I did uh in India why I did my Bachelor of Science where I started off
2:51
taking math um statistics and economics as my
2:57
subjects in the second year I dropped economics because the college wouldn't offer economics in second year and then
3:03
the third year I dropped maths because I found it more uh difficult than statistics so I enjoy doing statistics
3:11
so I graduated in statistics um and then uh again the sentiment is a
3:18
graduation alone load like your job so you go and do your post graduation and I wasn't that Keen to do Masters in
3:25
statistics because that would have been even more difficult so I said okay what is the thing that I will appear to me
3:33
and I realized that being an MBA would be a good thing so I did a MBA in
3:38
marketing so the combination of graduation and stats and post graduation in uh marketing
3:45
kind of helped me to funnel myself into advertising I got plays on campus with
3:53
rakana gray which uh later on went on to become gray and mediocre and because of
4:00
my background in statistics and an MBA in marketing I was assigned to work on
4:05
media team on PNG business and that's how my journey started uh so it's a bit of a selection then attrition and it's
4:13
been a fun ride since then wow um we're gonna talk a lot more about
4:19
statistic and advertising uh and what are some of the
4:25
so would you say because of your background in statistics um it actually prepares you much more easily for you
4:31
know addressing the data driven part in the advertising space
4:37
I would wish to think like that but I think uh it's more over time uh I have
4:45
met a lot of people who have not necessarily must majored in statistics but purely on the basis of their passion
4:51
for the subject they have actually embraced data analytics at a later stage in their career so
4:57
um if you hear a beginner in the space and your don't have the academic
5:03
training it's pretty uh okay for you to embark on this journey because there are
5:08
enough uh training materials out there for you to self-learn and a lot of things that we do in advertising is
5:14
applied statistics you're applying and interpreting rather than going through the the science and the principles of
5:22
Statistics right of course it helps if you understand how these theorems work but what is more important does iot
5:28
cable to interpret data are you able to analyze data from the context of consumer so
5:33
um for me it was an advantage but that doesn't mean it's going to be a disadvantage for somebody who has not
5:38
done then can you tell us a little bit about what
5:45
are the unique insights that you have learned over the years right that help to drive effective advertising
5:51
strategies that you have used and if there's any success story that
5:56
you can share with us that will be amazing it all starts with the business problem
6:03
that you're trying to solve right it doesn't start because I want to apply statistics so if you if that's the
6:08
starting point then more often than not you get into analysis paralysis because you don't know what you're trying to
6:14
find so any analytics project any data science project starts with the problem
6:20
that you're trying to solve a bit of business and when you start that way you're able
6:27
to frame hypothesis properly then you're able to structure the analytics
6:32
framework properly choose the right kind of techniques to analyze the data properly and then whatever outcomes come
6:38
you isolate a few times before you see that it makes sense we've done a lot of Market mix modeling
6:45
where purists come in and take all of the data do multiple runs come back and make recommendations purely based on
6:52
what the statistic says we realize that those findings are not applicable or not
6:57
usable because it's too theoretical so when a practitioner of marketing a
7:04
practitioner of media uh looks at collaborates with
7:10
um Market picks modeling expert you are able to frame the right kind of hypothesis you are able to restructure
7:17
the data to see how the results look uh it is almost like uh you know cooking
7:23
where you're putting in the right ingredients to end up with the right outcome right
7:30
um and you have to put that in a sequence you got to Stage that data in the analytics so that you are able to see
7:36
the layered effects so that's what the art of analytics comes from and that's where your understanding of the market
7:43
the understanding of the consumer even simple things like seasonality understanding season understanding
7:49
festive times and if somebody is going to be taking a trip
7:55
back home what is the implications of that on uh user behavior all of those things get layered and all of those
8:01
don't come in data form it comes in some kind of an Insight how do you layer that in your analytics and look at a big
8:08
picture becomes an important part so um you asked me about what are the you
8:14
know the successful um application of data analytics right I
8:20
think for me one of the favorites and was the work that we did when I was at
8:27
Omnicom Media Group uh and my team at OMD Singapore did this work for McDonald's uh where the
8:35
business problem was that they were launching online orders and delivery
8:41
service right but then the delivery was being managed by the retail outlets
8:46
and there were peak times where the online orders as well as footfalls in
8:52
the retail store would be coming in at the same time so the kitchen capacity
8:57
was not able to service the online delivery and uh retail demand
9:03
so that was a business problem and we had a very smart data scientist the very good strategist instead of giving up
9:10
saying it's not a marketing problem it's not a media problem they
9:15
how if I mirror the capacity information with
9:21
search volumes and uh reroute traffic based on that right
9:27
so first we went to the client the team went to the client asked them are they able to share it with us the capacity
9:33
data by each McDonald's restaurant in Singapore any give us on a minute by
9:39
minute basis how the order is coming in and the client was very Innovative and
9:45
ahead of their times and they were very keen to provide that support then we went and negotiated and
9:52
discussed with the Google team to say hey can you break down the search volumes by the McDonald's restaurant
9:58
Geographic zones localization based on rather than their own breakdown of the country and with a few conversations
10:05
with various teams including their engineering team they managed to give us that information we add the kitchen data
10:12
and then on a real-time basis we were able to match volumes of search and demand on ground
10:18
another messaging as well as the offer uh
10:23
tied in synchronized with the capacity when there was no capacity we were bidding aggressively when there was high
10:28
capacity they were offering in people to stay on for a longer time so that kind
10:33
of balanced out um the demand and supply and uh it was a
10:40
brilliant marketing Effectiveness solution right and even uh
10:46
um uh it was recognized at Khan it was recognized at Festival of media Global
10:51
is one of the best in class data driven marketing solution but the icing on the cake was even their
10:58
Global CFO uh said these are the kind of innovations that we would like to see uh
11:05
in their uh you know earnings call so I think um that I think is uh one of the
11:10
best practices I have come across uh I'm very extremely proud of the work that my team did extremely proud of the
11:17
collaborative mindset our partners and our clients had to try something different right and we came up with the
11:24
solution so to me data driven solution looks like that it starts with a business problem you see if their data
11:30
is available otherwise go and figure out where to get it how to get it and it's a you know
11:36
um process once you get it how do you line it up we couldn't have managed this
11:41
manually it had to be code driven somebody had to write a code to synchronize volume of search and uh
11:48
capacity and that kind of automated the entire surge bidding process so to us it
11:55
was a very uh meaningful solution that was traded and we created a very happy client we had a
12:02
happy team and uh becoming famous was a good side benefit of
12:10
I hope that kind of gives you a good example this is amazing
12:16
um I like the fact that how when you start actually giving the context of the scenario my first impression was also
12:22
the same oh is this is this a marketing problem it sounds like an operation problem but uh like you say it is is
12:29
really interesting how the marketing actually solves this in a meaningful way
12:34
um you you talk just now uh on this note you also introduced to our audience Market Market mix modeling
12:41
um do you see that in general uh Market mix modeling is only something like say bigger bigger companies are able to
12:48
apply or and can you also tell us a little bit more for those audience who have never not quite uh not quite
12:55
familiar with this Market mix modeling can you tell us a little bit more about it sure see at the end of uh a day what we
13:02
do in marketing is we use all the resources on hand and deploy it with the desired outcome to drive more sales
13:09
right um with that in mind we want to know what are those variables that we put in
13:14
whether it is uh investment behind distribution investment behind marketing investment
13:20
behind pricing investment behind uh gift promotions we do a lot of those things
13:25
to get the attention of the customer and get them to buy our solution now trying to figure out which of those
13:33
elements of inputs are contributing to the sales is what Market make modeling does broadly
13:40
um now for that to have analysis to happen you need to line up all of that
13:46
data you need to have your pricing data you need to have your distribution data you need sales data you need to have
13:52
your competitive media weights data all of that going as your input variable
13:57
right and uh when then you put it through a modeling process statistical
14:03
process at a very simple level it's a regression modeling and there are enough
14:08
software the tools that help you do that so don't get overwhelmed when you hear these statistical terms
14:16
um then once you put that into a framework you get the process done you're able to look at
14:22
um which of these input variables have the highest impact on sales right now
14:27
sometimes you might not get to see the direct impact on sales but you will start seeing impact on intermediary
14:33
variables brand awareness up late brand preference so you're able to then look at the
14:41
various input variables and see the impact on sales
14:47
now to your question can anybody do it uh or only Brands who are big can have a
14:53
lot of budget can do it now the answer to that is to prompt one is
14:58
everybody can do it because everybody has got input data right you know your
15:04
distribution data you know your pricing strategy data you know your marketing
15:10
spends data all of the input data is something that you control as long as you're organized
15:16
when you capture that in a even as simple as an Excel sheet that's
15:21
a good starting point now
15:26
whether all organizations can do it with a big company or small company to me the
15:32
answer is not the person it's not a function of scale it's a function of mindset
15:37
are you a data driven organization do you want to take decisions that you can actually hold yourself accountable
15:44
to the organization culture is driven by data you will be able to pull this off
15:52
if your organization culture if folks in the organization are a little bit more
15:57
driven by gut by experience even gut comes from experience right um then a data becomes an excuse of why
16:07
you shouldn't do something rather than why you should do something so going back and reflecting on your
16:13
urbanization culture all the way from top of the organization leadership to
16:19
functional leaders to see if we want to be data driven we want to take decisions
16:25
and hold ourselves accountable to those decisions by looking at data and outcomes then your organization will
16:31
have more success when we actually do Market mix modeling or any Analytics
16:37
um having said that the last say 10 years or so
16:43
um or even 15 years in Southeast Asia we were on a growth trajectory as
16:49
businesses right most businesses tended to have a positive
16:55
cycle in sales and growth and it kind of became exponential during
17:01
the pandemic period coming off the pandemic period there is normalization and potentially admins
17:09
that businesses are seeing due to macroeconomic environments geopolitics supply chain so on and so
17:16
now when the businesses were on upward trajectory uh
17:22
just being in the market gave you growth so unless you were looking at deficient
17:29
growth you didn't really have to look at data to tell you how to grow just being present available and uh having a very
17:37
Basic Marketing presence gave you a upward trajectory but over the next few quarters and few
17:45
um years there will be course connection there will be softening in some sectors there will be headwinds in some sectors
17:52
till the time the macroeconomic as well as geopolitical had been stabilized
18:01
with that happening uh when you're
18:07
not able to figure out which element of your input is giving you the Maximum Impact
18:12
you will end up having a much more tougher business situation
18:19
in the contrast from the past 15 years to how the next three years probably look you didn't need to do a market mix
18:26
modeling to say I can grow and you didn't really worry about which element of my input was giving Maximum Impact
18:32
but as we go into a little bit more uh constrained driven marketing and
18:39
business environment over the next uh say 24 months 36 months it's important for you to understand which variable
18:45
that you are inputting is giving maximum bang for the buck and therefore deploying a Marketplace modeling end of
18:51
a solution will give you much better insights on which labor to push up which lever to bring down and therefore you're
18:58
getting maximum Roi uh and in the western markets when the
19:04
markets were soft they were not growing as exponentially as you know uh
19:09
strongly as in Southeast Asia they have started using mmm Market mix modeling uh
19:16
for a few years now uh uh because when the markets have plateaued you got to
19:23
squeeze the blood out of your marketing efforts and therefore you try to figure out which one to uh really push I think
19:30
in the next three years we probably will be in that environment um across the uh region and um it's
19:38
important for you to understand what you're doing it's part of what you're doing is working the most for your
19:43
business and therefore you can control those decisions it was a long answer Julia I hope it kind of gave me some
19:49
content oh I left a contact in fact I think you answered the questions that I always
19:55
hear like a lot of times um when talking about say other than using mmm or other
20:01
than using other data driven strategies right uh some companies uh see I've been
20:06
doing so well in the past like why would I need to do it at an additional
20:11
um service like this or additional strategy talk like this and you just answer it um but if you're pursuing a Christian
20:18
group I like this work if uh efficient growth then actually doing
20:23
this is important um when do you see what are the what are
20:29
the few challenges or what are the top challenges that you see company face even when they try to apply mmm uh and
20:37
if you if there is any case studies that we can learn from it that would be great as well
20:42
on how you resolve that so we typically attend extreme um see
20:49
it's a very early stage for many companies in this area right or to use some deep statistical techniques to
20:56
actually get uh Market picks modeling is one tool that people use uh attribution modeling
21:04
techniques that people use especially if you're up only digital organization where
21:10
um entire operation end to end is on online uh you use a lot more application
21:15
work than Marketplace and uh
21:21
um the challenges that marketers face is
21:27
um the lining up of the data collections of the data storing of that data uh
21:33
retrieving of the data reporting of the data and interpreting that data right
21:40
um each one of those stages requires a little bit more conscious effort who
21:47
um ensure that you're getting the right information placed in the right place uh analyze the
21:55
right way interpret it the right way now again I'll give you an example of how it used to be before and how it is
22:01
today in the past um when before the digital era right
22:08
2007 2008 uh where we were still in the early days of digital uh
22:14
data in marketing a lot of analytics used to happen with historic data
22:20
so we used to work with time series in the sense that you would get sales data two months
22:26
later used to get AdSense data um again a month later uh your reach
22:34
frequency data a week later from when the events actually happened so you're dealing with a lot of past
22:40
data and you're lining up those past data and trying to figure out the interplay
22:46
between reach share a voice share of spends distribution pricing oh sales
22:54
so a lot of past data we used to bring together and analyze and the
23:00
speed at which the data came was very clear it will come in a week's time or a fortnight's time or a month's time or a
23:06
quarterly basis whatever was located it was very clear with the answer to data coming from
23:14
digital environment the characteristic of data changed
23:19
it started coming real time it started running fast then it started coming from multiple directions
23:26
so suddenly the data which we were used to dealing with which was past beta also
23:32
started building a characteristic of fast data so we ended up having passed data past data and bringing all of that
23:39
together required a little bit more rigor in data management right that was
23:45
the big that was beginning the challenge for most marketers if you don't line up your historic data you don't line up
23:52
your real-time data in the right form the outcomes that you get is going to be
23:58
a lot of noise a lot of rubbish so that requires
24:03
um that that required marketers or data analysis analysts to
24:11
bring in an additional skill set which is a bit that of coding that of
24:16
artificial intelligence machine learning so that the tool was
24:22
able to the software was able to analyze synchronize and organize data much
24:28
faster much real time than humans could do right so yeah
24:33
especially in the last three four years um the organization of data with the
24:38
support of AI machine learning becomes a very critical element for you to be successful if you're still try to do everything
24:45
manually like you did 10 years ago uh you're going to struggle to actually come up with any meaningful outcomes
24:52
because you're dealing with historic data which is very probabilistic and you're looking at real-time data
24:57
which is very deterministic and then you're trying to bring all of that together synchronize it and then predict
25:04
the future you will need machine learning and AI to help you to deal with the volume of data to deal with the
25:11
complexity of the data so if as a marketing organization you're at
25:17
an early stage um then what I would recommend is you start organizing your data
25:25
um you start at least reporting your past data properly uh bring it all
25:30
together and try to make sense of what you have done and what's that doing to the business
25:36
if you're in a intermediary stage then start
25:41
looking at dashboarding capturing all of the data into some place visualizing it
25:47
in a cleaner minor integrate uh analog data and Digital Data together start
25:53
looking at it together as a connector then if you are in the advanced stage then you need to bring in in addition to
26:01
very simple techniques uh that you use in statistics and Excel start using
26:07
platforms which are Ai and machine learning driven so that it's able to deal with the complexity
26:13
also in terms of culture you start with telling people the data is important at
26:19
an early stage and start showing Health brand Health measures at an intermediary
26:25
level you start doing some level of predictive work to say this is what this investment is going to translate to and
26:32
at an advanced level you start optimizing real time your inputs and
26:37
outcomes so that you're able to dial up dial down almost a live basis and this
26:44
the Nitro boost of that advanced level is where the system starts blowing it for you and you can just monitor if it's
26:51
doing brighter thank you for sharing different advices
26:57
for different types of companies I think the the audience will definitely behind because they will find one in in the
27:03
three options that you just mentioned um it does looks like in order to pull this off rate there's a lot of different
27:10
types of skill set that is required um not just on the typical say
27:16
communication side of things in terms of marketing right um how would you structure a marketing
27:21
team uh leveraging what kind of Talent OR skill set that are able to leverage this
27:27
kind of strategy
27:32
see first is um we have to start thinking beyond our own function
27:38
but I haven't wider perspective because um the way the finance organization
27:44
collects data the way the sales organization collects data with the research organization collects data the
27:52
customer service department collects data they're all in different forms so as a data analyst you need to have a
27:59
wider perspective of what all data the organization is creating how it is being captured right if I were to rephrase
28:06
data into interactions what is essentially happening between a company and its user is interaction either the
28:13
company initiated interaction or the customer initiated intervention they all are coming into the organization as data
28:20
streams identifying which are which function in my organization is triggering that
28:26
interaction if we are initiating it what are the ways in which it is going
28:31
out what are the characteristics of those interactions what is the Cadence of those interactions all of those
28:37
things the data analyst uh or the marketing specialist who's working on data leads to understand
28:44
the second element is when the customer is triggering a risk uh interaction with the company
28:50
likewise what are the things that the customer is triggering user is triggering how are they triggering from
28:57
what sources they are triggering and how What is the characteristic of that Source it how predictive is that Source
29:03
how deterministic is that Soul understanding those interactions become very important
29:08
so if I were to just pull a number of the air uh you probably will end up having about you know 40 to 50 unique
29:18
interactions that the brand triggers towards the user and the user triggers towards it so being able to understand those
29:25
interactions become an important element for a marketing specialist who is trying to interpret their relationship of these
29:33
interactions and how those relationships translate to a business outcome which in this case would be say
29:41
um so wider perspective of what's happening is one Element understanding the
29:46
characteristics of this important is an important element having some element of coding expert
29:52
experience is an element that you need to build you might not have it in-house then use Partners you know this is a
29:58
page this is a fast growing space and you might not have all the capability in
30:04
so for you to be successful don't be afraid to be resourceful seek help right
30:10
there are Tech organizations platforms uh analytics Specialists and marketing
30:17
Services Partners who can collaborate with you to make it right so be resourceful is a very
30:24
important part of this because you're not going to have all the capabilities in-house be able to build this right and
30:31
then the very fundamental level have an ambition right but I want to be a data driven marketer inputs account
30:39
so that that goes back to culture right so a wider perspective resourceful and
30:47
having a culture to be accountable are the key elements I think you will need
30:52
to have in the marketing optimization now as we Embrace data driven marketing
30:58
oh thank you um I now we we also spoke a little bit
31:04
about mlai um what are the trend that you are seeing or things that will change in the
31:11
coming few years because of AI in the advertising space or maybe
31:17
um it doesn't actually will change but there is a myth that it will change but it actually will stay the same I'm curious about your thoughts
31:23
from few conversations around what AI can do and big part of that AI conversation today is around generative
31:29
Ai and how is that going to impact marketing or business I think
31:36
any kind of uh the reason why all of this are relevant and important but
31:41
where to start from that point of view is because it the amount of data that is
31:46
being captured today amongst on these interactions that Brands and users are having
31:51
this become tending to Infinity right that volume is tending to Infinity
31:57
those micro events of those interactions are happening at such a scale that it's
32:04
humanely impossible to organize that and analyze that interpret
32:12
so if you want to be on top of all these interactions and assess which of those interactions have the strongest
32:18
relationship to fail that you want to Leverage there is no other option but to embrace
32:23
uh machine learning India now
32:29
if I were to then take it to so that is the answer which comes within the data analytics space but the overall impact
32:36
of generative AI on marketing Services is an important question that people are
32:42
asking and I think how generative AI will impact his
32:48
um make the process faster more efficient and people who are truly capable are
32:54
then able to invest their time in building that idea uh and which will be
33:00
super company right uh generative AI is not going to
33:08
create an innovation for you because it is based on all the things that happened
33:13
in the past whatever its recommendation is going to have a history right most innovations
33:22
that create path breaking Solutions and accelerated growth is going to come from
33:30
the fuzziness it's not coming it's not sitting in the binary one or zero it is somewhere sitting in the fuzzy and the
33:37
ability for Gen AI to operate in the fuzzy is still limited and it will uh
33:43
the moment there is a precedence to that fuzzy that becomes one or zero and that becomes predictive for the AI to then
33:50
use to actually uh recommend something till the time the
33:56
the why is answered by uh intuition the Y is answered by
34:02
um an observation that is already not been captured the human spirit will keep
34:08
the animation ahead of what a generative AI can do it so to me
34:13
generative AI is a great accessory great asset for somebody highly competent to
34:20
take all of that grunt work use that inside and project an idea which has never been done before right
34:26
if I were to exaggerate on the other side it will surely make the media current
34:33
now if I'm operating purely on a data in
34:38
the data is what I'm presenting as an idea without necessarily making the leap into uh meaningful elevation that's a
34:46
generative way I can do and you don't need a mediocre resource to actually give you some solutions right so I
34:55
believe what this will do for the industry it will separate the wheat from the
35:02
shaft as they say they will basically have the good quality folks uh become
35:09
better so the good will become great in that average you'll get churned out of the system so
35:16
um that's a good thing right um people don't have to pretend to be an
35:21
expert when they are not because uh if I'm a marketer who's using gen AI to
35:28
actually create Solutions if I've been using an average service provider I will I can do that myself
35:34
right but I'm actually using a very path breaking Innovative partner who's able
35:40
to take all of this data and LeapFrog into something that's never been imagined uh that uh Jenny I can't take
35:45
place and that kind of a solution was what one again the exponential growth will work you work with a lot of different
35:51
companies and I can see that a lot of companies probably have different success metrics or how do they define
35:58
success in their marketing as an organizations how do you see this as evolved over time
36:03
or is there a pattern to how a company defines what is the right success metrics when it comes to their marketing
36:10
still the mental struggle for a lot of marketers to define success metrics a lot of conversations in the organization
36:16
is around what should be the Matrix and uh now that's a evolving conversation
36:22
right um it becomes a little bit more sophisticated again in the fully digital
36:28
uh brand where the all the inputs in the outcomes are happening in the online ecosystem becomes a little bit more
36:35
brittle the process when it is offline to online to offline where we not having a full view of those
36:42
interactions when they go off the online to offline grid right so
36:50
so there are two kinds of measures which I would actually bucket any kind of outcomes to one is called Mind
36:56
measures and the other is operational measures so one needs to measure both the Mind measure and operational measure
37:02
mine measures are all the things that customers think about you whether they love you whether they hate
37:09
you whether they like a service that you give or they they want something else
37:14
all of the things that they have in their mind it gets captured in brand Affinity studies brand awareness studies
37:21
spontaneous awareness data attributing the product efficacy to the
37:28
right brand all of those things are sitting in a consumer's mind and you need to measure that to see which of
37:35
your messages are sticking and those messages converting to business right so
37:41
those are mind measures anything that people are thinking in the mind so you need to figure that out and that typically happens through a panel study
37:47
you run a panel you ask these questions and you get that information back and they are probabilistic data you can't do
37:54
a you know for you to be efficient in marketing you don't need to go and do a census you do a panel you then run an
38:00
estimate on it and see how this thousand people questionnaire uh reflects on the
38:06
overall uh population or the target audience right so that's a mind measure that you use a panel to run
38:13
cabin is a determinist operational measure operational measures are more often than not sitting within the
38:18
organization which is uh what is my distribution data how many markets how
38:25
many stores am I present and water level what sizes of uh units that I'm
38:30
presented what SK use that I am presenting um and then
38:36
uh what is the off take data that is how much sales is going on um from the factory so you collecting
38:43
the data from your own source I have shipped um and then you get a retail outlet data
38:49
to say how many people in the retail environment have bought that from the shelves right so those are hardcore
38:55
operational measures or uh so that you need to layer the Mind measure through
39:00
panel um and which is more directly probabilistic an operational measure which internal variables are more
39:07
deterministic and when you look at retail media data that probably has a mix of deterministic and probabilistic
39:14
data so these are the two broad buckets that one needs to operate with and see
39:21
how they come together uh in terms of uh
39:26
uh interplay between mine measure to operational measure and uh from there
39:31
the impact on business so that's uh that synchronization of the
39:38
data stream analyzes analysis of that data stream that creates the magic
39:45
I like how you bucket in the mind measures and the operational measures I think in the past I heard a lot of
39:52
companies a lot more actually focusing on operational measures uh because they can see the ROI tying very uh deeply
40:00
into that but my measures sometimes is less it's a little bit more weak see there is a saying in the marketing
40:07
and advertising that consumers make up they they decide with their heart and
40:13
rationalize with their mind and when they decide with their heart
40:19
um you cannot you don't have any deterministic data as to what are those triggers that made them decide right and
40:28
uh when you actually run a brand Affinity studies uh usage and attitude studies you understand the why because
40:38
what so that's when uh uh mind measure
40:43
studies become important if you are operating optimizing purely
40:50
on operational measures that is am I pushing my distribution all my pushing my pricing or my pushing my uh
41:01
you know off take data so on and so forth you get the what what's really happening
41:06
in the business uh and that's why uh as long as in the growth environment that kind of gives you decent enough or a
41:13
lever to actually play around with um but it doesn't give you an answer on why people are taking your product off
41:20
the shelf why are they buying it why they repeat buying it uh so on and so so
41:25
mind measures become their food right uh approach measuring the Mind measures
41:30
become the right approach to figure out uh how to push improve your operation so
41:36
uh and for that you need to be able to work
41:41
with um the uh
41:48
consumer response data right and you're asking a question and you're getting a response and that is when where the quality of
41:55
Mind measures become good or bad if you run a you become very greedy and you run
42:00
a two-hour questionnaire trying to understand what the customer thinks about you you are only going to get a
42:06
tired customer responding in a very high level of critique so you got to be very
42:11
smart about then choosy about unselective about what do you want to really understand so keep
42:18
your russianized or very shortland match 25 minutes 30 minutes and then that is a
42:23
stretch who's going to say 10 answer you for 30 minutes ideally you can pull it off in 10 minutes great which means that you need
42:30
to really spend a lot of time understanding what I really want to understand sharpen that
42:35
will very easy to respond um
42:41
the kind of will improve the quality of the responses so having the right panel partner having
42:47
a right uh questionnaire design I will give you that uh
42:53
strategic Advantage meanings mind missions right that also will give you the why behind the what which is the
42:59
operation thank you uh this has been amazing
43:04
conversation stronger I have last three very quick questions for you um first one what are some of the most
43:11
important skills that a marketer should have or what advice would you would you give to that if someone would like to
43:17
pursue a career in marketing
43:22
uh three things or two things [Music] um first is to have empathy right uh to uh
43:31
want to know what's really going on in your customers mind in your team's mind in your Market in your organizational
43:37
functional mind um and putting yourself in other shows a fundamental
43:43
um human uh need it becomes even more
43:48
exaggerated in the marketing environment because in marketing you're trying to win over the hearts and minds of your customer and uh unless you are
43:56
empathetic towards them you're not unless you're listening to them you don't know what you're selling to them
44:02
it's relevant so empathy becomes one key ill the second element is now uh to be a
44:10
little bit more risk-taking um and not be risk covers what do I mean
44:16
by that a lot of times you're persuading another
44:21
person uh with a lot of logic
44:27
and that is a comparative advantage everybody has access to the same logic
44:33
data the compa data has the same logic data you have the same logic data so when you go to a customer and says
44:40
hey use my shampoo because you're gonna get more confident uh because I'm going
44:45
to have a good Air day well competitor is going to say the same thing because they also have the same base
44:52
for you to be able to innovate uh you need for you to be able to build a
44:59
competitive Advantage you need to operate on in the space that is not evident for everybody and that's
45:05
requires you to go beyond comfort zone try something that you never tried before run experiments around it in a
45:13
successful scale so risk taking experimenting is the second level of attribute the good marketable
45:20
right and um if I were to add a third level to a
45:27
marketer's thing is put yourself at the top of the table of
45:32
the organization right um what do I mean by that is a lot of
45:39
the conversations tend to become very efficiency driven especially if it is driven in the context of financial
45:46
planning it is driven from the context of operational efficiency because
45:54
um the easiest thing to cut in a organizational operational expense expenditure is a marketing budget
46:01
and the reason why it is easiest to cut is because we have been uh shockingly uh
46:07
unaccountable for our actions right um we get away by saying 50 of what we
46:15
do we cannot explain and that is uh not uh acceptable anymore you have to be accountable
46:21
you have to make yourself come to the table in an organization and say if I'm
46:28
spending this much in the marketing space this is the impact on the business that means that you are bringing data to
46:34
the table and not judgment you're bringing um qualified uh insights powered by data
46:42
to the table rather than an opinion right so that is a super critical element for you
46:49
to then come back to a body level and push the right agenda so that as a
46:55
business you're able to create the right Marketing Solutions uh have the right communication strategies and win
47:02
customers and build a competitive advantage I believe in the last two decades the function of marketing is
47:09
eroded essentially because we have not owned data analytics we have not held
47:14
our students accountable for our recommendations we have used judgments more than data so
47:20
bringing in that element to our marketing flare is going to
47:26
bring this back onto the table uh and uh it's on us uh to basically leverage this
47:32
uh very powerful capability because uh I also aspect as I say that
47:38
we have not used we have the most competent to use it also right in of all the departments in the organization we
47:43
have a perspective of external internal and all stakeholders we can bring this together and actually create the right
47:50
uh competitive Advantage for the company my second question is what is the one
47:57
marketing book or marketing resources that you would recommend one of my close friends and marketing
48:04
experts Prashant has written a book made in future I recommend that as
48:12
prerequisite for all marketing Professionals in the region please don't read it it speaks very simply about why
48:19
are the various Dynamics in marketing what has changed what do we need to do to win and very compellingly to me
48:26
that's one of the updated books written for the region by somebody in the region
48:33
awesome um one final question so where can people find you if they want to reach out to you and learn more about what
48:39
you're up to I'm on LinkedIn um and uh you can reach out to me on
48:45
LinkedIn uh it's um the place that um I respond to I model it to her pretty
48:52
often um so please go ahead and reach out to me on LinkedIn
48:57
amazing uh ranga thank you so much for being here it is a very delightful and
49:03
insightful conversations that we have thank you once again actually Judy it was a pleasure to chat
49:09
with you as always um and I'm super excited uh to see how
49:15
we can help marketers in this region get the best out of data analytics to power
49:20
their business so look forward to hearing from you uh all the audience who
49:25
are listening to the podcast also reach out to me on LinkedIn so any specific questions that you want me to respond to
49:31
more than happy to support you thanks a lot Monique for giving this opportunity appreciate it
49:37
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49:54
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50:00
the next episode [Music]
50:19
thank you [Music]