In this episode, Julie interviews Ravi Shankar, the CMO of CARSOME Group and former Chief Growth Officer of AirAsia, about his data-driven marketing approach and how he uses data and technology to solve business problems.

About our guest:

Ravi is a versatile branding & AI marketing professional with a diverse skill set that spans across branding, customer experience, data science, AI, growth, technology, and commercial. 

He possesses a unique combination of marketing, business, and technical expertise that enables him to connect the dots between business needs and develop a marketing strategy that complements business and product strategies. 

With over 15 years of experience in the marketing & branding industry, Ravi has a proven track record of success in driving growth, improving customer satisfaction, and increasing revenue for companies in the APAC Region.

Well-noted works for Ravi Shankar Mallavarapu include launching a brand campaign for CARSOME with legendary footballer Eric Cantona as the brand ambassador, which raised awareness in the SEA region. 

Another critical milestone of Ravi is his role in transforming AirAsia's traditional marketing team into a growth marketing team. During this period, the company pivoted from the first aviation company to a super App.

With strong commercial acumen, Ravi can understand and communicate the business impact of marketing initiatives, making him an effective leader and partner for cross-functional teams. 

His unique skill set allows him to bring a fresh perspective to marketing efforts and deliver tangible results.

In today’s episode, we discuss :

• Ravi's background - Yahoo, Google, Lion & Lion, AirAsia, CARSOME
• First step before applying any marketing strategy is to uncover the core business problem
• Case Study on AirAsia : How Ravi achieved organic ranking and traffic growth for AirAsia using data-driven SEO techniques
• How to approach channel attribution problem with incrementality by using a test and control method
• Importance of setting up a good measurement framework to measure the efforts of marketing through business lens
• Why marketers should learn how to speak the product & engineering language (without the need to know how to code)
• Ravi's advice for marketers
• The book Ravi's recommend

Where to find Ravi Shankar:

LinkedIn

Where to find Julie:

LinkedIn

References:

• AirAsia - https://www.airasia.com
• CARSOME - https://www.carsome.my
• The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers - https://www.amazon.com/Hard-Thing-About-Things-Building/dp/0062273205
• Ben Horowitz - https://a16z.com/author/ben-horowitz
  
  

Transcript:

00:00
welcome to winning with data driven marketing podcasts this podcast is
0:05
brought to you by 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 we'll bring you to our speakers shortly after a quick word from our sponsor once
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a i and use a promo code podcast to get your first 10 off now back to the show
1:19
okay very excitingly is Ravi Shankar the current CMO of Carson Group which we all
1:26
know is southeast Asia's largest car e-commerce platform uh Ravi is also previously Chief growth officer of Air
1:33
Asia so Ravi is having 15 years of experience in reaching the gap between
1:38
not just marketing but technology and data and that's why you know we are very
1:43
excited to have you today uh thank you for joining us Ravi thank you thank you Julie thanks for having me since 2008
Ravi's background - Yahoo, Google, Lion & Lion, AirAsia, CARSOME
1:50
you're already in marketing analytics which I presume at that moment marketing analytics is not really a thing yet
1:59
um so I'm very curious you know why do you choose that career and can you take
2:04
us back to your career starting point and share with us how do you get to where you are today okay okay that's that's 50 hour long
2:11
journey so but yeah I'll try to keep the shot uh basically I'm a I'm a computer
2:17
science engineer uh and uh I've started my job as a PHP developer
2:24
uh and and I started to play with my computers because my dad used to own a computer
2:29
Institute that teaches computers right it was a thing back then uh so uh and
2:36
then I started coding and I've got my first job as a PHP developer and I started to build websites and and then and it was a startup and the the CEO
2:43
said hey uh you build the website for us so can you make it popular it's like okay I am not a marketing guy I back
2:52
then the concept of marketing is just sell a physical good right so now you created a digital good
2:58
and how do you Market it and where do you Market it and then I started researching about it uh then then that's when I understood uh
3:06
SEO that's when I understood paid marketing and in search ads and stuff
3:11
right so uh at the moment I went into the paid ads uh side of things the data
3:19
started to flow in right so uh it it the data was initially very little
3:26
right so oh you could optimize with your Naked Eyes okay this is this is high this is low let's make this change to
3:33
stand right until and then slowly that data started to grew uh quite a bit
3:39
because we started to run more campaigns right so uh and I started to play with it a bit more and I joined a different
3:44
startup who solely focuses on paid marketing uh and that's when I shifted
3:50
from my PHP Development Career to uh paid marketing career
3:55
and I started to play a bit more and sort of spend a lot more right back then
4:01
I used to spend about 150 000 you as a day uh 15 years ago that was a huge amount
4:08
uh so and I was uh coming from a small town in India that was a huge money
4:14
right that's like what celebrities make so I was a bit over and about the amount
4:20
of money I used to spend every day right so I used to be very careful on uh the decisions that I made and I started to
4:27
kind of slowly uh structure the data in the bed I used to optimize campaigns in
4:34
a way I used to wake up at 2 am and see okay if the campaigns are performing well and then make changes and stuff uh
4:41
and then I cracked the interview with Yahoo uh who were a dominant search
4:46
engine back then as well and then that's when I started to handling multiple clients
4:51
uh campaigns but there was no way I could optimize those campaigns with my
4:56
naked eye so I started putting all the data into Excel and then uh and started to analyze the
5:04
data in Excel by visualizing by learning some macros and you know I used to be that power user without using Mouse
5:09
using a lot of shortcuts and stuff uh and then uh that was a really good
5:15
experience for me on and a really good revelation of okay this data is getting out of hand
5:21
app and I spent a couple of years in Yahoo and then a couple of classified companies there and then joined the
5:28
Google programmatic team which Google was actually setting up so I was part of
5:33
that team where uh Google just acquired a business called invite media and like
5:40
any other product they're painting with Google colors and trying to make it like a googly thing and then I was working
5:46
with the with the product managers to help them out in in building this
5:52
platform and enhancing the features of this platform so it's basically a DSP it's a demand side platform to buy ads
5:59
and stuff so I was working with the product managers and stuff and then the challenge there was uh I was working
6:04
with these huge clients like Netflix Google Chrome was one of the client and Facebook was one of the clients for them
6:11
but the problem is the data was out of hand it's out of Google Sheets it's out
6:17
of uh Excel it was too huge so then that and then I was introduced to bigquery
6:24
where you know uh the data is sitting in the databases and then for me to request
6:29
the right amount of data or the right data sets I had to learn SQL and I
6:34
started to query and stuff and then uh connect with the data visualization platforms and everything
6:41
uh so uh it suddenly what I thought would be a marketing job
6:47
kind of started evolving to some analytics job right but analyzing
6:53
marketing data right and then I moved to uh Malaysia for uh for a startup which is an agency
7:01
which kind of gave me the opportunity to still keep in touch with the analytics
7:06
stuff and also do some marketing Performance Marketing and stuff which is what I perfected over the years uh and
7:13
then I set up my analytics team for them as well as a performance team it went really good about for two years there's
7:20
a company called Lionel lion uh and then I've uh I met the chief data officer of
7:27
AirAsia and he told me that hey we have huge amount of data and uh and and I
7:36
think we can use it uh in marketing and apply uh a lot more optimizations to the
7:43
marketing spends and AirAsia is known for its uh marketing right so and it's a
7:49
huge brand and that challenge really intrigued me because uh what you get from Airline back then
7:58
was a very structured data right so uh and how do you apply that structure data
8:04
into marketing campaigns was a challenge and I went there I worked with multiple teams uh slowly my rolly wall from
8:12
marketing to commercials to growth to product owner and so on so forth
8:19
and then after that uh after a good five years there and then I moved to Carson
8:24
pretty much doing very similar things there so yeah I think that's the
8:31
uh the story of how it evolved and where I am right now really well I'm curious in
First step before applying any marketing strategy is to uncover the core business problem
8:38
a lot of your career I noticed actually at each point of time it is actually a tough challenge
8:46
at least at that point of time because it wasn't figured out yet so if I were to take a point of time an example let's
8:54
take the Air Asia example when you go in how do you how do you go about what's
8:59
your thought process how do you think about what do you start first given that you know all the structured data are not
9:06
necessarily in one place for you to make sense of actually by the time I went there uh
9:12
they already started a bit of their digital transformation uh because they're moving moving from their uh
9:19
on-premise to Cloud so which made my life very easy and the data was very
9:25
structured but the challenge was always uh the data is there and the platforms is there but
9:32
what do I do to make uh this better for me to
9:38
get their understanding I had to understand the business well and I am not a travel guy uh never interesting uh
9:47
so I'm not a travel guy and I mean I I'm not still not into cars as well uh so I
9:52
learned driving a couple of years ago so it's just been there right so uh
9:58
it is it is it is very interesting problem for me because uh it's a very
10:04
complex business it's a huge business there is ground operations there is uh flight Ops uh there is Network there is
10:09
commercial there is brand and everything so uh again one I think one good thing
10:14
was I was uh based in uh an uh awesome office called AirAsia Redkey which is
10:20
one of the best offices in the world and the opportunity for me to go around the
10:26
office and talk to multiple teams and multiple people from treasury to engineering from procurement to network
10:35
really helped me understand the business uh and and uh I used to clock in about
10:41
10 000 steps a day in the office because it was huge and I was going around and talking to people and stuff
10:48
and then I figured out okay we'll do the top priorities uh for marketing uh and
10:56
how do we apply that what is the roadmap and then I started doing things because
11:01
is data available I could have taken decisions and stuff but if I don't connect with the business problem uh
11:09
it doesn't really make any sense because it just gives you the very local uh
11:15
local Maxima right what we want is a global language that impacts at a business level right so uh that helped
11:21
me and in short understanding the business first uh and then figuring out
11:27
which data to pick uh from the humongous copies of data that you have
11:34
and apply to marketing was the difficult part but once you figured it out the
11:40
steps ahead for me was very clear and my boss back then was a travel veteran who understands the
11:48
data well who could see things much ahead of any of us so that that kind of
11:54
helped me to kind of you know Skip some of the steps and mistakes a nice gacha so combining the business
12:01
problem uh and and making sure we dig from that before we choose what that had to look into
12:06
and on that note right um so you obviously tried a lot of marketing strategies I can see on your
12:12
LinkedIn you know I know you you say you know this is not for human this is for algorithm purposes and boom so among
12:19
those right like are are maybe more than those even what are some of the most effective marketing strategies you have
12:25
used so uh basically most of the marketing strategies that I've used
12:31
from a from a growth perspective right so I see there are
12:37
quite a lot of CMOS out there who came from very uh uh traditional media
12:44
perspective like they are really good at uh coming up with uh good media of
12:51
elbows TVs and stuff exactly know what TV spot to pick up and stuff they're they're that experts and they are CMOS
12:58
who really good at Brand strategy right so they can build the brand and they can create a person around the brand and or
13:04
enhance the person around and they have a really good answer creative and and I come from a different kind of
13:12
uh uh place where I I I I use data and
13:18
Technology uh and those are the levers that I pull the most right so I I I'm
Case Study on AirAsia : How Ravi achieved organic ranking and traffic growth for AirAsia using data-driven SEO techniques
13:25
not saying I'm an expert in the data and Technology I know
13:31
that the right way for for some of these strategies that I kind of tested over
13:38
the years uh but I had fortunately I had a really good team that
13:43
knows the brand well that owns a creative well that knows the media well
13:49
so a couple of strategies are going back to okay what exactly is the business problem the business problem let's say
13:56
for one of the year but Erasure was again uh quite a few uh routes right so Kuala
14:05
Lumpur Singapore is a rule saying Port Kuala Lumpur as a route and then we wanted to rank organically in all the
14:10
search engines and then uh then we figured out okay how many pages do we need and then
14:16
Pages were about came up to almost uh 7000 Pages because every root every
14:22
combination every airport and stuff so it's like okay so if I have to write 7 000 pages in multiple languages uh where
14:31
it is present across that multiplies to about 50 000 right so how do I solve
14:37
this it's not something like I'm gonna hire a a huge uh a Content company
14:42
somewhere in India or in China to write this stuff so uh what we decided is to
14:49
okay let's take a technology approach uh we worked with a couple of companies uh one based
14:56
in us one New Year and took all the data that we have the route information and stuff and pass it to it to an API I
15:03
build an API on top of that so it basically created uh
15:09
all the combinations of the routes and and it also created the pages and what
15:14
it also did is it created a template of content right meaning okay uh you fly
15:21
from here to here uh and this is the conditions of the airport this is the good this is the
15:27
weather of the destination this is the currency using this so it's a template of content it's a Content like you said
15:32
it's not for human human for human is a very Bland informational content it
15:37
doesn't give any excitement for them but it's a really good content for search engine so uh the moment we figured it out we
15:45
launched those pages the first seven thousand Pages uh Google started crawling it and then we started ranking
15:52
for it uh and uh and then we
15:57
built all multiple uh other languages as well so there are about 40 000 Pages now right so all
16:05
those pages would programmatically launched and not really uh hand written
16:10
by a human right so which would have not been possible so then again uh some of the pages
16:17
started to rank quite well and then what we realized is if these
16:23
Pages started to rank and the content is bad it's it's not going to be it's not been
16:30
sustainable right so then we the human content writers spent only time on those pages that are ranking on
16:37
the top and making the content they know a bit more appealing and not really robotic
16:43
right so uh so the approach was uh to
16:48
build all the pages first launch all of them first figure out which one to focus on and then put the human effort in that
16:55
uh uh but the the basic problem for business we're trying to solve is we
17:00
need to get more organic traffic uh and and you know and uh the problem again is which
17:08
which rules to choose from they are quite quite a lot which one to prioritize and what are the so for uh if
17:16
I say hey if the business says this particular route is really important for me build a page for it and make it rank
17:22
I can't it's not up to me whether the page ranks or not but for me to launch all the pages all the combinations
17:28
together and figure out which one ranks first and applying human content after
17:33
it ranks and started really enhance it it's also a lot of business problem address is a business problem as well so
17:40
that's one of the strategies it's a typical marketing problem solved by
17:45
technology and with uh some of the amazing folks uh who who held me from
17:52
technology standpoint and uh and again uh the culture of the organization to
17:58
kind of embrace that right so it's a mix of that I love this case study
18:06
and if my may ask this is a strategy you use back in the years do you think such
18:13
strategies will still work nowadays If Today listener are listening to this and try to emulate some some portion of it
18:20
uh it is a it's a very specific uh it will become a very different question
18:27
at this point right so uh if organizations have a scale to
18:32
build lots of pages and deploy a lot of pages when you really have the problem
18:37
of having so many products and skus and stuff right so this solution might still
18:44
work uh because it's not like a paid uh feature right
18:52
hey I have if if I'm talking about shopee a shoppie goes and asks the SEO man is saying I want this product
18:59
to be organically ranked so go build a page make it rank it's not it doesn't
19:04
work like that but if there's your manager or a head of shopkeep builds
19:09
Pages for every single product out there and then five percent of them brand that
19:15
makes a huge difference right so the approach is uh you are
19:20
building for algorithms right so when you when it comes to search engine optimization you can't really dictate what ranks you
19:28
just have to figure out throw everything at it see which one ranks and enhances later I think it's
19:34
pretty much still applies to the current state of
19:40
search right now but with with search moving from Google search to bar and
19:47
chat gbt I don't know it's it might not be the case for the next coming couple of years
How to approach channel attribution problem with incrementality by using a test and control method
19:54
or so so there is one common question that some marketers face especially when
19:59
they haven't figured out a channel that would work to see Drive Acquisitions of leads right uh then they would have to
20:06
actually figure out which channel works I'm curious if you have any thoughts
20:12
here that you can impart for for marketers who are going onto this journey do not know which channel Works
20:18
yet uh and how do they actually gone on to figure out which channel will actually
20:23
works for the acquisition there are multiple answers to this question right so and
20:31
it's very subjective uh because would you essentially asking is the famous or
20:37
like question of marketing attribution right so uh and marketing attribution
20:43
like you just mentioned is a rabbit hole right so uh there is never end to it uh
20:50
from a macro standpoint you are trying to see which channel works
20:56
by but when you're using the two biggest wall Gardens right or now three we have
21:02
to think about there and stuff all right so these guys don't talk to each other right so and and
21:09
no amount of data will give you 100 accuracy of which channel is the best performing channel right uh the second
21:16
one is uh uh there is an impression level attribution right so someone saw
21:22
something and then went on bought something which is there is no digital footprint there but there is an intent
21:28
right so you can't track that right that uh that is number two and uh the Third
21:36
problem for attribution is again you need to invest time and a lot of
21:43
resources into it right so and and it becomes complex for businesses which uh
21:50
which involves an offline touch point uh uh so if you're a pure SAS company
21:56
any pure e-commerce player your attribution problem I mean the third problem might get
22:02
easier because you still have the first two problems the third problems will get easier but if you're a company with an
22:07
offline touch point where you can buy both online and offline uh you are in that Rabbit Hole
22:14
uh and then you might never come back so uh it just depends on the size of the
22:19
business and the type of the business and the size of the business is small and it's pretty straightforward uh any Google analytics platform will
22:27
actually help you give you the basic attribution uh but again uh I would suggest them to
22:33
go with incrementality rather than attribution so uh so this do the simple
22:39
tests of turn off this channel figure out what's your Baseline turn it on again see if it's getting incremental
22:45
as long as all these mixes are getting giving the the incrementality uh
22:50
you can come out of the mindset of what is my CAC for this channel what is my
22:56
cat but this channel is like no just look at the cat for the entire marketing efforts right so and then see
23:02
if you can get incrementality out of it so yeah it's just summarize it don't
23:08
fall into the activation hole it's expensive attack Tech effort and data
23:13
effort and focus on incrementality oh loving this loving this because I I
23:20
used to hear a lot of debates around attribution and and let's face it until today that is really still unsolved so
23:29
this is before AirAsia trans transformed into a super app I was part of that
23:35
Journey that was super exciting but this was a very specific example of Airline
23:41
so the moment a flight takes off with an empty seat it's a perishable you can't get that back
23:47
right so you can't sell it right you can't monetize it right so uh what
23:52
we were figuring out is okay uh there was a what should Google search prioritize
24:02
which which route should I prioritize or which route should I invest more
24:07
and again it's a problem of scale because you have thousands of routes right and and uh
24:14
you can't it's not a human decision so an amazing data team there uh uh was was
24:22
having sitting without a structure data and we give gave them the problem of hey
24:27
product based on the previous data predict what routes are going to be the focus in the next 10 days or the 20 days
24:35
right and that's a simple prediction for them and then they did an amazing job for it and we picked out those rules uh
24:42
and I already mentioned you the example of very specific pages that we have for SEO
24:48
right so uh and uh we work with Google and Google has this product called
24:54
doubleclick search so with combination of all these tools together what happens is let's say if
25:00
the algorithm came out with hey this route needs an attention to date right
25:06
then what it triggers is it triggers the Google search uh double click search
25:11
which creates an ad from the page itself so no
25:16
one writes the ad creates the ad by the page itself which is a feature already valuable because we have very specific
25:23
pages and very specific content for each product uh it's easier for them to it's
25:30
easy for the tools to write very specific ad right and then uh the
25:35
campaign template is already set that we created in the in the platform and uh
25:41
the campaign is live that's best if someone searches for a specific log that big base because this
25:47
campaign is live our bids are high we appear on the top uh and uh we tend to
25:55
get more clicks for that specifically what we are doing here essentially is
26:01
again Bridging the Gap between the identification of business problem
26:07
and going to Market so this is the moment that you figured
26:13
out that this is the problem there is a solution it might not be the best solution out there because you know I can launch a
26:19
campaign about Japan and which takes a lot of time and creative and stuff but there is something running immediately in the
26:26
next five to ten minutes but uh uh take that in a typical
26:32
scenario of I can identify the business problem what happens then the the pricing team then conveys the problem to
26:38
uh the marketing team and marketing team briefs uh the the team in-house on this
26:45
is the problem figure create a solution for it worse if they have an agency because they have to go for an agency
26:51
and then brief the agency and they come up with a solution they create approval process and and the campaign goes live
26:57
and stuff right so but there is nobody addressing the problem immediately all right and these
27:04
automated Solutions will have those things live uh and doing something
27:11
uh because the problem already started so yeah so so Bridging the Gap was the
27:19
uh the business problem and we kind of figured out a solution a
27:24
marketing technology solution that addresses the problem immediately I how do you decide if a certain
27:32
experiment or a certain Solution that's going well or not so like you say not everything that's really goes as planned
27:39
and not necessarily everything would would rank as high as what you wanted right so how do you pick and choose
27:45
I mean in in this in this specific example in the previous example of uh
27:51
uh pages I did not choose I launched everything right so the my solution was to address
27:58
everything at scale uh an imperfect solution but addresses everything and
28:03
then figure out later on which to focus on right so uh yeah in it is very hard to
28:13
decide what to do when you have a lot of products and a lot of problems so you yeah
28:20
that's why the lever you pull is using data and Technology
28:25
your 2K study helped us to see you know the ROI of leveraging this liver instead
28:31
to achieve skill and a lot of times answer the uncertainty with data itself
Importance of setting up a good measurement framework to measure the efforts of marketing through business lens
28:37
what's the biggest challenge you face when using data itself to help solve
28:44
I haven't seen it challenges aren't really with data right
28:50
the challenges are really applying the data and building products
28:55
on top of it right so uh a couple of challenges is when
29:02
organizations don't have a good measurement framework right on how
29:09
to measure marketing or the efforts of marketing right all different channels of marketing
29:15
right so uh for for example
29:20
a campaign a bank is running a campaign to
29:25
uh December small loans right so whether running is a lead campaign right and the
29:33
success of the campaign is how many loads loans are finally approved right so
29:39
the what typically that business measures is
29:44
what is the cost for each loan approval right so I spent them a hundred thousand
29:51
on marketing dollars on Google and Facebook I've uh approved hundred thousand loans so it's just one dollar
29:58
but algorithms like Google and Facebook can't optimize the loan approval process
30:03
right it cannot collect leads it can optimize up to the leads funnel what happens
30:10
after they fill the lead and given the information is something manual people
30:15
checking the the loan uh applications and figuring out with the right guy or like little to kind of give the loan
30:22
isn't algorithmic so your measurement of cost per loan
30:28
approved loans itself is wrong you just should be just looking at how
30:33
many what is the cost per applications right so uh but the data is available
30:40
for both so it's the challenge is not the data it challenges what to measure
30:46
what's the measurement framework then when you give that
30:51
to the marketer saying that you know you know cosplay is high they tend to make changes so the campaigns that might
30:57
negatively impact the applications itself so that that is a challenge not having a
31:03
measurement framework is the challenge and I know you built
31:08
management framework from scratch so so um I I wanted to go in a little bit
31:13
deeper on it is can you share a little bit of your thought process or methodology see how do you build a measurable
31:21
framework that actually accurately reflect
31:26
like you say yeah the measurement was wrong just now yeah yeah so uh uh see uh
31:34
measurement Frameworks uh typically builds uh
31:41
it's first to understand the business well right so uh if you really have
31:47
someone in the business for a while uh who understands uh the entire Journey
31:55
and at the same time who knows a bit of a marketing on how it works will will be will really help solve the
32:02
problem immediately they can tell you exactly what to measure right uh otherwise you really have to go down
32:10
by yourself and try to map the entire journey of the business right meaning where does the product come from from
32:16
supply chain to actually going in Back to the customer right if you as a marketer can write it down of the entire
32:24
journey of that product in your business you know the business then when you try to measure the impact
32:30
of your marketing I'm pretty sure the way you measure it's going to be better is different right so uh do that
32:37
exercise first because before coming up with a measurement framework I don't
32:42
really want to give a very specific example for it because uh it is very unique to for for every business so
32:50
understanding the business is a very key primary thing and then coming up with
32:56
framework anybody can come with a framework as long as you understand the business uh but coming up with framework isn't
33:03
really uh is the biggest challenges adapting it right and convincing people
33:10
to measure a new thing rather than an old thing was the e was and is the
33:16
biggest challenge you let's go to the example of the bank right so if the CEO is measuring cost
33:23
per loan approval as the marketing efficiency for 10 years you're saying
33:29
hey no I'm gonna only uh measure the number of applications not the approved
33:35
loans and the first comment that she was gonna be hey are you trying to make your life easier are you trying to make your
33:41
job easier you're trying to escape from the responsibilities but in reality
33:47
the algorithms of Google Facebook or the marketers sitting down and optimizing the campaign or the guy clean creating
33:53
the creative or a product manager who's trying to optimize the the flow of the website
34:00
can't do anything beyond the loan application all right so
34:08
convincing the top guy and convincing the entire company is what it takes uh
34:16
it's what called implementing the framework coming up with it anybody can
34:21
come up with it all right so it took me a day because I had a really good focus
34:27
in aberration to uh to help me come up with the framework but it took me a year
34:32
to fully implement it across all the markets what a comparison
34:37
what a comparison oh what are so we spoke now about
34:45
actually this is like part of the challenges in your role I'm curious what are some of the challenges you face in
Why marketers should learn how to speak the product & engineering language (without the need to know how to code)
34:51
your role other than this or or this could be one of the bigger ones but this is the I mean this is one of
34:58
the biggest ones they are the biggest ones is uh which I've seen in the industry out there which fortunately I
35:05
haven't faced is uh how much time the tech teams are
35:10
spending on marketing right so uh it's very simple for
35:16
algorithms to work properly you have to properly pass the data right for Google and Facebook algorithms
35:23
uh the ad campaigns algorithms to work really well for your business you have to pass the right data from your website
35:29
all the events all the triggers uh proper pass in a structured way to or
35:36
from the app sdks may be passed it to the platforms to work well right but it's not done by marketers it's done by
35:43
the tech folks right and Tech folks are in that cycle of enhancing the features
35:50
clearing out the bugs doing experiments and stuff and when you go and put your requirement
35:56
in there Ben then don't really see value usually they don't because they have
36:01
different pressures of launching the features and products and when
36:06
uh a marketing guy or a growth guy uh can't have the same language
36:13
to the product folks this is really important uh and explain why it's really important it's probably more important
36:20
than the feature that you're about to Launch because it's impacting the marketing dollars and the efficiency of the
36:25
business right so uh the challenge in the industry is the the marketing growth
36:33
marketing folks can't really articulate the problem to the tech Force
36:38
attack folks don't really spend much time on marketing that's where my role of growth uh sitting between these two
36:46
really helped for me uh everywhere uh wherever I go right so I don't have those problems in every year I never had
36:53
problems in in classroom so uh I'm fortunate enough to not have those
36:58
problems but I've seen uh a lot of problems where the root causes specifically this
37:04
I can definitely see that as as you mentioned you know you started at computer science
37:10
background become a PHP developer then your boss asks you the million dollar questions that set you all the way here
37:16
yes it makes me wonder and when you say this challenge right it makes you wonder like as as the trend goes do you see
37:25
actually marketers need to actually learn um you know some form of coding or at
37:30
least the language of coding in order to be able to to leverage that data and
37:36
Technology because if not um how would be they be able to soft it being being a marketer that only speak
37:42
marketing language I don't think markets needs to understand how to code uh they just need
37:47
to understand technology on how it works if you really think about it
37:53
most of the product managers can't code but they're building products right so uh you just need to
38:02
talk in that language of product and Engineering folks and explain them why
38:08
technically this makes sense right so uh
38:14
I mean if there are a lot of no code repositories and tools out there that
38:19
you can do uh right so uh I don't think coding is going to be a really
38:25
particularly unique skill in the next five to ten years so yeah I wouldn't spend time learning how to cope
Ravi's advice for marketers
38:32
now we're going to move into advice kind of sessions where we call it the
38:37
lightning round so we have I have four questions for you are you ready I think so
38:45
awesome so number one what what do you think are some of the most important
38:50
skills that a marketer should have it keeps you always right so probably uh
38:57
for getting out and crunching the data was the most important skill uh
39:03
but now maybe prompt Engineering in the next
39:08
might be the important skill and and but the most important skill is to have uh
39:14
be curious and be empathetic to the consumer is is the most most skill that
39:20
I try to always uh you know call and on practice
39:25
what advice would you give to someone who is interested in pursuing a career in marketing
39:30
uh be curious uh it's it's it's not uh I
39:36
do a course you just have to keep learning it's evolving so fast so yeah
39:41
yeah it doesn't stop I I you know following a linking post I
39:47
can see you keep evolving uh you definitely live by this philosophy
39:53
good question what are the key data or metrics that you monitor when it comes
39:58
to growth building oh I business metrics not marketing
40:04
metrics basically uh all the dashboards that I ever built for a marketing team has no marketing metrics no clicks no
40:10
Impressions nothing it's all business metrics you already have that in your
40:15
marketing platforms on and stuff right so uh how do you
40:21
use it and impact the business is something uh you should measure not the other way
The book Ravi's recommend
40:27
around I it's not a specifically a marketing book I I like the book hard things about hard
40:34
things by Ben Horowitz uh uh I don't know I don't think but yeah I
40:41
I always going back to it and you know uh it's
40:47
very unusual source that gives me inspiration yeah I really really enjoy
40:52
our pieces so much um so thank you so much Ravi for this sessions today where
40:59
can I will listen to find you if they want to reach out and learn more about what you're up to uh thanks for having me uh firstly and
41:06
yeah I mean I'm moderately active on LinkedIn usually my rants all go there so you can try to follow me and all my
41:15
socials are with the tag ravi's book so you can search in any socials I use the
41:21
same handle so you might find me but my most active on LinkedIn okay we will
41:26
definitely put on the links for all the listeners to get access to uh on our page thank you so much for listening if
41:34
you find this valuable you can subscribe to the show on Apple podcast Spotify or Google podcasts also please consider
41:41
giving us a rating or leaving us a review because this really can help other listeners to find the podcast you
41:48
can find all the episodes or learn more about this podcast at Vase.ai see you in
41:54
the next episode thank you