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The Ingenious SEO Story: How Ravi Shankar and His Team Boosted Traffic and Revenue with SEO At Scale
40 Plays1 year ago
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.
Ravi’s Career Journey : Ravi shares his career journey from being a PHP developer to becoming a marketing leader, and how he learned to use data and technology to optimize campaigns, generate content, and rank organically on search engines.
The AirAsia SEO Case Study : Ravi also reveals the AirAsia SEO case study, where he and his team used a programmatic solution to create tens of thousands of pages for different routes and destinations, and then enhanced the content for the pages that ranked well on Google. To solve business problems, Ravi takes a mix of technical and organizational approaches to bridge the gap between identification and launch. This led them to boost their organic traffic to their website with minimal human involvement!
Importance of Having a Measurement Framework that Aligns with Business Goals : Ravi also explains the importance of having a measurement framework that aligns with the business goals and metrics, and how he convinces the stakeholders to adopt it. He also gives some tips on how to avoid falling into the attribution trap and focus on incrementality instead.
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Transcript
Introduction and Overview
00:00:00
Speaker
Welcome to Winning with Data-Driven Marketing Podcast. This podcast is brought to you by WaaS.ai Market Research. I'm Julie, your host in this podcast, and in every single episode, we talk to industry leaders, marketers, and growth experts in Asia about how to use data to enhance the ROI in their marketing activities.
00:00:22
Speaker
We bring you real case studies while giving you background on how these leaders built their career to where they are today. We will bring you to our speakers shortly after a quick word from our sponsor. WaaS.AI Market Research is an AI-powered digital research platform that can help you understand your target customer as quick as 24 hours from as low as US$1,000.
00:00:47
Speaker
For those who are having questions about what your target customer think about your brand, your ads, or your product, you are guaranteed reliable findings that can help you build better branding, advertising campaign, and launch a more successful product. Find out more at www.vase.ai. It's www.vase.ai. And use promo code PODCAST to get your first 10% off.
00:01:17
Speaker
Now, back to the show.
Meet Ravi Shankar
00:01:20
Speaker
Very excitingly is Ravi Shankar, the current CMO of Carson Group, which we all know is Adobe's Asia's largest car e-commerce platform. Ravi is also previously Chief Growth Officer of AirAsia. So Ravi is having 15 years of experience in bridging the gap between not just marketing, but technology and data. And that's why we are very excited to have you today. Thank you for joining us, Ravi.
00:01:47
Speaker
Thank you. Thank you, Julie. Thanks for having me. Since 2008, you're already in marketing analytics, which I presume at that moment, marketing analytics is not really a thing yet. So I'm very curious, you know, why do you choose that career? And can you take us back to your career starting point and share with us? How do you get to where you are today? OK. OK, that's a long journey. So I'll try to keep it short.
00:02:15
Speaker
Basically, I'm a computer science engineer and I've started my job as a PHP developer and I started to play with my computers because my dad used to own a computer.
00:02:30
Speaker
Institute that teaches computers, right? It was a thing back then. So, and then I started coding and I've got my first job as a BSB developer and I started to build websites. And, and then, and it was a startup and the CEO said, Hey, you build a website for us. So can you make it popular? It's like, okay, I am not a marketing guy. I, back then the concept of marketing is just sell a physical good, right? So now you created a digital good.
00:02:58
Speaker
and how do you market it and where do you market it? And then I started researching about it. Then that's when I understood SEO, that's when I understood paid marketing and in search ads and stuff, right? So at the moment I went into the paid ads side of things, the data started to flow in, right?
00:03:23
Speaker
It, it, the data was initially very little, 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
Career Path and Transition
00:03:33
Speaker
stand. Right. So, and then slowly, uh, that data just started to grow, uh, quite a bit 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 startup who solely focuses on paid marketing. Uh, and that's when I shifted from my PHP development career to a,
00:03:54
Speaker
paid marketing career. And I started to play a bit more and had to spend a lot more, right? Back then I used to spend about 150,000 US a day. 15 years ago, that was a huge amount.
00:04:08
Speaker
So, and I was coming from a small town in India, that was a huge money, right? That's like what celebrities make. So I was a bit overwhelmed about the amount of money I used to spend every day, right? So I used to be very careful on
00:04:24
Speaker
the decisions that I made. And I started to kind of slowly structure the data in the bed. I used to optimize campaigns in a way. I used to wake up at 2 a.m. and see, okay, if the campaigns are performing well and then make changes and stuff. And then I cracked the interview with Yahoo, who were a dominant search engineer back then as well. And then that's when
00:04:48
Speaker
I started to handle multiple clients campaigns, but there was no way I could optimize those campaigns with my naked eye. So I started putting all the data into Excel and started to analyze the data in Excel by visualizing, by learning some macros. And I used to be that power user without using a mouse, using a lot of shortcuts and stuff. And then that was a really good experience for me and a really good revelation of
00:05:18
Speaker
this data is getting out of hand. And I spent a couple of years in Yahoo and then a couple of classified companies there and then joined the Google programmatic team, which Google was actually setting up. So I was part of that team where Google just acquired a business called Invite Media and
00:05:40
Speaker
like any other product they're painting with Google colors and trying to make it like a googly thing. And then I was working with the product managers to help them out in building this platform and enhancing the features of this platform. So it's basically a DSP. It's a demand-side platform to buy ads and stuff.
00:05:59
Speaker
I was working with the product managers and stuff. And then the challenge there was I was working with these huge clients like Netflix, Google Chrome was one of the client, and Facebook was one of the client for them. But the problem was the data was out of hand. It's out of Google Sheets. It's out of Excel. It was too huge. And then I was introduced to BigQuery, where the data is sitting in the databases.
00:06:27
Speaker
And then for me to request the right amount of data or the right data sets, I had to learn SQL and I sent query and stuff and then connect with the data visualization platforms and everything. So it's certainly what I thought would be a marketing job kind of started to evolve into some analytics job,
Digital Transformation at AirAsia
00:06:51
Speaker
right? But analyzing marketing data.
00:06:54
Speaker
And then I moved to Malaysia for a startup, which is an agency, which kind of gave me the opportunity to still keep in touch with the analytics staff and also do some marketing, performance marketing and stuff, which is what I perfected over the years. And then I set up an analytics team for them as well. I set up a performance team. It went really good about for two years. There's a company called Lionel Lion. And then I,
00:07:25
Speaker
I met the chief data officer of Erasure, and he told me that, hey, we have huge amounts of data, and I think we can use it in marketing and apply a lot more optimizations to the marketing spans. And Erasure is known for its marketing, right? And it's a huge brand. And that challenge really intrigued me because
00:07:55
Speaker
What you get from an airline back then was a very structured data, right? And how do you apply that structured data into marketing campaigns was a challenge. And I went there, I worked with multiple teams, slowly my role evolved from marketing to commercials, to growth, to product owner and so on and so forth.
00:08:19
Speaker
And then after that, after a good five years there, and then I moved to Card Zone, pretty much doing very similar things there. So yeah, I think that's the, that's the story of how it evolved and where I am right now. Really well. I'm curious in a lot of your career, I noticed actually at each point of time, it is actually a tough challenge.
00:08:46
Speaker
at least at that point of time, because it wasn't figured out yet. So if I were to take a point of time or an example, let's take the AirAsia example when you go in. How do you go about what's your thought process? How do you think about what you start first, given that you know all the structured data are not necessarily in one place for you to make sense of?
00:09:09
Speaker
Actually, by the time I went there, they already started a bit of their digital transformation, which is moving from their on-premise to cloud, which made my life very easy, and the data was very structured. But the challenge was always, the data is there, the platforms is there, but what do I do to make this better for me to,
00:09:38
Speaker
get the understanding I had to understand the business well. And I am not a travel guy. Never. Interesting. So I'm not a travel guy. And I mean, I'm not still not into cars as well. So I learned driving a couple of years ago. So it's just been there, right? So
00:09:59
Speaker
It is a very interesting problem for me because it's a very complex business. It's a huge business. There is ground operations, there is flight ops, there is network, there is commercial, there is brand and everything.
00:10:13
Speaker
Again, one good thing was I was based in an awesome office called Ereshia Redkir, which is one of the best offices in the world. And the opportunity for me to go around the office and talk to multiple teams and multiple people, from treasury to engineering, from procurement to the network, really helped me understand the business. And
00:10:40
Speaker
I used to clock in about 10,000 steps a day in the office. It was huge and I was going around and talking to people and stuff. And then I figured out, okay, what are the top priorities for marketing? And how do we apply that? What is the roadmap? And then I started doing things because
00:11:02
Speaker
Is the data available? I could have taken decisions and stuff, but if I don't connect with the business problem, it doesn't really make any sense because it just gives you the very local, local maxima. What we want is a global maxima that impacts at a business level.
00:11:20
Speaker
that helped me and in short understanding the business first and then figuring out which data to pick from the humongous corpus of data that you have and apply to marketing was the
00:11:37
Speaker
the difficult part, but once you figured it out, the steps ahead for me was very clear. And my boss back then was a travel veteran who understands the data well, who could see things much ahead of any of us. So that kind of helped me to kind of, you know, skip some of the steps and mistakes. A nice gotcha. So combining the business problem and making sure we dig from that before we choose what data to look into.
00:12:06
Speaker
And on that note, right, so you obviously tried a lot of marketing strategies, I can see on your LinkedIn, you know, I know you say, you know, this is not for human, this is for algorithm purposes and boom. So among those, right, like, or maybe more than those even, what are some of the most effective marketing strategies you have used?
SEO and Marketing Attribution
00:12:26
Speaker
So basically most of the marketing strategies that are used from a growth perspective, right, so see there are
00:12:37
Speaker
Quite a lot of CMOs out there who came from a very traditional media perspective. Like they are really good at coming up with good media of well-bought TVs and stuff. They exactly know what TV spot to pick up and stuff. They are experts. And they are CMOs who are really good at brand strategy, right? So they can build a brand and they can create a person around the brand or enhance the person around the brand.
00:13:06
Speaker
and they have a really good eye for creative. And I come from a different kind of a place where I use data and technology and those are the levers that I pull the most, right? So I'm not saying I'm an expert in data and technology.
00:13:29
Speaker
I know to use it the right way for some of these strategies that I kind of tested over the years. But I had fortunately, I had a really good team that knows the brand well, that owns the creative well, that knows the media well. So a couple of strategies are going back to, okay, what exactly is the business problem? The business problem, let's say for one of the,
00:13:58
Speaker
was, again, quite a few routes, right? So, column port, same port as a route, same port, column port as a route, and then we wanted to rank organically in all the search engines, and then we figured out, okay, how many pages do we need? And then
00:14:16
Speaker
pages were about came up to almost 7,000 pages because every route, every combination, every airport and stuff. So it's like, okay, so if I have to write 7,000 pages in multiple languages where it is present across that multiplies to about 50,000, right? So how do I solve this? It's not something I'm going to hire a huge content company somewhere in India or China to write this stuff.
00:14:47
Speaker
So what we decided is to, okay, let's take a technology approach. We work with a couple of companies, one based in US, one in here, and took all the data that we have, the route information and stuff, and pass it to an API, build an API on top of that. So it basically created,
00:15:09
Speaker
all the combinations of the routes, and it also created the pages. And what it also did is it created a template of content, meaning you fly from here to here, and this is the conditions of the airport, this is the weather of the destination, this is the currency using this, so it's a template of content.
00:15:31
Speaker
It's a content, like you said, it's not for human. For human, it's a very bland, informational content. It doesn't give any excitement for them. But it's a really good content for Search Engine. So the moment we figured it out, we launched those pages, the first 7,000 pages. Google started crawling it. And then we started ranking for it. And then we built all multiple.
00:16:00
Speaker
other languages as well. So there are about 40,000 pages now, right? So all those pages were programmatically launched and not really handwritten by a human, right? So which would have not been possible. So then again, some of the pages started to rank quite well.
00:16:20
Speaker
And then what we realized is if these pages started to rank and the content is bad, it's not going to be, it's not been sustainable, right? So then we, the human content writers spent only time on those pages that are ranking on the top and making the content a bit more appealing and not really robotic.
00:16:43
Speaker
So the approach was to build all the pages first, launch all of them first, figure out which one to focus on, and then put the human effort in there. But the basic problem for business we're trying to solve is we need to get more organic traffic. And the problem again is
00:17:08
Speaker
which routes to choose from. There are quite a lot which one to prioritize. So if I say, hey, if the business says, this particular route is really important for me, build a page for it and make it rank, 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 together and figure out which one ranks first and applying human content after it ranks and started to really enhance it,
00:17:36
Speaker
solves a lot of business problem and addresses business problem as well. So that's one of the strategies. It's a typical marketing problem solved by technology and with some of the amazing folks who helped me from technology standpoint. And again, the culture of the organization to kind of embrace that, right? So it's a mix of that. I love this case study.
00:18:06
Speaker
And if Miami asks, this is a strategy you used back in the years, do you think such strategies will still work nowadays if today's listener are listening to this and try to emulate some portion of it? It's a very specific, it would become a very different question at this point, right? So if organizations have a scale to build lots of pages and deploy a lot of pages,
00:18:36
Speaker
when you really have the problem of having so many products and its KUs and stuff, right? So this solution might still work because it's not like a paid feature right here. If I'm talking about Shopee, Shopee goes and asks to the SEO manager saying, I want this product to be organically ranked. So go build a page, make it rank. It doesn't work like that.
00:19:05
Speaker
But if the SEO manager or head of shop, he builds pages for every single product out there, and then with 5% of them rank, that makes a huge difference. And so the approach is you are building for algorithms, right? So when it comes to search engine optimization, you can't really dictate what ranks. You just have to figure out
00:19:30
Speaker
throw everything at it, see which one ranks and enhances later. I think it's pretty much still applies to the current state of search right now, but with search moving from Google search to bot and chat GPT, it might not be the case for the next coming couple of years or so.
00:19:55
Speaker
So there is one common question that some marketers face, especially when they haven't figured out a channel that would work to, say, drive acquisitions of leads, right? Then they would have to actually figure out which channel would work. I'm curious if you have any thoughts here that you can impart for marketers who are going on to this journey. Do not know which channel works yet. And how do they actually go on to figure out which channel will actually work for the acquisition?
00:20:25
Speaker
There are multiple answers to this question. And it's very subjective because what you're essentially asking is the famous question of marketing attribution. And marketing attribution, like you just mentioned, is a rabbit hole.
00:20:46
Speaker
So there is never an end to it. From a macro standpoint, you are trying to see which channel works, but when you're using the two biggest wall gardens, or now three with TikTok out there and stuff. So these guys don't talk to each other. And no amount of data will give you 100% accuracy of which channel is the best performing channel.
00:21:15
Speaker
right? The second one is there is an impression level attribution, right? So someone saw something and then went on bot something, which is there is no digital footprint there, but there is an intent, right? So you can't track that, right? That is number two. And the third
00:21:36
Speaker
problem for attribution is, again, you need to invest time and a lot of resources into it. And it becomes complex for businesses, which involves an offline touchpoint. So if you are a pure SaaS company and a pure e-commerce player, your attribution problem
00:22:00
Speaker
I mean, the third problem might get easier because you still have the first two problems. The third problems will get easier. But if you're a company with an offline touchpoint where you can buy both online and offline, you are in that rabbit hole and then you might never come back. So it just depends on the size of the business and the type of the business. If the size of the business is small and it's pretty straightforward, any Google Analytics platform will actually help you give you the basic attribution
00:22:29
Speaker
But again, I would suggest them to go with incrementality rather than attribution. So do the simple tests of turn off the channel, figure out what's your baseline, turn it on again, see if it's getting incremental. As long as all these mixes are getting, giving the incrementality,
00:22:51
Speaker
you can come out of the mindset of, oh, what is my CAC for this channel? What is my CAC for this channel? It's like, no, just look at the CAC for the entire marketing efforts, right? So, and then see if you can get incrementality out of it. So yeah, just summarize it. Don't fall into the activation hole. It's an expensive, multi-tech, tech effort and data effort and focus on incrementality.
00:23:17
Speaker
Oh, loving this, loving this. Because I used to hear a lot of debates around attribution. And let's face it, until today, that is really still unsolved. So this is before AirAsia transformed into a super app. I was part of that journey that was super exciting. But this was a very specific example of Airline. So the moment a flight takes off with an empty seat, it's a perishable. You can't get it back.
00:23:47
Speaker
So you can't sell it. You can't monetize it. So what we were figuring out is, OK, what should Google search prioritize? Which route should I prioritize or which route should I invest more? And again, it's a problem of scale because you have thousands of routes.
Automation and Challenges in Marketing
00:24:12
Speaker
And it's not a human decision.
00:24:17
Speaker
So an amazing data team there was having sitting with the data structure data and we gave them the problem of it.
00:24:27
Speaker
predict, based on the previous data, predict what routes are going to be the focus in the next 10 days or the 20 days. And that's a simple prediction for them, and then they did an amazing job for it. And we picked out those routes. And I already mentioned you the example of very specific pages that we have for SEO. And we work with Google, and Google has this product called a Click Search.
00:24:55
Speaker
So with combination of all these tools together, what happens is, let's say if that algorithm came out with, hey, this route needs an attention to date, right? Then what it triggers is it triggers the Google search, do a double click search, which creates an ad from the page itself. So no one writes the ad.
00:25:17
Speaker
creates the ad by the page itself, which is a feature already valuable because we have very specific pages and very specific content for each product. It's easier for them to, it's easy for the tool to write the very specific ad, right?
00:25:34
Speaker
and then the campaign template is already set that we created in the platform, and the campaign is live. If someone searches for a specific log, that is because this campaign is live, our bids are high, we appear on the top, and we tend to get more clicks for that specific group. What we are doing here essentially is again,
00:26:02
Speaker
bridging the gap between the identification of business problem and going to market. So this is the moment that you figured out that this is the problem. There is a solution. It might not be the best solution out there because I can launch a campaign about Japan and which takes a lot of time and creative and stuff, but there is something running immediately in the next five to 10 minutes.
00:26:29
Speaker
Uh, take that in a typical scenario of I can identify the business problem. What happens then? The, the pricing team then conveys the problem to, uh, the marketing team and marketing team briefs, uh, the team in house on this as the problem figure create a solution for it.
00:26:48
Speaker
worse if they have an agency because they have to go for an agency and then breathe the agency. And they come up with a solution, they create approval process and the campaign goes live and stuff. But there is nobody addressing the problem immediately. And these automated solutions will have those things live and doing something because the problem already started.
00:27:15
Speaker
So bridging the gap was the business problem. And we kind of figured out a solution, a marketing technology solution that addresses the problem immediately. How do you decide if a certain experiment or certain solution is going well or not? So like you say, not everything necessarily goes as planned and not necessarily everything would rank as high as what you wanted, right? So how do you pick and choose?
00:27:45
Speaker
I mean, in this specific example, in the previous example of pages, I did not choose. I launched everything. So my solution was to address everything at scale, an imperfect solution, but addresses everything, and then figure out later on which to focus on.
00:28:07
Speaker
It is very hard to decide what to do when you have a lot of products and a lot of problems. That's why the lever you pull is using data and technology. Your two-case study helped us to see the ROI of leveraging this lever instead.
00:28:32
Speaker
to achieve skill, and a lot of times, uncertainty with data itself, what's the biggest challenge you face when using data itself to help solve? See, the challenges aren't really with data. The challenges are really applying the data and building products on top of it.
00:28:59
Speaker
A couple of challenges is when organizations don't have a good measurement framework on how to measure marketing or the efforts of marketing, all different channels of marketing. So for example, a bank is running a campaign to
00:29:27
Speaker
December's more loans, right? So what they're running is a lead campaign, right? And the success of the campaign is how many loads loans are finally approved, right? So the
00:29:40
Speaker
What typically that business measures is what is the cost for each loan approval, right? So I spent 100,000 on marketing dollars on Google and Facebook. I've approved 100,000 loans, so it's just $1. But algorithms like Google and Facebook can't optimize the loan approval process, right? It cannot collect leads, it can optimize
00:30:08
Speaker
up to the leads funnel. What happens after they fill the lead and give the information is something manual. People checking the loan applications and figuring out if it's the right guy or like to kind of give the loan, isn't algorithmic. So your measurement of cost per loan approved loans itself is wrong. You just should be just looking at how many, what is the cost per applications.
00:30:36
Speaker
Right. So, uh, but the data is available for both. So it's the challenges, not the data challenges, what to measure, what's the measurement framework. Then when you give that to the market as saying that, you know, you know, cost alone approval is high. They tend to make changes for the campaigns that might negatively impact the applications itself. So that, that is a challenge. Not having a measurement framework is the challenge.
00:31:05
Speaker
And I know you built measurement framework from scratch. So I wanted to go in a little bit deeper on it. Can you share a little bit of your thought process or methodology? Say, how do you build a measurement framework that actually accurately reflect? Like you say, the measurement was wrong just now. Yeah. Yeah. So see,
00:31:34
Speaker
Measurement framework typically builds, it's first to understand the business well, right? So if you really have someone in the business for a while who understands the entire journey and at the same time who knows a bit of a marketing on how it works,
00:32:00
Speaker
will really help solve the problem immediately. They can tell you exactly what to measure, right? Otherwise, you really have to go down by yourself and try to map the entire journey of the business, right? Meaning, where does the product come from? From supply chain to actually going in back to the customer, right? If you as a marketer can write it down of the entire journey of that product in your business, you know the business.
00:32:27
Speaker
Then when you try to measure the impact of your marketing, I'm pretty sure the way you measure it is going to be better. It's different. Do that exercise first before coming up with a measurement framework. I don't really want to give a very specific example for it because it is very unique for every business.
00:32:50
Speaker
Understanding the business is a very key primary thing, and then coming up with framework. Anybody can come with a framework as long as you understand the business. But coming up with framework isn't really, is the biggest challenge is adapting it, right? And convincing people to measure a new thing rather than an old thing was and is the biggest challenge.
00:33:19
Speaker
Let's go to the example of the bank, right? So if the CEO is measuring cost per loan approval as the marketing efficiency for 10 years, you're saying, hey, no, I'm gonna only measure the number of applications, not the approved loans. And the first comment that CEO is gonna make, hey, are you trying to make your life easier? Are you trying to make your job easier? You're trying to escape from the responsibilities. But in reality,
00:33:48
Speaker
algorithms of Google, Facebook, or the marketers sitting down and optimizing the campaign, or the guy creating the creative, or a product manager who's trying to optimize the flow of the website, can't do anything beyond the loan application.
00:34:08
Speaker
Convincing the top guy and convincing the entire company is what it takes. It's what we call implementing the framework. Coming up with it, anybody can come up with it. And so it took me a day because I had a really good focus in average to help me come up with the framework, but it took me a year to fully implement it across all the markets. What a comparison.
00:34:38
Speaker
What a comparison. So we spoke now about, actually, this is part of the challenges in your role.
Tech-Marketing Collaboration
00:34:49
Speaker
I'm curious, what are some of the challenges you face in your role other than this? Or this could be one of the bigger ones. This is one of the biggest ones. The other biggest ones is which I've seen in the industry out there which fortunately I haven't faced
00:35:09
Speaker
How much time the tech teams are spending on marketing? It's very simple. For algorithms to work properly, you have to properly pass the data. For Google and Facebook algorithms, the ad campaigns algorithms to work really well for your business, you have to pass the right data from your website, all the events, all the triggers.
00:35:33
Speaker
a proper, pass in a structured way to, or from the app as decades may be passed it to the platforms to work well, right? But it's not done by marketers. It's done by the tech folks, right? And tech folks are in that cycle of enhancing the features, clearing out the bugs, doing experiments and stuff. And when you go and put your requirement in there,
00:35:57
Speaker
when they don't really see value, usually they don't because they have different pressures of launching the features and products. And when a marketing guy or a growth guy can't talk the same language to the product folks, this is really important. And explain why it's really important. It's probably more important than the feature that you want to launch.
00:36:22
Speaker
because it's impacting the marketing dollars and the efficiency of the business, right? So the challenge in the industry is the marketing folks can't really articulate the problem to the tech folks. The tech folks don't really spend much time on marketing.
00:36:42
Speaker
That's where my role of growth sitting between these two really helped for me everywhere, wherever I go, right? So I don't have those problems in Asia. I never had problems in Carson. So I'm fortunate enough to not have those problems, but I've seen a lot of problems where the root causes specifically this.
00:37:05
Speaker
I can definitely see that as you mentioned, you started a computer science background, become a PHP developer, then your boss asks you the million dollar question that set you all the way here. It makes me wonder, and when you say this challenge, it makes you wonder, as the trend goes, do you see actually marketers need to actually learn some form of coding or at least the language of coding in order to be able to
00:37:33
Speaker
to leverage data and technology? Because if not, how would they be able to software being a marketer that only speaks marketing language? I don't think marketing needs to understand how to code. They just need to understand technology on how it works. If you really think about it, most of the product managers can't code, but they're building products, right? So you just need to
00:38:02
Speaker
talk in that language of product and engineering folks and explain them why technically this makes sense, right? So I mean, if there are a lot of no code repositories and tools out there that you can do, right? So I don't think coding is gonna be a really particularly unique skill in the next five to 10 years. So yeah, I wouldn't spend time learning how to code.
00:38:32
Speaker
Now we're going to move into advice kind of sessions where we call it the lightning round.
Skills for Future Marketers
00:38:39
Speaker
So I have four questions for you. Are you ready? I think so. Awesome. So number one, what do you think are some of the most important skills that a marketer should have? It keeps evolving, right? So probably a
00:38:57
Speaker
Figuring out and crunching the data was the most important skill. But now maybe prompt engineering in the next, might be the important skill. But the most important skill is to have, be curious and be empathetic to the consumer is the most skill that I try to always hone and practice.
00:39:25
Speaker
What advice would you give to someone who is interested in pursuing a career in marketing? Uh, be curious. Uh, it's, it's, it's not, uh, I could do a course. You just have to keep learning. It's evolving so fast. So yeah, it doesn't stop. I, you know, following a LinkedIn post, I can see you keep evolving. Uh, you definitely live by this philosophy. The question.
00:39:54
Speaker
What are the key data or metrics that you monitor when it comes to group building?
Focus on Business Metrics
00:40:01
Speaker
Oh, business metrics, not marketing metrics, basically. All the dashboards that I ever built for a marketing team has no marketing metrics, no clicks, no impressions, nothing. It's all business metrics. You already have that in your
00:40:16
Speaker
marketing platforms and stuff, right? So how do you use it and impact the business is something you should measure not the other way around. It's not a specific marketing book. I like the book, Hard Things About Hard Things by Ben Horowitz. I don't know, I don't think it's a marketing book, but yeah, I always,
00:40:44
Speaker
going backward and you know it's an unusual source that gives me inspiration.
Conclusion and Contact Information
00:40:51
Speaker
I really really enjoy our pieces so much. So thank you so much Ravi for the sessions today. Where can our listeners find you if they want to reach out and learn more about what you're after? Thanks for having me firstly and yeah I mean I'm
00:41:08
Speaker
moderately active on LinkedIn. Usually my rants all go there. So you can try to follow me and all my socials are with the tag rubbies book. So you can search in any socials. I use the same and also you might find me about my most active on LinkedIn.
00:41:25
Speaker
OK, we will definitely put on the links for all the listeners to get access to on our page. Thank you so much for listening. If you find this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or Google Podcasts. Also, please consider giving us a rating or leaving us a review because this really can help other listeners to find the podcasts. You can find all the episodes or learn more about this podcast at was.ai. See you in the next episode.
00:41:55
Speaker
So,