“Being data-led is core to becoming product-led…”
Arpit is a non-engineer who loves data and APIs and believes that everybody, irrespective of their technical chops, should gain data literacy to stay relevant. He likes to spend his days thinking, writing in simple terms, and answering questions about data. He also firmly believes in the power of delayed gratification.
In this episode we talked about how Arpit refines his ideas through writing, the importance of product analytics tools for growth marketing, what is product-led growth, the four core elements of a “data stack,” his definition of “data spaghetti,” and much more!
Here are four keywords to reflect on after listening to this episode: collect, store, analyze, and activate…That’s how marketers need to use data to make it worth something…
Kenny Soto 0:00
Huh, I can never get used to how loud she is. We are now recording 5432 Hello, everyone and welcome to Kenny Soto’s Digital Marketing podcast. As mentioned in the, in the introduction of this podcast interview, I am very excited to speak to Arpit Arpit. Howare you?
Arpit Choudhury 0:23
I’m good, Kenny, thank you. Thanks for having me excited to chat with you.
Kenny Soto 0:27
Likewise, and I’ve been doing a lot of research in preparation for this interview, particularly looking at your LinkedIn bio, which I love to read. And you wrote a list of facts about yourself. And that bio, two of them stood out to me the most number eight, I am good at selling my ideas and writing about them. And number nine, I get paid to help others sell their ideas. Could you describe these two points in more detail so that the listeners can get more context about who you are?
Arpit Choudhury 0:59
Yeah, that’s really interesting that you read through my, my LinkedIn bio. Great, so. Okay, so the first one, I am interested, I like to work with it again, I’m interested in stealing my ideas and writing about them. Right. So yeah, yeah. So you know, like, I mean, just like all of us, I’ve also had a lot of ideas.
Throughout the years, I’ve worked in, some haven’t worked on, on some, but I always try to, like, irrespective, irrespective of how, how sold, I am on a new idea, I try really hard to sell my idea to others. Now that idea can be a business idea. It could be anything else, it could be an idea for a trip, it could be essentially anything that I feel is is a little out of the box, or you know, not very easy to sell to others, you know, like I find it really interesting and challenging to sort of sell ideas to people that they don’t expect to hear hear about, right. So it’s not just about, like I said, just not just about business or work.
But in general. It could be about moving homes, you know, I’ve sold the idea of moving homes several times to my wife, right? So stuff like that. And when it comes to writing, sure, like, every time I have had an idea that I’ve pursued, like from a business context, I’ve tried, and I have written a lot about it, you know, I haven’t always succeeded. But I have sort of tried to articulate my idea through through the written word. And that has helped me a lot. Personally, it has helped me shape my ideas better.
But it’s also helped others understand my ideas like, like, again, all of us have ideas, and we like to talk about them. We love to share our ideas with people, but I’m not sure how, what percentage of people write down their ideas, and then rewrite those ideas and try to sort of write them in a manner such that anybody who reads that piece of content could be nautical.
Yeah, typically is an article, they they’re, they’re actually sold on the idea to not just understand the idea of the director sold on the idea. And this process. It’s an iterative process, not like a one time thing. But it really helps me personally shape my ideas better. And then it’s really fun. Because a lot of times when I’m having a conversation and someone like, oh, okay, yeah, that makes sense.
And I think you can also try this, right? And I’m like, Well, I’ve already already written about analysis, can you send them this article? And they’re like, Oh, great. This is what I had in mind. So it’s really, it’s a great way to also sort of connect with others, you know, because, like, you’re obviously having a conversation, and there’s only so much you can talk about in a conversation, whether it’s, it’s a podcast interview, or just like a one on 130 minute conversation. So yeah, that’s that.
Kenny Soto 3:57
One was the second one. The second one is I get paid to help others sell their right. Yes.
Arpit Choudhury 4:04
Yeah. So that’s just a fancy way of saying that I help companies with their like, basically, I work with very specific companies, companies do building products in the data, infrastructure space, and I help them with, you know, their content, strategy, community strategy, product, messaging and positioning.
So So yeah, I mean, I get paid to help others sell, sell their ideas and sell their products, primarily, like, my, my expertise lies and in helping, you know, companies trying to think through their go to market strategy or content community strategy. I’m not like if you talk about digital marketing, like obviously, paid ads is a huge part of digital marketing. I know nothing, or I should say know very little about paid marketing. I’m not an expert when it comes to that. So so yeah. Yeah, so I get paid to help people fail the night Yes. Through content committee marketing 30 say
Kenny Soto 5:05
you are an advisor to Mixpanel. Could you describe the importance of product analytics and product analytical tools as it relates to growth in marketing?
Arpit Choudhury 5:19
Yeah, absolutely. This is something I love talking about. And, you know, I keep talking about all day. So in simple terms, the product analytics tool enables people to understand user behavior and identify points of friction in their app.
Now, that’s the sort of load centers but in simple terms, any anybody that has that, that that works in the SAS space, specifically, it’s really important for them to understand how people use their products. It’s also equally, I would say, important for consumer technology companies to understand how users use the product. When it comes to SAS, it’s actually more complicated, because you’re not just looking at a particular an individual users usage, you’re actually looking at the account usage.
So in SAS, almost always, you don’t just have users, but you have accounts, right? Your customer is a company, and that company can have multiple users. So you want to understand user behavior, which actually could could also be account behavior. You know, it’s not just about how an individual user is using a product. But you also want to understand how all the users in a company origin and account or using my product, where people are dropping off in the in the user journey? How are people becoming active active users, you know, or what are the actions that they’re performing in the app that are helping us retain those users or do the council.
So product analytics tools, essentially help people understand all of these things, is really important. When it comes to growth and marketing. Let me first talk about growth. So in terms of growth, unless you you know, exactly how people are using your product, what problems they’re facing, how many people are sort of, you know, going from signing up for a free trial to like, you know, performing the core events that lead them to the activation event or the aha moment, unless you know, that unless you can see, you know, what percentage of people are dropping out? Or exactly, at which step? are they dropping, or dropping or right after signing up? Or are they dropping after a specific step in the onboarding, right, unless you have the data, which you essentially get through a product analytics tool, you can’t really do much when it comes to growth, right?
Because growth is all about looking at data, understanding data, and then acting upon data, using that data to improve your product, but also to improve your, your, your customer experience, you know, also to improve all the outreach, you know, and to improve your your onboarding emails or your in app onboarding flow, right. So that’s why product analytics tools are super important for growth people.
It’s like, if you don’t have a product analytics tool, and you you’re working in growth, there’s there’s really not much you can do. You’d be flying blind. When it comes to marketing, well, marketers primarily wanting to understand how they’re kept, how their campaigns perform, and what is the impact of their campaigns, right? It’s not just enough to see that, okay, you wrote this blog post, we’ve got, you know, 1000 views.
Great. That’s not enough, right? Because you want to see if that job post actually resulted in people signing up for your product or scheduling a demo, right? There are cases, I have seen where a blog post have got a ton of views. But the conversion, which could be someone signing up for a free trial, or scheduling a demo is really, really low. Whereas there, there are articles that haven’t gotten a lot of views per se, right.
But the conversion is really, really high, right. And that’s where product analytics tools really shine, because you can actually see the entire user journey, you can see it someone sort of landed on a blog post blog post, and then became a free user, or schedule a demo and then eventually became a paying customer. And that’s really, really important, right?
And marketers who are interested in data who are interested in actually measuring the impact of their work, definitely benefit from a product analytics tool, because they can actually go much beyond they can actually show they can actually build reports that prove that someone who first landed on our website on this particular page, it could be a blog post, it could be any web page, eventually became a customer and eventually became one of our most valuable customers. Stuff like that. So hope that makes sense.
Kenny Soto 9:57
Certainly, my next question is Is how do you define product lead growth?
Arpit Choudhury 10:05
Yeah, well, product lead growth is, it’s really it’s a really wide sort of term. You know, it means a lot of things. But in simplest terms, it means that you’re essentially using your product as your growth lever, right? So instead of, you know, spending money on ads, or spending money on influencer marketing or whatever else, you’re trying to use your product as a vehicle for growth right? Now, how can how can how can one actually do that? Well, to do that, they need to understand they need to have data, like data is basically the centerpiece of product lead growth, like you can’t actually implement a product lead growth machine if you don’t have data, right.
And that’s why I love to say that, you know, being data lead is core to becoming product lead, right? Because being product lead means to to actually, again, understand how people are interacting with your product. What is the onboarding experience? Like? Where are people dropping off, which, which particular campaign or like, outreach, email is actually resulting in people coming back in the product? And then using the product? Which particular feature or like, again, outreach, or campaign is getting people to, to add more users or like, like, refer other users? Right? So product lead growth is essentially a way to grow your product using data. That’s, that’s how I like to sort of sign up.
Kenny Soto 11:49
And speaking of data. Can you also define for the audience what a data stack is?
Arpit Choudhury 11:59
Yeah, absolutely. So a data stack, essentially, is a combination of all the tools that you would need to essentially become data lead. So it has four core elements, starting with data collection, right? If you don’t have data, if you don’t collect data, culturally, you would have data, you can’t do anything with data.
So the first step is to collect data, right? Which typically happens when people interact with your product or use your product. Or it’s also known as product data, product usage data, event data, but this is the data that you would actually use in a product analytics tool to understand user behavior, right.
So that’s that, then you also collect data from other sources, right. So if you have a CRM, or if you’re doing ads, if you’re using an email marketing tool, all of those different tools are also generating data, right? You also want that data, right, you want to, you don’t want the data to be in those two, you want to collect that data and ingest that data into what we call a data warehouse.
So basically, once you’ve collected the data, you want to store the data, right? You don’t want want the data to be hanging midair, you want to store it in your own sort of environment in your own database, which is essentially a cloud data warehouse, where, where all of all the data that you collect is available in its raw format. Once you have that, you obviously want to derive insights from the data.
And to do that you need to analyze the data. In some cases, you also want to transform the data, right? Because to analyze the data, you oftentimes have to transform the data. And there are specific tools to do that. But let’s assume that you already have clean transform data, which which is hardly true. But let’s just say you do, you want to analyze the data and derive insights from it.
Once you derive insights from the data, you want to, again, activate the data or take action on the data, right? Like, it’s great to derive insights, great to look at all these dashboards and be like, Okay, we need to do this, we need to do that we need to change this, when you actually go ahead and do it. And you want to use data to actually do that. So, so we often call it data activation, that’s the step that comes after you analyze the data, right.
So, a data stack comprises all the tools that you would need to collect, store, analyze and activate data. And then of course, you have other aspects, where, you know, once you have a basic data stack in place, there, there are other things you want to you want to have a specific tool for data discovery. Now, this is very applicable to large companies that have you know, a lot of data and or different data sources, or different teams, you want to make data accessible within your organization.
You want to make it easy for people to find exactly the data that they’re looking for. And there are specific tools for that. Also referred to as data discovery tool. And then you also have data observability tools, data monitoring tools are these are the tools and all of them fall under the data stack, right. But for product lead growth companies, I would say these four stages of collecting, storing, analyzing and activating data are key
Kenny Soto 15:20
in those four elements, and when we consider those four elements, what are some common challenges, mistakes or pitfalls you see startups make prior to you coming in and helping them fix their data stack?
Arpit Choudhury 15:41
Yeah, I think the number the biggest mistake people make when it comes to, to data collection, because because I haven’t seen many companies. And this is pretty much true industry, right? That the act of collecting data is is. So there are two aspects of collecting data. So first, you want to know what data you want to collect. Just because you can collect 1 million data points, you don’t want to do that.
Because firstly, it costs money, it takes time. And then having more data is also a problem. So you need to know what data you want to collect. And that has to come from people who are actually going to use the data, if you don’t know what you’re going to do with the data, you don’t know what data to collect. Right? So not a lot of teams spend a lot of time thinking about what data to collect, they actually think about what tools to use.
And once they find like, Okay, we want to use this tool or that tool or this this, you know, set of tools, they will typically hand it over to their engineering team to implement the tools. Now, that’s the biggest problem because engineers know how to implement tools, right? But they don’t know what data you want to collect, right? They don’t know what data is useful for you as a marketer, or as a growth person or as a product manager, like you have to tell them, right, it’s not their job to know that.
So that’s the biggest gap, you know, where a lot of companies sort of, you know, miss this step where they just spend a lot of time evaluating tools, they spend a lot of money on those tools, but then they don’t spend enough time implementing those tools properly, which is the biggest problem. And if you don’t do that, then everything goes for a toss, right, you might have the best tools out there.
But if you don’t have the right data, it becomes a challenge you, then basically, people lose trust in the data, you’re not able to, you know, get answers to your questions, or you’re getting incorrect answers to your questions, you’re unable to use the data the way you want to use the data. Because, again, data is not just about analyzing it, it’s also about activating the data and using the data to build better customer experiences.
You have to engage customers at every touchpoint using the data. So yeah, the biggest challenge would be to ensure that the data collection step is is done thoughtfully and mindfully and in a planned approach, rather than, you know, like not spending enough time on it.
Kenny Soto 18:07
I don’t want to assume that this term, is the answer to what you just discussed. But I would love for you to dive deeper into data spaghetti, and how does it negatively affect the business?
Arpit Choudhury 18:23
Yeah, it is sort of, it builds into what I just said, right? Like, if you if you don’t plan, your data collection, your data strategy, you end up collecting a lot of data.
But oftentimes, you don’t even know what the data means. Right? Like, and I’ve seen this many times, I’m sure many, many of you, many of the listeners have also seen seen this inside whatever tool they use, whether it’s a, you know, web analytics tool, product analytics tool, data warehouse, where they have this data, right, but they don’t exactly understand what this data is about. Right?
So I guess that caused data, spaghetti, because let’s say I’m a product manager, I’ve joined a new company, I’m super excited. And I want to like look at all all the data, but I don’t understand anything. So I’m like, Okay, nevermind, I’m just going to, like, collect these data points again.
And what happens is, you actually end up collecting the same data multiple times, it just looks different. But you don’t even know that that is the same data point. Right? They because they have different event names and different property names. So all of this leads to data, spaghetti, because, again, there’s a lack of planning, there are too many people involved.
But you know, the right people are not involved, or the person who took care of the implementation. Initially is no longer with the company, or new person has come in and you know, there is no there’s no documentation as a big problem. There’s a lack of documentation so they don’t exactly understand the data that’s that’s available that has been trapped and collected and stored.
So they sort of you know, take a crack at it. They Maybe you have a different approach. So all of these things lead to data, spaghetti, which is essentially what exactly what it sounds like, it’s a spaghetti of data where there is a lot of data, but you don’t know what to do with it, you don’t know if you’re using the right data point. And that essentially, causes people to lose trust in the data.
And when that happens, then then basically nobody wants to use that data anymore, right? Because you don’t trust it, you might, you might like, spend a lot of time working on a campaign and, you know, like, you’ve done a great job with with all of your content and your outreach and your research, but you’re using the wrong data, and you don’t even know it. Right? And you might like, you might, you might conclude that you didn’t do a great job with the campaign or your content wasn’t right, or your target audience wasn’t right, maybe everything was right, you just didn’t have the right data.
So there are endless problems that that sort of take place when when there is data spaghetti, and it’s pretty common to have data spaghetti is, yeah, it’s more common than you can imagine.
Kenny Soto 21:09
Two more questions, what are some core skills, and this can be hard or soft skills that you have leveraged throughout your entire career?
Arpit Choudhury 21:21
Um, okay, I would say for me, it’s been about like, the, the most important skill that I have leveraged is communication. And both written communication and will be communication, I think, is the most important skill, irrespective of the role, or industry working, especially now that, you know, like, a bulk of bulk of the workforce is remote, you really, really need to be able to communicate well.
And you need to often over communicate, whether there’s communicating internally with your team, or even communicating with customers. And then obviously, writing stuff like I’m a huge fan of writing stuff, sharing my ideas, through through, you know, the written word, not just, you know, talking about my ideas, or sharing my ideas in a structured manner, you know, not just sharing a bunch of bullet points on a slack, Slack chat, but actually, you know, putting down my thoughts in the document and like, punishing, so that when I’m presenting the idea, I don’t have to spend much time explaining it, you know, someone who reads this should understand it, ask the right questions.
And, you know, like, basically, we should be on the same page. So I would say, yeah, like communication, but like, definitely written communication. And then also, like, networking, and like, you know, being out there and having conversations with people, like, one of the things that’s worked really well, for me, is community, like being active in communities. Irrespective of the company I represent, I’ve been very active in few communities.
And that has been really great, because it has helped me. You know, build a lot of connections helped me gather a lot of feedback on stuff I’m working on. It also helped the company that I’ve worked with, you know, for example, when I used to lead lead growth at Integra mat, which is, which was acquired late last year, the workflow automation tool like Zapier, we actually, all our growth was was what happened to aren’t enough to meet your marketing, right? We, because we spent a lot of time in the communities where our prospects and customers hung out.
And we just made ourselves available to our prospects and customers and partners. And we’re always there to answer their questions. So, so yeah, just just being more open. And like, you know, like being more responsive, across these communities, has been really helpful. I see a lot of people, you know, simply ignoring messages like, or like being super selective about people they add on LinkedIn, for example, I have a very simple rule. I just add everybody on LinkedIn, you know, it doesn’t cost me anything.
And LinkedIn has a pretty generous limit to the number of people you can have in your network. So you never know, the person who has just sent you a request, they may not have a fancy background, or like, you know, an impressive LinkedIn profile. But they might, they might, they might, you know, have a unique perspective. Or they might bring something unique to the table. So yeah, just having a more open mind and like, yeah, just just being open to like conversations and yeah, communicating, I would say.
Kenny Soto 24:44
Ask question, and this one is hypothetical. If you had access to a time machine, you can go back 10 years into the past with everything you know, now, how would you get to where you are today? Just faster.
Arpit Choudhury 24:59
Oh, Oh, that’s a good question. Well, yeah, 10 years back with everything I know now, how would I get to where I am faster? I would just like, you know, make better decisions. I mean, like, decision making is a hard skill to get right. And everybody, you know, like, no matter how good they are, they end up making the wrong decision.
And I have done that as well whether in my past startup endeavors, or, you know, choosing the companies to work with. So I would just, you know, make sure I make better decisions. And one of the things I would do is to, like, delay, bigger decisions, you know, like, a lot of times, I have personally, you know, taken big decision without sort of spending a lot of time thinking about them.
What I’ve learned in the last few years, is that if you have a big decision to make, and if you can delay it, you should delay it as much as you can. Right? It sounds counterproductive, but it’s actually it actually works really well. You know, if you have one week to decide on something, you should spend that one week, even if you if you’re sure about a decision, like two days, you should still sit on that decision for five more days before you actually communicate your decision. So I would definitely take that into account and you know, try to take better, better decisions.
Kenny Soto 26:31
Amazing. Thank you, Arpit, for your time today. And thank you, to you the listener for listening to another episode now. Arpit. If anyone wanted to find you online, where can they say hi?
Arpit Choudhury 26:44
Well, they can reach out to me on LinkedIn, they can reach out to me on Twitter on both you can find me as I can automate this my LinkedIn and Twitter handle, I can automate. You can also check out my website. There’s data lead dot Academy, da ta led dot Academy. Yeah, and I’m pretty responsive. I’m also super active in a bunch of slack communities. So if you find me in any of those slack communities, feel free to say hi.
Kenny Soto 27:12
Amazing. And as always, you’ve just listened to another episode of Kenny Soto’s Digital Marketing podcast. I hope everyone has a great week. Bye.