Transforming Data into Meaningful Insights

We sat down with Neerav Vyas, VP Head of Data, Insights and Analytics at Homer to learn his tips and tricks on how to turn data into tangible strategic insights.

Guests:

Neerav Vyas, Vice President – Head of Data, Insights, & Analytics, Homer: Neerav helps organizations to accelerate innovation, drive growth, increase operational efficiencies, facilitate large scale transformations, and turn around ailing business units/ brands. He’s an award winning leader, using a combination of Human Centered Design and Advanced Analytics to help ensure these transformations and product launches are more likely to succeed and have a meaningful impact.

Finally, he’s served as an advisor to a number of Fortune 500 CMOs. CEOs, CFOs, COOs, CTOs, and several of their boards. As well as served on the Executive Team of a Fortune 500 company and a successful series B (now series C) venture back startup. He’s twice lead teams to become finalists for the AiConics Awards for Innovation in AI (in 2019 with Realogy Holdings and in 2020 with HOMER Learning). Ive also won the Ogilvy Award for research in advertising twice.

Host: Anish Shah is the CEO & Founder of executive search agency Ruckus. Anish has worked in-house in Growth roles at Snapfish and Getable. He started Bring Ruckus as a Growth consultancy 11 years ago working with 40+ clients, then re-focused his firm on executive recruiting for Growth leaders.

Highlights

• 1:10 – Neerav shares how machine learning plays on decision making
• 2:27 – Neerav speaks on how Homer uses Data to reach growth targets. (Reduce churn, increase retention & more)
• 6:30 – “If the data isn’t driving decisions, tangible strategic insight it’s just a fancy algorithm”
• 8:37 – Neerav adds on how Data Scientists should always enter each project with a high degree of skepticism
• 8:40 – Advice from Neerav: Take a design ladder human centric approach and try to see how each team would use and adapt to it
• 13:13- Neerav speaks on the ideal approach should be to design to solve broader business problems (Similar approach to product designers)
• 14:10- Neerav shares when is best to hire a Data Scientist & how to know what to look for
• 18:45 – Tip from Neerav : When a fresh hire, take your data scientist through case interview phase to be able to see how they’re able to resolve thinking outside the box.
• 24:44 – Neerav talks about CPRA and how things will change for Growth Marketers in the next 2-3 years due to restrictions
  

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Anish Shah (00:01):
All right. Amazing. so we have Neerav Vyas here. who’s been working in data analytics, data science for quite a few years, and we just wanted to dig in with them and understand, you know, what does a data scientist do on a regular basis? How do you end up in this? And you know, what are the right tools to use and how do you communicate even what you do internally, which I’m sure is confusing to a lot of people, but let’s start off here with just your background and how you sort of ended up here.

Neerav Vyas (00:30):
Yeah. So I serve as the head of data insights and strategy for Homer learning mostly, or kind of a team of product owners, data scientists, data analysts, data engineers. But my background is actually in strategy innovation and growth consulting, practically kind of helping clients to launch and relaunch new businesses, new products, physically and digitally increasingly helping them do customer centric, digital transformations often, you know, over the last decade in change, kind of moving more and more towards using advanced analytics. and so that’s kind of like how I started shifting into the AI and data science space, but worked a lot with different agencies CMOs, marketers across the board.

Anish Shah (01:09):
Okay, awesome. And, and that word AI gets thrown around a lot. So in your opinion, is there, is there a good way how, how it should be defined whether someone is actually utilizing actual AI instead of just sort of rules based insights and analytics versus, you know, actual AI, I’d love to hear what your thoughts are and on where that kind of separates and or whether it’s even important to separate it and, and maybe not.

Neerav Vyas (01:35):
Yeah. I mean, I think I, I separate out the, the rules based kind of rudimentary stuff with any form of kind of data science machine learning, right. Whether or not it’s true AI or not. I think shouldn’t matter. I think it’s really about, can we get to a point where we’re using analytics in a way that would help us to uncover patterns and make decisions that we would not normally have been able to do with a rules based approach or that we wouldn’t have been able to do as humans alone because we we’d be looking at true narrow set. So I think that’s important part, and I think it’s really important to not get caught up on the hype on it because a lot of times there is a lot of hype associated with, with what’s being done.

Anish Shah (02:12):
Okay. I like that. I, I think so maybe a distinction there is if, if a human can, can build it right off the bat using, you know, pure code, maybe it’s not real AI and AI takes a while to kind of build up on its own.

Neerav Vyas (02:25):
Yeah. Usually,

Anish Shah (02:26):
And, you know, run through a little bit of, on kind of Homer’s product sets and how Homer integrates with customers. And then also, and then digging into, based on that, how does machine learning AI and data science speak to that as well? Because, you know, when you think of eCommerce or DtoC or, or consumer oriented products, sometimes you don’t think that, you know, deep data science or AI is built into the decision making. So I’d love to learn more about that. So yeah. Start off with just like the products and then how your role kind of dovetails and then helping with the, with decisions on what to do.

Neerav Vyas (03:01):
Yeah. So for those folks who don’t know us, we’re eLearning company, we focus specifically on early childhood education. We also would acquire a number of other companies over the course of our history, particularly a company called seedling, which has a physical Teddy bear product in the augmented reality space. So kind of, you know, predominantly subscription based eCommerce product, but some physical goods as well for us actually, you know, if you think about it as an early state startup being very, very growth centric, the primary use case for advanced analytics and AI for us is marketing, right? So can we better understand cocktail LTD? Can we understand where we’re acquiring customers from where we can profitably acquire our customers? Can we predict the lifetime of our customer or the lifetime value of the customer relative to what it costs us to bring them in?

(03:45):
And, and if we can do that, then we can optimize our, spend efficiently, try to think about more responsible growth and look at that balance between, you know, hitting our growth targets versus potentially wanting to hit margin or profitability targets. Right? So that’s really the, the predominant use case that we focus on. And the secondary use case is kind of on churn analytics and then driving inter personalization. So again, you know, getting the customers is, is one part of the challenge, but on a subscription based offering, you know, monthly churn can kill us. So, you know, thinking about how do we improve retention, how do we use their engagement with the product to understand, you know, how do we drive new content, which content which content is resonating with with individuals. And how do we use marketing and communications potentially to triage the likelihood of someone leaving?

(04:32):
Like, for example, we know that if someone increases their usage with us week over week and the trial month, they’re with us, their probability of leaving significantly decreases. So we’re improving convert to pay and therefore likely to want retention. And then we know that certain types of engagement with content free for all, a la carte content, rather than some pathways that we’ve created. If we get them to do that, they’re much more likely to stay with us in subsequent months. So that’s kind of a way that we’ve used the analytics to kind of guide marketing coms, but also think about, you know, how do we think about product development and think about experience.

Anish Shah (05:11):
Okay. And thanks for that, by the way, super helpful. And with that, are the analytics is it, is it more analytics or is it more data science? And do you feel like there, there, there should be, or a kind of discrepancy between the two or it’s just really vernacular what you mentioned kind of okay.

Neerav Vyas (05:30):
Yeah. I mean, look, I mean, look, I think the challenge that a lot of times with both data science teams, but also business unit teams, not, you know, be marketing growth regardless. Right. It’s getting to an algorithm, isn’t an answer, right. It’s not a decision making person, right. It’s the very first step it’s taking that and saying, you know, how might we engender a new experience? How might we reduce you know, churn? How might we improve personalization? Right. taking it to those steps, really integrating into business decision making. That’s the challenge. So I think that, you know, a lot of times folks come in and say, oh, I got you to a number, right. Or I got you a risk score with a person. And they don’t take it beyond that to say, you know, what do we do with this? How do we interpret this?

(06:12):
How do we take this to potentially make a causal interpretation of what’s going on, or three steps, four, which is they’re gonna AB test this, right? Like, is it worth putting this model into production, integrating into our app? Is it the lift that we’re gonna get meaningful? Right. I think those things start to really push the boundary of the data science and good data science teams will begin to integrate that in. And they’ll begin to have those conversations with their partners across the business to say, let’s really, really dig into that because at the end of the day, if it’s not driving decisions, if it’s not driving tangible strategic insights, then it’s gonna be a fancy algorithm that doesn’t really drive a lot of value. And a lot of those projects can become science experiments. And, and I, and I try to avoid that as much as possible.

Anish Shah (06:54):
Yeah, no, that, that, that makes a lot of sense. And I love that you, you bring up, you, you bring that up that a lot of, a lot of the criticism of data science teams, particularly for consumer products, is that a lot of what they’re doing is pie in the sky and not, not not exactly actionable to say, oh, based off of this data, we’re gonna make decision A, B, C, or D. Right. And so it’s great that you’re, you’re taking that extra step and not just saying, Hey, here’s, here’s some great charts. Here’s some great histograms. Here’s some great kind of, you know, data, but you’re actually going one step further. And it’s like, okay, now based on the data, this is what we’re gonna do. Number one, number two, number three, number four, number five.

Neerav Vyas (07:35):
And look, I mean, I think that requires the teams to begin to develop an understanding of the business, right? And how they’re gonna work and how they’re gonna operate. The statistic I give out when I, when I do talks is seven more between 77% and 90% of advanced analytics projects fail, right? 77% don’t get industrialized or production wise. If you’re using the threshold as did it get

Anish Shah (07:57):
And define failed, what does, what does that necessarily even mean?

Neerav Vyas (08:01):
Yeah, so the, the, well, Gardner reports is 77% won’t get put into a product or IT won’t scale it up. And 90% of them won’t actually make it into business operations or customer experiences. And I kind of use that as the actual threshold, right. Cause if I didn’t change operations, I don’t change the way that my employees experience the world or my customers experience the world. Then it really hasn’t manifested itself to create a strategic impact if that’s the statistic, right. Then that means that the odds are against most data science teams when they’re doing projects. And you fundamentally have to go into every single project with a high degree of skepticism. And what we’ve found over the past, you know, four to five years is that if you take a design led or human-centric approach to how you’re coming through it, and you’re really being thoughtful about how are people going to use this, right?

(08:52):
How is this, like, how is it gonna manifest itself into business? How is my marketing team gonna use it? How is my, my employee gonna experience this, right? How’s my customer gonna experience this and how is it gonna get integrated into my app or my product, right? Then you’re much more likely to be thoughtful about how the recommendations are gonna come in. You’re gonna become a willing partner in helping to shape those recommendations and turn it into something tangible. So I think that it’s one of those areas where, you know, data science teams tend to think about things in terms of being very, very quantitative analytical and things like design thinking seem inept to them. And I think it’s actually the opposite where that level of creativity will actually help them to understand their business partners better and actually helps kind of make their solution much more viable.

Anish Shah (09:34):
Yeah. And hopefully it’s also fun saying the work you’ve done. Yeah. You know, not just end up in a deck or a research paper or in a very academic ish kind of end point, but more so, oh, Hey, this is in the product I’m using on a day to day basis. You know, we change this algorithm, we change this user experience. We change this, you know, whatever welcome screen based on all of this data. So that that’s, that’s really useful.

Neerav Vyas (10:00):
Yeah. I mean, and, and I, I add that, I think it add, has one other, an additional benefit, which is tend, it makes you like your employees and your, your colleagues more, right. Because you’re much more collaborative in how you develop things, you generating stronger relationships. So, you know, it’s funny because a lot of times teams will build these things and they’ll be like, oh, I can’t get anyone to use it. And I think part of that is, is that they don’t bring that level of collaboration in earlier. So those teams that operate in that manner are they, they have tighter relationships across the org.

Anish Shah (10:31):
It makes a lot of sense. And I guess the way you, and in, in, you know, how do you create those relationships? So you’re not sort of just creating research in a silo and that you are sort of able to communicate and understand what does the product team need. What does the marketing team need in terms of creating things that are going to be actionable and reach the end consumer? Are there any tips on how to make some of the work that you’re doing more digestible or more actionable or anything like that? What have you learned maybe has worked and hasn’t worked in, in those processes?

Neerav Vyas (11:07):
Yeah. So, you know, I have a background in both quantitative qualitative research as well as kind of advanced analytics. So I actually spend the first part of projects that I’m doing well, the, the doing a little bit of ethnography. So couple years back a client of ours asked us to change how their financial forecasting function was gonna work. And basically they were like, we don’t think it’s working well, but can you, can you help us dig in, help us figure it out? So we basically spent a couple days being like, what are they doing? What does the day in the life of the analyst look like? what are their pain points? We then created an employee journey map. and then we created a customer journey map in this case, the customers being their internal board, their internal members. Right. And from that, we took that and said, okay, how do we then translate this into a data science solution?

(11:53):
Where is data science applicable towards solving this problem? Or is it just a data of insights problem? Like we don’t need to overengineer this. So we don’t need that. And, and one of the key kind of findings that came out of it was it was taking them hundreds of hours to get to a number in forecast. And for most folks have gone through QDR processes, doing monthly forecasts, quarterly forecasts, yearly forecast. It’s like hundreds of hours to, to get to just a number. And I’m sitting there and I’m like, look, I know for a fact that I can make this process faster for you, easier, better, and more accurate. Now that data science can solve this out. I think there’s enough meaningful value here that we’re gonna do that, but I get you to that number. So what, what do you do with right?

(12:33):
And they’re like, well, if you get us to a number, right, we wanna understand why that number’s there. We wanna understand what levers would be change to potentially alter that number. Like we spend more marketing dollars. Is that gonna help get us to, to a higher growth target, higher margin target? Right. We wanna do simulations. What if work? I was like, all right, cool. Now I understand what you’re doing with them. Like what what’s truly valuable, right. So I might not be able to solve all that for you, but if I know what you’re gonna do with it, I can build it in a way that it’ll be more valuable for you. Right. And it’s much more like it’ll make you enjoy what you’re doing and for you to provide more strategic value to our senior leadership team. So, you know, it’s not a concrete set of steps, but I think if you’re approaching it from that way, in terms of like, how do I really understand the context that’s coming in here and how do I design to solve for the broader business need and broader, broader business problem. Right. I think you get to something more meaningful. And I think it’s the same way that good product team’s designed your products, right. You’re looking at that underlying need of the consumer, not literally taking what the consumer’s telling me, and you’re trying to figure out and say, what’s the right way to solve for these pay points. How can I create something it’s gonna be more durable than, you know, just like, you know, giving them, you know, a horse when, what they need is a car. Right.

Anish Shah (13:45):
Mm-hmm okay. Thanks for that. That makes a lot of sense. And obviously I, I run a firm that, that focuses on talent. so, you know, if a company is kind of hitting that, that point to, to hire data scientist, actually, number one, how do you know you you’ve hit a point where you should actually bring in someone who’s, who’s focused in data science, you know, like my best assumption, it’s not one of your first five hires in a company now. Correct. If I’m wrong, because it might be. But how do you know when you’re, you’re hitting that point and then number two, when you do decide that you need to hire this person, what are you looking for within this hire?

Neerav Vyas (14:24):
Yeah, I mean, I, I think unless the company’s gonna be heavily analytic centric, shouldn’t be an early hire. In that case, you probably have a founder. who’s got a strong background there. So, so less of, of a pertinent need. I think, I think about it in, in two directions, right. Which is, are we gonna start first from kind of just getting a data foundation in place, right. And if I just need to get the, the pipes running basic analysis and stuff like that, then you probably don’t need to bring the Ferrari in, which is a true data scientist, and they’re probably not gonna be happy, so they’re gonna leave. So I think it’s understanding where you are in that journey, but as you get to that point where the insights work that you need, right. Either a, the company is now large enough and there’s not critical, massive data. you’ve been running for a year or two you know, got tens, if not hundreds of thousands of customers. And there’s a lot of data to

Anish Shah (15:15):
Let’s, let’s stop right there. So are you kind of saying tens to hundreds of thousands of users, maybe the, the baseline of when to start thinking when to hire this person

Neerav Vyas (15:28):
Caveat slightly, right? Which is if you were a subscription based physical service, right. and the number of data points that you have coming in is relatively low. Then most anomalies that you’re gonna be able to do can be handled through off the shelf solutions. You can use an amplitude for example, right. Or vertical, right? Your CDP platform, some of these box startup centric analytics solutions can actually serve you for a fair amount of time. And they’ll be really, really cost effective for those first couple of years. So you may not need that data scientist right. On the flip side, if you’re someone like us, where there’s a lot of data coming through, you’ve got tens of thousands of engagement points coming in. You’re gonna end up needing a data scientist or data science team sooner rather than later. So I think there’s the nature of the business and the volume of the data and how granular you’re getting with the interactions, right.

(16:20):
That kinda judge where you’re at. and then the second point I would make is that as you give in, you know, a lot of organizations come in and hire data scientists that are super junior. my advice would be, try to find someone that’s a little bit more experienced and more senior, right? Because you hire someone junior, they’re not gonna be able to really lead their way. And if the team itself doesn’t have a lot of experience with analytics products, then you’re not gonna be able to give them meaningful guidance. There’s a, a tendency for a lot of wheels to be spinning. Right. So, and it’s one of those things where that initial hire, you may want to, to get someone it’s a little bit more expensive than, you know, the kind of the analyst ran outta school or analyst right. Outta the bootcamp.

Anish Shah (17:04):
Okay. Which also speaks to the point of, you know, this shouldn’t be, or one of your first five hires, you know, it’s when you do have the, the, the capability to afford someone good. And being yeah. And, and, and not only afford someone, but attract someone good. Right. You know, to do what you do. You probably are a bit handicapped if the sample sizes of data are not very large. Right. if, if a company has, you know, 150 customers, there might be pretty handicapped on what you’re able to do for them.

Neerav Vyas (17:34):
Yeah. I mean, I think particularly the earlier you are in your journey and the smaller you’re looking at it if you’re, you’re not only is the sample size gonna be in there, but you basically don’t wanna bring a data scientist that’s gonna fit a neural net or an artificial intelligence algorithm is the answer to every solution. Right. AB testing works really, really well for a very long period of time. Yeah. So, so, you know, like, so I think it’s, it’s also an issue of like, you’re gonna bring in a hammer. You you’re gonna need guidance to make sure that everything doesn’t.

Anish Shah (18:08):
Okay. Awesome. And then when you are ready to hire this person, you already mentioned your first, your first hire, shouldn’t be too junior. What, what are certain things you look out for when talking to, to different candidates and you know, where you understand, okay, this person is, or is not the right fit to, to join a startup.

Neerav Vyas (18:27):
I think the, the fit on the startup is more about the culture of the startup itself, right? So I think culture, fit’s always gonna be really important particular if they’re early on or one year, your first did science fires, right. That integration with company fit, I think is an aspect that’ll vary company company. One bit of advice I would give is that, and again, I come from consulting space, so obviously heavily biased. but I take all my data scientists through what we would call case interview fits. and the reason I do that is not because I wanna take a McKinsey or a BCGs approach on MCK case interview, but what I’m actually testing for in that is are they creative in terms of solving up problems and thinking about the business, right. If they can think out the box there if they’re willing to kind of work through and understand the business context of things like they’re gonna ask the right questions back to you as they’re going through that, that for me showcases their ability to tackle a whole host of different problems, particularly early on in startup.

(19:26):
Like lots of things are gonna, right. Like we’re gonna be scrappy and lots of different ways if they can think out the outside of that box. Right. Then that for me is an indication that they’re gonna be able to be flexible and durable for a lot of the things they’re gonna come at. And so that’s kind of like the, the mantra that I hold with not only here at Homer, but I kind of use that across companies that I’ve worked with big and small. And I tend to find that, that those teams serve me really well. And it also means that, you know, I’m not using a team of 20 to solve a problem. I’m using a very small team, you know, that that’s gonna be focused on the things that are most important

Anish Shah (20:01):
Makes a lot of sense. And I like the fact that you put in that, that, that case study that, that I, I, I guess is a little bit more management consulting ask just to show creativity instead of just using a template. And then also, you know, no matter what you’re doing this in you’re, you’re supporting a stakeholder, right. and so can this person actually show creativity and solving a and creating a solution? You’re solving a problem. That’s gonna please a stakeholder instead of just, you know, as, as you mentioned earlier, kind of U utilizing whatever the coolest technology is in building a neural net for no reason any advice for people who want to get into data science, it’s, it’s, it’s a pretty hot field right now in terms of people trying to pivot into it would love to hear what has made data scientists good that you’ve worked with. so someone who is trying to get into it can learn what makes them good and then other general advice for how to get into it.

Neerav Vyas (20:59):
Yeah, I would, I would give them two broad guide pieces of advice and guidance, right? One is try to avoid being enamored with the technology itself, right? The technology is a tool to an end outcome, right? And if you focus on what those outcomes are, you’re gonna be much more likely to be successful and happy as a data scientist. Mm-hmm the caveat I’ll say there is, if you really, really wanna do cutting edge data science, self driving cars, stuff like that, you probably wanna get a PhD like, like, you know, there’s gonna be a small subset of firms that are recruiting in that area, and you’re gonna need a very deep theoretical foundations come through 98%, 99% of the world doesn’t need that. and so if from that perspective, you’re gonna be using a lot of open source tools.

(21:51):
you’re gonna leverage that to get to a business outcome relatively quickly. So think of that as that’s your goal, right? So you’re gonna stay on top of a lot of new frameworks. You’re gonna understand what’s going on in that community, but it’s not necessarily creating things from scratch. Right. And I think you need to understand where you can leverage what’s available rather than rebuilding what’s new and getting familiar with that. the second thing I would say is for, for folks who are trying to get in or doing career switching, whatever it ends up being I think a lot of the bootcamp programs that have come out are really, really good. I think they do excellent job on getting folks ready for that. so folks like the Meis bootcamp I’m huge, huge fan of GA does a great job.

(22:31):
So not only do they teach you what you need to do, no, but they’re really, really focused on what’s your job gonna actually look like, right? What your bosses gonna need you to do? And they get you ready for that hit the ground running. So I wouldn’t underestimate them, definitely. you know, a lot of really good boot camps that are in there. And I would focus on talking to some of the alumni who’ve gotten recruited there from there, who’ve gotten jobs. And I think they’ll give you an idea how ready they were on day.

Anish Shah (23:02):
I love that. I think some people who feel like they did get a CS degree from, you know, a top tier school, you know, they’re their kind of Sol when it comes to this career path, but you’re saying, no, you you’ve worked with seen interacted with people who come outta the boot camps and felt like they were able to do the, their, their job well and learn and grow.

Neerav Vyas (23:22):
I mean, three quarters of my team at one point I ke and I had been recruited out one of two boot camps. So, you know, I think that’s a Testament to how much we trusted them. And I’ll also add you know, for folks who are not from the stem background, some of the best hires I’ve ever had, right. had design backgrounds or were arts majors who switched into it. And that level of creativity was fantastic. I, myself, and I’m an international relations econ major. So you can maybe argue the econ is math, but I am very much arts and sciences as well. So again, I, I would not, I would suggest to folks who don’t have a stem background, don’t be afraid of it. There’s a ton of value you can provide. just bringing that other perspective.

Anish Shah (24:02):
I love that. That’s great advice. And yeah, I, I mean, I think of econ and any humanities kind of like realm, you know? Yeah. So yeah, I, I, I, I agree with what you’re saying.

Neerav Vyas (24:14):
We’re equally hated compared to the world, so…

Anish Shah (24:18):
That’s a very good point. That’s nice. Your trailblazers cool. I think I’ve, I’ve kind of dug into everything that, that I have. I don’t know if you have any sort of like exciting, like kind of groundbreaking closing statements here to tell everyone about the, the world of analytics, data science and so forth. But

Neerav Vyas (24:40):
I, I would just say that for, for marketers and growth marketers, it’s gonna be a really interesting net two to three years with all the changes that are happening in the landscape. So you know, I think everyone’s just gotta understand that you gotta roll with the punches a lot is gonna change, just expect it and try to be as flexible as possible because between apple, Google, California the, the, the landscape is, is literally shifting underneath our feet. So on the plus side, it’ll be exciting for everybody.

Anish Shah (25:09):
How, how is it shifting, do you think, or any predictions on how it’ll shift the next, you know, 24 months?

Neerav Vyas (25:15):
Yeah, I mean, you know, look with, with CPR, a moving forward for those folks who don’t know that it’s California strengthening their CCPA law, their privacy laws to be stronger than GDPR. And in particular, they’re telling folks that are sharing data, not just selling data, that they’re gonna have an increasing degree of scrutiny against them. I think what it’s gonna mean is that sending data out of our ecosystems to our advertising partners to do matches, to do attribution is increasingly going to be restricted. If not prohibit, apple is already said with their deprecation of IDFA, that they don’t want you sending data out to third parties, unless the, the customers explicitly opted in. So I think what that’s gonna mean is a lot more folks who gonna have to pull things onto their platforms. Primary data is gonna become much more important and creating that data foundations both an analytics perspective, but from platform perspective is gonna be increasingly important. The problem is, is that CPR a hasn’t been fully written yet. So over the course of the next 12 to, you know, 14 months, we’re gonna get more and more guidance on what it’s gonna look like. so, you know, there’s a, you know, landscape itself is shifting, but I would expect that if you’re building kind of in-house first party solutions, we’re building within your private cloud you’re gonna be safer because sending things out is gonna become harder and harder,

Anish Shah (26:36):
Makes a lot of sense they’re even the playing field so that, you know, hopefully the companies, the best products can sell more of those instead of the companies with the best marketers. I think, I think, I think that’s the direction all the platforms are trying to go in.

Neerav Vyas (26:51):
I think there’s always a room for, for really good marketers. I think it’s just gonna get marketers to, to realize that they’re gonna have to be a lot more creative, but you know what, look, I, I think that, you know, the one silver lining around all of this right, is that in the last seven years, as we became more performance centric, right, it was really easy to do insights on folks that we had direct attribution on. And so we lost sight of the holistic sense of marketing that we had, right? Our, our, our earned and kind of non performance related paid marketing kind of fell by the wayside cause we couldn’t measure it easily. And I think now it’s gonna require us to take more of a holistic approach to saying these things were always really valuable. Maybe even became more cost effective because demand for those channels were, were a lot lower. And so they didn’t scale up in CPM as high as performance marketing did. So I think it’s just gonna get folks to be a little more holistic and realize that they can reach for some of those other tools, right. Without feeling like they’re gonna be judged negatively because you know, performance marketing itself is gonna get leveled out relative to, you know, TV and, and kinda PR. So I I’m, I’m optimistic that the new world will, will eventually be better for everybody.

Anish Shah (27:56):
I am too. And I think it’ll make everyone try harder and be a little more creative and be a little more thoughtful and you know, less hacky less, less hacky in a black hat way, maybe more hacky in a, in a creative, you know, reach people where they are kind of way. So, yeah, I, I agree with you on that. And good.

Neerav Vyas (28:16):
Real relationships without having to worry, like this thing doesn’t go viral. Like I failed, right? Like, cause we don’t wanna feel like that either.

Anish Shah (28:24):
No, no. And I think, luckily at least in the last, like two or three years, I’ve had less conversations with founders who just like, just, just hire someone. Who’s gonna make it go viral. I’m like, it’s not going fucking viral. You know, you sell, you sell, you know, you sell insults fors for shoes. That’s not going fucking viral. figure out how to figure out how to build a good product and figure out how to sell it to people who want what you want. But it’s not gonna…

Neerav Vyas (28:48):
Yeah. Think about your brand position, your brand. Right. I think that’ll, hopefully that’ll become much more important

Anish Shah (28:54):
Ya, I hope so as well. amazing. Well, thanks for taking the time here. and thank you again.

Neerav Vyas (29:00):
Thank you an absolute pleasure. Thanks again. Have a great day.

Anish Shah (29:04):
You too. Bye.