NYSE Media Week CXO Series, theCUBE + NYSE Wired | Tejas Manohar, Hightouch

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Tejas Manohar

Co Founder & CEO

Hightouch

play_circle_outline Hightouch co-founder on motivation to start the company due to data usage struggles in businesses

play_circle_outline Overcoming Challenges in BI and Data Warehouses with Hightouch's Business Tool Integration Approach

play_circle_outline Hightouch's unique IP with sitting on top of data warehouses without copying data

play_circle_outline Differentiation from traditional CDPs and focus on composable CDP concept

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Tejas Manohar, Hightouch

Tejas Manohar

Co Founder & CEO Hightouch

Tejas Manohar, co-founder of Hightouch, discusses the challenges faced by business teams in utilizing data and the solutions provided by Hightouch. Hightouch enables marketing teams to access data from data warehouses like Snowflake, Databricks, and others without copying the data. The platform helps harmonize data and identify key customer information across different systems. Their unique IP sits directly on data warehouses, providing an easy-to-use interface for marketing teams. Hightouch partners with major cloud data warehouses like Snowflake, Databricks... Read more

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What issue did the speaker notice in many enterprises across different industries regarding the use of data by business teams? add

What is the role of BI (Business Intelligence) software like Tableau or Looker in helping marketing teams access and operationalize data from data warehouses to power their campaigns and grow their business? add

What are the benefits of a platform that sits directly on top of data warehouses like Snowflake, Databricks, without copying the data at all? add

What inspired the creation of a new data platform solution aimed at bringing technology used by large enterprises like Netflix and Walmart to smaller companies? add

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Tejas Manohar, Hightouch

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Dave Vellante

>> Hi, everybody. Welcome back to our NYSE and Cube series here. We're here high above court side, as they say, at the New York Stock Exchange. This is the NYSE Wired plus CUBE CXO series. My name is Dave Vellante. Super excited to be here with Tejas Manohar, who was the co-founder and co-CEO of Hightouch. Welcome to the Cube at NYSE. Thanks for coming on.

Tejas Manohar

>> Thanks for having me.

Dave Vellante

>> I always like to ask co-founders, why'd you start the company?

Tejas Manohar

>> Yeah, great question. So I saw this pattern across tons of enterprises in all sorts of different industries that companies have a ton of data, right? They have so much data sitting around in data warehouses, data lakes. They're using all this data for analytics, like forecasting revenue, earnings reports, but when you go to a business team, like marketing or sales, and you ask them how they're using data for their job, like to figure out what to send customers, they're all struggling. They're asking their peers on the data team for CSVs, they're trying to get technical teams to support them, and they're unable to just use data effectively. So I saw this gap, right? We have all the data technology in the world, and tons of data from an analytics perspective, but from the perspective of business teams actually using it, there was a problem, and that's what motivated me to start the company.

Dave Vellante

>> Well, I mean, there's a multi tens of billion, maybe it's a $50 billion industry called BI that's supposed to be solving this problem, but it actually doesn't, right? So let's talk about why today's BI, and today's data warehouses, and data marts, and data lakes, and Hadoop clusters, and et cetera, and big data. Why doesn't that solve the problem?

Tejas Manohar

>> Yeah, great question. So, from a marketing team's perspective, which is one of the biggest teams that we serve, but also any sort of business team at enterprise, they want to have easy capabilities to access all of this data that's in data warehouses like Snowflake, or Databricks, or Google Cloud, or AWS, and then they want to be able to easily operationalize this data, so take it and use it to power a marketing campaign that they're sending customers, or use all this data to decide who to give what discounts, and ultimately use this data to grow their business. What BI provides, so software like Tableau or Looker, we don't really consider it a competitor to high touch. What those tools provide are the ability to make charts, or reports, or answer questions on your data, but what we are really providing companies is a way to connect that data about your customers to Salesforce, to Facebook ads, to Adobe, to all the tools that are actually sending communications to those customers, and to give you intelligence on top as a marketing team that allows you to decide what's the right treatment per customer to really grow your revenues as a business?

Dave Vellante

>> So you're at the top part of that iceberg. Is that right?

Tejas Manohar

>> Exactly, the last mile.

Dave Vellante

>> But the last mile, which is always the hardest, but you still have to absorb, don't you, all that hidden information underneath. First of all, is that correct?

Tejas Manohar

>> Yeah, it is 100% correct. So we sit on top of structured and unstructured data in companies, data warehouses, data lakes, and we also give companies like PetSmart, like Warner Music, some of the large enterprises we serve, tools to help them merge that customer together, figure out what is a customer across my point of sales, and e-commerce, and website and loyalty program systems? And really figure that out, and then we do the last mile of putting that in the hands of business teams to actually use to tailor what you're going to send customers, what you're going to say to them in stores, whether you're going to give them discounts or not, and all those decisions you have to make about customers.

Dave Vellante

>> Okay, so when we talk about last mile, a lot of times you think about last mile, you think of things like cable TV or the landlines. That was always the hardest thing; you have to just lay physical connectivity, but there's more to your story, because not only is the last mile that last piece of the puzzle, but you have to somehow, I think, harmonize all that data. You got data, like you said, and data lakes, and data warehouses, et cetera. How do you know that a customer means a customer, or not a prospect, or a division of a company, or that revenue actually equals revenue, and not AR, or NR, or price equals price? Now at the discounted price. Am I correct that part of your job is to harmonize all that data?

Tejas Manohar

>> It's a great question. So I'll answer this in two parts. A lot of the other solutions that exist to help marketing teams and business teams use all their data at an enterprise first say that step one, you need to get all your data into their system in their format. So Salesforce is an example of this. Adobe-

Dave Vellante

>> Snowflake.

Tejas Manohar

>> Snowflake is actually a little bit more of a flexible, open-ended platform.

Dave Vellante

>> Oh, interesting that you say that. We'll come back to that.

Tejas Manohar

>> Yeah, we'll come back to it. So most of the business software out there that says, "Hey, we'll identify a customer. We'll help you personalize your marketing to them," they require you to first get the data in their format. That is a huge effort for an enterprise that never really ends up happening, right? Because there's so much data, every company's different, and the work required to get it all into a system, like a SAS system, is just crazy high. So what Hightouch does is we tap into the work that data and analytics teams are already doing at companies on top of technologies like Snowflake, Databricks, and broadly the cloud that every enterprise is investing and figuring out their data in the cloud. We allow marketing teams to tap directly into that data without requiring you to translate all that data into high-touch format, and that's kind of some of the core IP of our technology and why really large enterprises like Warner Music, PetSmart, NBA, Spotify, all adopt high-touch, versus something like Salesforce or Adobe to manage their data. The second thing is, yes, we do have some tools that help with the harmonization of data, particularly one of the big challenges you mentioned of identity resolution or determining what is a customer across all these different systems.

Dave Vellante

>> So, okay, let's get into the how you do that. Can you talk about your sort of secret sauce to some extent, to your unique IP? What makes you guys different?

Tejas Manohar

>> Yeah, so, from some of the how from our platform, so, first and foremost, we have the ability to sit directly on top of data warehouses like Snowflake, Databricks, et cetera, without copying the data at all. So a big challenge in enterprises is that they don't want to copy all their data to some SaaS system. Whenever that's the paradigm, which is the case for most SaaS software today, every SaaS system has its own database. You need to copy the data into it. Enterprises always limit the amount of data they put in those systems, but, because we actually sit directly on the warehouse, we don't have a database of customer information at Hightouch, we use your data warehouse as that database. Enterprises are willing to give us a lot more access to information, which means marketing teams are actually able to use a ton more information to personalize how they engage with their customers and drive them to the outcomes they want. The other aspect of it is that we don't store any data, right? We don't store any data at all. It all lives in the customer's cloud and the customer's premises. On top of that, a big selling point of the software is that it's really easy to use for marketing teams. We actually have a UI that allows marketing teams to do everything they need from a data perspective. So querying the data, building audiences of customer information, syncing those to various different systems that they use, like marketing tools or ad networks, or even using that data on your website to personalize the customer experience, running experiments, so figuring out if A is better than B, and lastly, starting to use ML and AI to actually determine what's the best treatment on a per-customer basis using all that data.

Dave Vellante

>> Talk about, I mentioned your go-to-market strategy. Are you partners, for instance, with Snowflake and Databricks, you run on top of those platforms, other partners? Describe your go-to-market.

Tejas Manohar

>> Yeah, great question. So a few things. We are, first and foremost, partners with kind of all the major clouds and cloud data warehouses, so Snowflake, Databricks, Google, AWS, you name it, and, from a go-to-market perspective-

Dave Vellante

>> Microsoft, too?

Tejas Manohar

>> Yeah. Yeah, we work with Azure as well.

Dave Vellante

>> Because you never know. Microsoft can be competitive, but you got to be in the Microsoft ecosystem.

Tejas Manohar

>> No, no, for sure. It's an ecosystem, right? And you got to be in it. It's what the enterprise sees.

Dave Vellante

>> Okay, so, of all the cloud players-

Tejas Manohar

>> We partner with them-

Dave Vellante

>> The data platforms?

Tejas Manohar

>> Mm-hmm.

Dave Vellante

>> Okay.

Tejas Manohar

>> And even some of the business tools. So different ad networks, like Facebook or TikTok, or different software providers, like a HubSpot is actually an investor, a strategic investor in Hightouch. So we partner with those folks, and when their sales teams or their customer success teams see a challenge in their customer base, because they're talking to the big companies like the PetSmarts, NBA, the Warner Musics of the world, they'll actually loop Hightouch in and say, "Hey, this company has a lot of data in Snowflake, but they're having some difficulty using it from a marketing perspective, and figuring out how to take that and personalize all the marketing, and advertising, and website experiences they're delivering to their customers to grow their LTV and grow their revenues. So that's one part of our go-to-market strategy. Another part of it is that there is a big category of software called CDP or customer data platform. Before founding Hightouch, I was actually an early engineer and product leader at a company called Segment, which helped found that space. It got acquired by Trilio for $3.5 billion a few years ago, and-

Dave Vellante

>> Congratulations.

Tejas Manohar

>> Thanks, and-

Dave Vellante

>> Was that your first exit?

Tejas Manohar

>> Yeah, it was. I mean, I wasn't the founder, but I was an early employee of it.

Dave Vellante

>> That's awesome. Yeah, you learned a lot, I bet.

Tejas Manohar

>> Definitely learned a lot. I joined when it was about 20 people.

Dave Vellante

>> Amazing.

Tejas Manohar

>> And what I found is that that category of software has a lot of demand. Every enterprise marketing team is saying, "I want a CDP, because I want the ability to get all my data in one place and use it for marketing," but that category of software doesn't work super well in the enterprise due to the complexity around getting all your data into a SaaS system. So the big innovation we have at Hightouch is saying, "Hey, over the last five years everyone's been investing in these cloud data warehouses. How do we get that data in the hands of marketing teams?" So we do a lot of marketing and we get a lot of inbound around this concept of a composable CDP, or a CDP that sits on the warehouse.

Dave Vellante

>> And when did you start the company?

Tejas Manohar

>> We started the company about five years ago.

Dave Vellante

>> Okay, and you raised 92 million, is that right?

Tejas Manohar

>> Yeah.

Dave Vellante

>> Is that an accurate number?

Tejas Manohar

>> Yeah.

Dave Vellante

>> And, at this point, you've got product market fit, would you say?

Tejas Manohar

>> For sure. Yeah, we work with over 700 companies at this point, hundreds of enterprises, and dozens of the Fortune 500.

Dave Vellante

>> They're renewing, presumably, right? So that's

Tejas Manohar

>> 100%. Yeah, our GRRs are high 90%, actually, so...

Dave Vellante

>> Awesome. And so how did you know when you had product market fit, and then how did you decide when to scale, go to market? Because the reason I ask that is I see a lot of companies, the biggest mistake they make is they try to scale, go to market, before they even have product market fit. So how did you know and how did you manage that transition from where you and your co-founder were probably the key salespeople, you were consultants, you were building the process, belly to belly, to then transitioning to scale, go to market?

Tejas Manohar

>> Great question. So, man, five years ago feels like a long time when you're working at a startup, but I think one of the key indicators for us that we had product market fit, actually, there were two things that really come to mind. First and foremost was some of the early customer traction. I felt like, actually, at the time that we had just three customers, we had the sense that there was something here, because we noticed that these customers were in completely different industries, but had the same problem. So one customer was a SaaS company out in Silicon Valley that's pretty notable, another one was a healthcare DTC brand, they made different healthcare medical products for consumers, the conglomerate of them, actually, and another one was a newspaper. And then we looked at the pipeline of customers we're talking about, and they were also in tons of different industries, and the fact that they all had the same problem, which is that it's difficult for me to use all my data for marketing, I have to keep going to engineering, to keep asking data, to merge data together, and it takes weeks and weeks to just try and experiment of an idea I have, that told us, "Hey, there's something here. These companies are completely different." One of them was in Norway, out of all places, and had the same problem that we saw in Silicon Valley. The second thing that we found was we did a lot of customer discovery, because there's a lot of companies out there trying to solve this, Segment, Salesforce CDP, Adobe CDP. I've seen many companies rise and fall around the idea of helping businesses use their customer data, and that was really important to us that we're not just going to build the same thing at the end of the day and face the same consequences, but what really inspired us was that we talked to some of the largest enterprises in the world, so companies like Netflix and Walmart, and what we found was that everyone was saying the same thing: eventually, they can't get all their data into something like Salesforce, Adobe, or CDP. They need to invest in their own data platform, like a Snowflake, like a Databricks, and build a CDP on top of that, and we thought, "Okay, if the biggest companies in the world are doing this, and all the small companies are facing a challenge, why don't we be the solution to bring that tech from the Facebooks, from the Netflix of the world, down to the rest of the market?"

Dave Vellante

>> Well, it's interesting you mentioned Walmart. We've featured Walmart on theCUBE several times now in our Palo Alto studio, and what they've done with their triplet model to create a common experience, we call it a super cloud, across their clouds, but also, they've done something similar with AI, and so they can't just throw it all into one platform. It's just not practical, and now you're seeing, I'm sure you see this with your customers, open data formats bringing the engine to any compute, to any data. So that's key.

Tejas Manohar

>> Iceberg and Delta Lake.

Dave Vellante

>> Yeah, absolutely, and so that's, to your point about Snowflake before, the market's forcing them to open up.

Tejas Manohar

>> We can generalize that statement to any sort of data lake or data platform, but the truth is that companies need to invest in something and standardize around that. We're a big believer in open data platforms.

Dave Vellante

>> Five years ago, when you and your co-founder started the company, what type of AI were you doing, and how has that evolved today?

Tejas Manohar

>> It's a great question. It's a great question. So we're actually very excited about AI, first and foremost. I mean, who isn't? But I would say that we're excited about it in a way that I think is different than a lot of the players in the marketing space.

Dave Vellante

>> How so?

Tejas Manohar

>> So, in marketing and AI, or just in AI in general right now, the hot topic is around generative AI, right? Generating content, helping marketing teams write emails, generating more creative than they could have before, and more or less just trying to emulate an army of people doing something through an AI. What we're really excited about is using AI to do stuff that it just wasn't possible to do with humans before, and I believe that the opportunity there is decisioning. So we launched a product recently called AI Decisioning, and the high level of that product is that we sit on top of all the customer data, and we look at an individual customer basis to determine what is the best treatment for that customer. So should we give them a discount? To what extent? What kind of email should we send them? What should we show them on the website? What's the right goal to these customers? Should I be encouraging them to download an app, or should I be encouraging them to go into the store? And we do that all optimizing towards lifetime value of a customer, and we don't actually generate content and stuff like that in this usage of AI. We're all focusing on the decisioning problem or the quantitative side, and I believe that's actually an under-looked opportunity with a lot of the AI discussion today, is the traditional machine learning, reinforcement learning, propensities, predictive ML, I think can be a step further than a lot of the market is using today.

Dave Vellante

>> So the next best action, if you will, and cause and effect. Causal AI is obviously something that is sort of-

Tejas Manohar

>> Yeah, that is a part of the technology as well.

Dave Vellante

>> Really, you guys are doing stuff in causal.

Tejas Manohar

>> So we're doing research to figure out the best tactics to use, and causal inference, stuff like that, is a part of the equation.

Dave Vellante

>> I'd like to connect you to our causal AI analyst, Scott Hebner, who's based a little upstate.

Tejas Manohar

>> Would be great.

Dave Vellante

>> Yeah, so we'll do that.

Tejas Manohar

>> And a lot of it also is experimentation, right? I think the way a lot of companies are approaching AI right now is, "Give us all your historical data, and we'll tell you what discount you should offer or what marketing you should send, what's the next best action." That doesn't actually work a lot of the times. What you need is an autonomous system that is actually just doing experiments, right? Is actually running experiments of, "What if I send this customer this and that customer that?" And running these mini A-B tests effectively, continuously.

Dave Vellante

>> And, essentially, in real time.

Tejas Manohar

>> Exactly.

Dave Vellante

>> And now, you've not only got the historical analytic data, but you've got the sort of real time data of digital representation of the business, some people call it a digital twin, and then you can do those real time A-B tests, and then really figure out the next best action before all the data changes, right?

Tejas Manohar

>> Exactly, and that experimentation part is critical to how the product works from a technical perspective, because the historical data often actually doesn't have what you need to really interpret what you should do in the future.

Dave Vellante

>> What's next for you guys? Are you well-funded? Are you doing a raise? Are you doing an IPO? What do we look forward to?

Tejas Manohar

>> Great question. So we have an aspiration of going public. We want to take this company big. We believe there's an opportunity to create a next player in the market of the level of the Salesforce or Adobe on top of this data and AI revolution that's going on right now, but, as it stands right now, we're growing the business. Proud to say that it's growing at 100% plus year-on-year, and we are focused on taking on more and more enterprise customers, and really building towards this vision of customer experiences being decided using ML and AI on a per customer basis.

Dave Vellante

>> Yeah, well, you're five years in. As you know, it takes five to seven years, at least, to build a company, and then another few years to really get to scale.

Tejas Manohar

>> I think it takes at least 10 years to build a company

Dave Vellante

>> I would say so. I think these days, it's funny, in this day of AI, where everything's supposedly so compressed and happening faster, it's hard to get to a public market inside of 10 years, at least in a way that is really attractive for investors.

Tejas Manohar

>> We'll see. A lot of things are happening fast in AI, but things need to happen durably as well, and things are happening so fast, but we have to see how that market pans out over time, right? If technology starts to plateau, or anything like that, there can be massive changes in the players in the market. So we're really focused on building a long-term business here.

Dave Vellante

>> Well, it's fantastic having you on theCUBE. Best of luck and I hope to have you back.

Tejas Manohar

>> Thank you. I appreciate it.

Dave Vellante

>> All right. You're very welcome. All right, keep it right there. This is Dave Vellante for theCUBE + NYSE Wired here, high above the New York Stock Exchange trading floor. We're right back right after this short break.