Allison Pickens' Podcast

As part of my Substack series on Growth Endurance, I wanted to speak with the most successful late-stage SaaS companies out there. ServiceTitan is #7 on the Forbes Cloud 100 list of the world’s top private cloud companies. So I was psyched to sit down with CEO Ara Mahdessian and learn how he has been able to grow his company so fast for so long.

This conversation is super relevant to any CEO or other executive looking to sustain high growth rates long into the future.

It will also have particular relevance for CEOs and executives running vertical SaaS companies, since ServiceTitan offers software for the trades — initially residential plumbing maintenance and repair businesses, and ultimately many other verticals.

In this conversation we discussed:

* How to showcase the ROI of your product — a topic particularly relevant in this macro-environment.

* The role of private equity in driving software sales — and inversely, the role of software in driving private equity acquisitions.

* How to design and manage your board.

* And many other topics that fascinated me!

You can read the lightly edited transcript or listen to the audio version. Let’s dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

At the start of a new year, many people are thinking about how they might grow in their careers in 2023. Employers need to stay ahead of those aspirations, even with the prospect of a more significant economic downturn. Employers must also adapt to employees’ changing expectations about career growth, resulting from the shift to remote work, social justice movements, and the talent shortage.

I’m reflecting back on frameworks that used to be the gold standard for managing career growth:

* Annual promotion cycles

* The path to promotion requiring individual contributors to become managers

* Forced ranking of employees

None of these make sense anymore…and maybe they never did! So where does that leave us?

In this conversation, Barbra Gago, Founder & CEO at Pando, shared with me her new paradigm for career growth: “just-in-time” career progression. We covered topics such as:

* Why is continuous management of career growth important?

* Why does a downturn make it more important to invest in employees’ career growth?

* Is it unreasonable for founders to expect employees to be intrinsically motivated, as opposed to driven by extrinsic factors like compensation or seniority?

You can listen to our conversation or read the lightly edited transcript below. Let’s dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

Many startups are aspiring to “grow into their valuations” from 2021. For most of them, this will take a couple of years, at minimum. Even once they reach that target revenue level, these startups will need to show that at the end of that period, they can continue to grow fast — since their valuation presumed they would be able to sustain high growth rates into the future.

Mary D’Onofrio, a partner at Bessemer Venture Partners, coined the term “growth endurance.” The idea is: If you’re a venture-backed SaaS company, it’s not unusual to grow fast to $1M or $3M in ARR. What is unusual is growing fast to $100M ARR and continuing to grow fast after that. Growth endurance is about sustaining those high growth rates.

This isn’t an argument for being a turtle instead of a hare. It’s also not an argument for being a hare for longer distances — because the essence of a hare is the lack of long-term perspective. Companies that sustain high growth endurance blend the best attributes of the hare (speed) and the turtle (longevity).

In this conversation, Mary and I discuss the origin story of the term growth endurance and why it matters. We cover:

* How does growth endurance factor into her investment decisions?

* What counts as “good” growth endurance?

* What are the primary factors that allow a startup to have high growth endurance?

You can listen to the podcast or else read the lightly edited transcript of the conversation below. Let's dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

This is the first post in a series on the topic of Growth Endurance — fast growth, many years in a row.

We’re accustomed to hearing about startups that have tripled, quadrupled, quintupled, or grown even faster in the early stages. But once you’ve reached the tens of millions of ARR, it’s very hard to grow fast. That’s why very few companies have reached $100M in ARR, and why even fewer continue to double when they’re at that scale.

Bessemer Venture Partners pointed out in their annual State of the Cloud report a few months ago that while 520 unicorns were minted in 2021, only ~60 new $100M ARR companies were added. They gave those companies “Centaur status.” Centaurs are 7x more rare than unicorns. And Centaurs with real growth endurance are even more rare.

Despite their rarity, the imperative to grow fast is stronger than ever before. Companies have raised money at astronomical valuations, which assume they will continue to grow fast for years to come.

But because there are very few companies that have done this successfully, the best practices on Growth Endurance are hard to find. That’s why I launched this new series.

In this episode, I sat down with Ariel Cohen, the CEO of TripActions. TripActions was named to the Forbes’ Cloud 100 list of the world’s top private cloud companies several years in a row. They are also rumored to have filed confidentially to go public at a $12 billion valuation.

You can listen to the podcast or else read the lightly edited transcript of the conversation. Let's dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

Leaders are accustomed to having control over information: who sees it, how much is shared, and when. Arguably, an important purpose of leadership is to explain context to people in a narrative form.

But there are major trends in our society that run counter to this idea. For example, there is a movement to make data on employee compensation more accessible. Some people believe that governments should openly share data on the status and results of funded programs. And certainly employees want to hear the unfiltered truth about how their companies are performing. Should we provide that kind of radical transparency to our customers about the value that they’re getting from a product?

In this post, Gary Lin, the CEO and Co-Founder at Explo, makes the case for sharing usage data with your customers. We discussed:

* What are the benefits of openly sharing this data?

* What are the best practices for doing so?

* What are the implications for CSMs, sales reps, and other customer-facing folks?

You can listen to the podcast or else read the lightly edited transcript of the conversation below. Let's dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

Historically, customer-facing teams treasured every moment they could get with their engineering counterparts — because those moments were rare. Engineers were focused on building the core product, and any other initiative could be considered a distraction. Support teams in particular were often frustrated because they felt that Engineering wasn’t spending enough time fixing bugs, gathering customer feedback, and giving visibility into product changes. Getting Engineering’s help in building internal tools was so far from the realm of possibility that it wasn’t even on the agenda.

But today, Engineering teams are spending more time supporting Support teams. And there is a strong movement behind that.

In this post, Simon Rohrbach, the CEO and Co-Founder at Plain, explains the drivers of this movement and what best-in-class collaboration between Support and Engineering looks like. We discussed:

* Why are engineers now more open to helping their colleagues in Support?

* How can Support teams motivate Engineering teams to prioritize their projects?

* How can CEOs encourage collaboration between the two functions?

You can listen to the podcast or else read the lightly edited transcript of the conversation on my Substack. Let's dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

I remember that around 2013, everyone was talking about “CRM 2.0” — what CRM company would displace Salesforce? SFDC was clunky. Implementation took forever. And it never really ended. It seemed like only a matter of time before SFDC would be forced to pass the torch to a hot new CRM company.

As it turns out, SFDC is here to stay. They’ve been an incredibly successful company. When you go to hire your first VP of Sales, they’ll very likely tell you that SFDC is their tool of choice.

But people still love to complain about SFDC. They’ve been successful in spite of the continued cost of implementation and maintenance. So how can you use SFDC but reduce the cost of ownership?

In this post, Taylor Lint, the CEO and Founder at Swantide, shares the best practices for setting up and maintaining your CRM. We discussed:

* How should you set up your CRM early on so that you don’t accumulate a ton of tech debt?

* What are the costs of switching to a new CRM as you grow?

* How can you automate future changes to your CRM, so that you’re not spending a ton of money on admins and contractors?

You can listen to the podcast or else read the lightly edited transcript of the conversation in my Substack newsletter. Let's dive in!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

Many startups are thinking about conserving cash. Some are reducing their R&D spend by cutting headcount. That might be because those product and engineering folks were working on projects that were more aspirational, less proven out; because their companies are now focused more on top-down sales than product-led user growth; because the company’s new trajectory didn’t justify senior leadership; or because it didn’t justify the current number of people in management positions. Regardless of the reason, when you let people go, you often have to re-org. And even if you’re not cutting headcount, you might want to re-org in order to help your team run more efficiently.

Aaron Erickson, the CEO and Co-Founder at Orgspace, has studied the art of the re-org. The topic might make your stomach churn, but it’s an essential one for many companies right now, and not enough leaders re-org the right way. Poorly executed re-orgs can be disastrous for the company; thoughtful re-orgs can actually be highly motivational and become the substance of positive stories about company leadership for years to come.

In this conversation, Aaron and I discussed:

- What are the signals that indicate you have an opportunity to trim spend on your R&D team?

- What factors should influence your re-org decisions: for example, the value of different functional roles, the number of direct reports per manager, the ratio of spend on direct hires vs. contractors?

- How can you manage through a re-org well, and what mistakes should you avoid?

You can listen to the podcast or else read the lightly edited transcript of the conversation below. Let's dive in!

If you’d like to hear more about topics related to scaling SaaS and other B2B businesses, you can subscribe to my Substack newsletter (for free).

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

Data teams and the modern data stack have attracted large sums of investment over the past few years. Everyone has taken for granted that data teams are valuable, and that they deserve a multi-layered stack of best-of-breed software. I’ve been wondering: with the significant changes in the macro-environment this year, will data teams (and their software vendors) soon be required to justify their ROI more concretely? And how should they go about doing that?

Che Sharma, the Founder & CEO at Eppo, was the perfect person to discuss this question with. As an early data scientist at Airbnb and later at Webflow, Che was knee-deep in proving the value of data teams to the rest of the business. Most recently he founded an experimentation platform to help data and product teams figure out how to impact business metrics — thereby building a category within the modern data stack that is arguably much closer to revenue than other categories.

In this conversation, we discussed:

* Should data teams align themselves with revenue or with cost reduction?

* If you led a data team and you were making your case to your CFO that your budget shouldn't be cut, what would you say?

* How can data teams prove their value in companies that don’t quite “get it” yet?

You can listen to the podcast or else read the lightly edited transcript of the conversation below. Let's dive in!

If you’d like to hear more about topics related to scaling SaaS and other B2B businesses, you can subscribe to my newsletter (for free) here:

Leadership Roles

As always, I’ll share a few leadership roles at companies I’m excited about:

* Eppo (Che’s company!): Founding Marketer

* dbt Labs: Sr. Director of Professional Services

* Bubble: Director of Product Marketing, Director of Partnerships

* LaunchNotes: Director of Engineering

* Kingdom Supercultures: Head of People

* Guide: Head of Marketing

Transcript

Allison: Che, I'm so excited to have you on the podcast today to talk about the ROI of data teams and how we can prove it out, especially in this macro environment. Thanks so much for joining us.

Che: Great to be with you, Allison. Topic near and dear to my heart here. So, as you probably mentioned in the show notes here, I'm an early data scientist at Airbnb and then Webflow, which both have interesting relationships with data teams and ROI. So I'm excited to dig into it.

A: Awesome. To start off, can you tell us a little bit more about background and what you're doing today?

C: Absolutely. I am the founder and CEO of Eppo. We're a next gen A/B experimentation platform. It came from my feeling that the best way for data teams to have ROI is to heavily invest in experimentation and plug that throughout the org. But before I was at Eppo, I was, as I mentioned before, a data scientist at Airbnb, quite early. I saw a lot of the growth journey play out and worked across the stack, from machine learning for a couple years, then analytics for a couple years, and data tools for a couple more. I saw the data team progress as we went from mostly focusing on reporting and supply growth, to being part of every major pillar of the company, from growth in China to business travel, marketing, and everything else.

After Airbnb, I consulted for a bunch of companies from a wide array of domains. And then most recently I was at Webflow, which is a no-code website builder, a SaaS tool, where again I got to see the data tool ROI journey.

A: You're the perfect person to speak to the subject. Often when people think about ROI, they think about one of two levers, and maybe both: revenue and cost reduction. Do you think data teams should associate themselves with either of those? And if so, why?

C: I think data teams need to associate themselves with the goals of the company at the time. If you look at Airbnb from 2012 to 2017, it wasn't necessarily a revenue-oriented place, but it was extremely focused on growth, on pursuing the land grab. Airbnb understood that there were incredible network effects in an international travel platform. So the most important thing was to become the de facto standard, not just in America, but in Europe, in Asia, Australia, everywhere. I wrote a blog post about this: if you focus too much on revenue, then actually it will distort the initiatives you pursue. In our case at Airbnb, focusing on revenue might have made us invest a lot more in America and Western Europe, which would have taken away from the land grab initiative.

So I think in general, it's good to just focus on the company's priorities. At Webflow, the priority was ARR, so our data team was much closer to revenue. I think this is what leadership exists to do: to outline those priorities so that teams can orient themselves around it.

A: So do you think this literally might mean the data team saying, there are these five OKRs for the company this quarter, we are contributing to some or more of those in these particular ways?

C: Yeah. 100%.

A: And do you think that data teams should measure themselves numerically based on some kind of impact on those numbers?

C: I don't really think, so because data teams don't actually do things. It's funny to say that, but they don't actually build anything. The actual product-building process is: understand, design, build, and measure. Data teams are really involved in understanding and measuring, but not design and building. Even on the marketing side elsewhere, we're about guiding hygienic decision-making. So in that regard, if you are not the one necessarily making the decision, how do you kind of pin the dollar tag on yourselves?

I think what you need to do is, have really tangible demonstrations of how you are improving decision-making. It's hard to put a number on it, but you can list all the places in which you guided a decision in a way where you had a really big role here.

A: Oh, interesting. I'm trying to think through exactly what that might look like. Is that saying something like, before the data team got involved, this business team was on track to make this particular decision, which would have resulted in X, Y, Z impact on our OKRs, but then we got involved and actually now there's A, B, C impact on our OKRs?

C: Yep. That's exactly it. Concretely, the way this turns out is less of a sharp “before, after” and much more of a, “here's a scope of type of decisions that are happening, unguided by data, that are now going to be guided by data. And we can understand that level of decision making in aggregate.”

I'm the CEO of an experimentation company. In experimentation, there are a bunch of decisions like, would you launch this thing or would you not launch this thing? When you do experimentation and you actually say, let's see what the metrics say about it, and metrics are obviously very much in the data domain, that's a tangible demonstration of how the data team affected a decision.

There are examples in other parts of the organization. For example, customer service: would you route this ticket to this person, or that person? In marketing: would you spend more on this campaign, or that campaign?

There are decisions that are happening all over the place. As you plug data into those places, then those are, to me, tangible demonstrations of guiding decision-making.

A: I'd imagine there would be some people out there who would say, look, it's really hard in most instances to really prove that the data team had an impact on decision-making in a way that impacted company OKR. So these people might say, instead, we should think about data teams as essential infrastructure that just has to exist and should be funded, regardless of circumstances. What would you say to that?

C: I actually don't disagree with it too much. The place where I would counsel a little bit of temperament of that, is that just because you have numbers doesn't mean you changed the decision. Data people often need to make sure that the decisions are actually changing — especially early in their journey, before companies fully embrace data as a core constituent. Our common friend, Elena Grewal [former leader of data science at Airbnb] and I talk about this, that when you're hiring early data scientists, a really important trait that makes a big difference is communication ability. Can you take these results and make them compelling so that people change their decisions?

A: I'm thinking more about the different ways in which you could conceive of the impact of data teams and how you should budget for them, which obviously in this macro context is potentially controversial and a very important thing to think through. Where would you say the consensus is today, about how you should budget for data teams? Perhaps on the one end of the spectrum, it should be hire data folks regardless of circumstance, there should be a fixed budget, or a percent of revenue. And then, on the other end of the spectrum, there might be a need to prove out the impact on certain financial metrics. Where do people's opinions lie?

C: I think right now there is not that much idea of plugging in data teams as direct drivers of revenue. I think that the nature of the way the data landscape has progressed is, that the tools like Snowflake and dbt have made it so that you can spin up reporting really easily. And so, I think as a result, you're seeing that all across the globe, there are reporting teams. They're called data teams, but they're mostly doing core reporting that's going to guide a board deck. Oftentimes that reporting is so macro that it’s not driving operational decisions.

That's the state of the world today, but I think with likely a recession happening, things could change fairly readily. A lot of teams have made many investments in purchasing all these data tools, hiring these data teams, and there was a clearly justifiable reporting need. But everything beyond reporting was a little bit more speculative. The thinking was, “PMs need numbers, so let's give them a data person.”

What’s going to happen in the future is that a CFO is going to look at headcount request and ask, tell me how money comes out the other end? Data teams need to provide the answer very concretely.

I've seen data teams early on, when they need to win political capital, generate short-term revenue wins. For example, you're spending marketing dollars all over the place. Is there some way you can establish attribution models? That kind of analysis always reveals some channel that's bad. Whenever you do that process, especially early on, there's always low hanging fruit. “Here's the example where we're spending $500K and don't have to.” That is tangible revenue improvement. It pays off a small data team's salary and feels good. That's a decent enough place to get started.

The casualty of recession-style thinking of being very short-term focused is that some of the most powerful things coming out of a data team is when you actually reveal an entire strategic direction that could be pursued. That's the thing that in the boom times you get more leeway to pursue, but in the lean times, you have to make sure that's part of a portfolio of much more direct revenue drivers.

A: All of that makes a lot of sense. I'm thinking about, more broadly, how data teams can advocate to their CFO to retain budget in this type of economic environment. And I imagine one of the frequent inputs into this conversation coming from go-to-market leaders — the people who own revenue at the end of the day. Sometimes I hear rumblings in the SaaS world from GTM leaders that the modern data stack is really just tools for data people. And that data people all have this love fest with each other, and are very excited about their own profession and all these software products that are being built, but the GTM leaders in some of these instances feel that they haven't really benefited from the modern data stack.

So you could imagine a data leader and a sales leader being in a room with the CFO, each advocating for their own software and headcount budget. I wonder how that conversation will go.

Do you think that go-to-market leaders are mistaken about the modern data stack and data teams?

C: The funny thing is, I'm not even necessarily sure they are mistaken. I would say that with the economic boom times over the last several years, and the boom in innovation and the data tool landscape, suddenly people have brought on a bunch of data observability tools, or next gen BI tools, or data catalogs. The data team liked them and had a lot of discretionary spending.

But the key question is, Do all of these data tools add up to better decision making? This is again why I was so excited to start an experimentation company. Suppose you snap your finger and you had a pristine data warehouse that was always up to date that could plug into everything. Suppose you took Snowflake, dbt, Hex, Hightouch, etc., had the whole package, and you snapped your fingers and it all worked. What would happen next? You have to have that theory of change in your mind. As a data team, you need to say, “we think we are going to affect a bunch of product launch decisions.” That's the experimentation play.

Data teams should be thinking about how to model the segmentation of users in order to dramatically improve the ROI of ad spend. They can completely change the way marketing teams deploy dollars.

Once you are affecting decision-making, and your company leaders like that, they’re going to see the value of the other data tools that you want. They’ll see that you need your data pipelines to reliably work, and that you need other underlying investments on top of your data warehouse. That’s when you can have nice conversations about the ROI of more foundational data pieces.

A: I'm wondering how you think these budgeting conversations will unfold from the perspective of where in the P&L a data team should be accounted for. You could make the argument that data team should be a part of the G&A budget, but then again, maybe if you're trying to prove an impact on the business, you should be allocated to the various functional cost categories. What's your take?

C: A data team can kind of start off by doing some sort of cost sharing. With Eppo, for example, the most common setup we've seen is that half the budget comes from the product team and half the budget comes from the data team. This shows that the decision-makers are literally bought into the process.

There may be some amount of money that you would spend on standard reporting that could count as G&A. But when you're affecting decision making and when you need to justify having more than a 3-person data team, you should have a lot more cost sharing.

A: Che, I know there are some companies where the data teams are revered internally. Airbnb, where you used to work, is one of these companies where the data team is considered to be a core part of the broader organization. It's much easier to justify headcount and spend there. It's a default assumption that they're adding a ton of value. But, if you are on a data team at a tiny startup where there maybe only a few data people, it might be a lot harder at that point to prove, why do you need that additional head count? Or why do you need that additional spend? What would your advice be for founders and data leaders at earlier stage companies, in terms of how to get to where Airbnb is?

C: I think it's a great question because I was the fourth data scientist at Airbnb and so when I joined, we did not have a personal brand, internally. We did not have this default assumption that we should be hiring data scientists everywhere and pursuing opportunities that were not just contributing to short term revenue, but rather long term strategic. The advice I give is that you have to treat it like a ladder. Step by step. What do you do to reach up to that “data team nirvana,” where you're held in high esteem as a crucial part of every decision?

I think the starting point is — and this is why your initial data hires are really important — what is the easiest path to showing wins? Tangible wins that are going to lead to further investment. Those are places where you have sufficient data volume to do something meaningful. In the world of data, sample size is forever a thing you have to care about. And so, where do you have good data volume?

Secondly, based upon your previous experience doing this type of work, where do you believe a few months of investment will lead to tangibly improved decision making or ROI? That could be improving your marketing campaigns. It could be taking the customer service operation and making them more efficient. Pick one or two areas that are going to show value quickly, staff them up, and show value. From there, you build up bit by bit. Once the data team has a brand of, “everything it touches, it is transforming and driving a lot of value,” that helps you get the political capital to pursue things that are a little bit more long-term, to figure out some underlying deep question of the business.

At Airbnb, for instance, after showing a whole bunch of successful experiments improving search ranking — because at the time search ranking was literally a handmade score, literally it was just a column of the database with a number in it and we were ranked by that number — we then moved to machine learning and kept improving revenue.

And then we got the leeway to spend a month investigating seasonal pricing, which we kind of always knew was going to be a big deal. If you look at the rest of the travel industry, they do seasonal pricing. And if you look at our price filters, people obviously use them differently across seasons. That was a lot of months of work to make that case and then ultimately justify a dynamic pricing model. I can't imagine going into the CFO's office and saying, we want to get two of our most senior data people and give them three to six months to go work on a project that may or may not work. You get there by first knocking out a bunch of singles and doubles.

A: So it's a journey. Thinking about the early stages of proving out the brand and value of your data team, what are some other examples of early wins that ultimately the best data teams generated when they were early stage?

C: Webflow is a great example. It's a SaaS business, it was three data scientists at the time when I joined, and the whole company was operating as if every user was basically the same — part of one homogenous pool. But, all the early employees knew there was this central divide of people who are website builders for others — contract website builders —and then there were people who were going to build a website themselves, like a business owner. One of the earliest wins we had as data team was to model that out. Once we did that segmentation, it started having dramatic ramifications for how we spent marketing dollars and how we prioritized products. For example, we saw that if you spend a lot of marketing dollars on a small-business CEO who has never built a website before, it is probably not going to go very far. It’s better to spend marketing dollars to get a website builder who's going to make 500,000 websites. We saw that these non-technical CEO types wanted Webflow to look like Squarespace, to be very easy but limited. Whereas the website builders wanted it to look like Photoshop, very powerful and complex.

There was immediate tangible benefit in terms of allocating marketing dollars. But there was also a strategic question that emerged: who are we building this product for? Who do you want to serve?

That was an early win. Another early win related to our affiliate program. There was literally a bug in how it was set up that was wasting $500K a year. There were little wins like that here and there.

At Airbnb, when I first joined, the name of the game was a land-grab growth strategy. The question was, in all the different metros, whether it's Bangkok, Thailand or Paris, France, what do we consider to be a healthy balance of supply and demand? And, where should we be spending dollars on boosting supply in those places? We supplied all the boots on the ground with insights to help guide the resourcing. That was a very early way to demonstrate value.

The other early win that I hadn't mentioned before was in search ranking. We replaced a very manual process with an actual machine learning model that did things like penalizing hosts who never responded and boosting hosts who often accepted people. Those were immediate revenue gates.

A: It's amazing how many times I'm talking with a founder and they're looking for advice on some operational topic. My first tip is, get a data person to actually look into what's going on. It's very difficult to form a churn playbook or create a certain type of go to market strategy if you don't actually understand what's happening. And I can imagine seeing many founders hiring data people earlier and earlier in their startup journey.

C: Yeah. One of the things about Airbnb is that even though the founders were designers and weren’t really data people, they did hire Riley as the first data employee. There was enough kind of investors in the ear to make an investment there that they went and did it. The trap I see all the time is that when the people start a data team, they do it with a “dip their toe in the water” fashion: let's just bring on a data scientist and do some reporting. And, they'll hire someone a little bit junior or maybe mid-career or something. Someone who knows SQL and worked at a couple places.

But, when you're first deciding on how to invest and start this flywheel, you need someone who can think more strategically, because the core decision is, where are you going to go to notch your first wins? And how do those ladder up to the other ones. And, to be able to make that judgment, you need to understand that “this business is a marketplace whose goal is margin improvement” or something else. You have to be able to see a few steps ahead to be able to say, “the best investment we can make right now is marketing ops” or another area. Failure mode is when you’re simply building core metrics for the board decks.

A: My last question for you, Che. You started an experimentation company, Eppo, which almost by definition is intended to help data teams prove their impact on priorities, since the nature of experimentation is that there's some output variable that you're trying to impact, and you see new mechanisms for how you can best impact it. I'm sure we can have a whole conversation about best practices for experimentation, but if you had one tip to leave people with today, what would that be?

C: Besides buying Eppo, what I would say is, the reason why experimentation has a great effect is that it takes the metrics that underlie the business — the stuff the board cares about — and it plugs it into all of these product investments you're making. If you ran an experiment and it showed a successful result, would your metrics be ones that would actually get attention? If you go to the CFO and say hey, I just boosted click through rates 10%, that's not going to get you very far. It's not going to lead to this ladder-up of capabilities and brand. When you're running experiments, make sure you use business metrics. And then, that logically leads you to think about the experiments that will drive those business metrics.

A: Che, thank you so much for joining us today. This was an awesome conversation. I know that people will benefit a lot from it.

C: This has been great, Allison.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com

Update from Allison: I’ll be taking a break from the newsletter for a couple of weeks, first for PTO and then to think more carefully about the next content series I want to publish. I’d be grateful for your feedback on what to keep doing, stop doing, and start doing. Reply to this email - I’d love to hear from you!

Here’s a question I’ve often wondered about: When I publish a podcast like this one on Substack and then post it to LinkedIn, how can I integrate the LinkedIn comment threads into the Substack post itself? That way we could all have one big conversation about this topic, rather than siloed ones across social media platforms. Even better, wouldn’t it be valuable if anytime my article was discussed anywhere on the internet, anyone could easily keep up with and participate in that conversation?

That’s exactly the topic that I explored in this podcast episode with Anudit Nagar, Co-Founder at Convo Space, which is backed by ConsenSys, Ethereal Ventures, NfX, Protocol Labs, and other Web3 investors. Crypto tokens might be under fire right now, but Web3 builders in the B2B world like Anudit are heads-down building. We discussed:

* What is the value proposition of decentralized conversations, for both consumers and developers?

* What exactly are the Web3 aspects of his company now, and what might they be in the future?

* What will his go-to-market motion look like?

As always after these Web3 conversations, my brain is exploding. Feel free to reach out to me with any thoughts. You can also read the lightly edited transcript of the conversation below. Let's dive in!

If you’d like to hear more about topics related to scaling SaaS and Web3 businesses, you can subscribe to my newsletter (for free) here:

Leadership Roles

As always, I’ll share a few leadership roles at companies I’m excited about:

* Convo Space (Anudit’s company!): Multiple roles

* dbt Labs: Sr. Director of Professional Services

* LaunchNotes: Director of Engineering

* Kingdom Supercultures: Head of People

* VendorPM:

* VP of Product

* Head of Marketing

* Head of Finance

* Guide: Head of Marketing

Transcript

Allison: Anudit, I'm so excited to have you on the podcast. Thank you so much for joining us.

Anudit: Thanks a lot, Allison. Stoked to be here.

Allison: So to get started, can you tell us, how did you get into the Web3 world?

Anudit: I've been in the Web3 space for about four years now. I primarily started working in the Web3 space for a college project, and I randomly typed in the words "decentralized storage" on Google, and out came IPFS and Ethereum. I fell into that rabbit hole and have never looked back since then.

Allison: Tell us about what your company does.

Anudit: Convo Space actually started out as an early hackathon project. But the idea really came when I was talking with my co-founder, and we were ranting about the design of a website. But then I was on that website, and she was on WhatsApp, and we had to jump into these different silos to be able to communicate about really anything on the internet. We said, "Hey, what if you could be on that website and talk about whatever we are seeing right there on that interface? And not have to jump through all of these silos? What if we could pick up that same conversation wherever we are?"

So we started building this interoperability layer, wherein I could have a conversation on a website, and then switch to another website and continue the conversation. The goal is to continue that conversation freely, wherever I go. And we wanted to do all of this in a completely decentralized manner, wherein we have all the goodness of Web3, like self-sovereign identity and ownership, where people have sole control over their data.

Allison: I could see why a lot of people on the consumer side, consumers would really appreciate being able to chat with their friends wherever they are. You can imagine having a conversation about a topic of interest and exploring multiple websites together, for example, that relate to that topic. Might there be business use cases for Convo Space as well?

Anudit: Absolutely. One of the biggest problems that we’ve face when we're building out Convo Space is that we realized was there weren't any tools, libraries, or really anything for us to able to build this consumer product. So first, we had to actually build out the infrastructure, the code libraries, tools that developers can actually use to be able to build such a decentralized conversation layer for the internet.

Now it’s as simple as a single line of code that people can drop into their websites to spin up a mini comment section on their blog. It's really all you need to build and get started hacking in the space.

Allison: I'm wondering from a personal standpoint, why I might actually benefit from using Convo Space, and I haven't used it yet. As you know, and the readers know, I have a Substack and there are community features that you can enable there, so I can allow people to comment on my post. But what's a little bit frustrating is that I might post an article or a podcast to LinkedIn, there might be a really interesting conversation that people are having about the article in LinkedIn, but because people may be primarily reading these articles or listening on their phones, they're not often commenting on the Substack. So if I use Convo Space, could I enable conversations to move between platforms?

Anudit: Absolutely. Our core focus with Convo Space is interoperability. We don't want to build another silo. In the Web2 realm, to be able to build an incredibly popular, scalable business, you had to build a silo. WhatsApp has become so big, only because you have to be on WhatsApp, because only then can you talk to other people on WhatsApp. That was the only way to actually scale it to billions of people.

But within Web3, we finally get this concept of positive-sum games, wherein everyone can contribute data to this decentralized conversation layer, and any other platform can pull and continue those conversations anywhere else. For example, in the traditional world, if I had to talk to you, and if I was a WhatsApp, then you would also to be on WhatsApp. But in the Web3 world, we could have somebody on WhatsApp, somebody on Telegram, somebody on a website, somebody in the VR world, somebody on your Substack, all talking about that same conversation in the same group. They have completely different interfaces and completely self-owned data structures, identity that only they can control and be able to do all of those same things in that same seamless Web2 ecosystem.

Allison: This is really fascinating. I can see now from my own standpoint why this actually might be very useful.

Who exactly are you marketing your product to? Who are your customers?

Anudit: For now, it's just developers. If we talk about the Web3 niche that we are focusing on right now, it's primarily towards this ecosystem of NFTs. Because right now, one of the biggest problems in the ecosystem for Web3 is the information asymmetry, wherein you have to follow those popular Twitter accounts, the right accounts, to be able to understand what's happening in the ecosystem. Or let's say there is a certain hack that has happened, how do you alert your users? That's where Convo Space has become this way in which people can propagate information in a much faster manner, because you could just leave a comment on Etherscan or anywhere else on the web, and it can travel to wherever the actual consumer is using the product or consuming information.

Allison: It sounds like I wouldn't be a direct customer of Convo Space using my Substack. Probably Substack would have to work with you directly. Is that right?

Anudit: Yes. But with Substack, I think it has this capability where you just drop in an HTML code that you copy from Convo Space, so you can use Convo Space independently. So you aren't directly dependent on Substack to be able to integrate Convo Space.

Allison: Oh, interesting. So I literally could use your product by incorporating the HTML. And would you be charging me directly for that?

Anudit: No. The small little embeds and different websites are all free. Anyone who wants to hack on top of the raw APIs and libraries can just grab that API key. And it's also free. Only when you're scaling up to very massive user loads, then it's mostly a paid service, and we still haven't built that up.

Allison: Got it. Tell us a bit about your pricing model. How exactly do you make money?

Anudit: For now it's mostly free, but when you upgrade, it's priced for every single request. So if you make a hundred thousand requests that day, maybe it's roughly $20 or $30 a month. Because we’re in the developmental stage right now, we aren't really charging for any of it. But that's where our head would be at.

And especially within Web3, it opens up this idea for tokens and crypto to be incorporated into it. Imagine a specific application on your own computer. Imagine a mini version of Convo, wherein on your own local computer, you'll be able to store all your social data, like the way it's stored on Twitter service, you'll be able to do that on your own computer. You can build and hack on top of that local node so that you don't have to depend on us to be able to host that application data.

To simplify that a little more, we would be decentralizing the entire infrastructure down to the community, so that people don't have to depend on us, just the way you can spin up an Ethereum node and independently participate in that network. You'll be able to do all of that, be able to make fees from all the API requests, be able to post it for yourself or for others, and actually incorporate a token to be able to do all that. That's the Web3 aspect of it, of being able to be decentralized the community. But right now, it's purely focused on SaaS.

Allison: I see. So would you consider yourself to be running a Web3 company right now?

Anudit: Pretty much, yes. But the investors and people that we have on board always know that even though Web3 is the part that we are starting with, none of our libraries, none of the products that we built out are just for Web3 companies. It can used by anyone up there.

Allison: Got it. So you don't currently have a token?

Anudit: Do not.

Allison: So tell us, in what way exactly are you Web3?

Anudit: We are Web3 in a way where most of our entire stack is using a decentralized service. That includes how data is stored, and how we link data to an actual user — on that last note, in the traditional Web2 world, actual users are identified with Gmail IDs. You can sign up your account using Facebook, Google, or use your own emails, but then Google can any time revoke your access to it, or if you lose your access to it, then you lose data. Within the Web3 word, that entire ownership of that data lies with you: you own your own identity, you hold the private key, so nobody else has access to it.

This important because of rules like GDPR and CCPA. But also we have the single button on Convo Space, so that within a single click, you can erase your data from every single interface provider and developer out there that you've given access to. You can go anonymously, because you own all that data. You could revoke access at any time, or even just delete it from every single interface. That's the power that we want to give to users that almost no Web3 platform is really capable of.

Allison: So literally with a click of the button, I could eliminate anything I had ever said on any platform on the internet.

Anudit: Any and all conversational data that is passing through Convo Space.

Allison: That's powerful. Wow. On that note, do you think that the category that you're in tends toward monopoly? Do you think that Convo Space will ultimately own all conversation that happens on the internet, or will there be multiple players? And if so, why?

Anudit: Even right now, there have been multiple different companies way bigger than Convo Space who have tried doing decentralized social media conversations. Platforms like Status, Streamer, and many others have tried. None of them have actually caught on. That's simply because Web3 has this thing where we love to sort of ship and build things, but to actually build products that are in need is a completely different category and a problem that both Web2 and Web3 companies face. So it really has to address a core need for people. And right now, Discord, Slack, Twitter are all great. I mean, there isn't really any core use case that Web3 brings to the table for which people would say, "Hey, I need this." Most of them are nice to have.

So that's where we bring about this idea of self-sovereign data, identity, and also this interoperability that you get along with it. That's where you sort of, by default, create these positive sum gains where even though everyone's using Convo Space, the entire code space is open source. Anyone can hack and build on top of Convo Space, they don't need our permission for it.

It sort of tends into monopolies, but those monopolies are beneficial for everyone else because everyone else can benefit from it, contribute to it and then gain benefit out of it. There’s a mutual economy behind it. Building public goods that are beneficial for all people — it's far, far better. And with Web3 you have this inherent economy where you can literally program money into your entire code base and define how people would use it and be able to make money out of it.

Allison: I want to distill the value proposition of what you're doing into a few words. Do you think that the real demand, the need to have this, is coming from the consumer asking for the protections you mentioned earlier — anonymity, the ability to delete things — and then developers in turn are responding to that consumer need by embedding Convo Space into their sites?

Anudit: Let’s say someone is building their platform, and they embed an entire comment section or they create a simple social media product. The biggest problem is the cold start problem: once they start getting users, those users have to sign in, write their entire profiles, start commenting on things, start interacting, and building their entire community around it.

But let's say those users want to try out a different platform. Now they have to start again from scratch, create their entire profile, start commenting. Because Convo Space becomes this interoperable layer across multiple different platforms, people can just integrate with it so that conversations and communities can move along with the user. That’s what the core proposition has been for people to integrate with Convo Space. Now developers have libraries and APIs to easily make that happen.

Allison: I want to switch gears to your go-to-market. Can you tell me a bit about what go-to-market means at "The Convo Space"?

Anudit: Convo Space started out as a personal need, and then it gradually became bigger and bigger, where more people started using it, and we started getting feedback and incorporating it. Since then, as we started getting that flywheel moving, most of the feature ideas have come directly from the actual market and the users. So for go-to-market for Web3 right now, especially because it is such a small ecosystem and such a small niche that you can literally just talk to the people and be like, "Hey, what do you need?" And once they start building on your platform, they can be like, "Hey, we want more insights about who the user is. Maybe if you can just send us their Twitter address and their Twitter profile. Or maybe if they're doing something on OpenSea?”

To go on a brief tangent, I’ll say more about that specific request from developers. Even before you talk to people, you have this inherent need to establish trust. Even before we came on this podcast, you knew about me, we looked up each other and we were like, "Hey, this might be a good conversation to have." So you're mentally trying to establish this trust. And people want to do that also in a decentralized manner. So we started building out this product called Omnid, which comes from omniscient ID.

Going back to how you interact with your users: To reduce the gap between ideation and execution for literally any company is very, very important. But it’s even more important in Web3, because the attention span and the speed of innovation is 10 times more than any other industry. So if you build something right now, it'll probably be outdated in the next week. Maybe someone will come up with even a better idea and everyone will start using that.

So go-to-market in Web3 is very interesting because you have to talk to so many people, get that feedback instantly, incorporate it, and be able to ship out that improvement as fast as you can.

Go-to-market is interesting also because right now nobody knows what they're doing. Everyone's trying to figure it out.

Allison: It sounds like being an open source company is perhaps really beneficial to you, given the requirements to act very quickly, is that right?

Anudit: Very, because of that and also because you're building these incredibly complicated protocols, so putting things out to the wild wherein anybody can test it, break it and figure out their way through it. They can see if they can gain access in some way, or if they can do something malicious. Because everything is open source, if anything breaks people can just file an issue or figure out why it’s not working, independent from us.

You don’t have to trust us or indirectly verify the code that is running. You’re not bound by the black box of not knowing how Twitter works — how are they using my data, how are they processing it, where are they sending it?

Allison: How exactly do you engage with your community? What product are you using, who is actually messaging people? How exactly do you gather feedback?

Anudit: For now, we were running on a very lean team. In terms of social, it's just us and our team managing all of it for now. Most of our feedback comes from Discord, wherein we can very easily talk to people, understand what they need. In terms of broadcasting to the world, it's primarily Twitter. But I would say we aren't very active on the social presence primarily because both me and my co-founder aren't very social people. So we would definitely like to up our game and hire better community managers. So far it's just been word of mouth.

Allison: It sounds like from what you've said so far, your go-to-market will be primarily self-serve or maybe with the support of your community. And I imagine other help guides that you could provide. Do you expect at some point to have really large customers that are trying to incorporate Convo Space into what they're doing and if so, how will you support them?

Anudit: Absolutely. Our core focus from the get go was to be able to onboard even the biggest of partners. Let's say if Twitter one day wants to switch their entire stack to Convo Space, all of that is possible. We would be able to happily migrate all of their existing data very easily onto Convo Space. All of our libraries are in compliance with most of the industry standards that institutional partners require for compliance.

Allison: Wow. And apart from social media platforms using you, what would an enterprise customer look like?

Anudit: An enterprise customer might say, "Hey, I want to view all the conversations that are happening about this specific feature of a product that we are building out, across the world. I want to be able to catalog them and process the feedback."

Allison: If I'm interpreting you correctly, it sounds like there's an enterprise use case, as with Twitter, which might be to incorporate conversations about particular topics into my website or application. But there might also be use case, which is to monitor conversations that are happening about specific topics, perhaps for consumer research purposes. So if I'm an e-commerce company trying to understand a bit more about fashion trends for a particular segment, it sounds like I might be able to tap into Convo Space's data, to understand what my target demographic is saying about products that I might be launching in the future. Is that right?

Anudit: Exactly.

There are public conversations and private conversations. Public conversations are things that can be queried and understood by everyone. Private are just between the people that you allow. So let's say if it's a personal group between you and your family, and you're talking about the specific website, it is only accessible by them. But public conversations can be pulled by other companies, to be able to easily understand what's happening anywhere on the web. So a company can ask, "Hey, I want to know all conversations that are happening about XYZ artist’s new album on Spotify.”

Allison: Did you ever think about helping consumers, or the people actually participating in these conversations, be able to monetize the data about their conversations, since that data might be interesting to companies?

Anudit: Yes. Users have this ability with Convo Space to convert their data into a data token, which essentially means package it up and upload it to a service called Ocean Protocol, which allows platforms to be able to invest in your data, and so you can monetize it. Platforms could also use it for private machine learning algorithms.

Allison: On the hiring front, I remember you mentioning that you're thinking about hiring some community managers to help you and your co-founder. Who might be the people that you would be hiring after, that are doing something community- or customer-facing in some way? Basically I'm wondering, what roles will exist at your company over the next year or two that are not product and engineering?

Anudit: Both me and our co-founder have an incredible interest in design. So we wanted to shift our entire infrastructure so that our personal roles are focused more on designing and building beautiful products. The primary new roles will be user research, design, animation, and people who have spent their time and energy on how users interpret the world. Because technical work and actually working with users are two completely different worlds.

Allison: As my last question, I'd love to know, what are your tips for people in the Web2 world who are looking to get into Web3?

Anudit: My biggest tip would be to just build something and get it out there. It might sound like probably the worst advice anyone can give, especially because you don't really want to get out things that are half baked. But it's important to experiment and see what the space is capable of before you make that entire decision of whether or not you want to actually focus your efforts into it. And I have personally found out the best way to understand about this space is to just build something.

If you're active in the social ecosystem, look at the more creative people who are building these incredible services for people to be able to harness the power of Blockchain, Defy, NFTs. Find a product idea for something that wasn't really possible in the realm of Web2.

With Web3, users have more control and say with the products that they're using, and that can be an incredibly powerful motivator, but also a demotivator for products. So it needs a lot of mental attention to be able to figure out, but at the same time, it is very fruitful in terms of understanding more about your product.

Allison: Anudit, thank you so much for this conversation. I learned a great deal and I'm sure others who are listening to this will as well.

Anudit: Absolutely. Thanks a lot.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit allisonpickens.substack.com