并购、竞争、定价与投资 | Julia Schottenstein (dbt Labs)
M&A, competition, pricing, and investing | Julia Schottenstein (dbt Labs)
Julia Schottenstein: M&A is always about creating plan Bs. And the way I would think about it is for any one company, there’s only ever two to three buyers that find what you’re building to be extremely strategic. And the strategy that I would do in how do you get noticed is I would figure out the area that you bring a competitive advantage. And I would inflict pain on that potential buyer. Make it impossible for them to not notice you because that’s when they’re going to have their ears perk up and say, “Well, what’s going on with this company?”
The really important piece here is you want to do that in a way that’s still friendly and open. I see a lot of founders get this wrong and they prematurely will shut down a conversation or they won’t talk to an incumbent or a potential future buyer because they take too competitive of a stance. But that’s a mistake because M&A is all about creating plan Bs and you don’t want to shut that door down prematurely because you don’t know if you can really go the distance and be an independent company. So you want to have optionality.
Career Shift: Investing to Product
Lenny: Welcome to Lenny’s Podcast, where I interview world-class product leaders and growth experts to learn from their hard one experiences building and growing today’s most successful products. Today my guest is Julia Schottenstein. Julia is a product leader at dbt Labs where she leads the dbt Cloud product. She’s also the co-host of the dbt Labs podcast called Analytics Engineering Podcast, a show about data trends that impact analytics engineers work. As you’ll hear in this episode, Julia actually led the acquisition of a startup that I’m an investor in called Transform from the side of dbt Labs.
And in our conversation, we dig into the M&A process and get into a bunch of advice for how to improve your odds of having a good outcome and just approaching M&A broadly. We also dig into the story of dbt, which is one of the most successful startups out there that you probably don’t know about. And we talked about what they did right to get to where they are now. We also cover how to best think about competition, a bunch of frameworks for thinking about product and advice on how to approach pricing and also open source. Enjoy this episode with Julia Schottenstein after a short word from our sponsors.
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Julia, welcome to the podcast.
Julia Schottenstein: Super excited to be here.
Discovering dbt Labs
Lenny: So you have a really interesting career path in that you went from VC into product management. Usually it’s the other way around. Usually PMs become VCs and it’s right to see this version of it. So I wanted to start with just a question of just how did that come to be?
From Investing to Joining dbt
Julia Schottenstein: I do have an unusual background, but it doesn’t surprise me that people who are interested in product are also interested in investing and vice versa. For me, I’ve always had three interests broadly and that’s an interest in business, an interest in technology and an interest in markets. And I get to express those interests both in investing and in product, but just with different weights. So in product you go a lot deeper on the tech and markets is less of a focus, but you still get to do all three. So I have a unusual background and I used to be a professional investor at NEA. Spent all my time investing in early stage startups that built for technical audiences. So think dev tools, infra data companies.
And in 2019 I first discovered dbt, which was an open source data transformation framework. And I got really very, very excited about dbt because when I talked to people that were using it, the way that they described their experience on dbt was like unlike anything I had heard before. It was much more of an identity for them than just a tool that they were using to get their job done. And that really struck me. And as I thought about what was happening in the market, there was a lot going on in 2019. The markets were changing quite a bit. Cloud data warehouses were starting to explode. This was the year where Snowflake went from a 4 billion company to a 12 billion company. And I thought to myself, if dbt worked, it could work in a really extraordinary way. And I naturally tried to spend all my time getting close to Tristan, who’s the CEO and founder and I wanted to invest.
Lenny: Okay. So at this point you’re a spec at NEA, you’re trying to invest in dbt and okay, keep going.
Four Dimensions for Evaluating Startups
Julia Schottenstein: Yeah. So I was very, very excited. I thought if this worked, it could work in a really extraordinary way and I spent all my time trying to get Tristan to like me so that I could invest in the company. And then in 2020, I finally got the call that I was waiting for. Tristan said, “We’re going to raise some money.” He had a term sheet from a firm that he liked. It was a good venture fund, but he also liked me and he wanted to give me a shot and I was super excited to get that message because this is my chance. And unfortunately I ended up losing that deal to Sequoia. It’s a formidable partner.
Signs of a Product Spark
Lenny: Reasonable loss.
Julia Schottenstein: I was just so convicted that if was going to be a special company that I asked to even put my personal money in and I asked to put a very irresponsible, irrational amount, nearly like 20% of my liquid net worth into dbt because I was so convinced that this was special. And sometimes when deals don’t work out as an investor, you can create this narrative in your head like, “Good, I dodged a bullet or better off without them or screw them.” But for me that was quite the opposite with dbt. I really felt like dbt was this runaway train and it was special and I wanted to jump on board. So a few months later I ended up calling Tristan and asked him if I could be a part of the company that built the product that I thought was so special. So that was my unique path into product and to dbt.
Advice on Mergers and Acquisitions
Lenny: So you invested in dbt and then an opportunity opened up where you ended up working there?
Julia Schottenstein: No, I think if I was able to invest I wouldn’t be here. So I got no, the board ended up vetoing my personal investment, but it’s okay because I ended up dedicating all of my personal time to building the company.
How to Think About Competition
Lenny: Awesome. Okay. So something you touched on there that was really interesting of what you saw about dbt that was so interesting and I think this is maybe a broader question of just in your time investing and finding a company like dbt early, what have you learned about just picking well, finding companies early especially? What are signs in your experience of just that this is going to be something really interesting that you might want to join? And this is more for people listening that are thinking about joining a company early on. What do you think they should look for?
Julia Schottenstein: So the way I would look at joining an early stage company would be the same way I would evaluate investing in one. So there are four things that I care about when I’m looking at really early stage companies and it’s people, market, product and distribution. And I’ll touch on each of those four to say a little bit more about what specifically I’m looking at. So people, this is really the CEO, the founder of the company put simply do you trust this person to lead. And for me, Tristan had this really rare ability to paint a very compelling future of the industry and how dbt was going to be a part of making that vision a reality. But he also was really, really detailed in the day-to-day work of the analytics engineering work. And it was that range in scale that made me feel like this is a founder that’s very rare and compelling.
The next is markets. We touched on it a little bit, but what I’m looking for in markets it’s like is it growing? Is there space for new entrant to make its mark? And when it came to dbt, it was an explosive time in cloud data warehouses and it was that chaos that was really the opportunity for dbt because they created some orderliness and structure to the way that people worked with their data in the cloud data warehouse. So that was very compelling. The next is product. Everyone who’s listening hopefully either is interested in products or has a product background. So I won’t say too much there, but can you talk to users or potential customers, are they building something that’s really special, unique? Can you hear that, spark that enthusiasm and figure out if this is going to be special.
And then the last, I think this is more important arguably than if they have a good product but is distribution, do they have an advantage on how are they going to get to market because that’s really, really hard? And think about how they think about their competitive advantage on either the ecosystem or distribution and how they’re going to ultimately sell the product. You’re not going to get a 10 out of 10 on all four dimensions. So when you’re joining a company, you also have the benefit of dedicating your time. So try to think if they’re weaker on one dimension, what is it that you bring to the table or what are you special at that could potentially de-risk the success of the company
The Secret to dbt’s Success
Lenny: In terms of spark with a product. Right now on product market fit, now how do you know if you have product market fit? And a lot of it often comes down to there’s a emotional reaction from someone you’re talking to about the product you’re building. There’s like, “Holy shit, I want this now.” Is there anything even more specific you’ve seen of just what is a sign that this, there’s a spark that people are just really enthused? You talked about people made dbt part of their identity. Is there anything else there?
Julia Schottenstein: Yeah. It’s can they not stop talking about it and that’s the chatter about a product, they want to share it with their teammates or to other people at different companies. That just top of mind love and wanting to share what they’ve found with others is really a great sign that you’re onto something. And then that spark will help do a lot of the work on how do you get to market because your evangelists are really your users of people that love what you’re building.
Flywheel and Network Effects
Lenny: What about in terms of the distribution bucket? What are some examples of just really important, I don’t know, unique or effective distribution strategies or I don’t know, unfair advantages you’ve seen maybe with dbt, maybe other companies? What are some examples of that?
Julia Schottenstein: So dbt had an ecosystem advantage and they were open source and this helped really dramatically for lots of people to have low barrier friction to just try it out and spread organically. They first got started with very horizontal. People could just get started without ever even talking to sales and think that was a competitive advantage. But not all companies need to be product led. Some companies are enterprise top-down sales. So in those situations think about does the team really know how to land a complex enterprise sale? Do they have a background in that particular space? Do they have a network of connections? Can be different depending on what the company is selling, but you either want to see a company that’s really strong at enterprise or really strong at the bottoms up.
Transitioning from Consulting to Products
Lenny: Okay, cool. So I want to shift a bit to talking about an area that you have a lot of experience in which a lot of people are also really interested in right now, which is M&A. I’ve invested in a lot of companies and maybe, I don’t know, once a month I’m getting an email from a startup I’ve been investor in. There’s just like, we’re looking at maybe selling the company, things aren’t working out the way we were hoping. And you’ve been on I think maybe all sides of the table of M&A transactions, including I think you led the acquisition of a company… I was an investor in the dbt acquired that I think is public company called Transform. So my question is just for founders who are currently thinking about M&A meaning acquisition essentially. What’s your best advice for them for how to be most successful in M&A outcome for themselves?
Julia Schottenstein: When it comes to acquisitions, the time to start thinking about an M&A strategy is hopefully when you don’t need one. And the best strategy that I could give a founder is to have a really strong offense in building their company. And when founders start their businesses, they don’t usually set out to start a company to sell it to another business. They start it to be an enduring independent standalone company. And if you have that path, then you’ll have the upper hand in absolutely every single M&A conversation because you have a viable alternative, which is do nothing, stay the course, you don’t have to sell. But of course that’s not the case for most companies. Most companies don’t have a viable path to being an independent company forever. So they have to think about M&A. So M&A is always about creating plan Bs.
And the way I would think about it is for any one company, there’s only ever two to three buyers that find what you’re building to be extremely strategic. And the strategy that I would do and how do you get noticed is I would figure out the area that you bring a competitive advantage and I would inflict pain on that potential buyer. Make it impossible for them to not notice you because that’s when they’re going to have their ears perk up and say, “Well, what’s going on with this company?” We just bought this company Transform. They are playing a really good playbook here.
And the really important piece here is you want to do that in a way that’s still friendly and open. I see a lot of founders get this wrong and they prematurely will shut down a conversation or they won’t talk to an incumbent or a potential future buyer because they take too competitive of a stance. But that’s a mistake because M&A is all about creating plan Bs and you don’t want to shut that door down prematurely because you don’t know if you can really go the distance and be an independent company. So you want to have optionality.
Understanding Algorithms with String and Sticky Notes
Lenny: I love this term with pain on your potential acquirers. What are some examples of that? Who’s done that well or what’s an example of that interaction?
Julia Schottenstein: Yeah. I mean I can share the transform stories of a company we just acquired, we announced it in February. So dbt Labs, we build transformations, that’s our main product. And we were venturing into a new product area that we call the semantic layer. And to describe what that is quickly, it’s allowing companies to define their business metrics and so that whenever anyone queries it, we always serve back consistent data on those business metrics and transform. They were a pure play company only in this metrics layer, semantic layer. And they had a really strong product.
They had figured out some of the technical challenges and they had solved it early on. They had the benefit of having worked at Airbnb, which and Airbnb in the data world is famous for having a really successful semantic layer metrics layer called Minerva. And what we had at dbt Labs is really good distribution and ecosystem, but we were a little behind in bringing a product to market and we felt that pressure from Transform because they were doing such a great job at being vocal and loud about how their semantic layer solves these really hard technical problems.
But they didn’t have any distribution. So that was really tough for them and they were putting pressure, but they were still positioning their company as a partner to us because they wanted our community to be excited about what they were building and hopefully lure them over to use their product. So because they had positioned themselves as a friendly partner when really we were trying to compete for this similar use case, when it came time to do an acquisition, we were really excited because we knew their product was good and they had already done a lot of the work to make integrating with dbt possible and that helps us post acquisition do the integration much more easily.
Boundaries of Open Source vs Proprietary
Lenny: How do you just think about either as a startup or even an incumbent about how to think about competition, how much emphasis, how much energy to put into thinking what competitors are doing and just how that informs your strategy?
Julia Schottenstein: So we recently codified our philosophy when it comes to competition. And I’ll give Nick Handel the founder of Transform who led this exercise at dbt Labs. But we really have three pillars when it comes to competition. So the first is hold true to our vision. We’re really excited about the path and the journey that we’re going on at dbt Labs and we don’t want the distraction. So occasionally you’ll have competitors maybe throw shade or throw stones, but most of that is just noise. If you have a lot of conviction that you’re going in the right journey, you want to just keep your eyes straight ahead and run your best race and not be too distracted by what maybe some critics are saying.
The second philosophy we have is really a grow the pie philosophy. So we want to work with our partners in our ecosystem to make the opportunity set even larger and we see that today. We mostly serve reporting and BI use cases, we’re seeing lots of companies start to operationalize their data. Now with this big wave of ML, clean Transform data assets are being used to train machine learning models so that the pie continues to grow. Let’s focus on that as our target and work with people to make the opportunity set really attractive and not try to slice it up too thinly.
And then the last one is we want to lean into our strengths. So we have an ambition to be a platform company and we know what we’re good at, but we also want to leave space for our ecosystem to offer solutions to our users that help them out. And we really want to foster an ecosystem where we can partner with lots of companies in the modern data stack. And generally speaking, when it comes to competition, we take a really long-term view and there are a few areas that we do want to hold our ground and that’s in our transformation standard as well as our semantic standard because we believe those two are better served together for the user’s sake. But for everything else, we really feel like we can work with our ecosystem and accomplish what we want to accomplish and also help them accomplish their goals too.
Pricing and Willingness to Pay
Lenny: This might be a good time to just chat about dbt and the success the company has had. So many startups have tried to become a standard default layer of what’s now called the modern data stack. And I don’t know any startup doesn’t use dbt or planning to use dbt. It’s just a incredibly rare success story somewhere to snowflake where it’s just like it’s the default for building large data startups and most startups these days work with a lot of data. So my question is just like what do you think dbt did most right to win in this and continue to win?
Julia Schottenstein: So I think dbt did a lot of things right, but I’ll point out too that really stick out to me. And the first is just power and simplicity and the second is a commitment to being open. And I’ll touch on what I mean by those two things. So when dbt was first getting started, you would hear a lot from companies, I don’t understand, what’s so special about dbt? We have a SQL templating tool at our company, we built one in-house. Like this is really straightforward and simple and it’s true like dbt is really simple, but that is the power of it.
So our founders, Tristan, Drew and Connor, they had a belief that the people who do data analysis work, that really work closely with their business stakeholders should also be the ones to contribute to creating clean data assets in production because that data prep work is a necessary prerequisite for any analysis that you do. So dbt was really this belief that if you know SQL, we want to invite you to do these workflows that were traditionally held by data engineers but you had to earn that. So dbt has this nice framework where it’s harder to mess up, keeps data quality really high, but it is pretty simple to get started and learn and learn. And that was really the unlock in the industry. We were definitely solving a pain point at the right time.
And then the second thing is this commitment to being open. So dbt is open source and that’s the main guts of dbt where you write your business logic and it helps in a number of ways. Specifically it helps with flywheel, keep the flywheel running and also with network effects. And I’ll explain what that looks like. So dbt is really easy to get started with at your company with reduced friction. We’re building a product that people, so they talk about it, they want to share it both at their organization and with other companies. Other companies get started with dbt again with reduced friction.
We now get to see this really diverse set of use cases for dbt across company sizes, across industries and it allows us to build a truly horizontal company. As our company grows, we get to invest back into our community and our product and the flywheel begins to spin faster. And then meanwhile we have a really large user base. So we have 20,000 companies using dbt every single week and that attracts partners to want to build for dbt and so they share best practices, build workflows, and now if you’re a company and you’ve standardized on dbt, you’ve really unlocked an integrated modern data ecosystem that wasn’t available for you before. So that has a flywheel and also benefits everyone that decides to be on the standard. So it’s those two really important trends that made dbt so powerful today.
The Pricing Adjustment Process
Lenny: So what I’m hearing there is essentially the product was right for what people needed to solve. There’s also a product led component, open source, free self-serve piece that people adopted, used and started working and then scaled and started paying for it. And then there’s an alignment of the vision of where this was going and how it fit with how people wanted this to work for them. Is there anything else? Because a lot of startups do that and that all sounds really smart and good, but a lot of startups try to do those things and no one cares. Maybe their product isn’t necessarily what people are looking for, maybe they don’t get the right distribution. I don’t know. Is there anything else that you think they did really well that helped them kickstart this to even be a thing? Is it timing that was really great? Is it specific influencers early on?
Julia Schottenstein: Yeah. I think timing was really important with the success of dbt. That they were there when the cloud data warehouses were really exploding and growing in an enormous way. And dbt Labs started as Fishtown Analytics, a consulting firm. So they worked really, really closely and hands-on with all of their consulting partners to get the pain point and really solve firsthand challenges that they saw. I think that combination of being at the right place at the right time and also getting to work really closely with people’s day-to-day problems created a really special experience.
Public Strategies in M&A
Lenny: I didn’t know that. That’s a really important element of the story is basically they were focused… How long did they do that Fishtown Analytics consultancy?
Julia Schottenstein: Board consulting part of the business was almost two years.
Plant Seeds Early
Lenny: Okay. So they basically spent two years solving this problem basically manually for people, and that’s such a great way to understand real pain and figure out how to solve it.
Julia Schottenstein: Totally.
What to Do When Cornered
Lenny: Awesome. Okay, so that’s a really interesting insight. Just spend a few years. It sounds like it was almost manually helping people transform their data using whatever tools already existed.
Julia Schottenstein: Yeah. Well, they were building dbt and using dbt to help them do their jobs better and supporting their clients. And whenever they encountered paper cuts or friction or the workflow was taking longer than they expected, they would build that into dbt. And that really matured the experience of the product because the people who were building it, the founders were also day-to-day working with these customers or clients that had pain points.
Current Active Acquirers in M&A
Lenny: That reminds me of a story you told me about how you made your eng team do some manual work of an algorithm involved in transformation. Can you share that?
Julia Schottenstein: Okay. So I’m going to preface this story by sharing that I’m a huge math nerd and one of my favorite books on logic is called Girdle Escher Bach. And in this book there’s a fun scene where there’s an ant farm that bands together to do the work of a computer flipping bits from zero to one to solve logic gates. So this chapter of that book was really the inspiration for an exercise that I ran my team through. So about a year ago we were doing a big zero to one new project at dbt Labs and we were going to change the algorithm for how we built customers data transformation graphs. And I needed a way for the team to really internalize all of the changes that we were going to be making and I needed them to own it because otherwise they wouldn’t be able to anticipate all of the edge cases and it wouldn’t be quite as durable. You couldn’t copy paste the algorithm.
So I showed up to a team offsite with a spool of rope and sticky notes and I think my team looked at me crazy, went with two heads when I started to tie people up to create a graph. So each note of the graph was an engineer and the rope was the edges of the graph to connect them. And then we worked through the new algorithm extremely slowly, step by step. And it was a way that you couldn’t leave that exercise without knowing exactly what was going on because everyone had a role to play.
So I think a lot of times when you’re starting something new, you get into a situation where a few people really understand it and they’re running way ahead of the rest of the pack. But I needed a way for the whole team to go along for the journey. So I’m constantly trying to create these important moments or memorable moments for the team so that it’s centered around our mission and they can have the ownership of taking the project and making it successful. So it was perhaps a overly creative or kooky way to spend the day, but it was really successful.
Timing the M&A Market Recovery
Lenny: What was the actual algorithm you were trying to implement?
Julia Schottenstein: We were trying to figure out how to make flipping the way that we run people’s dags from an imperative way to a declarative way. So instead of running things left to when data arrived in your warehouse. You think about it as reverse, like what would need to happen to make your data SLAs be materialized in time.
Company Values at dbt Labs
Lenny: Awesome. And sounds like the team found that valuable.
Julia Schottenstein: Yeah.
Co-Building Products with Strong Communities
Lenny: Okay. Reminds me of a clip from this last season of Ted Lasso where they have used red strings and I won’t get into it, but if you’ve seen it you will know what I’m talking about. I want to come back to your chatting about open source versus not open source. So some part of dbt was is open source and some isn’t. I’m curious how the team decides what is open source and what should be open source, what isn’t open source and what to charge for?
Julia Schottenstein: We think about dbt open source. It’s really the guts of the data transformation. It’s where you describe your business logic. And then on the cloud side we build proprietary software that supercharges the development life cycle and the productionization of dbt at scale. So what we think about as leaving for our cloud offering is we deal with state, so stateful interactions and also any cross team or structural collaboration. We want to reserve that for our proprietary offering. And I think it’s really important to have that distinction of what do you believe should be open source or what is the open standard that really matters? And ecosystem to us is really important. So it’s important that that remains open source, but then we want to supercharge that experience with an open core model and build proprietary software that makes people much more successful at using dbt.
Product Development Frameworks and Philosophies
Lenny:
So many PMs I know are considering or already building with AI and AssemblyAI is the fastest way to build with AI for audio use cases. Now’s the time to check out AssemblyAI, which makes it easy to bring the highest accuracy transcription plus valuable insights to your customers. Just like Spotify, CallRail and writer do for theirs. Visit assemblyai.com/lenny to try their API for free and start testing their models with their no code playground. That’s assemblyai.com/lenny. I know you also spent a lot of time thinking about pricing and willingness to pay and things along those lines.
Is there anything you could share about which is what you’ve learned about how to think about pricing for a tool like this or even in general?
Julia Schottenstein: Pricing and willingness to pay is such a hard conversation and lots of startups don’t do this early enough in their company journey. Likely a side effect of zero interest rates where investors were happy funding GitHub stars and usage and companies never thought about how am I going to make revenue or make money, which is it’s important. So for us at dbt Labs, I don’t claim to be perfect at it, but we’re trying to get a better muscle around it. And I always think about Madhavan who wrote this book Pricing Innovation. I know you’ve had him on the show before, but he shares that you don’t get to decide if you’re going to have a pricing or willingness to pay a conversation. You only get to decide what. So it’s much better to have that conversation before you build the product than have it when your sales team’s trying to sell something and people aren’t excited about what you’ve built, aren’t willing to pay for it.
And at dbt Labs we have this value, it’s one of our core values that says we are more concerned with value creation than value capture. And we really mean this. When we talk about what is the value of dbt Labs to our customers, they often talk about how it’s either 20 to 35% as valuable as what they spend on their cloud data warehouse. But what we charge our customers is a very small fraction of that 20 to 35% and that’s by design. And last year we did our first ever pricing change in the company history and you learn a tremendous amount when you have that event because you get to test the price elasticity by of your customers.
And it’s so important to learn that lesson while the company is still smaller or the stakes are lower because pricing just is always evolving. It’s not a fixed thing, it gets more complex over time. So we have to think about it quite a bit because my team builds proprietary software for dbt cloud and when we lose a deal we most often lose it to dbt open source and we like it that way. We’re happy to lose to ourselves, but we have to really think very deeply about what are people willing to pay for and what moves the needle for them and focus on that.
Transferring Investing Skills to Product
Lenny: I’m so curious what that pricing process was like to figure out what to change. Is there anything you could share about just what that was for you or what are some just surprises that came out of that process or just like, “Wow, we didn’t expect that in these conversations?”
Julia Schottenstein: It’s really like an all hands on deck conversation. Pricing is so cross-cutting because it’s a finance discussion as well. You’re modeling out things in spreadsheets and figuring out how that might impact the business. But you can’t just solve these problems in spreadsheets. You have to go talk to customers, test the waters, understand where people’s appetites are and sussing that out is really hard. And then of course there’s a product piece to it too where you have to affect it and communicate it as well. So it was very cross-cutting. We learned a lot. We track our conversion rates really carefully. We track our turn rates very carefully and I think largely we were happy with the change that we made and we felt like one of the big things that we were trying to solve for was have our pricing catch up to how people valued the tool.
Lightning Q&A Round
Lenny: How many people did you end up talking to, who was doing these conversations and is there anything really important you learned about how to ask these questions and these sorts of leanness to pay conversations?
Julia Schottenstein: So we talked to dozens. It was combination of product and product marketing, having the conversations and people aren’t very willing to share explicitly what they will pay. But there’s some tools that we use on relative value. People most think about what is the relative value of dbt in their cloud warehouse. And we also tried to employ some of the tactics that we tried to suss out what do people view as very inexpensive? What’s a price point that’s very cheap or a no-brainer? What price point is maybe fair or comfortable for them and what would be too expensive? And then we had all the data back to figure out where we landed.
Lenny: I want to come back to M&A for a bit. I had a few more questions there and I moved on from that. So again, you’ve done a lot of work within the M&A realm. Something that I saw recently, and this connects to the fact that a lot of startups now are looking to get sold. This VC hunter walk, he’s a founder of Home Brew Ventures, had this interesting blog post where he basically suggested you should actually think about being public about the fact that you’re selling your company. Which is crazy because in the past you never want to come across as too selly. I don’t want people to think I’m desperate. And his point is many startups are desperate right now and it’s okay to be public about that. And then in theory creates more of a bidding situation where many people know versus keeping it secret and in closed rooms. So my question is just what do you think of that? Do you think that’s effective strategy? Is it not?
Julia Schottenstein: I think it’s good advice. If you’re in a Hail Mary situation where you’re looking for a home or you need an exit for your business, it’s better to be transparent and cast a wider net. And you’re right, in previous times founders tried to be a little bit cute or obfuscate that they were evasive the situation that they were in because they wanted to drum up some competitive interest when there really wasn’t any. But unfortunately in today’s climate, too many companies are in that spot so it’s impossible to hide it. So the better approach is just to be transparent and I see it pretty regularly and there’s absolutely no shame in sending a note that says something like, “Hey, we’re looking for an exit for our company X, Y, and Z didn’t pan out as we expected. We built a really interesting product and we want to keep the team together. We’re running a process. Are you interested?” Really just as simple as that.
Lenny: I’ve seen a lot of companies put together these really detailed decks or even websites of just like, here’s the team, here’s what we built to be very promotional about it. Is that something you’ve seen? Is that something you’d recommend?
Julia Schottenstein: Yeah. Usually in these situations people are acquiring the teams and so having your data room together, really the most important thing is these are the team members that are going to come along with the acquisition is the biggest motivator for why a buyer would get excited.
Lenny: You made this point earlier that a lot of the seeds need to be planted early for you to have the best outcome. And this reminded me… So I had a startup local mine that we sold to Airbnb and I met Airbnb initially at a year before we actually started exploring the process at a random party at South by Southwest where I was, I actually have no memory of this party, but the head of product at Airbnb, Joe bought, remembered it and came back to us a year later and just like, “Hey, what are you guys up to? Maybe we could collaborate on some stuff,” and that led to an acquisition. So I think that’s just wanted to touch a ban on that point of just the power of, the way I thought about it is let a thousand flowers bloom, just like meet everyone. Get the word out that you’re around and what you’re doing so that in the future when someone has that problem they’re like, “That company, maybe we should talk to them.”
Julia Schottenstein: Yeah, you want to make sure that the buyer knows who you are before the acquisition moment and hopefully that’s because you’ve made an impact and they like what you’re building. Maybe you’ve inflicted some pain on them. But yeah, certainly creating those connections well ahead of an exit event is important.
Lenny: And specifically there it’s meet people at your competitors potentially. For us, Airbnb was never even a potential company we would sell to because it had nothing to do with it but ended up making sense down the road. What’s like the realm of the companies and people that you think people should think about meeting?
Julia Schottenstein: If the company is buying a lot or they’re active, they often have corp dev teams and so use that corp dev team to your advantage. It’s their job to meet absolutely every company that could be potentially interested. So take that meeting, say you’re not interested in an acquisition just yet, but push them to make an introduction to someone that could sponsor the deal. So usually that’s someone in product or maybe a GM and use that as a starting point for maybe just a conversation, maybe something more like a partnership but get the corp dev team to work for you.
Lenny: What if you’re at the other end of the spectrum and you’re in dire straits right now and you’re just like, “Man, what do we do to potentially sell this company?” I know odds are not going to be great, but just what do you suggest folks do in terms of I guess finding connections to potential companies that might be acquirers?
Julia Schottenstein: Yeah. I mean use your network. Usually your venture capitalist has a really big network and one of the things that I hear founders feeling the most nervous about is a duty to return the money to the investors. And maybe this is a unpopular thing to say on a podcast, but your investors understand that they’re not making their money back. And what they want to do instead is have you end up at a really great company like an Airbnb because that will help them down the road. So it’s all about the long game, but use your investors to help you find connections at different companies that could be buying and don’t worry so much about disappointing them or being really realistic about where you are in your company journey.
Lenny: To build on that, I’m just an angel investor, I’m not like the lead fund and they feel differently I imagine. But the last thing I want for a founders to get stuck at a company they hate and just so that they could return some money or have some outcome rather they just give up, move on. Life is short.
Julia Schottenstein: Yeah. I think founders forget that it is so risky investing in early stage companies that 50% of portfolios investments don’t return anything. And that’s just part of the game and it’s a very acceptable path where hey, we gave it our best shot. It didn’t work out and moving on.
Lenny: Which is tough a lot of times for founders that are told like it’s all about grit and not giving up and don’t quit. Sometimes you should quit.
Julia Schottenstein: Gosh, yeah. I don’t want to say that. I think being founders, it’s an extremely lonely role sometimes and it is very hard to know what your next chapter will look like or what the journey will look like and sometimes you really are out of cash and you do have to find a home. But I hope that you can continue to fight and find a way forward.
Lenny: Agreed. Specifically for people thinking about selling their company, are there any companies you think are good companies for people to look at right now that are actively acquiring or open to M&A?
Julia Schottenstein: This is a tough one. I think a lot of companies are on the sidelines for a number of reasons. And they could be on the sidelines because they just did an acquisition and they’re trying to digest or integrate that company. They could be on the sidelines because they’re not growing headcount as much and M&A is often org chart gymnastics of folding the target companies headcount into your budget and plan and maybe just don’t have a lot of space. Or probably more common right now is there’s just a general uncertainty about the future. And in highly volatile markets people want to take care of their own and even the best M&A deals at a level of complexity that a lot of buyers are just not looking to take on right now.
So there are a number of reasons why people might sit out, but what I would do if I were in a position of wanting to sell my company is I would come up with a buyer set of maybe a dozen and really there aren’t more than a dozen companies that will find what you’re doing to be a very good fit. Start with your buyer set and then start calling the list by looking at some of the criteria that might count people out. And then go have those conversations. And if you’re in a Hail Mary situation, be very transparent about it. Maybe open up the buyer list. But if you still have some room, I would maybe focus on two to three partner or buyers and really play a different playbook, which is inflict pain. Make it really hard for them to not notice you but do it with a smile and be friendly at while you do that.
Lenny: Sometimes with M&A discussions, there’s a lot of subtlety to the way you communicate where you don’t come out and be like, please we’d love to sell our company to you. Any advice on just the phrasing and how to approach it or do you think it’s just, it’s fine, just tell us we’re looking to sell our company. Are you interested in being buyer?
Julia Schottenstein: Yeah. I just wouldn’t be too clever here. Everyone understands like I’m evaluating strategic alternatives. It means you’re looking to sell your company and it depends. Do you have time? If you have time, yeah. Don’t come out and say, “Hey, I’m for sale.” That’s not going to end up in a good outcome. But depends where you are in your company journey. If you have time, then don’t talk about M&A at all. That’s the last thing that you want to speak about. Instead, you’re talking about maybe collaborating or partnerships. How do we work together? Knowledge sharing and M&A is a dirty word if you have a lot of runway and you’re going to try to continue to pursue an independent path, but if you’re out of time, you’re out of time.
Lenny: That’s such good advice. I remember the term we used was we want to explore a strategic partnership and everyone’s just like, I knows what you mean by that, but you just don’t want to say it.
Julia Schottenstein: Yeah. It’s like everyone in the room’s looking like yeah. Okay. Strategic partnership or a strategic alternative, it’s like we all know these code words and we understand the situation you’re in.
Lenny: Yeah. When do you think M&A market might pick up again? I know it’s impossible to predict. Do you have any sense?
Julia Schottenstein: Yeah. I wish I had a magic ball. That’d be pretty sweet. I think what is happening though is we’re far enough out from the peak of the markets. So the peak was really November of 2021. And why does that matter? Two things. One, founders are coming to terms with valuations that they maybe received at the highest of the market, are no longer going to hold in this market and companies are out of cash or maybe out of options. We’ll be better assets entering the market soon. And at a certain point the opportunities will just be too great. That will incentivize a lot of the buyers that have been on the bench to start participating again in the M&A market. And I don’t know exactly when that will be, but I think we’re pretty close. I think we’re pretty close.
Lenny: And you mentioned a value that you have at dbt. I forget exactly what it was, but I’m curious what are the other values that you have at dbt, whatever you can share. Always curious what principles and values companies come down to help drive the way they think.
Julia Schottenstein: The value that I shared is we are more concerned with value creation than value capture and that really drives everything that we do. We try to put a lot of good out into the world and it pays back slowly. The whole mission of dbt Labs is to help analytics engineers disseminate organizational knowledge through data. So we really believe also being participants of sharing that information and getting more people knowledgeable about all sorts of things.
Transparency wins. We’re a really transparent company. We share our board decks. We have lots of communication and participation in all of our Slacks. We’re writing culture. We have hard conversations in the open. So that’s another a big one. Transparency always wins. We are humble. We don’t ever feel like we are successful. We come at this from a very humble space where we feel like we have to serve our community and our users and that really motivates us. And then another one is just work done well as its own end and it’s really focusing on the journey and not the end destination.
Lenny: Awesome. I want to do a post someday of just like, here’s the values all these different successful companies have come to and see if there’s any patterns. I’m actually doing a post right now on Snowflake and on Figma and what you touched on. There’s such a connection and a thread of obsessing with your users and making sure they’re happy at Figma. I forget exactly what it is, but it’s just like in this article, it’s going to come out tomorrow actually. So people get a sense of when we recorded this where they think of their company as software as a service where service is their number one goal. They actually provide service and software is just a way to do that. And then it’s Snowflake. Their number one core value is put customers first and they talk a lot about how they just actually informs their prioritization all I think, and all their thinking. So it’s interesting. That also comes up a lot in what you’re talking about, which I think it’s easy to say, but I think what actually separates companies that succeed is they actually put this into practice.
Julia Schottenstein: It’s really interesting too because a lot of the people who work at dbt Labs came from the community, so they feel this real ownership in making the experience an excellent one because they were so compelled to come join dbt Labs because the product changed the way that they work or changed their lives. So that commitment to the community and product experience is really, really strong.
Lenny: This isn’t another thread. Maybe we’ll go down real quick. I’m working on a different post around Reddit and how to work with really opinionated users that strong opinions about changes to the product and the way they described it, these guys were there for five years working with the community on the product. Is that to your users, the product is their baby, basically. It’s like they think it’s theirs versus the companies. And I’m curious, having a really strong opinionated community, what it sounds like you also have, what have you learned about just working with them to build product that makes them happy and avoid revolts and really upsetness?
Julia Schottenstein: It’s hard as you grow. I think it’s just a challenge cause our community gets bigger. You can’t service everybody’s needs. But I think what we’ve done is everyone is very deep in our own product. I think one of the cool stats is at dbt Labs we have over 30% of our employee headcount has contributed to our data transformation workflow. So that’s across every discipline. It’s in obviously product, obviously in our data team. Our marketing team also contributes to data transformation. And our engineering team will also contribute to our internal dbt analytics project and that sense of really understanding what the experience is like and then soliciting as much feedback as we possibly can. We have a dbt Slack community of 50,000 and all of our employees are in that Slack channel regularly and can feel when we mess up or we don’t quite deliver an experience that we’re proud of, you will just see dozens of people trying to jump on board and try to make it better.
Lenny: Is there any other frameworks or just general processes that you found to be release useful in building awesome product running teams?
Julia Schottenstein: I’m not a big framework person, but there’s two sayings that I find myself repeating or I either to myself or to others. And it’s worse is better and tech debt is a champagne problem. And what do I mean by that? It’s really to help me combat this perfectionism because perfect doesn’t exist and you should instead go with good enough because when you ship, that’s the moment when you get to learn a lot from your users and you just can’t anticipate it. You try very hard to understand exactly how people will use the product and get all the edges ironed out. But you can’t until you ship. And I’ll share an example. So my team helps support the dbt cloud scheduler and the initial version of the dbt cloud scheduler was pretty naive. We were a little embarrassed by it.
It was a big old for loop over a big old jobs table. So we would look like is it time for this job to run? Okay. Yes, run this job. Okay. It’s not time for this job to run next, continue on. Is it time? Yes, run this job. And it would just loop over and it’s extremely naive and very simple, but it got the job done. And I try to remind the engineers, we would be so lucky to have tech debt because that means people are using the product. And now we’ve had to rebuild our scheduler several times over because we do have meaningful scale. We have 8,000 companies using our scheduler. We have to manage 10 million runs per month. But what we didn’t need at launch was a distributed scheduler with coworkers and RabbitMQ. We just didn’t need it because we had no users. So these two sayings that worse is better and tech debt is a champagne problem, just really reminds people like, let’s ship, let’s get it out into the user’s hands and then we’ll learn and iterate and it’ll be a better experience for them.
Lenny: That’s a good segue to my last question. So weren’t a PM before this role. You have strong experience in investing, investment banking business in general. I’m curious what you think product managers should maybe focus on more or learn more or lean into to become stronger product leaders based on the experience you’ve had moving into product.
Julia Schottenstein: So I pull a lot on my experience or some of the things that I did was as an investor in my current role in product. And maybe I’ll touch on what is the scale of a venture capitalist might be a little bit foreign for people, but venture capitalists, they spend all their day meeting lots of different companies, context switching. They have to know a little bit about quite a lot of different things. And they do this to refine their investment tastes or find their investor judgment. And they’re also investing a lot in their network and connecting people, supporting people, and mining people for ideas that are way smarter than they are. So you do that all the time in Venture and I’ve brought a lot of those skills with me into product and it translates really well.
The first is I still spend a lot of time investing in my network and I think it’s an underrated way for a PM to spend their time. And I try to build a network of operators at other companies that are like dbt Labs that are growing nicely, maybe a little bit ahead of where we are. And I ask them questions, how did you navigate open source? How did you navigate pricing? How did you navigate acquisitions? And then I take the best ideas, figure out which ones I can apply and bring it back to dbt Labs. The second thing is I really think my special T or my superpower is that I’m a T-shaped generalist. So I know a little about a lot of things from finance to business to product. I have to go a lot deeper in product in the areas that I specialize in. That’s where the tail of the tea comes in. But it’s precisely because I’ve had a diverse background that makes me more effective when I’m trying to get things done within the organization. Because I have just more credible experiences that I can pull from.
And then the last thing that I think maybe doesn’t show up day to day in my product work, but in investing, you’re constantly thinking about risk and the power laws, and we touched on this before, but most investments don’t work out. You lose the dollars that you put in, but all the returns come from these rare events that make up for all the losses. You have to think about what are the uncapped upside opportunities in investing. And I think in product, you still have to do the same thing. If 50% of the things I worked on went to zero, we’d have a problem. But it encourages me to continue to make bets for the company that has the chance of bending the trajectory of our business.
Lenny: We’ve reached our very exciting lightning round. I’ve got six questions for you. Are you ready?
Julia Schottenstein: Yeah, let’s do it.
Lenny: What are two or three books that you’ve recommended most to other people?
Julia Schottenstein: Okay. So two books that helped me learn a lot about myself. Range, it’s a book about generalist and also Quiet, it’s a book about introverts. And then I like a lot of biographies. So a few of my favorites are Snowball about Warren Buffet, Made in America, about Sam Walton and Leonardo da Vinci.
Lenny: What is a favorite recent movie or TV show?
Julia Schottenstein: So I almost watched a movie in preparation for this podcast, but I really don’t watch things except in the holiday. During the holidays, I like Succession, but I have not seen the latest season.
Lenny: Wow. You’re in store for a treat. Favorite interview question you like to ask?
Julia Schottenstein: When’s the last time you had to teach yourself something new and how’d you do it? So I like to test for growth mindset and a thirst for learning. And then also why dbt Labs. I think a lot of people who come to dbt Labs have very authentic reasons why they’re drawn to the company. And in moments where in things are tough, it’s the answer to that question of why are you here, it’s going to make all the difference.
Lenny: And sounds like what you look for is just like an genuine enthusiasm.
Julia Schottenstein: Yeah.
Lenny: Awesome. What are some favorite products you recently discovered that you really like?
Julia Schottenstein: I like Belly. It’s a consumer social app that lets you find and discover restaurants and rate them with your friends. It’s been a lot of fun looking at the New York City restaurant scene.
Lenny: I’ve never heard of that. Awesome. What is something relatively minor you’ve changed in the way you all do? Product that has had a lot of impact?
Julia Schottenstein: Do fewer things and try to single thread the team as much as possible.
Lenny: And single thread meaning like one main priority, one goal?
Julia Schottenstein: One mission. Yeah. We’re all working rowing in the same direction.
Lenny: Final question. You have a podcast, first of all, tell us what it’s called, but second of all, what’s a favorite podcast of yours other than this podcast and your podcast?
Julia Schottenstein: Yeah. It’s called the Analytics Engineering Podcast, so if you want to learn more about the data industry, I host it every other week with our CEO Tristan Handy. It’s a lot of fun. Check it out. Other podcasts that I really like are In Depth, it’s First Rounds podcast by Bretton. He interviews a lot of operators about how they do their very best work. And another podcast that I really like is the Logan Bartlett Show, which touches on timely trends in tech.
Lenny: And In Depth I think Todd Jackson actually hosts a lot of the episodes too. Also, a huge fan of the podcast. Definitely check it out. And then say your podcast again and how can folks find it?
Julia Schottenstein: It’s called the Analytics Engineering Podcast.
Lenny: And it’s just in podcasting apps?
Julia Schottenstein: Yes.
Lenny: Amazing. Check it out. Julia, we’ve talked about inflicting pain and strategic partnerships and why worse is better. Thank you so much for being here. Two final questions. Where can folks find you online if they want to reach out and how can listeners be useful to you?
Julia Schottenstein: You can find me on Twitter J_Schottenstein, and you can also find me in the dbt community Slack also, Julia Schottenstein. Send me a note, reach me there and I’d love to hear from you if you have data problems or we can help serve your needs better, would love to chat.
Lenny: Thank you so much for being here, Julia.
Julia Schottenstein: Awesome. Thanks Lenny.
Lenny: Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
Glossary
| English | 中文 |
|---|---|
| Analytics Engineering Podcast | Analytics Engineering Podcast(播客名称) |
| Belly | Belly(消费社交应用) |
| board decks | 董事会材料(board decks) |
| bottoms up | 自下而上(bottoms up) |
| Bretton | Bretton(In Depth 播客主持人) |
| corp dev | 企业发展战略(corp dev,即 corporate development) |
| DAG | DAG(有向无环图,Directed Acyclic Graph) |
| data room | 数据室(data room) |
| data transformation graphs | 数据转换图(data transformation graphs) |
| data transformation workflow | 数据转换工作流(data transformation workflow) |
| declarative | 声明式(declarative) |
| enterprise top-down sales | 自上而下的企业级销售(enterprise top-down sales) |
| evangelist | 布道者 |
| Fishtown Analytics | Fishtown Analytics(dbt Labs 前身,咨询公司) |
| flywheel | 飞轮(flywheel) |
| growth mindset | 成长型思维(growth mindset) |
| Hail Mary | 绝地求生(Hail Mary,源自美式橄榄球的”万福玛丽传球”,指孤注一掷的尝试) |
| Homebrew Ventures | Homebrew Ventures(风险投资机构) |
| Hunter Walk | Hunter Walk(Homebrew Ventures 联合创始人) |
| imperative | 命令式(imperative) |
| In Depth | In Depth(First Round 播客) |
| in-place company/in-place enterprise | 在位企业(incumbent) |
| Joe Zadeh | Joe Zadeh(Airbnb 前产品负责人) |
| Leonardo da Vinci | 《Leonardo da Vinci》(达·芬奇传记) |
| liquid net worth | 流动净资产 |
| Localmind | Localmind(Lenny 创办的创业公司,后被 Airbnb 收购) |
| Logan Bartlett | Logan Bartlett(播客主持人) |
| Logan Bartlett Show | Logan Bartlett Show(播客名称) |
| logic gates | 逻辑门(logic gates) |
| M&A | 并购(Mergers and Acquisitions) |
| Made in America | 《Made in America》(山姆·沃尔顿传记) |
| materialized | 物化(materialized) |
| metrics layer | 指标层(metrics layer) |
| Minerva | Minerva(Airbnb 的语义层/指标层产品名称) |
| modern data stack | 现代化数据技术栈(modern data stack) |
| network effects | 网络效应(network effects) |
| Nick Handel | Nick Handel(Transform 创始人) |
| offsite | offsite(团队外出活动) |
| org chart gymnastics | 组织架构体操(org chart gymnastics) |
| portfolio investments | 组合投资(portfolio investments) |
| power laws | 幂律分布(power laws) |
| product led | 产品驱动(product led) |
| Quiet | 《Quiet》(关于内向者的著作) |
| Range | 《Range》(关于通才的著作) |
| runway | 跑道(runaway,即创业公司的资金可维持时间) |
| semantic layer | 语义层(semantic layer) |
| single thread | 单线程(single thread) |
| SLA | SLA(服务等级协议,Service Level Agreement) |
| Snowball | 《Snowball》(巴菲特传记) |
| South by Southwest | 西南偏南(South by Southwest,即 SXSW,科技/文化/音乐盛会) |
| spec | 投资分析师(spec,即 special associate/principal track) |
| strategic alternatives | 战略替代方案(strategic alternatives) |
| Succession | 《继承之战》(Succession,HBO 剧集) |
| T-shaped generalist | T 型通才(T-shaped generalist) |
| term sheet | 条款清单(term sheet) |
| Todd Jackson | Todd Jackson(In Depth 播客联合主持人) |
| Transform | Transform(被 dbt 收购的公司) |
| Tristan Handy | Tristan Handy(dbt Labs CEO) |
Reformatted by reformat_english.py
并购、竞争、定价与投资 | Julia Schottenstein (dbt Labs)
文字稿
Julia Schottenstein:
并购始终是关于创造备选方案。我的理解是,对于任何一家公司,永远只有两到三家买家会认为你所构建的东西对他们极具战略价值。至于如何被注意到,我的策略是——找到你能带来竞争优势的领域,然后给那个潜在买家制造压力。让他们无法不注意到你,因为只有在那种时候,他们才会竖起耳朵说:“这家公司到底怎么回事?”
这里很关键的一点是,你要以一种仍然友善、开放的方式去做这件事。我看到很多创始人在这点上犯了错——他们过早地关闭对话,或者拒绝与在位企业或潜在的未来买家沟通,因为他们采取了过于竞争性的姿态。但这是个错误,因为并购的核心就是创造备选方案,你不想过早地关上那扇门,因为你不知道自己是否真的能走到最后、成为一家独立公司。所以你要保留选择权。
Lenny:
欢迎收听 Lenny’s Podcast,在这里我采访世界级的产品负责人和增长专家,从他们打造和发展当今最成功产品的宝贵经验中学习。今天的嘉宾是 Julia Schottenstein。Julia 是 dbt Labs 的产品负责人,负责领导 dbt Cloud 产品。她同时也是 dbt Labs 旗下播客 Analytics Engineering Podcast 的联合主持人,这是一档探讨影响分析工程师工作的数据趋势的节目。正如你将在本期节目中听到的,Julia 实际上从 dbt Labs 这边主导了对一家名为 Transform 的创业公司的收购,而我是这家公司的投资人。
在我们的对话中,我们深入探讨了并购流程,并给出了大量建议,帮助你提高获得良好结果的机会,以及如何从更宏观的层面看待并购。我们还深入聊了 dbt 的故事——这是你可能不太了解的最成功的创业公司之一。我们谈了他们做对了什么才走到今天。我们还讨论了如何最好地看待竞争、一系列思考产品的框架,以及如何处理定价和开源的建议。稍作休息后,与 Julia Schottenstein 一起享受本期节目。
从投资到产品的职业转型
Lenny:
Julia,欢迎来到节目。
Julia Schottenstein:
非常高兴来到这里。
Lenny:
你的职业路径很有意思,你是从 VC 转入产品管理的。通常方向是反过来的——通常是 PM 去做 VC,很少见到这个版本。所以我想先问一下,这是怎么发生的?
Julia Schottenstein:
我的背景确实不寻常,但我并不觉得奇怪——对产品感兴趣的人往往也对投资感兴趣,反之亦然。对我来说,我一直有三个广泛的兴趣:对商业的兴趣、对技术的兴趣、以及对市场的兴趣。在投资和产品这两个领域,我都能表达这些兴趣,只是权重不同。在产品领域,你在技术方面扎得更深,市场相对不那么重要,但三者都能涉及。所以我的背景确实不寻常——我曾经是 NEA 的职业投资人,把所有时间都花在投资面向技术用户的早期创业公司上,也就是开发者工具、基础设施和数据公司。
发现 dbt
2019年,我第一次发现了 dbt,这是一个开源的数据转换框架。我对 dbt 非常、非常兴奋,因为当我与使用它的人交谈时,他们描述自己在 dbt 上的体验方式与我之前听过的任何东西都不一样。对他们来说,这更像是一种身份认同,而不仅仅是用来完成工作的工具。这一点深深打动了我。而当我思考市场正在发生什么时,2019年确实发生了很多事情。市场在相当大的程度上变化着。云数据仓库开始爆发式增长。这一年,Snowflake 从一家 40 亿美元的公司变成了一家 120 亿美元的公司。我对自己说,如果 dbt 能成的话,它可能会以一种非常非凡的方式成功。我自然而然地想把所有时间都花在接近 Tristan 身上——他是 CEO 兼创始人——我想投资。
Lenny:
好的,所以这个时候你是 NEA 的投资分析师,你想投资 dbt,好的,继续。
Julia Schottenstein:
对,我非常非常兴奋。我想如果这能成的话,它可能会以一种非常非凡的方式成功,我把所有时间都花在想办法让 Tristan 喜欢我上,这样我就能投资这家公司。然后在 2020 年,我终于等到了那个电话。Tristan 说:“我们要融一些钱。“他拿到了一份他喜欢的某家机构的条款清单。那是一家不错的风投基金,但他也喜欢我,想给我一个机会。收到那条消息我超级兴奋,因为这是我的机会。但遗憾的是,我最终把那笔交易输给了 Sequoia。他们是一个强大的合作伙伴。
Lenny:
合理的失利。
从投资到加入 dbt
Julia Schottenstein:
我非常确信这将是一家特别的公司,于是我甚至请求用自己的个人资金投入,而且我要求投入一个极其不负责任、不理性的金额——几乎是我流动净资产的 20%——投到 dbt 里,因为我深信这家公司是特别的。有时候当交易没有谈成时,作为投资者你会在脑子里编造一种叙事,比如”很好,我躲过一劫”或者”没有他们也更好”或者”去他们的吧”。但对我而言,dbt 恰恰相反。我真的觉得 dbt 就像一列疾驰的火车,它是特别的,我想要跳上去。所以几个月后,我给 Tristan 打了电话,问他我能不能加入这家打造了我认为如此特别的产品的公司。这就是我进入产品领域、进入 dbt 的独特路径。
Lenny:
所以你投资了 dbt,然后出现了一个机会,让你最终去那里工作了?
Julia Schottenstein:
不,我想如果我成功投资了,我就不会坐在这里了。我被拒绝了,董事会最终否决了我的个人投资,但没关系,因为我最终把所有个人时间都投入到了建设这家公司上。
Lenny:
太棒了。好。你刚才提到了一点非常有趣,就是你在 dbt 身上看到了什么如此特别的东西。我觉得这可能是一个更广泛的问题——在你投资和早期发现像 dbt 这样公司的经历中,关于如何做出好的选择、尤其是如何早期发现公司,你学到了什么?根据你的经验,有哪些信号表明这可能是一家非常有意思的、你可能想加入的公司?这个问题更多是给那些正在考虑早期加入一家公司的听众的。你觉得他们应该关注什么?
评估早期公司的四个维度
Julia Schottenstein:
我评估是否加入一家早期公司的方式,和我评估是否投资一家公司是一样的。我在看早期公司时关注四个方面:人、市场、产品和分发。我会逐一展开,说说每个方面我具体在看什么。
首先是人,说简单点就是这家公司的 CEO、创始人——你信任这个人来领导吗?对我来说,Tristan 有一种非常罕见的能力,他能描绘出一幅极具说服力的行业未来图景,以及 dbt 将如何参与实现这个愿景。但与此同时,他在日常的分析工程工作中又非常非常细致。正是这种跨度上的表现,让我觉得这是一个非常罕见、非常有吸引力的创始人。
第二个是市场。我们之前稍微提到过,但我在市场中寻找的是——它在增长吗?有没有新进入者留下印记的空间?就 dbt 而言,当时正值云数据仓库的爆发期,而正是那种混乱局面构成了 dbt 的真正机会,因为他们为人们在云数据仓库中处理数据的方式创造了一种秩序和结构。这非常有吸引力。
第三个是产品。正在收听的各位,希望你们要么对产品感兴趣,要么有产品背景,所以我不多说。但关键是——你能否去和用户或潜在客户交谈,他们是否在打造一些真正特别的、独一无二的东西?你能否听到那种火花、那种热情,从而判断这是否会变得特别。
最后一个,我认为可以说比产品好坏更重要,那就是分发——他们在如何触达市场上是否有优势?因为那真的非常难。想一想他们对自身竞争优势的看法——无论是在生态系统方面还是分发方面——以及他们最终打算如何销售产品。你不可能在四个维度上都拿到满分。所以当你加入一家公司时,你还有一个优势:你可以贡献自己的时间。试着想想,如果他们在某个维度上较弱,你能带来什么?你擅长什么,可能帮助降低公司成功的风险?
产品火花的信号
Lenny:
关于产品火花这个问题。现在说到产品市场匹配,你怎么知道你是否达到了产品市场匹配?很多时候这其实归结为你与之交谈的人对你正在打造的产品有一种情感反应。就是那种”天哪,我现在就想要这个”的感觉。你有没有见过更具体的信号,表明这里有一种火花、人们真的非常兴奋?你之前说过人们把 dbt 变成了自己身份的一部分。还有什么其他的吗?
Julia Schottenstein:
有。就是他们能不能停止谈论它——关于一个产品的议论,他们想和队友分享,或者分享给其他公司的人。那种发自内心的热爱、想要把自己发现的好东西分享给他人的欲望,是一个非常棒的信号,说明你找到了一些有价值的东西。然后这种火花会在很大程度上帮你解决如何触达市场的问题,因为你的布道者其实就是那些热爱你所打造之物的用户。
Lenny:
那在分发这个维度上呢?你有没有见过一些非常重要的——我不知道怎么说——独特的或有效的分发策略,或者不公平的优势?也许是 dbt 的,也许是其他公司的?能举些例子吗?
Julia Schottenstein:
dbt 有一个生态系统优势——他们是开源的,这极大地降低了门槛,让很多人可以轻松试用并有机地传播。他们一开始走的是非常横向的路线。人们甚至不需要和销售团队沟通就可以直接上手使用,我认为这是一个竞争优势。但并非所有公司都需要走产品驱动的路线。有些公司是自上而下的企业级销售。在这种情况下,想一想这个团队是否真的懂得如何拿下复杂的企业级交易?他们是否有那个特定领域的背景?是否有足够的人脉网络?这取决于公司在卖什么,但你要么希望看到一家在企业级销售上非常强,要么在自下而上上非常强的公司。
关于并购的建议
Lenny:
好的,好。我想稍微转换一下话题,聊一个你非常有经验、现在也有很多人非常感兴趣的领域,那就是并购。我投资了很多公司,大概每个月我都会收到一封来自我所投资的创业公司的邮件,说”我们在考虑可能卖掉公司,事情没有按我们期望的方向发展”。而你在并购交易的桌子两边——我想可能所有位置——都坐过,包括我认为你主导了 dbt 对一家公司的收购……我作为投资者投资的那家公司被 dbt 收购了,是一家叫做 Transform 的公司,我认为是公开的。所以我的问题是,对于正在考虑并购——基本上就是被收购——的创始人来说,你最好的建议是什么?他们怎样才能为自己争取到最好的并购结果?
Julia Schottenstein:
关于收购,开始考虑并购策略的最佳时机,最好是当你还不需要它的时候。我能给创始人最好的建议是,在建设公司的过程中保持强势的进攻姿态。创始人创办企业时,通常不会一开始就以卖掉公司为目标。他们的初心是打造一家持久独立的、能够独立运营的公司。如果你拥有这条路,那么在每一次并购对话中你都会占据上风,因为你有一个可行的替代方案——什么都不做,继续走自己的路,你不必出售。但当然,大多数公司并非如此。大多数公司并没有一条可以永远独立运营的可行路径,所以它们必须考虑并购。所以,并购本质上就是创造备选方案。
我会这样思考:对于任何一家公司来说,真正觉得你所做的事情极具战略价值的买家,永远只有两到三家。我会采取的策略——以及如何让自己被注意到——是找准你带来竞争优势的领域,然后给那个潜在买家制造压力。让他们不可能不注意到你,因为只有在这种情况下,他们才会竖起耳朵说:“这家公司怎么回事?“我们刚收购了 Transform 这家公司。他们在执行一个非常出色的策略。
而这里非常关键的一点是,你要以一种友好、开放的方式去做这件事。我看到很多创始人在这方面犯了错——他们过早地关闭对话,或者拒绝与在位企业或潜在未来买家交流,因为他们采取了过于对抗性的立场。但这是一个错误,因为并购的核心就是创造备选方案,你不应该过早关上这扇门,因为你并不确定自己是否真的能一路走到底成为独立公司。你需要保留选择权。
Lenny:
我很喜欢”给潜在收购方制造压力”这个说法。能举些例子吗?谁做得好,或者这种互动具体是什么样的?
Julia Schottenstein:
好的。我可以分享一下我们刚收购的 Transform 的故事,我们在二月份公布了这起收购。dbt Labs 的核心产品是做数据转换(transformations)。我们当时正在拓展一个新的产品领域,我们称之为语义层(semantic layer)。简单来说,它可以让企业定义自己的业务指标,这样无论谁查询,我们都能始终返回这些业务指标的一致数据。而 Transform 是一家纯粹专注于这个指标层、语义层的公司,而且他们的产品非常出色。
他们很早就攻克了一些技术难题并找到了解决方案。他们有一个优势——团队成员曾在 Airbnb 工作过,而 Airbnb 在数据领域以拥有一个非常成功的语义层、指标层 Minerva 而闻名。而 dbt Labs 拥有的是非常好的分发渠道和生态系统,但在产品推向市场方面我们稍微落后了一些,我们感受到了来自 Transform 的压力,因为他们在积极、高调地宣传他们的语义层如何解决这些非常困难的技术问题。
但他们没有任何分发渠道。这对他们来说非常困难。他们在施加压力的同时,仍然把自己定位为我们的合作伙伴,因为他们希望我们的社区对他们正在构建的产品感到兴奋,并希望能吸引这些用户来使用他们的产品。正因为他们在定位上把自己塑造成了友好的合作伙伴——尽管实际上我们在竞争类似的使用场景——当进入收购阶段时,我们非常兴奋,因为我们知道他们的产品很好,而且他们已经做了大量工作使与 dbt 的集成成为可能,这帮助我们收购后更轻松地完成整合。
如何看待竞争
Lenny:
不管作为创业公司还是在位企业,你怎么看待竞争这件事?应该在多大程度上、投入多少精力去关注竞争对手在做什么,这又如何影响你的战略?
Julia Schottenstein:
我们最近把我们的竞争理念系统化了。Nick Handel 值得一提——他是 Transform 的创始人,他在 dbt Labs 主导了这次梳理。我们的竞争理念有三个支柱。第一个是坚持我们的愿景。我们对 dbt Labs 正在走的道路和旅程感到非常兴奋,不希望被干扰。偶尔竞争对手可能会暗箭伤人、挑衅攻击,但那些大多只是噪音。如果你对自己前进的方向有很强的信念,你应该做的就是目视前方,跑出自己最好的成绩,不要被一些批评者的言论分散注意力。
第二个理念是”做大蛋糕”。我们希望与生态系统中的合作伙伴一起把机会盘子做得更大,我们现在也确实看到了这一点。我们目前主要服务报表和 BI 场景,我们看到越来越多的公司开始将数据用于业务运营。现在随着机器学习的大浪潮,干净的数据转换资产正被用来训练机器学习模型,所以这个蛋糕还在持续增长。让我们把这个作为目标,与各方合作把机会空间做得极具吸引力,而不是试图把它切得太碎。
最后一个是我们希望发挥自身优势。我们有成为平台公司的雄心,我们清楚自己擅长什么,但我们也想为生态系统留出空间,让它们为我们的用户提供解决方案。我们真心希望培育一个能与现代化数据技术栈中众多公司合作的生态系统。总的来说,在竞争方面,我们采取的是非常长期的视角。有几个领域我们确实想坚守阵地,那就是我们的转换标准以及语义标准,因为我们相信这两者结合在一起对用户来说更好。但在其他所有方面,我们真的觉得自己可以与生态系统合作,实现我们想要实现的目标,同时也帮助他们实现他们的目标。
dbt 的成功之道
Lenny:
这也许是一个很好的时机来聊聊 dbt 以及这家公司取得的成功。有那么多创业公司试图成为如今所谓现代化数据技术栈(modern data stack)的标准默认层,而我不知道有任何创业公司不使用 dbt 或者不计划使用 dbt。这是一个极其罕见的成功故事,堪比 Snowflake——它就是构建大型数据型创业公司的默认选择,而如今大多数创业公司都需要处理大量数据。所以我的问题是,你认为 dbt 在这场竞争中做对了什么,才能赢得并持续赢得这个位置?
Julia Schottenstein:
我认为 dbt 做对了很多事情,但我要指出两点特别突出的。第一是力量与简洁,第二是对开放性的坚守。我来展开说一下这两点是什么意思。当 dbt 刚起步的时候,你会听到很多公司说,我不明白,dbt 有什么特别的?我们公司内部也有一个 SQL 模板工具,是我们自己搭建的。这东西真的很简单直接。确实,dbt 确实很简单,但这恰恰是它的力量所在。
dbt 的核心信念与开放性
所以我们的创始人 Tristan、Drew 和 Connor,他们有一个信念:做数据分析工作的人,那些真正与业务利益相关者紧密合作的人,也应该是那些为生产环境创建干净数据资产做出贡献的人,因为数据准备工作是任何分析的前提条件。所以 dbt 背后真正的信念是,如果你会 SQL,我们就想邀请你来参与这些传统上由数据工程师把持的工作流程——但你得自己争取到这个资格。所以 dbt 有一个很好的框架,让你不容易搞砸,能保持很高的数据质量,同时上手和学习的门槛又相当低。这真正是行业里的一个突破口。我们确实在对的时机解决了一个痛点。
第二点是对开放性的坚守。dbt 是开源的,而开源的部分正是 dbt 的核心——你在这里编写业务逻辑,它在多个方面提供帮助。具体来说,它有助于飞轮(flywheel)的运转,也有助于网络效应(network effects)的形成。我来解释一下这意味着什么。dbt 在公司内部非常容易上手,摩擦很小。我们打造的是人们愿意谈论、愿意分享的产品,无论在组织内部还是与其他公司之间。其他公司又以同样低的摩擦开始使用 dbt。
飞轮效应与网络效应
我们现在能看到 dbt 在不同规模、不同行业的公司中有着极其多样化的使用场景,这使我们能够构建一家真正横向发展的公司。随着公司成长,我们可以把资源回馈到社区和产品中,飞轮就越转越快。与此同时,我们拥有一个非常庞大的用户基础——每周有两万家公司在使用 dbt——这吸引了合作伙伴愿意为 dbt 生态构建产品,他们分享最佳实践、构建工作流程,于是如果你是一家标准化采用 dbt 的公司,你就真正解锁了一个以前无法获得的、集成的现代化数据生态系统。这就是一个飞轮,也让每一个选择站在这个标准上的人都受益。正是这两个重要趋势使 dbt 今天如此强大。
Lenny:
所以我听到的是,本质上产品正好切中了人们需要解决的问题。同时还有一个产品驱动(product led)的要素——开源、免费、自助使用的部分,人们采纳它、使用它、开始运作起来,然后扩大规模并开始付费。还有一个愿景上的对齐——关于事情的发展方向以及它如何契合人们希望它为自己工作的方式。还有别的吗?因为很多创业公司都这么做,这些听起来都很聪明、很好,但很多创业公司也试图做这些事情,却根本没人在乎。也许是他们的产品不是人们真正想要的,也许是他们没有找到正确的分发方式。我不确定。你觉得他们还有什么做得特别好的地方,帮助他们把这个事情启动起来,让它成为一个真正的存在?是时机特别好?还是早期有特定的意见领袖?
Julia Schottenstein:
是的。我认为时机对 dbt 的成功非常重要。他们恰好出现在云数据仓库爆发式增长的时期。而且 dbt Labs 最初是 Fishtown Analytics,一家咨询公司。所以他们与所有咨询客户非常紧密地、亲手一起工作,去理解痛点,真正一线解决他们看到的挑战。我认为这种在对的时间出现在对的地方、同时又能够深入接触人们日常问题的组合,创造了一种非常特别的经历。
从咨询公司到产品公司
Lenny:
这个我不知道。这是这个故事中一个非常重要的元素——基本上他们专注的是……他们做 Fishtown Analytics 咨询做了多长时间?
Julia Schottenstein:
纯咨询那部分业务差不多两年。
Lenny:
好的。所以他们基本上花了两年时间为人们手动解决这个问题,这真是一种理解真实痛点并找到解决方案的绝佳方式。
Julia Schottenstein:
完全同意。
Lenny:
太好了。好的,这是一个非常有意思的洞察。就是花上几年时间。听起来他们几乎是在手动帮人们用已有的工具转换数据。
Julia Schottenstein:
对。不过他们也在构建 dbt,并用 dbt 来帮自己更好地完成工作、服务客户。每当他们遇到小的摩擦点或障碍,或者工作流程比预期耗时更长的时候,他们就会把这些改进构建到 dbt 里。这使产品的体验变得非常成熟,因为构建产品的人——也就是创始人——自己也在日常与这些有痛点的客户一起工作。
用绳子和便签理解算法
Lenny:
这让我想起你给我讲过的一个故事,说你让工程团队手动做一个涉及转换算法的工作。能分享一下吗?
Julia Schottenstein:
好的。我先铺垫一下这个故事的背景——我是一个超级数学迷,我最喜欢的逻辑学书籍之一叫《哥德尔、埃舍尔、巴赫》。在这本书里有一个有趣的场景,一个蚁群聚在一起扮演一台计算机的角色,通过把比特从 0 翻转到 1 来实现逻辑门运算。这本书中的这一章,正是我在团队中进行一项练习的灵感来源。大约一年前,我们在 dbt Labs 做一个很大的从零到一的新项目,我们要改变构建客户数据转换图的算法。我需要找到一种方式,让团队真正内化我们即将做出的所有变更,而且我需要他们真正拥有它,因为否则他们就无法预见到所有的边缘情况,做出来的东西也不会那么可靠。你不能只是复制粘贴算法。
所以我带着一卷绳子和一堆便签出现在团队 offsite 活动上。我觉得我的团队看着我像看疯子一样——当我开始把人绑起来构建图的时候,他们大概都以为我疯了。图中的每个节点是一个工程师,绳子是连接他们的图的边。然后我们极其缓慢地、一步一步地走完了新算法。你不可能离开那个练习还不知道到底发生了什么,因为每个人都有自己的角色。
所以很多时候当你开始做一件新事情时,会出现一种情况:少数几个人真正理解了,他们远远跑在其他人前面。但我需要一种方式让整个团队一起跟上这段旅程。所以我一直在努力为团队创造这些重要的时刻、令人难忘的时刻,让它围绕我们的使命,让他们能够拥有推动项目走向成功的所有权感。所以这或许是一种过于有创意的、甚至有点古怪的方式来度过这一天,但效果真的很好。
Lenny:
你们实际要实现的算法是什么?
Julia Schottenstein:
我们想弄清楚的是,如何把运行用户 DAG 的方式从命令式翻转为声明式。也就是说,不再是按照数据到达仓库的顺序从左到右执行,而是反过来思考——要做什么才能让你的数据 SLA 按时物化完成。
Lenny:
听起来团队觉得这个方法很有价值。
Julia Schottenstein:
是的。
开源与专有软件的边界
Lenny:
这让我想起 Ted Lasso 上一季的一个片段,他们用了红线——我就不展开说了,但看过的人应该都知道我在说什么。我想回到你刚才提到的开源与非开源的话题。dbt 的一部分是开源的,一部分不是。我很好奇团队是如何决定哪些应该开源、哪些不应该开源、哪些应该收费的?
Julia Schottenstein:
我们对 dbt 开源版的理解是:它真正构成了数据转换的核心,是你描述业务逻辑的地方。而在云端这一侧,我们构建专有软件,用来增强开发生命周期,并支撑 dbt 在大规模场景下的生产化部署。因此,我们为云端产品保留的部分涉及状态管理——即有状态的交互,以及任何跨团队或组织层面的协作,我们都保留给专有产品。我认为非常重要的是要明确区分:你认为什么应该是开源的,或者说什么样的开放标准才是真正重要的?生态系统对我们来说非常重要,所以这部分必须保持开源,但我们希望通过开放核心模型来进一步增强体验,构建专有软件,让用户在使用 dbt 时更加成功。
定价与支付意愿
Lenny:
你在定价、支付意愿等方面也花了很多时间思考。关于如何为这类工具甚至更广泛的产品思考定价,你有什么可以分享的经验吗?
Julia Schottenstein:
定价和支付意愿是一个非常难的话题,很多创业公司在公司发展的早期阶段都没有足够重视这件事。这很可能是零利率时代的副作用——投资者乐于为 GitHub star 数和使用量买单,公司从来不需要思考”我要怎么赚钱”这个问题,但这其实很重要。在 dbt Labs,我不敢说自己做得完美,但我们正在努力锻炼这方面的能力。我经常想到 Madhavan,他写了《Pricing Innovation》这本书。我知道你之前请他上过节目,他分享过一个观点:你不是在决定是否要进行定价或支付意愿的讨论,你只能决定什么时候进行。所以最好在产品开发之前就进行这场讨论,而不是等到销售团队去卖东西的时候,才发现人们对你做出来的东西不感兴趣、不愿意付费。
在 dbt Labs,我们有这样一个价值观,也是核心价值观之一:我们更关注价值创造,而非价值捕获。我们是认真的。当我们谈到 dbt Labs 对客户的价值时,客户常常会说 dbt 的价值相当于他们在云数据仓库上支出的 20% 到 35%,但我们对客户的收费远低于这个 20% 到 35%,这是有意为之的。去年我们做了公司历史上第一次定价调整。当你经历这样的事件时,你能学到很多东西,因为你有机会测试客户的价格弹性。在公司还比较小、 stakes 还比较低的时候学到这一课是非常重要的,因为定价始终在演变,它不是一个固定的东西,会随时间变得越来越复杂。所以我们必须反复思考这个问题,因为我的团队为 dbt Cloud 构建专有软件,当我们输掉一个订单时,最常输给的对手是 dbt 开源版。我们乐见其成——输给自己没问题,但我们必须深入思考人们愿意为什么付费、什么对他们真正有影响,然后集中精力做好这些。
定价调整的过程
Lenny:
我非常好奇那次定价调整的过程是怎样的——如何决定要改什么。你能分享一些当时的经历吗?或者在那些对话中有没有什么让你意外的地方,比如”哇,我们没想到会是这样”的发现?
Julia Schottenstein:
这真的是一个全员参与的讨论。定价的影响面非常广,因为它同时也是一个财务问题——你需要在电子表格里建模,推算可能对业务产生的影响。但你不能只靠电子表格解决这些问题,你必须去跟客户谈,试探一下水温,了解人们的心理预期,而要把这些摸清楚是非常困难的。当然还有一个产品层面的工作,你需要去落地执行,也需要向外界沟通。所以这是一个非常跨职能的过程。我们学到了很多。我们非常仔细地追踪转化率,也非常仔细地追踪流失率。总的来说,我们对这次调整是满意的。我们觉得要解决的核心问题之一是:让我们的定价能够跟上用户对这款工具的价值认知。
Lenny:
你们最终跟多少人谈了?是谁在做这些对话?在如何提问、如何进行支付意愿的讨论方面,有没有什么特别重要的经验?
Julia Schottenstein:
我们跟几十个人聊了。参与对话的是产品和产品营销的组合。人们不太愿意直接告诉你他们愿意付多少钱,但我们有一些工具来衡量相对价值。人们最常思考的是 dbt 相对于他们的云数据仓库的价值是多少。我们也尝试用一些策略来探明人们的认知——他们认为什么是非常便宜的,什么价格是毫无犹豫就能接受的,什么价格是合理或可以接受的,什么又太贵了。然后我们把所有数据汇总,确定我们最终应该落在哪个位置。
并购中的公开策略
Lenny:
我想再回到并购的话题聊聊。我之前跳过了一些问题。你确实在并购领域做了很多工作。我最近注意到一件事,也跟当下很多创业公司想被收购有关。一位叫 Hunter Walk 的 VC,他是 Homebrew Ventures 的创始人,写了篇有趣的博客,他基本上建议你应该公开表明你想卖掉公司这件事。这听起来很疯狂,因为过去你绝对不想让人觉得你急于出手。你不想让人觉得你走投无路。但他的观点是,现在很多创业公司确实处于这种境地,公开说出来也没关系。而且理论上这样做能创造更多的竞价局面,让更多人知道,而不是关起门来悄悄进行。所以我的问题就是,你怎么看这个建议?你觉得这是有效的策略吗?
Julia Schottenstein:
我觉得这是好建议。如果你处于绝地求生的境地,正在为你的公司找一个归宿,或者需要为你的业务找到一个退出路径,那么保持透明、撒更大的网会更好。你说得对,过去创始人往往会稍微遮掩一下,或者模糊自己真实的处境,因为他们想在没有太多竞争兴趣的情况下制造一些竞争氛围。但遗憾的是,在当前的市场环境下,太多公司都处于这种情况,所以根本藏不住。更好的做法就是坦诚相告。我经常看到这种情况,发一封短信说类似这样的话完全没有丢人的:“嘿,我们正在为我们的公司 X 寻找一个退出路径,Y 和 Z 的发展没有达到预期。我们做了一个很有意思的产品,我们希望团队能保持完整。我们正在进行这个过程。你有兴趣吗?“就这么简单。
Lenny:
我看到很多公司会做非常详细的演示文稿,甚至搭一个网站,展示这是我们的团队、这是我们打造的产品,非常主动地推销自己。这种情况你见过吗?你会推荐这样做吗?
Julia Schottenstein:
是的,见过。通常在这些情况下,收购方主要是看中团队,所以把你的数据室准备好,最重要的是展示这些是会跟着一起过来的团队成员,这才是买家会感兴趣的最大动因。
种子要趁早播下
Lenny:
你之前提到过,要获得最好的结果,很多种子需要提前播下。这让我想到——我自己有一个创业公司 Localmind,后来卖给了 Airbnb。我最开始接触 Airbnb 是在我们真正开始探索出售流程的一年前,是在西南偏南的一个随机派对上认识的。我其实完全不记得那个派对了,但 Airbnb 的产品负责人 Joe Zadeh 记得,一年后他主动找到我们说:“嘿,你们最近在做什么?也许我们可以在一些事情上合作。“这最终促成了一次收购。所以我觉得这一点值得再次强调——让千朵花盛开,去见所有人。让大家都知道你们的存在和你们在做什么,这样将来当有人遇到那个问题时,他们会想到:“那家公司,也许我们应该找他们聊聊。”
Julia Schottenstein:
是的,你要确保买家在收购时机到来之前就知道你是谁。理想情况下,这是因为你已经产生了影响力,他们喜欢你正在做的事情。也许你还给他们制造了一些压力。但确实,在退出事件之前建立这些连接是非常重要的。
Lenny:
具体来说,是不是也应该去见竞争对手那里的人?对我们来说,Airbnb 原本根本不在我们潜在买家之列,因为它跟我们的业务完全没关系,但后来却变得合理了。你觉得创业公司应该考虑去接触的公司和人的范围是什么?
Julia Schottenstein:
如果一家公司在频繁收购或者比较活跃,他们通常会有企业发展战略团队。利用好这个企业发展战略团队。他们的工作就是去见每一家可能感兴趣的公司。所以去参加那个会面,告诉他们你暂时不打算被收购,但推动他们把你介绍给可能支持这笔交易的人——通常是产品部门的人,或者可能是某个业务总经理。以此作为起点,也许只是一次对话,也许更进一步发展为合作关系,但让企业发展战略团队为你所用。
走投无路时怎么办
Lenny:
如果你处于另一个极端,你现在确实陷入困境了,心里想的是:“天哪,我们该怎么做才能卖掉这家公司?“我知道成功的概率不会很高,但在寻找潜在收购方的连接方面,你有什么建议?
Julia Schottenstein:
利用好你的人脉网络。通常你的风险投资人有一个非常大的网络。我听到创始人最焦虑的事情之一,是对投资者的责任——要把钱还给投资者。也许在播客上说这个不太讨喜,但你的投资者心里清楚他们收不回那笔钱了。他们更希望你能加入一家很棒的公司,比如 Airbnb,因为这会在未来帮到他们。这是一场长期博弈。所以利用好你的投资者帮你找到各个潜在收购公司的连接,不要太过担心让他们失望,或者对公司所处阶段过于坦诚。
Lenny:
补充一下,我只是天使投资人,不是领投基金,他们的感受可能不同。但我最不想看到的就是创始人困在一个自己讨厌的公司里,就为了能返还一些钱或者制造某种结果,然后就放弃了,继续前行吧。人生短暂。
Julia Schottenstein:
是的。我觉得创始人常常忘记,投资早期公司本身就是极其高风险的事情,50% 的组合投资不会带来任何回报。这就是游戏规则的一部分,而且这是一条完全可以接受的路径——我们尽了最大努力,没有成功,然后继续前行。
Lenny:
这对很多创始人来说很难,因为他们一直被告知要坚持、不要放弃、绝不退出。但有时候你确实应该退出。
Julia Schottenstein:
天哪,是的。我不想轻易说那句话。我觉得做创始人有时候是一个极其孤独的角色,你很难知道下一篇章会是什么样子,或者这段旅程会走向何方。有时候你确实已经没有资金了,不得不为你的公司找一个归宿。但我希望你能继续战斗,找到一条出路。
当前活跃的收购方
Lenny:
同意。具体来说,对于正在考虑出售公司的创始人,你觉得现在有哪些公司是值得关注的、正在积极收购或对并购持开放态度的?
Julia Schottenstein:
这个问题比较难回答。我觉得很多公司目前都处于观望状态,原因有很多。可能是因为他们刚刚完成了一笔收购,正在消化整合那家公司。也可能是因为他们的人员增长放缓了,而并购本质上往往是一场组织架构的体操——要把目标公司的人员编制纳入你自己的预算和计划中,也许他们确实没有太多空间。或者更普遍的情况是,目前大家对未来存在一种整体性的不确定感。在高度波动的市场中,人们想先照顾好自己人,即使是最好的并购交易也带有一定程度的复杂性,很多买家现在不想承担这些。
所以人们选择观望的原因有很多。但如果我处于想要出售公司的位置,我会做的是:先列出一份大概十二家左右的买家清单,因为真正会觉得你所做的事情非常契合的公司,其实不会超过十二家。从这个买家清单出发,然后根据一些可能排除某些买家的标准来逐一筛选。然后去展开那些对话。如果你已经处于绝地求生(Hail Mary)的境地,那就对此非常坦诚,也许可以扩大买家名单。但如果你还有一些余裕,我建议集中精力在两到三个合作伙伴或买家身上,采用一套完全不同的策略——制造痛点。让他们很难忽视你,但在做这一切的同时保持微笑,保持友善。
Lenny:
有时候并购(M&A)的对话中,沟通方式会有很多微妙之处,你不会直接站出来说”拜托,我们想把公司卖给你们”。关于措辞和沟通方式,你有什么建议吗?还是说你觉得直接告诉对方”我们正在考虑出售公司,你们有兴趣做买家吗”也没什么问题?
Julia Schottenstein:
是的。我觉得在这方面不要太耍聪明。大家都明白”我正在评估战略替代方案”是什么意思——这意味着你想卖公司。但这取决于你的具体情况。你还有时间吗?如果你还有时间,那就不要一上来就说”嘿,我要出售”。那不会有好的结果。但也要看你处在公司发展旅程的哪个阶段。如果你还有时间,那就根本不要谈并购。这是你最不应该提及的话题。相反,你可以谈合作或伙伴关系——我们怎么一起合作?知识共享。如果你的跑道还很长,并且打算继续走独立发展的路线,那并购就是一个忌讳的词。但如果你已经没有时间了,那就是没有时间了。
Lenny:
这个建议太好了。我记得我们当时用的说法是”我们想探讨一下战略合作关系”,所有人都心知肚明你的意思,你只是不愿意直说而已。
Julia Schottenstein:
是的。就像房间里所有人都在互相看,心照不宣。战略合作关系,或者战略替代方案,大家都知道这些暗语,也都理解你所处的处境。
并购市场的复苏时机
Lenny:
你觉得并购市场什么时候会回暖?我知道这不可能精确预测,但你有什么感觉吗?
Julia Schottenstein:
嗯,我希望自己有个水晶球,那就太好了。不过我认为目前的情况是,我们已经距离市场顶峰足够远了。那个顶峰实际上是在 2021 年 11 月。为什么这很重要?有两点。第一,创始人正在逐渐接受一个事实——在市场最高点获得的估值,在当前市场中已经不再成立了,而很多公司已经耗尽了现金,或者耗尽了选择。很快会有更好的资产进入市场。而在某个时刻,机会将变得太过诱人,这将激励很多一直坐壁上观的买家重新参与到并购市场中来。我不知道具体什么时候会发生,但我认为已经不远了。我认为已经很近了。
dbt Labs 的公司价值观
Lenny:
你之前提到过 dbt 有一个价值观,我忘了具体是什么了,但我很好奇你们在 dbt 还有哪些其他的价值观,能分享的都可以说说。我一直很关注各个公司最终确立了哪些原则和价值观来引导他们的思维方式。
Julia Schottenstein:
我之前分享的那个价值观是”我们更关注价值创造而非价值获取”,这确实驱动着我们所做的一切。我们努力向世界输出很多好的东西,它会慢慢地得到回报。dbt Labs 的整个使命是帮助分析工程师通过数据传播组织知识。所以我们也非常坚信参与这种信息分享,让更多人了解各种各样的事物。
透明制胜。我们是一家非常透明的公司。我们分享董事会材料。我们的 Slack 里有很多沟通和参与。我们是一种写作文化。我们在公开场合进行艰难的对话。所以这是另一个很重要的价值观——透明永远制胜。我们保持谦逊。我们从不会觉得自己已经成功了。我们以一种非常谦逊的姿态来面对这一切,觉得自己必须服务好社区和用户,这确实是我们的动力源泉。还有一个就是”做好工作本身就是目的”,专注于旅程本身,而不是终点。
Lenny:
太棒了。我以后想写一篇文章,就是列举所有这些成功公司确立的价值观,看看有没有什么规律。我现在其实正在写一篇关于 Snowflake 和 Figma 的文章,正好跟你提到的有关。Figma 那边有一种很强的联系和主线——对用户的痴迷,确保用户满意。我忘了具体措辞,但在那篇文章里——实际上明天就会发布,所以人们大概能猜到我们录这期节目是什么时候——他们把自己的公司看作一种服务即软件(software as a service),其中”服务”是他们的第一目标。他们实际上提供的是服务,而软件只是实现服务的方式。而 Snowflake 呢,他们的第一核心价值观就是把客户放在第一位,他们大量谈论这一点——这实际上指导了他们的优先级排序和所有思考。所以这很有意思。这一点在你说的内容中也反复出现。我觉得这说起来容易,但真正区分成功公司的,是他们真的把这些付诸实践了。
Julia Schottenstein:
这确实很有意思,因为 dbt Labs 的很多员工都是从社区来的,所以他们对打造卓越体验有一种真正的主人翁感,因为他们当初正是被产品改变了自己工作方式甚至改变了生活,才被深深吸引而加入 dbt Labs 的。所以这种对社区和产品体验的承诺是非常、非常强烈的。
与强势社区共建产品
Lenny:
这又是一条线索了,也许我们可以很快展开聊聊。我正在写另一篇关于 Reddit 的文章,讲的是如何与那些对产品变更持有强烈意见的用户合作。他们的描述方式是这样的——这些人在那里工作了五年,和社区一起打磨产品。用他们的话说,对你的用户来说,产品就是他们的孩子。他们觉得产品是自己的,而不是公司的。我很好奇,面对一个非常强势、有自己鲜明立场的社区——听起来你们也有这样的社区——在和他们一起打造让他们满意的产品、避免社区暴动和强烈不满方面,你学到了什么?
Julia Schottenstein:
随着规模增长,这确实很难。我认为这就是一个挑战,因为我们的社区越来越大,你不可能满足所有人的需求。但我认为我们的做法是——每个人都非常深入地使用我们自己的产品。有一个很酷的数据:在 dbt Labs,超过 30% 的员工参与了我们的数据转换工作流(data transformation workflow)。这横跨所有职能部门——显然包括产品团队,显然包括我们的数据团队。我们的营销团队也参与数据转换工作。我们的工程团队也会参与我们内部的 dbt 分析项目。这种真正理解用户体验的感受,然后尽可能地收集反馈。我们有一个五万人的 dbt Slack 社区,我们所有员工都经常活跃在那个 Slack 频道里,能第一时间感受到我们搞砸了的时候,或者我们没能交付令自己自豪的体验的时候,你会看到几十个人立刻行动起来,试图改进它。
产品开发中的框架与理念
Lenny:
在打造优秀产品和领导团队方面,你有没有发现其他特别有用的框架或通用流程?
Julia Schottenstein:
我不是一个热衷框架的人,但有两句话我经常对自己或他人重复——“更差即是更好”和”技术债是香槟级的问题”。什么意思呢?这其实是为了帮我对抗完美主义,因为完美并不存在,你应该追求的是足够好。因为当你发布产品的那一刻,才是你从用户那里学到很多东西的时候,而在此之前你根本无法预见到这些。你会非常努力地去理解人们将如何使用产品,把所有边界情况都打磨好。但在发布之前你做不到。我举个例子。我的团队负责支持 dbt Cloud 调度器,而 dbt Cloud 调度器的第一个版本相当简陋,我们甚至有点不好意思。
它就是对一个巨大的作业表跑一个 for 循环。我们会看一下:这个任务到时间运行了吗?好,是的,运行这个任务。好,这个任务还没到时间,继续下一个。到时间了吗?是的,运行这个任务。就这样一直循环下去,极其简陋,非常简单,但它把活干了。我经常提醒工程师们,我们应该庆幸能拥有技术债,因为那意味着有人在用我们的产品。现在我们的调度器已经重建了好几次,因为我们确实有了可观的规模。我们有 8,000 家公司在使用我们的调度器,每个月要管理 1,000 万次运行。但我们在发布之初并不需要一个带有 worker 和 RabbitMQ 的分布式调度器。我们根本不需要,因为我们当时没有用户。所以”更差即是更好”和”技术债是香槟级的问题”这两句话,就是在提醒大家——让我们发布吧,把它交到用户手中,然后我们去学习、去迭代,最终为他们提供更好的体验。
从投资到产品的经验迁移
Lenny:
这个过渡很好,正好引出我最后一个问题。你在这个角色之前并不是产品经理,你在投资、投资银行等商业领域有丰富的经验。我很好奇,基于你转入产品领域的这些经历,你认为产品经理应该更多地关注什么、学习什么,或者更深入地投入什么,才能成为更强的产品领导者?
Julia Schottenstein:
我在当前的产品工作中,大量运用了之前做投资人时的经验和做法。也许我可以先聊聊风险投资人的工作方式,这对有些人来说可能比较陌生。风险投资人整天都在跟各种不同的公司会面,不断切换上下文。他们需要对很多不同的领域都懂一点。他们这样做是为了不断打磨自己的投资品味,锤炼投资判断力。同时他们也在大量投资自己的人脉网络——连接人、支持人,从比自己聪明得多的人那里挖掘想法。在风投行业你一直在做这些事情,我把很多这样的能力带进了产品工作中,而且转换得非常顺畅。
第一点,我仍然花大量时间经营自己的人脉网络,我认为这是产品经理容易被低估的一种时间投入方式。我尝试建立一个由其他类似 dbt Labs 这样增长良好的公司运营者组成的网络,这些公司可能比我们稍微走在前面一点。我会向他们请教问题:你们是怎么应对开源的?你们是怎么处理定价的?你们是怎么做并购的?然后我把最好的想法带回来,看哪些可以应用,带回 dbt Labs。第二点,我真的认为我的特长或者说超能力是——我是一个 T 型通才。我在金融、商业、产品等很多领域都懂一点。在我专门深耕的产品领域,我需要深入得多,那就是 T 字的那一竖。但正因为我的背景足够多元,这让我在组织内部推动事情落地时更加高效,因为我有更丰富的可信经验可以调用。
最后一点,也许不会每天直接体现在我的产品工作中——在投资领域,你不断在思考风险和幂律分布,我们之前也谈到过这一点——大多数投资都不会成功,你会亏掉投入的资金,但所有回报都来自那些弥补所有损失的罕见事件。在投资中你必须思考:什么是上行空间不受限的机会。我认为在产品中你同样需要这样做。如果我经手的项目有 50% 归零了,那确实有问题。但这鼓励我持续为公司下注,去押那些有可能改变我们业务轨迹的机会。
闪电问答环节
Lenny:
我们到了非常激动人心的闪电问答环节。我准备了六个问题。准备好了吗?
Julia Schottenstein:
好,来吧。
Lenny:
你向他人推荐最多的两三本书是什么?
Julia Schottenstein:
两本帮助我更好地认识自己的书:Range,一本关于通才的书;还有 Quiet,一本关于内向者的书。另外我很喜欢传记,几个最爱是写巴菲特的 Snowball、写山姆·沃尔顿的 Made in America,还有 Leonardo da Vinci。
Lenny:
最近最喜欢的电影或电视剧是什么?
Julia Schottenstein:
为了准备这期播客我差点去看部电影,但除了假期之外我真的不怎么看点东西。假期的时候我喜欢看《继承之战》(Succession),但最新一季我还没看。
Lenny:
哇,那你接下来有大惊喜了。你最喜欢问的面试问题是什么?
Julia Schottenstein:
你上一次不得不自学一项新东西是什么时候,你是怎么学的?我喜欢用这个问题测试成长型思维和对学习的渴望。另外就是”为什么选择 dbt Labs”。我觉得来 dbt Labs 的很多人都有非常真实的原因被这家公司吸引。在遇到困难的时候,“你为什么在这里”这个问题的答案会产生决定性的影响。
Lenny:
听起来你寻找的就是那种发自内心的热情。
Julia Schottenstein:
对。
Lenny:
太好了。最近发现并且非常喜欢的几款产品是什么?
Julia Schottenstein:
我喜欢 Belly。它是一个消费社交应用,让你和朋友一起发现餐厅并互相评分。用它来探索纽约的餐厅场景特别有趣。
Lenny:
这个我从来没听说过,很棒。在你们做产品的方式上,有没有什么相对较小的改变却带来了很大的影响?
Julia Schottenstein:
做的事情更少,尽量让团队单线程运作。
Lenny:
单线程的意思是就是一个主要优先事项、一个目标?
Julia Schottenstein:
一个使命。大家都在朝同一个方向划船。
Lenny:
最后一个问题。首先,你有一档播客,告诉我们它叫什么,其次,除了这档播客和你自己的播客之外,你最喜欢哪档播客?
Julia Schottenstein:
好,它叫 Analytics Engineering Podcast,如果你想了解更多关于数据行业的内容,我和我们的 CEO Tristan Handy 每隔一周主持一期,很有趣,欢迎收听。其他我很喜欢的播客有 In Depth,这是 First Round 的播客,由 Bretton 主持,他采访了很多运营者,聊他们如何做到最好的工作。另一个我很喜欢的是 Logan Bartlett Show,内容涉及科技领域的最新趋势。
Lenny:
In Depth 我记得 Todd Jackson 实际上也主持了很多期。我也是这档播客的忠实听众,强烈推荐。再说一下你的播客名字,大家在哪里可以找到?
Julia Schottenstein:
叫 Analytics Engineering Podcast。
Lenny:
在各播客应用里都能搜到吗?
Julia Schottenstein:
是的。
Lenny:
太好了,大家去听听吧。Julia,我们聊了制造痛苦、战略合作,以及为什么”更差”反而”更好”。非常感谢你能来。最后两个问题:大家在网上哪里可以找到你?听众怎样能帮到你?
Julia Schottenstein:
你可以在 Twitter 上找到我,账号是 J_Schottenstein,也可以在 dbt 社区的 Slack 里找到我,同样是 Julia Schottenstein。给我发消息,在那里联系我就好。如果你有数据方面的问题,或者觉得我们可以更好地服务你的需求,我很乐意聊聊。
Lenny:
非常感谢你来,Julia。
Julia Schottenstein:
太棒了,谢谢你,Lenny。
Lenny:
大家再见。非常感谢收听。如果你觉得这期内容有价值,可以在 Apple Podcasts、Spotify 或你喜欢的播客应用上订阅本节目。也请考虑给我们评分或留下评论,这对其他听众发现这个播客非常有帮助。你可以在 lennyspodcast.com 找到所有往期节目或了解更多关于本节目的信息。下期再见。
术语表
| 原文 | 中文 |
|---|---|
| Analytics Engineering Podcast | Analytics Engineering Podcast(播客名称) |
| Belly | Belly(消费社交应用) |
| board decks | 董事会材料(board decks) |
| bottoms up | 自下而上(bottoms up) |
| Bretton | Bretton(In Depth 播客主持人) |
| corp dev | 企业发展战略(corp dev,即 corporate development) |
| DAG | DAG(有向无环图,Directed Acyclic Graph) |
| data room | 数据室(data room) |
| data transformation graphs | 数据转换图(data transformation graphs) |
| data transformation workflow | 数据转换工作流(data transformation workflow) |
| declarative | 声明式(declarative) |
| enterprise top-down sales | 自上而下的企业级销售(enterprise top-down sales) |
| evangelist | 布道者 |
| Fishtown Analytics | Fishtown Analytics(dbt Labs 前身,咨询公司) |
| flywheel | 飞轮(flywheel) |
| growth mindset | 成长型思维(growth mindset) |
| Hail Mary | 绝地求生(Hail Mary,源自美式橄榄球的”万福玛丽传球”,指孤注一掷的尝试) |
| Homebrew Ventures | Homebrew Ventures(风险投资机构) |
| Hunter Walk | Hunter Walk(Homebrew Ventures 联合创始人) |
| imperative | 命令式(imperative) |
| In Depth | In Depth(First Round 播客) |
| in-place company/in-place enterprise | 在位企业(incumbent) |
| Joe Zadeh | Joe Zadeh(Airbnb 前产品负责人) |
| Leonardo da Vinci | 《Leonardo da Vinci》(达·芬奇传记) |
| liquid net worth | 流动净资产 |
| Localmind | Localmind(Lenny 创办的创业公司,后被 Airbnb 收购) |
| Logan Bartlett | Logan Bartlett(播客主持人) |
| Logan Bartlett Show | Logan Bartlett Show(播客名称) |
| logic gates | 逻辑门(logic gates) |
| M&A | 并购(Mergers and Acquisitions) |
| Made in America | 《Made in America》(山姆·沃尔顿传记) |
| materialized | 物化(materialized) |
| metrics layer | 指标层(metrics layer) |
| Minerva | Minerva(Airbnb 的语义层/指标层产品名称) |
| modern data stack | 现代化数据技术栈(modern data stack) |
| network effects | 网络效应(network effects) |
| Nick Handel | Nick Handel(Transform 创始人) |
| offsite | offsite(团队外出活动) |
| org chart gymnastics | 组织架构体操(org chart gymnastics) |
| portfolio investments | 组合投资(portfolio investments) |
| power laws | 幂律分布(power laws) |
| product led | 产品驱动(product led) |
| Quiet | 《Quiet》(关于内向者的著作) |
| Range | 《Range》(关于通才的著作) |
| runway | 跑道(runaway,即创业公司的资金可维持时间) |
| semantic layer | 语义层(semantic layer) |
| single thread | 单线程(single thread) |
| SLA | SLA(服务等级协议,Service Level Agreement) |
| Snowball | 《Snowball》(巴菲特传记) |
| South by Southwest | 西南偏南(South by Southwest,即 SXSW,科技/文化/音乐盛会) |
| spec | 投资分析师(spec,即 special associate/principal track) |
| strategic alternatives | 战略替代方案(strategic alternatives) |
| Succession | 《继承之战》(Succession,HBO 剧集) |
| T-shaped generalist | T 型通才(T-shaped generalist) |
| term sheet | 条款清单(term sheet) |
| Todd Jackson | Todd Jackson(In Depth 播客联合主持人) |
| Transform | Transform(被 dbt 收购的公司) |
| Tristan Handy | Tristan Handy(dbt Labs CEO) |
此文档由 AI 分片翻译(translate_long_document)