Janis Zech: Hello, and welcome to another episode of the RevOps Lab. We are here with Philip, and our guest today is Danny Schoenfeld. Welcome, Danny.

Danny Schonfeld: Thanks for having me. Excited to be here.

Janis Zech: Yeah. We are super excited. We have a fantastic topic today. But before we dive in, who are you? What do you do?

Danny Schonfeld: Sure. Yeah. I am a revenue ops leader. So I'm a VP of revenue operations at Glia, a unified interaction management platform that primarily works with financial institutions, helping contact centers engage with their customers to provide service across all different channels. My role in revenue operations is being the go to market strategy and operations leader, partnering with our go to market executive team. So I report to our COO and work with our heads of marketing, sales, customer success, alliances on envisioning the plan for the future, outlining what we need to get there, and then executing against that plan. I live in Minneapolis with my wife and two young daughters as well who keep me busy when I'm not dealing with all that go to market strategy and operations and analytics and enablement.

Janis Zech: Yeah. I mean, as a dad of two daughters here myself, we could probably spend the next, you know, forty minutes just talking about that and exchanging war stories, very similar to wrap ups sometimes. They always want something from you, and you never know what it is.

Danny Schonfeld: True. We'll see if they barge in during this podcast as well.

Janis Zech: Exactly. But, no, today, we wanna talk about a topic that's really dear to our hearts. It's like go to market analytics and how do you do data driven storytelling well. So, I mean, maybe, like, let's kick off with, like, why is this important in your mind?

Danny Schonfeld: Yeah. Well, you hear a lot from different organizations about how they are data driven. Right? All their decisions need to be data driven. That's something I fortunately hear frequently because it happens to be where my skills center pretty heavily. And I do think that what that can mean different things in different places. And in order for leaders to make effective decisions, they need access to up to date relevant signals around what is happening in the business. We often say we have great problem solvers at companies. That's meaningless if we don't know what problems we're trying to solve. So when I think about analytics, I think about, well, let's make sure that we know what is happening under the hood of the business so that we can actually go and solve those problems. Analytics in many ways is the canary that lets us see what's happening to go and then solve those problems, which is certainly parts of my team as well. But that's part of why I think it's so important. It helps orient our energy in the right places as a leadership team or as an overall organization.

Janis Zech: Yeah. I mean, I think it's a bit of a kind of fluffy term, but then rooted in fundamentals that matter a lot. And, I mean, if you think about, like, what strategic initiatives you spend your time with. Right? Like, how do you make those decisions? And I think, right, like, sometimes you go in one direction, but actually, that might not be the right direction the data tells you. So I'm wondering, like, kind of how do you, like, ensure that the go to market analytics function is both kind of operationally well set up, but then also helps you to be really strategic.

Danny Schonfeld: Right. That's a good broader question and theme for RevOps overall and somewhat of a common RevOps pitfall or excuse even is that, well, we didn't have time for the strategy because we were so focused on the ops. And to be clear, that's always a challenge, but it's also something that needs dedicated focus. I could speak at length about the operation strategy, for instance, being an overlooked component of the role. But your question about ensuring that it's both strategic and operational — that's the secret in many ways to success for a function like this. You think about the operational aspect that centers around building easy to use tools that have relevant information, accessible, consistent across them as well. That's getting that right. And also knowing the right level of detail to go into based on the audience. So my team has its own very deep tools where we can get into many layers of analysis when trying to infer root causes. My other stakeholders, if you think about our head of marketing or head of sales, they'll have specific versions that give them signals that they just need to know about the business for a snapshot to get a broader pulse on things. On the strategic side, that's more about monitoring signals. So I want to know how things have changed over time. I want to know why they have changed over time. I can, again, I mentioned root causes, go in, investigate, interpret them, and read them back out in a compelling way that then drives business decisions. So it's around having the capabilities and capacity to do that, to come up with those insights, and then figuring out how to interpret and use them to then drive the strategy of the business. That's the combination of those different things.

Janis Zech: I feel like, you know, like the GTM KPIs, I think they — I mean, once you have a clear idea of what you're actually looking for and how to retrieve those, I think you can actually set that up in a way that it will mostly require some maintenance work, maybe like a few updates here and there. Maybe there's a new metric coming once a year and then another one is leaving again, you know, how the things are. But I think once you set it up, right, like you can have that in a pretty automated kind of way. But then the real challenge in most of these cases is actually to bring those metrics really to life. And I think this is sort of like how I'm thinking about the strategic part of GTM analytics — okay, right, so you can spend a lot of time crunching the numbers, trying to get the numbers, putting them in a spreadsheet and then sending a spreadsheet around. And then I think the issue here is if you stop after that, right, then I think the impact, especially the strategic impact, would be pretty small. There needs to be someone that actually explains that spreadsheet, creates necessary context, makes sure that there's alignment across the different departments and functions, that there actually is an agreement of like what that KPI means in that spreadsheet or whatever tool you're using. So that's sort of like how I'm thinking. Like you have this operational part, but then the strategic part — that's actually like a soft skill, I would say, to some extent. So I'm just curious, like, how have you in the past tried to make this work?

Danny Schonfeld: You're describing one of the reasons why I'm not worried about AI taking my job, because it's absolutely true that it's an underappreciated aspect of an effective go to market analytics function — that data driven storytelling component as well. A good RevOps function can identify trends and signals in the data, can confirm causal relationships versus correlational ones. A great one tells the story about what happened and why that matters for the overall business. That can be a visualization. It can be a reference to a broader business problem that we're discussing. But doing so in a way that is understandable and actionable from your stakeholders is, again, what separates — one of the ways that I coach my team and that I pride myself on — being seen as good partners. I'll give you an example, which is, let's say we've launched a new product and it has an especially high win rate out of the gate. That's good to know. That's helpful. Oh, okay, it has a high win rate. Showing that in a chart relative to other new products that launched in the same timeframe, right, and what that gap looks like, and then saying, all right, well, actually, this has spanned across many different account executives that have sold this, or we have one who's been really effective in selling this over and over, or putting the logos of all the customers who bought it in its first quarter versus the few who didn't. Right? That's much more powerful than just, hey, new product we launched, good job, product team, it has a high win rate. We want to know what's happening because then they can say, oh, actually, all right, well, I have ideas on how we can improve that further to help scale it so that it's not just a high win rate, it's also a high revenue number. Or what we do for the other products that we're working on to try to replicate that success. Or what we do from an enablement perspective to scale what an individual is doing. Right? All of that is around bringing it to life as a result of that data driven storytelling to be a, again, strategic function.

Janis Zech: Those are really, really good examples. I like the one with the win rate and just, you know, just as a cross reference, like how did other products do in the past, and just picking one metric also — I think, I mean, of course you can look at multiples, but I think like, you know, first week, first two weeks, you know, like just keep it very focused, you know, first of all, for the product team itself, but also for the rest of the company to keep their heads maybe still and just be a bit more focused on the execution side of things. I'm curious, have you found like an arena that works well in an organization to share these kinds of insights — that you found like, that's sort of like a way for communication that others can also easily replicate?

Danny Schonfeld: Yeah. So one of the things that comes to mind is QBRs — our quarterly business reviews — that in past companies I've been at has been a really effective way for leaders across departments to understand what's happening and discuss it. And that can be good or bad signals. Right? But that's been an effective tool that I've used throughout now a number of different companies. One of the ways that we do that is surface up those key insights. Like you said, you can drown in data. But if you focus on a couple of key things that really help tell the story of what happened in, let's say, the last quarter — I'm talking about the QBRs — so what happened in the last quarter? What was the good? What was the bad? What did we learn from it? And what are our plans as a result of those learnings? That's been really effective in, again, driving an actionable discussion among a set of cross functional stakeholders by first grounding them in these key signals that we can then use to go and dictate the rest of the strategy for the overall organization.

Janis Zech: Yeah. I mean, I think on the QBR topic, I mean, can you share some best practices? Like, you know, how do you prepare them? Who should be in them? Like, you know, how do you actually run them? Yeah, I would be super curious because I think, you know, we have this discussion a lot, right? Like what are the forums where you have the right people in the room to ensure that the story is presented in the right way, and then actually you can take action and decide on specific initiatives. Right. Because there's always too many initiatives. And so you're always having these trade offs. Right? Like if you look from an executive perspective, right, they don't — I mean, they know there's a lot of stuff that could be better. The question is what has the biggest impact. So I'm super curious, like maybe we can digest the kind of best practice QBR preparation and running them a bit more.

Danny Schonfeld: Yeah. I'd be happy to. So I've run a number of them again across multiple companies and different styles. So I've gotten somewhat good appreciation for some of the things that work, some of the things that don't work. And often my sales ops previously and now RevOps teams have coordinated these. So that involves building templates for department leaders to fill out, managing pre-reads, identifying topics to deep dive into, heavily influenced by those analytical overviews that I alluded to earlier. Often, my team will help interpret what's happening for their specific department stakeholder. Right? So my marketing ops leader will work with our CMO. My sales ops leader will work with our head of sales and help orient. Alright, these are some of the key topics to focus on for your function so that we're trying to distill that information going into that section. The preparation is both tactical and strategic, right? It's surfacing that data in a way that people know what to do with it and can manage it. When you're actually in the session, I've seen a number of different approaches taken. So full day sessions with dedicated time for each leader to present, more concise two hour sessions focusing specifically on deep dive topics. I think both can be effective, but you really need to be thoughtful around — depending on the type of QBR that you're conducting and how in-depth to go into each department versus a couple of the most critical cross functional topics — dictates how specifically to prepare for them. So a little bit higher level, but I can go further in-depth if you'd like. Just wanna get a sense of how I think about it and some of the things that have worked for me over the years.

Janis Zech: Yeah. Maybe even more tactical, like, you know, how long do you prepare? When do you send the pre-read? Would you do it in person, or is it virtual? You know, what's a good length for a QBR? Do you recommend breakout sessions or not? I wanna know it all.

Danny Schonfeld: Sure. So as I mentioned, I've done a number of different types, and I don't think there's one size fits all that works for every organization, which is why I'm giving you a little bit of a multiple choice response to your multiple choice question. But some of the more tactical items that you're asking about — because, you know, often there will be a hockey stick toward the end of the quarter in terms of deals closing in the last couple weeks. It's very difficult to prepare for those until after the quarter ends. So the timeline for my team, it's almost like you think about the sales team closing until the last day of the quarter, and then that's when much of the work starts for my team when we can really interpret the signals that have come out of closing that quarter, when the dust has settled essentially. And sometimes there's even a couple day gap if you need finance to confirm that deals should be properly booked, for instance. Now that doesn't mean you can't set up all the infrastructure and templates in advance at that time, but when you can start working on the insights themselves, I'd say it's either the day or a couple days after the quarter ends. And then I would typically love to have two weeks — more realistically closer to one week — in order to do all that prep, which involves a combination of, again, working with stakeholders to make sure that they're completing their pre-work, my own team and myself working on some of our holistic insights into what happened, reviewing some of the key signals and trying to come up with that story. And I'll try to be really focused to make sure that it's a valuable, effective use of time, all leaning into a good discussion. And as far as how long the actual meeting itself could be, like I said, I think it's very company specific — whether it's two hours or eight hours — how deep you need to go and how many things are changing. But if you schedule the time, you'll fill it. So just make sure it's valuable for all of the stakeholders who are there for that full time and have a good discussion that has important action items. I think those sorts of meetings are more effective in person as well. That was another question that you asked. My company is remote, so it doesn't always work. I've done them remotely. I've done them in person. I think it's just a better way to keep the group engaged, especially when I'm going through some dense analytics items — I can force everyone to pay attention more often in a better way when they're in person than over Zoom. But, yeah, overall, like I said, there's a lot of different ways to do them effectively, but in person's ideal.

Janis Zech: Sounds a bit like the trade off. Like, do you wanna start the conversation so everybody's focused, but then everybody's focused so maybe you don't wanna stop the conversation. Right? So there's pros and cons, but yeah. Very interesting.

Danny Schonfeld: One other thing I'll add is just — I'm in ops, right, I'm biased toward operations — those thoughtful strategic discussions are critical to any business' success. But in order to really make them valuable, the operations and execution coming out of those strategic discussions are the backbone of a company. So what happens after matters more than what happens during the meeting, and that's easy to lose sight of. It's very easy to just say, okay, I did the work, my work stops here. I've heard it happen at companies — it's like you're working toward an IPO, then we did it, we solved it. It's no. In many cases, things are just magnifying complexity and challenge. And now we have a blueprint, but that's all meaningless unless we go and deliver what we agreed we were gonna deliver. And that's also a critical aspect of revenue operations more broadly.

Janis Zech: This is actually something I wanted to ask you about. So right, you can have obviously a lot of people in the room, you can also just have a few people in the room. Like, how do you think about involving the leadership team outside of the sort of core functions that are affected by a QBR? So sales, marketing, obviously, should be involved. COO, probably, if that person exists at the company, should be involved. But like, CEO — should they be involved? Like, to what kind of level? If you run like a six hour QBR, they probably would not want to sit through that. Maybe. Depends on the company and size and all this stuff. But if you're like five thousand people, then probably a CEO is not going to sit there for six hours. So just curious how you think about that. And is it something where you kind of share an executive summary in the end? Or, you know, how do you expand the message beyond the QBR?

Danny Schonfeld: Yeah. Look, I've worked in companies of very different sizes, and at LinkedIn, for instance, the CEO was not attending our marketing solutions QBRs. Right? So that is fair. That doesn't mean that the insights and decisions coming out of that aren't valuable to the most senior executive leadership team — at minimum just to communicate. Right? And almost by communicating what decisions came out of it, it helps hold you accountable to those follow ups like I talked about. It also gives you a blueprint of, well, next time we have a starting point. We said we were gonna do these things. We're building out these action plans. Let's make sure that we're auditing the team to deliver against those. Different sized companies, yes, you could see having more executive level input and guidance and even running those meetings. But it varies a little bit depending on the size of the organization.

Janis Zech: Yeah. I just wanna share one experience. So my previous company, we were around three fifty people when I left, and like, we basically had two layers. Right? So C-suite and then basically direct reports — kind of a broader leadership. And our common theme was always, the leadership team was essentially more for communication, sharing, and alignment. The C-level was, like, obviously a lot of decision making on an operational level. But to bring everybody together and make a decision is very, very difficult. Right? So like, we basically — there were a lot of decisions, like even strategic decisions, taken in product or in engineering. Right. But everybody together was almost impossible. So I'm curious, like, if you think about the QBR, right, and you present these strategic narratives or findings — like, do you then take decisions in the QBR on what to work on? Is there a follow-up discussion? Does it depend?

Danny Schonfeld: Yeah. So I fully agree. And not even mentioning that the more people are in the room, the maybe more challenging it could be for some individuals to speak up and share a certain opinion about a topic even if they feel comfortable, depending on the level of senior leadership there. So that also could impact the decision. I think a more effective approach is — it's more about, right, understanding what happened last quarter, hearing from the leaders what their plans are for the next quarter as a result of this, providing input into that, and then giving them a chance maybe later on to come back and say, all right, I refined my plan based on the conversation that we had, here's what I'm gonna move forward with. And at the end of the day, yeah, if an executive leader wants to overrule what that individual department leader's recommendation or plan is, that's a time and place to do that. But I think it also helps create maybe a safer environment for them to be able to say, hey, this is the area that I'm an expert in. Right? This is where I've spent my career, and this is what I've thought about for the last three months — these are my thoughts. You obviously have a broader purview, so I love your perspective, but I'm not coming in blind. I've thought about this. I'm all adapted based on some of the signals that my RevOps function has shared. But overall, this is what I think needs to happen. And if, you know, over time, you're probably more and more in sync with that leadership team of, okay, yes, that makes sense versus we're on completely different pages.

Janis Zech: I'd love to switch gears a little bit and just focus a bit on analytics tools as well. So you have, I think, a lot of experience working with those, and obviously I'm assuming some of them are needed to prepare something like a QBR. Maybe you could just spend like a few minutes on that topic. So how would you actually collect all the information? In your case, does it end up in a spreadsheet? Or, you know, have you found other ways? And — I'm sorry, but I have to ask this question — do you use AI at this point, or have you not let AI into the room or the conversation there yet?

Danny Schonfeld: Yes. So in terms of tools, I think there's a little bit of a mix between the platform that we're building the tool on and then the tool itself that is more custom built that we're using in my go to market analytics function, for instance. So in terms of the platform, right, that's where, yes, certainly some spreadsheets in certain instances, but a BI tool like Tableau and Looker Studio and Power BI. And honestly, I use each of those for different use cases in my function. For sales facing or even sales director facing, Tableau can be pretty heavy if trying to filter things. So something like Looker — if it's a lighter dashboard or platform — can make more sense. Or something where we need just a wall of metrics to see next to each other, that's more of a place for a spreadsheet. Something that's reading a system — that's where something like Tableau comes in, where we'll typically use Tableau to be able to manipulate a system and ensure root causes to get to the next level. Right? So there's different answers depending on the use case. You asked about the QBR specifically, and there it's sort of a combination of each of them. But one thing I'd like to do is publish just a recap of the prior quarter. So just all of the performance against our KPIs versus similar in the previous periods. And we'll just have that as a tool that we re-draft each quarter. And that is then typically referenced throughout the entire quarter. It's available to all stakeholders. They often use it as a reference. My team is more unearthing the key stories and signals associated with it, but just publishing something like that tends to be pretty valuable for these sessions. So, again, everyone's working from the same numbers.

Janis Zech: It is so important. Right? Like, it's funny you say this. I had a conversation yesterday with a PE guy, and he was like, you know, we somehow talked about QBRs, and he was like, look, one of the most important things you have to do is, like, you know, you put the numbers you actually planned for there, and then you show the actuals. Right? And you should always start with that because, you know, we all come out of annual planning and then you have the spreadsheet the finance team works on, but it's somewhere hidden. Right? So this is so, so important, I think.

Danny Schonfeld: I'm glad you mentioned that. I'm a big fan of setting targets because targets are essentially context in a way, similar to historicals as context. But even seeing one hundred percent growth — right, when you're dealing with twenty or thirty or fifty metrics, whatever it is, that can lose its meaning. So having that comparison of the target that we set and how we did against that is fundamental in the tools that I build. And that's part of my annual business planning process — I'll set pretty granular targets across those metrics as we're going on record.

Janis Zech: Totally agree. And on the AI part though, I'm curious — like, you — sorry, I didn't let you answer. I'm glad you're bringing it back. This is relentless journalism, you know, like we're asking a lot of questions.

Danny Schonfeld: I couldn't dodge it. I tried. Yes. So I have experimented with tools. I have plugged, for instance, what I mentioned into an AI platform to see if I could say, all right, well, what was my revenue growth from twenty twenty three to twenty twenty four? Just like reading this spreadsheet. How did — what did this customer spend in this timeframe? What was our win rate on this product? As an example. I have not found a tool that has been able to provide accurate results in that way, or that has been able to build a tool like this. The way that my team uses it is more for things like formula assistance. If you're building in Tableau or Google Sheets and need to, like, handle some complicated referencing of different fields, or to see where you're missing a comma to fix something or a dollar sign. Right? So more of like an assistant than an actual fundamental tool. As soon as it is capable of doing that sort of thing, I would love to implement it. I personally have not seen the level of success despite there being a lot of tools about it, because this data is often so unstructured and coming from so many different sources. And that's a place where you need that individual expertise that comes from just years of knowing what the exceptions are in our data and where things are breaking and going.

Janis Zech: Yeah. I mean, just from personal experience, right, we also have built AI into our tool, and one of the things that we're gonna launch sometime this year — not adding any timelines here now, but working on it — is also, like, you know, to look at a pipeline overview and then have AI summarize it for you, which is also something that Salesforce is working on and many other companies. But I think the key thing here is that you need to actually really provide a lot of context. So, you know, close dates — it's not just a date field, it could mean so many different things. A currency field — the number of currency fields some of our customers have, like, well into the multiple dozens. Right? And some are legacy, some are real, some are not. And it's crazy sometimes. And it feels like it's very easy to add fields, it's very hard to remove fields or columns or data entries and so on. And then to really make an AI understand that, I think you have to limit the scope massively and at the same time increase context information a lot for the AI to be capable of coming to somewhat of a solid conclusion. So it's not enough to just take a base model, but you really need to tweak it and train it yourself. And you need to train it on your own company data to become really excellent. So I think this is just a challenge. This is something that I think can be overcome, but it's not easy. And I think every business needs to solve that.

Danny Schonfeld: Well, not only that, but in analytics that are driving company strategy and decision making, accuracy is the most important thing in what we do. And it's a lot easier to lose trust than it is to gain it. We know all the stories of AI hallucinating, but I need to be confident in the numbers in front of my executive stakeholders. And if I were to do this, then I would spend as long, if not longer, just validating the accuracy of what it spit out. So like I said, I don't think it's quite there yet. And that's just not something I can sacrifice, because every time there's a data error, it leads to questions about every other number that you put on a page. And that's why I'm really careful — maybe even annoyingly so, my team would say — in terms of checking what we're putting together, comparing it, drilling into the specific numbers that are drawn up. That's also like — my marketing colleagues who are phenomenal will often use it to help with marketing campaigns. Right? Great use case. Doesn't have the same level of requirement that every single word is off by a decimal, for instance.

Janis Zech: Yeah. I think this is such an important point because, basically, I think kind of referring to what is the status quo — and the status quo you're putting out is probably highly accurate and very measured and very good already from a quality level. Right? Like, if you think about most sales workflows, right, like, you have conversations, all the transcripts, they're everywhere, but nobody looks at them. Nothing happens. And all the MEDDIC fields or MEDDICC fields, they're all empty. Right? That's a great use case. If there's a little error, it doesn't really matter as much. Right? So there you go from basically very bad to pretty good, actually very quickly. And I think the same, right, when we have the pipeline management forecasting conversation. Right? So we basically do the data piping and then the pipeline management forecasting layer. And, you know, our tests on when you run a deal review — most deal reviews are not done very well. There's so much context missing. Right? So we fix the context in terms of data, making sure that everything is mapped to the opportunity. And then you have a button, and that basically sums up all the context data that is already pre-wired. That goes from pretty okay to very, very good. Right? So I think it's a really good framing of how to think about AI use cases. And I think your use case you mentioned on the marketing side is very similar. Right. Like, I do these LinkedIn posts. I don't let AI write them, but I always check against it. And I think the variability of the result is very high versus analytics — it's not. So one question for you. I mean, we're getting at time. I know you're also super busy, but this is a lot of fun. So, I mean, I think a lot of our listeners listen to this and they're like, okay, this sounds like a lot of work. I'm firefighting left and right. Slack is pinging me the whole day, and I also get some emails and sometimes people even come into my office and want something from me. Right? So, like, how do you set up your team if you have the luxury of having a team, or how do you keep your head above water to actually do this? What you're just explaining — and how have you done it with what kind of team structure, and how much time do you personally spend on it as the leader of the team?

Danny Schonfeld: Sure. Yeah. So I've gone through a lot of iterations over my career in RevOps and SalesOps previously. I didn't get too much into my background, but I was a consultant for a number of years before transitioning over to LinkedIn to lead a sales ops function before moving into this RevOps role. When I started out, my day consisted of whoever came to my desk and was the loudest with whatever problem they were dealing with. Some dashboard was down, some data wasn't working. And I was just — it was just constant firefighting. Okay, I gotta go fix this. Oh no, this person's yelling at me, gotta go fix this. I have recognized over time that I needed to be more deliberate in how I was going to spend my day and not just succumb to, again, the loudest angriest voice, because often you have a better idea — by having visibility into all these things — you have a better idea of what's best for the company longer term. And there's always room for the short term, there's always an imperative to support the short term, but you can't sacrifice the long term as well. And I think it is on every RevOps leader or SalesOps leader to make sure that you are dictating your own priorities at the same time, which includes setting the business up for long term success. Because often we know better by being in this role, by not being tied to a specific number to achieve this quarter — which many of us are — but we'll also often have more holistic or qualitative targets along with that, ironically, given we tend to be one of the most analytical and quantitative functions in the business. But separate topic. What I've done as a result of that is created demand for analytics by showing what's possible. That's the quick answer. Specifically, I have built go to market strategy and analytics functions across my last few companies in these roles. There are huge benefits from a dedicated function, from that centralization, monitoring lead to audit the customer through the full customer life cycle, to allow for that sort of proactive analysis, to allow for building tools that all the other functions are gonna be able to use so that we can get faster, we can create more self-service options for these individuals, we can remove the errors — the root cause of the error versus just addressing the symptom of it. Most of my team is siloed into specific functions. RevOps is only able to operate effectively by living cross functionally, which is the nature of my go to market strategy and analytics team. That's one of the ways that I've been successful in doing that and what I'd recommend. Right? Create that demand by showing what's possible.

Janis Zech: I mean, working with you certainly sounds like something everyone should be looking forward to if they have a chance. So thank you so much for joining and sharing your thoughts, and yeah, I think a really interesting thought process. Loved it. We loved it. So, always one final question — what book would you recommend to our listeners?

Danny Schonfeld: Yeah. It's been a few years since I read it, but one — a business book, I guess — that