Ep. 323: Bhavin Shah is the Founder & CEO @ Moveworks, the cloud-based AI platform, purpose-built for large enterprises, that resolves employees’ IT support issues—instantly and automatically. To date Bhavin has raised over $108M with Moveworks from the likes of Mamoon Hamid @ Kleiner Perkins, Arij Janmohamed @ Lightspeed, Bain Capital, Sapphire Ventures and ICONIQ. Prior to Moveworks, Bhavin was the Founder and CEO @ Refresh which was later acquired by LinkedIn and then before that founded Gazillion Entertainment, a company he scaled to over 200 employees.
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In Today’s Episode We Discuss:
* How Bhavin made his way into the wonderful world of SaaS and came to found Moveworks.
* What are the core challenges IT teams are facing as a result of the move to remote work? Where do many make mistakes here? What can one do from a structural perspective to set them up for success when moving to remote?
* What does great change management look like in Bhavin’s mind today? Where do so many go wrong here? How does this change in the world of remote? Who should be involved in executing on the change management plan?
* How does Bhavin think about the role of customer success today? Why does Bhavin believe that customer success and product should be in one org? How does Bhavin think about the interplay of marketing and customer success? Is marketing moving closer and closer to customer success with their content?
Ep. 324: Join SaaStr CEO Jason Lemkin and Bessemer Venture Partners Partner Byron Deeter for a deep dive on what’s going on in Venture Capital and Cloud.
This episode is sponsored by TaxJar.
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This podcast is an excerpt from Jason and Byron’s webinar “Bridging the Gap: The Current State of Venture Capital and Cloud.” You can find the full webinar here.
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Below, we’ve shared the transcript of Harry’s interview with Bhavin.
Harry Stebbings: Welcome back to the official SaaStr podcast with me, Harry Stebbings. I’d love to see you behind the scenes here at the show on Instagram @HStebbings1996 with two Bs. I always love to see you there. But to our episode today, and we welcome an incredible three time entrepreneur now rocking the world of enterprise SaaS, and so with that I’m thrilled to welcome Bhavin Shah, founder and CEO of Moveworks, the cloud-based AI platform, purpose built for large enterprises that resolves employees’ IT support issues instantly and automatically. Today, Bhavin has raised over $108 million with Moveworks from the likes of Mamoon Hamid at Kleiner Perkins, Arif Janmohamed at Lightspeed, Bain Capital, Sapphire Ventures, and ICONIQ, to name a few.
Harry Stebbings: Prior to Moveworks, Bhavin was the founder and CEO at Refresh, which was later acquired by LinkedIn, and then before that, founded Gazillion Entertainment, a company he scaled to over 300 employees.
Harry Stebbings: Now I’m very excited to hand it over to Bhavin Shah, founder and CEO at Moveworks.
Harry Stebbings: Bhavin, it is so awesome to have you on the show today. As I said, I’ve heard so many good things from pretty much every board member of yours and investor, but especially Mamoon at Kleiner and Arif at Lightspeed. So thank you so much for joining me today.
Bhavin Shah: Thanks, Harry. First time caller, long time listener. Excited to be on the show today.
Harry Stebbings: I mean, my word, that is so good for my ego. But I do want to start today with some context. So tell me, Bhavin, how did you make your way into what I definitely think is the wonderful world of SaaS, but also what was that founding ah-ha moment for you with Moveworks?
Bhavin Shah: So I grew up in Silicon Valley here in the Bay Area, and my parents immigrated in the late ’60s, was exposed to technology from a very early age. This endeavor with Moveworks is my third company. My last company was in the mobile productivity space called Refresh.io. We were acquired by LinkedIn about five years ago. Moveworks came about through a very deliberate process, a process by the four of us founders to understand where we could use machine learning in a very useful and impactful way in the enterprise. And the journey took us to a conversation with over 30 CIOs and IT leaders and analyzing a bunch of their IT tickets before taking our first bit of capital.
Bhavin Shah: The discovery was that there’s now about a billion knowledge workers worldwide, and each of those knowledge workers submits about one ticket a month. But those tickets still take three days to get resolved, and so we saw billions of dollars being spent on better ticketing systems, better automation tools, which the world definitely needed. But these tickets were still relatively slow, and when we dug into the details, what we realized is that they’re still written in natural language, and so no one had taken the approach of trying to solve the tickets directly by understanding and building machine learning frameworks to read these tickets and then solve them completely autonomously. And so that’s been our journey. How do we go from three days to three seconds?
Harry Stebbings: Absolutely, and it’s been an incredible journey to see those since then. I do, sorry, I’m too intrigued not to ask, you mentioned the two prior companies there. How did that experience with those two prior companies impact your operating mindset today with Moveworks?
Bhavin Shah: My career has involved a variety of different industries. I started off in the toy business. I went into the gaming world and it went into mobile productivity, and then now into enterprise SaaS, and if I could do it all over again, I would have done enterprise SaaS from the get-go. It’s a domain that I really enjoy, and I think I thrive in. But along the way of company building, I think you learn a lot about yourself. You learn a lot about what matters, how you build culture, how you build teams, how you listen to customers, how you don’t listen to customers. A lot of different factors went into this. But when it came down to Moveworks, there was a variety of different viewpoints that each of us founders were bringing to the table. I had spent a bunch of years thinking about how to make people productive on mobile, how to leverage that platform.
Bhavin Shah: Varun, my co-founder, was at Facebook building machine learning tools with specifically chat bot tools that were being used to optimize and improve the interview process for hiring managers. They could talk to a bot about who they were interviewing, etc. Jiang, my other co-founder, was at Google and was one of the founding engineers of the question-answer system that shows those paragraphs at the top of the Google search results, basically doing NLP on the fly and then figuring out what to show you, which is very important to our product today, and then Vaibhav, who had also been a serial entrepreneur like myself, building large enterprise scale systems.
Bhavin Shah: So I think a lot of these perspectives came together in a way that allowed us to all build from there and build this company.
Harry Stebbings: I mean, it’s such a unique blend of both backgrounds and skills associated, and so just [inaudible 00:06:23] teams. I do want to ask, though, because if we start on the most important topic that’s actually the front of every single portfolio a founder of ours has mind is the obvious move to remote work and the rise of work from home. You were on the front lines of this movement, and as you said there about speaking CIOs, you speak CIOs across all verticals really, making the switch to work from home. So I guess the first question is what are the core challenges IT teams face as a result of having to move to fully remote pretty much overnight?
Bhavin Shah: There’s so many challenges, and I think as the weeks go by, some of these challenges are getting solved, but initially connectivity, VPN issues, bandwidth, access, policies, procedures, related to all this is support, and that’s our world here at Moveworks. Now what’s interesting if you read the news, which everyone is doing right now, you hear a lot about bars closing, schools closing, gyms closing, but one thing that you don’t hear about is the fact that every walk up bar in the enterprise is now shuttered. It’s closed. We’re not able to go into the office anymore and walk up to someone in IT and say, “Hey, can you fix this for me?” And so overnight, IT teams have had to shift their strategies and deploy things like messaging platforms, remote conf and webinar tools, digital channels for IT support, a lot of other systems to ensure that workers working from home are staying productive.
Bhavin Shah: And the data, as part of the challenges that we’re seeing them face, we’re seeing a 500% gain in requests that employees are making for video conferencing apps like Zoom. We’re seeing the demand for IT support services double. We’re seeing two to three X the number of troubleshooting tickets come into the help desk. And so the good news is there’s lots of good solutions, there’s a lot of companies that want to help here, but the real test that’s going on right now is whether IT teams can put these systems in place in a matter of days and weeks, not months or years.
Harry Stebbings: You’ve seen a variety of different rollout, so to speak, from the IT teams themselves. When you think about perfecting the infrastructure stack and process of these rollouts, where do you see many maybe make mistakes and go wrong in really transitioning to work from home effectively?
Bhavin Shah: Yeah, I think what we’re seeing is it’s mostly about speed. I think a lot of IT teams were thinking that they would make these changes over the course of several years, and manage change management accordingly. I think the ones that are moving quickly now to pick up best of breed best practices and roll it out are finding that employees are quite receptive. Employees are able to make switches very quickly. I think we’re all seeing this. I’ve got kids, and overnight they’re all switched to now doing classes over Zoom. Even my kid’s TaeKwonDo and gymnastics classes are doing them over Zoom.
Bhavin Shah: So I think that where people believe that people can’t change, I think ends up getting them stuck in deliberation and where they know that if they make the change, people will adjust, those groups are winning. One thing I’ll say though is you are seeing kind of a shift in terms of the economy. While many businesses are shuttering and slowing down or weakening, there is a new type of economy, a new type of business that is starting to see more traction and/or thrive in this environment, and that is for businesses that are helping employees and individuals get work done anywhere and anytime. And I think that’s something that we’re seeing play into our business, and in general, the world of SaaS.
Harry Stebbings: One thing I’m really intrigued by is is this a flash in the pan momentary realization from traditional large incumbents and enterprise that obviously fundamentally by law, they have to be work from home? And then more of back to their traditional standards of practice and work? Or do you think this is actually fundamentally a shift in how society operates with regards to its relationship to work?
Bhavin Shah: Yeah, I think it’s the latter, and here’s our experience. So when we started the company, about 20% of CIOs that we talked to had an enterprise-wide collaboration/chat strategy. They’d be going with Slack or Microsoft Teams or Glip or GChat, etc. Last year, that percentage was about 50/50. 50 had adopted something, 50 were still figuring out when to deploy a new tool and how to go about doing that. That debate is over. Basically the next two months, every company in America is going to have decided and implemented one of these tools, and if you’ve used these tools in enterprise, once you start using them, it’s very, very hard to go back. And I think for us, we made an early bet in this world of enterprise chat being a big deal and being something that would overtake traditional means of communication like email.
Bhavin Shah: And so we see that happening in our personal lives with WhatsApp and SMS and everything else. Why not in the enterprise? And so this is actually causing that shift to occur very rapidly, and people will start to see the utility of this. I think through chat, the UI is very intuitive for everyone. It’s not one that we have to learn. We’re all familiar with writing, we’re all familiar with communicating. And so it allows us to have more interactivity, it allows us to simplify work streams, and basically work at real-time. So I think this is a permanent shift, even when we do go back to the office building.
Harry Stebbings: You mentioned an early bet that you placed in terms of the market itself and its transition. I have to ask, because now it seems inherently obvious, but it wasn’t at the time, and it was early. Why did you gain such conviction as opposed to other channels of support, like email and voice? And what led that decision making conviction?
Bhavin Shah: Yeah, it’s a good question. We are a conversational AI company, and there’s a variety of ways that you can obviously engage in conversation. I think that enterprise messaging for us provided a lot of affordances that just made sense if we wanted to provide support to employees. So just going back to the behavior, employees submit on average about one IT ticket a month, and it takes about three days for them to get a result. Now there are portals, there are self-service catalogs and things that people can use, but when you only have an issue every few weeks to a month, you don’t really know how to navigate those. Those UIs are wrought with friction, and email obviously had its inherent delays as well and it’s not real-time.
Bhavin Shah: And so what we wanted to do was find a way that employees could just do what’s intuitive, which is shoot off a quick note and send it off to IT through a chat and have that picked up and have that resolved, but more importantly, by doing it in the enterprise collab tool, we could get their attention, because they’re already checking that tool 100 times a day, collaborating with team members on projects, checking updates, etc. So we wanted to find an interface that allowed for that high volume, high frequency, and we just didn’t necessarily see that with voice, and of course, email has been clogged for years, and you can very effectively find people on their mobile devices, on their tablets, on their laptops, with these new messaging platforms, which really does a lot to help improve our engagement and overall resolve more tickets for those employees.
Harry Stebbings: You said that about engagement, and you also said that about the three day ticket resolution being the standard. I’m really interested, because I always think the definition of what success looks like is crucial, and metrics are often fundamentally quite uniform across industries and verticals. What metrics do you use to define success today? And I guess why do you focus on those specific metrics over others?
Bhavin Shah: Yeah, so until recently, I think SaaS has been mostly focused on the delivery model of the software, to go from your data center to a cloud, which that itself led to some new metrics that have been used for the greater part of a decade, which is to measure active users. And it’s pretty logical, but the problem with active users is it doesn’t really tell you how much value you’re getting. And so I think for us, we’ve been charging forward in a new way of energizing SaaS insomuch as we’re taking ownership of the actual end to end results. We’re doing the implementation and configuring overall, so looking at how many tickets did we resolve for each customer? And so that is the metric that we picked. It’s got perfect alignment between us and the customer. How many tickets did Moveworks resolve yesterday at Broadcom? How many tickets did Moveworks resolve yesterday at Nutanix?
Bhavin Shah: And we measure that, our customers measure that, and it allows us to focus on whatever might be the case to make those metrics better. Sometimes that’s engagement, sometimes that’s an additional integration that we need to do, sometimes that is triggering some other automation that the customer might already have. But the goal ultimately is to provide more workload relief for the IT teams by having a system like this resolve more and more tickets. So for us, the alignment is at a higher order around what was our output, what was the results of our system each day, each hour?
Harry Stebbings: Can I ask you, I’m on the board of many companies that face the challenge of change management. In a normal world where services, revenue, and in-person visits, meetings, seminars are possible, change management is still fundamentally a challenge. In a remote world, how does change management change, and does it get inherently more difficult given the lack of physical services that one can provide in terms of coaching, training seminars to really allow for a smooth transition?
Bhavin Shah: Well, I think that’s absolutely right. What I see a lot of, especially in our own experience of using other SaaS tools, there’s a heavy burden placed on the customer to learn the tool, to implement the tool, to train the employees on how to use the tool, and only then do you actually see the full value of what was created. With our product, and something that we strive for, is how do we take more and more of that work off the plates of our customers and how do we give them more value right out of the box? And so sometimes, it’s not just us delivering a set of tools. I always think of this analogy like Home Depot. A lot of enterprise SaaS mindset is, “Hey, let’s just give someone a tool and have them go figure this out or extract value from it.”
Bhavin Shah: At Home Depot, you go buy your tools to go do whatever home improvement project you want. Maybe you’re building a birdhouse. Maybe you’re building an extension to your living room. It doesn’t matter to them as long as you bought their tools. For us, we actually want to provide you with the end result of what you’re looking for. If you want that living room extension, let’s go build that and give you the keys when it’s ready so you can walk in and enjoy it. And so from our standpoint, the customer success motion, the engagement that we have with customers, is not just about improving our machine learning to understand yet another type of ticket utterance or language pattern, but it’s sometimes us adding a new integration into a back end system.
Bhavin Shah: Sometimes it’s us showing those customers how to clean up an old process or to change a system or record. All of that is so that we can track and show that progress with each customer, and that metric gives us the ability to know where we stand, and that’s something that’s very intrinsic with what we do. Really it’s about customer success being company success as we build this out.
Harry Stebbings: Can I ask, in terms of both customer success, I guess also marketing, but in terms of horizontal application, this is not a vertically specific tool and it’s so horizontal across so many different industries. How do you think about effective marketing in customer success, given the inherently different use cases, applications, and some challenges the different verticals will face with it.
Bhavin Shah: So one of the premises that we had when we founded the company, and this was insight that we derived by looking at IT ticket data, was that IT was homogenizing globally. What does that mean? That means that, and largely due to SaaS products, people had the same tickets that they were solving every morning about Zoom, GoToMeeting, WebEx, or BlueJeans about MacBook, Dells, Lenovos, or HPs. And so as a result, we found ourselves really excited about the opportunity to solve this problem because everyone was experiencing the same problems. So we could build machine learning models at scale that could resolve it out of the box up front for our customers.
Bhavin Shah: And so while we have customers in pharma and semi-conductor and other high tech and retail and services, we have the same types of tickets. And so as a result, our customer success team works broadly across all customers, but we’re solving the same issues. Organizationally, we put customer success and product into one org, because it’s not just important to build a feature and then deliver it to the customer. It’s about building that feature, delivering it, and then seeing how much value it delivers and figuring out what needs to happen on our side, on the customer side, to maximize that value.
Bhavin Shah: So there’s a variety of metrics that we track from a customer success standpoint, but in this sense, it goes back to this core drumbeat heartbeat that we have at the company, which is everyone in the company gets a daily report. And every morning, we all look at it and it tells us how many tickets we resolved yesterday at Broadcom, how many tickets yesterday did we resolve at AppDynamics, at Nutanix, at Freedom Financial. And for me, I get that report every morning, but others in the company are looking at that every hour, every few minutes, to understand really what is the impact that we’re having and what can change and evolve as a result of knowing that?
Harry Stebbings: Can I ask, you said there about CS, like sitting actually with product? Do you engage with a product postmortem, and what do you do to weave that very tight fabric between product and CS strategically other than sitting together?
Bhavin Shah: The journey of building a product or a feature inside of our company is perhaps different than others. So instead of it being a top down motion of the market saying, “Build this use case,” and then the product marketers figuring out the details of that going down to a product manager, going down to an engineer. It’s actually quite the opposite. The engineers are looking at ticket data every day, and they’re saying, “Hey, there’s these tickets, these 300 tickets we skipped at this customer today. What could we build? What kind of machine learning models? What kind of integrations can we build to solve that?” And that leads to further research, further discovery, customer conversations, and ultimately ties into well, what’s the impact going to be? Are we going to increase overall customer resolution by 5%, by 2%? Great. Let’s go do that. Let’s put that in the next quarter’s product plan.”
Bhavin Shah: So today, we’re resolving between 30 and 40% of all tickets on average across customers, and it didn’t just happen overnight. We were two years ago bragging about three to 5%, and over time, the analysis of ticket data, the understanding of how customers perceive that value but ultimately building these features, it’s not a black box. It’s not one of these things where we build them, then we market them, and then we sell them, and then we all pray that people will buy it. It’s something that’s a lot more fluid and a lot more certain before we even start writing the first line of code.
Harry Stebbings: Gosh, the joy of doing the show is I can take the conversation in any way I want. You said there about the improving efficiency of the resolutions there. It made me think we chatted before the show about information network effects with regards to podcasting. When you think about network effects with Moveworks, is there not data network effects where every subsequent ticket actually improves the efficiency of the subsequent thousand tickets, building higher efficiency and resolution speeds.
Bhavin Shah: 100%, and I think that’s something that we haven’t seen a ton of in the enterprise world. In traditional settings, if I buy a database and you buy a database, the same database, the database doesn’t get smarter because we’re both using it. For us, we are absolutely benefiting from that, and our customers are, too. Think of different forms of AI. You hear about in the academic circles, weak forms of AI, which are basically making a prediction or a suggestion or better ranking things. Then you have strong forms of AI that are trying to take entire problems and solve them end to end. A level five self-driving car is a strong form of AI.
Bhavin Shah: We’re sort of like that in the IT world. You want a self-driving car that’s been driven a million miles before you get into it, or a billion miles. With us, similarly, our product is seeing now over 70 million tickets and we keep processing them. We keep getting better, we keep understanding, okay, what actions need to be taken for this kind of language pattern? What actually needs to be taken for this other? And so we are getting better. What that means is a variety of things. One is customers who become new customers of ours, and which today we’re doing almost one customer launch every day, a large enterprise, they’re seeing immediate time to value, like the first day we might resolve 20% of their tickets. The first week we might resolve 30% of their tickets. So they don’t have to wait around for six months, a year.
Bhavin Shah: And that’s where I think we’re breaking from the traditional SaaS model where you have a toolkit, have a lot of professional services, they get attached to it, and then you configure it, that’s custom, and then you see the results. You can’t get to the same results that we have with machine learning because we’re able to aggregate, but also we’re able to learn very quickly and to your point, have this true network effect, which ties into the homogeneity and ties into a lot of the other factors, which really gets down into how did we and why did we start this company? This is exactly why. We thought of all these things, these things were somewhat still hard to figure out how we were going to achieve, but the concepts were there, and that got us very excited.
Harry Stebbings: Can I ask, thinking large enterprise and some of their challenging requests, do you ever get pushback on the data sharing between companies in terms of allowing you to provide better efficiency across the client base because of the knowledge sharing and data sharing that allows you to do so?
Bhavin Shah: Yeah, so security’s super important to us from the get-go, very early on we made investments to ensure that we could keep customer data separate and not commingle data, but at the same time, build machine learning understanding across ticket data so that when we learned something in one environment, we can leverage it in the next. So we do a lot of abstraction tokenization, things that keep what’s proprietary proprietary, but also allow us to more generically learn from that. Our customers review that with us. We go through extensive discussions around how we handle that, and for the most part, I think people are starting to come around to the fact that they want an AI system that has been trained, that has learned, that delivers value out of the box, because if you’ve been tracking a lot of this AI space, there’s a lot of AI initiatives that crash and burn six months later, a year later.
Bhavin Shah: And it’s because these things are very hard to stand up on your own. They’re very hard to maintain. They’re very hard to make smarter and evolve. And so the real benefits of going SaaS with AI is that you get all that benefit right up front and you get that continuous improvement over time. So that’s how we’re seeing it and our customers are excited about getting those results without the kind of effort that they would normally have to put into it.
Harry Stebbings: We may be diving a little bit into the ML world here, but I am just interested, have you noticed this kind of asymptotic moment of data ingestion really where there’s decreasing efficiency with every additional dataset? Because once you hit 70 million, there must be some moment of asymptote. Does that make sense? And how do you think about that?
Bhavin Shah: Yeah, so instead of thinking about it as one machine learning model and a monolithic concept of 70 million tickets going to one model, think of it as thousands of models. Think about it as hundreds of techniques. Think about it as different capabilities, and so today, we’ve seen so many tickets, but we’re not solving 100% of tickets. In fact, we think the asymptote of tickets that can be resolved rises up to about 85 to 90%. There’s still some tickets that will always have to use people to better understand… Sometimes people are very vague in their tickets. It’s like, “Hey, I just got back from Hawaii, and everything’s broke. Can you help?”
Bhavin Shah: Well, in that case they really just want a hug from IT or they want someone to give them support, so it’s not intended for a machine to solve. But if you think about it from how we’ve gone about this is we’ve stitched together a framework of looking at all the intents in IT, all of the entities that we see in IT, and we’re building machine learning models to understand every intent entity pairing that’s out there. But as a result, it isn’t just a singular effort. We have to constantly review these things. We have to have human annotators that look at thousands of tickets a day and see are our models drifting? Are our models getting over fit? Do we have to inject noise into the training data? There’s a lot of other factors that go into this that we work on to ensure that this stuff works.
Bhavin Shah: But to your point, absolutely, models, techniques, are evolving very rapidly, and we see models peak in performance all the time, and so this idea that there’s one model and you just train it once and it’s modeling as a service, then you bring it back and you use it, forget about it. We’re changing stuff so often that sometimes we’ll remove entire sets of models and retrain them, some get retrained on a continuous basis, some on a weekly basis, some on a monthly basis, some don’t get retrained until we have a committee meeting, but it’s all towards the goal of what I’ve been saying this whole time, which is one metric. How many tickets did we resolve across all customers? How many tickets did we resolve across these customers? And that drives our behavior.
Harry Stebbings: Well, I can clearly chat to you all day about data asymptotes and the machine learning model. So I think it’s best we move onto the 60 second quick fire round, Bhavin. I say a short statement and you hit me with your immediate thoughts. Does that work well for you?
Bhavin Shah: Sure.
Harry Stebbings: So it’s a war for talent, as we always hear. What’s the hardest role to hire for today and why?
Bhavin Shah: That’s easy. Machine learning infrastructure. I think with machine learning systems, they’re both computationally and memory intensive, and so the challenges are just intense, and finding people with that experience is really hard.
Harry Stebbings: What’s the biggest challenge about your role today with the company and with Moveworks?
Bhavin Shah: We’re going through fast growth and I think nailing forecasting is still an art, still figuring that out. Sometimes we overshoot, sometimes we under call, and I think that’s something that we’re all focused on now, especially as we’re seeing everyone work from home and how to right size our investments accordingly.
Harry Stebbings: Sorry, this is kind of off schedule, but how do you think about transparency within the org, especially with regards to goals? Because you want people to drive towards something. You want to set ambitious goals. But you also don’t want to set too ambitious goals so people will be discouraged by it if they don’t hit them. How do you think that balance of ambitious but not discouraging if not hit?
Bhavin Shah: One of the folks that I had a chance to recently meet is Rob, the CEO of Coupa, and he has hit his target as a company I think for something like 44 quarters. It’s some larger than 40 quarters in a row. That is a hero. That is someone who has really figured out the right balance of making sure that you set a goal and that you hit it, even through good times and bad times. And I think that is super important. I don’t have a specific answer other than you do want to strike that balance, and the transparency is key, and so the good news is for us, a lot of our stuff is inherently transparent just given our product, and even today, later today I’m going to be reviewing the operating plan for the year with the team based on what we’re seeing now change in the market. And I think it’s just keeping everyone updated and keeping everyone tuned into the same channel.
Harry Stebbings: What’s the biggest area where investors have actively helped you and really moved the needle for the company?
Bhavin Shah: CIO introductions. Investors seem to know every CIO out there and their help with prospecting is huge.
Harry Stebbings: If you could change one thing about the world of SaaS today, what would it be?
Bhavin Shah: I think increase expectations from SaaS products. It’s like, let’s hold ourselves more accountable for the outcomes and deliver real results. Instead of just being a tools provider and making customers do the heavy lifting, let’s give them solutions. I think that’s something that I’d love to see the world of SaaS talk more about.
Harry Stebbings: Hit me. What’s the next five years for you and for Moveworks? Can you paint that vision for us?
Bhavin Shah: Yeah, so we’re doing IT today, but our goal is to go into many other domains and departments. I think ultimately, if knowledge workers need help, we want to be the company that solves it for them, and I think we’re on that journey and we’re super excited by the reception so far. So we’ll keep plowing ahead.
Harry Stebbings: Bhavin, as I said, I heard so many good things, as I said, from Mamoon, from Arif, so thank you so much for joining me today, and I’ve so enjoyed chatting.
Bhavin Shah: My pleasure, Harry, and these are obviously challenging times, but it’s been fascinating to see how SaaS apps and other products like that or like Moveworks are allowing folks to stay connected from afar.
Harry Stebbings: I have to say, I really could not be more excited for the time I had with Moveworks, and if you’d like to see more from Bhavin, you can find him on Twitter @Bhavinator. I do love that username. Likewise, it’d be great to see you behind the scenes here. You can do that on Instagram @HStebbings1996 with two Bs.
Harry Stebbings: As always, I so appreciate all your support and I can’t wait to bring you another fantastic episode next week.