Disambiguation Podcast Special Edition - SugarCRM Connected 2023 Transcript

Michael: Welcome to the disambiguation podcast, where each week we try to remove some of the confusion around AI and business automation by talking to experts across a broad spectrum of business use cases and the supporting technology. I'm your host, Michael Fauscette. If you're new to the show, we release a new episode every Friday as a podcast on all the major podcast channels on YouTube as a video. And we also post a transcript on the Arion Research blog in case you want to stop by and read it.

Welcome to this week's special edition of Disambiguation coming to you from London, England. And this week I've been here attending a couple of days with SugarCRM. First, a customer conference yesterday called Connected 2023. And then an analyst summit today where we spent some time with their executives taking a look at strategy and some of the things that they've been running in beta, some of the things that are coming out soon, et cetera. Just a little bit about Sugar and I'm sure that it's not a new name for most of you but SugarCRM is an interesting company.

They started as an open source product many years ago and over time, of course, have moved away from the open source approach and then not that long ago, a few years ago, they were acquired by a private equity firm, AKKR. And, they have evolved the product quite a bit. It was, they were sales force automation focused for many years as with most, CRM companies, when they first launch, they focused around sales force automation. And then over time they added a service module and they've also acquired a company that provides them a marketing module. So now you have sales, marketing and service. So a complete product suite and a platform underneath of course. And that platform really provides the connectivity. They have a real time CDP, customer data platform underneath. And that serves as a way for all of the data to be connected across all three applications. So that's the state today. Now, what we looked at this week, and first, in the customer conference, a lot of that was listening to and talking to and learning from customers about what they're doing. And I would say, from a target market, an ideal customer profile, whatever you want to call it Sugar is, they were, known as a small to medium business solution, but they've moved up market from that. Now they are a medium-upper medium business solution and also into the lower enterprise.

And they have quite a few larger customers as well. And then they also, from a vertical perspective, they've mostly been in manufacturing, high tech, financial services, a few others. And over time, it's a horizontal platform, it's evolved with a great deal of features and functions, typical features and functions, but also some things that I think are a bit unique. They've had a very strong focus over the last several years on the user experience and when I say user experience, it actually goes a little bit beyond the common user interface. So, it's not just the way you interact with it, but it's the whole package and, packaged up so that it's, for you, much easier to use. It's easy to learn and to get your employees to adopt. An elegant solution, nice solution overall. And then today, we spent a good bit of time looking at what they've had in what they call the private beta, controlled beta for a while now around generative ai and AI in general. They do have an AI layer in their platform. I'll put the diagram up as well, so you can see the architecture slide that walks through that, but just as an overview, there's predictive AI capabilities or predictive analytics that they've had that's focused around things like opportunities, lead scoring, that sort of thing. And then, as you move across, they they've added several generative AI capabilities that, like I said, have been in private beta. And really overall, they've thought of this as a move, an evolution of the CRM product. And I would argue that's true, that CRM has moved, from the early days, really focused around sales to a broader platform approach to now, a platform that has moved heavily into the automation AI what they call the assistant phase.

It's early, certainly we're seeing a lot of evolution progress with AI. That's the subject of this podcast. So, if you're listening, I'm sure you already know that, but they've added a lot of capabilities around AI and they're really focused around assistive capabilities. So how does it act as a force multiplier for your employees? How do you make it easier to onboard employees? How do you make, how do you add automation that removes mundane task or tasks that are time consuming. For example, there are a lot of things that a salesperson does that they have to enter into a sales force automation solution that, could really be done with some automation because the data exists in several places and could easily be updated for them rather than having to depend on the salesperson to update some of that information.

It also gives the capabilities to provide information to the different roles across the suite. Everything from the salesperson to a service rep to a marketer and can aggregate data, across all the different functions so that you get a deeper view of that customer, and you can see across all the issues. So, if you're a salesperson, you're calling on a customer, you could see the service call issues, any problems they've had, challenges in the past. And perhaps deal with that or help them with any issues they might have. If you're a marketer, you can get a good picture of profile and where they are, what they're looking for, that sort of thing. Intent data, you can incorporate that so that you can really start to understand the buyer's behavior, et cetera.

And I won't go through all of this in too much detail right now, because I will put a couple of interviews up that we recorded today. And one of those with Volker Hildebrand, who is their SVP of global product marketing. So, he has a good perspective on how they're going to market and, how they've messaged some of these capabilities. And also, we look at some use cases there. And then also I talked to Zach Sprackett, who is the chief product officer, and he gives us a much better look at how they went about this, what their philosophy is about incorporating AI into the product and, in general, some of the things they've done, and then more specifically, some of the enhancements they've made in the platform to help with trust, with personal identifiable information, PII to help with making sure that that things are filtered and masked correctly from the large language model. So again, we'll get into that more in the in the interviews, but I just wanted to set the stage a little bit so that you could understand a little bit more about sugar if you don't know them, SugarCRM and also just have a bit of where we're going with the show today.

So I welcome you to listen to the interviews. I think they were very insightful and frankly, I just had a good time. I love coming to London and also interacting with my analyst colleagues and with the executives at SugarCRM who are very passionate about what they do. And certainly they put together a very compelling story for that mid-market and lower enterprise market and I'm sure they'll continue to evolve that, and continue to evolve their capabilities as they go forward. So, I won't drone on at the introduction, but stand by and we'll get into the interviews.

So welcome. We're here at in London at the SugarCRM Connected 2023 event and analyst summit. Which today is the Analyst Summit. Yesterday was the Connected event. And I'm here with Volker Hildebrand, who is the SVP of Product Marketing, Global Product Marketing. Volker, can you just give us a little bit of background and talk a little bit about what you do at Sugar and then we'll jump into some questions.

Volker: Yeah, sure. First of all, thanks for having me. And thanks for joining us at our event in in London which was a great event. Yeah, it was a lot of fun. Yeah, I am, I have a global responsibility for everything product marketing, which is pretty much everything has go to market related packaging, pricing, messaging, positioning, talking with analysts and enjoying their company as well. But so that, that's mostly outbound, but we're also focused on a lot of what I call the inbound part, which is getting market insights and providing guidance for the product and engineering team with regards to roadmap, future direction. So bringing in outside analysts, point of view, marketing houses, et cetera.

Michael:

Yeah, that's great. And I should say we've known each other for a really long time. You and I go back into the, me at IDC and you at one of those other large vendors that we won't mention.

Volker:  That's right.

Michael: So good to have a chance to chat a little bit. And as I'm really excited about Gen AI and also just the implications across all the things that it's going to change and is changing. So I'd love to just get a perspective first on how SugarCRM thinking about it. What are, where are you? What are you doing right now? Not in a, no, you don't have to tear it apart architecturally, but I'm just curious kind of from an overview, what does it mean? And what does it mean for your customers? And how is this gonna, really make a difference in the marketplace? Yeah. So, I think

Volker: I'm a surfer. So, I think yesterday I said, I'm really stoked about it. That, that's the word for super excited about the possibilities of generative AI. We've probably all heard some stories where hallucination went in the wrong direction and we can maybe cover that later, but I think the potential it's undeniably there. One, one big thing is in my opinion the huge productivity gains that generative AI can bring to the table. Simply by according to our motto, let the platform do the work, let the API do the work. So instead of working on things for many hours, whether it's composing an email for a marketing campaign or finding, summarizing the customer history or a service ticket. And these things usually take a lot of time and now, boom, 30 seconds instead of 30 minutes. So, I think this is huge. And I think this is also important and we can talk about other benefits later because this is the part that everybody gets, right? It's you understand this. It used to take me 30 minutes, now it takes me 30 seconds.

Michael: Yeah, I use the term force multiplier, right? It helps you be more productive because it takes away or helps you in some way.

Volker: That's right. Yeah. And also from an especially from a software vendor perspective. It's easier for customers to understand and they can see the immediate value or benefit the, one of the bigger challenges on the predictive AI side, for example, is you need to explain that people don't really understand what's happening in the black box. And they don't really see the results the same way as with the generative side.

Michael: So, let's talk about this in the context of a seller. Cause that, that at least, that'll help I think from an audience perspective. Cause I think you're absolutely right, there's a lot of hype and a lot of sort of mythology around what we're doing. And yet there's a lot of really big benefits there. And, for years we've heard this sort of tribal knowledge of the seller spends more time doing administrative stuff than they do selling. Why is that? And so I was just thinking in the context of these new capabilities, how does that change? How does that make the seller's life better? Yeah.

Volker: What's the day in the life…

Michael: Yeah. Day in the life. I love it.

Volker: You get up in the morning your calendar tells you, oh, you have actually a meeting with a customer in an hour, right? And so, you gotta prepare for the meeting. Usually what you do is you look at your CRM records and you look at the order history. If it's a customer that maybe is new to you, right? Maybe you just took over a new territory and it's the first time you meet that customer, pretty much nothing. So, you may also do some Google search to understand, Oh, what's the customer actually doing, right? What's their business like? And then maybe, there's, Oh, there have been some service tickets and you read those. So you pretty much need the full hour, and probably, you probably needed more than that.

Michael: Yeah, that's right. But you waited until just in time, as we say.

Volker: Generative AI now can do all this work for you. It can look up the customer data that's available in their CRM system. And summarize it, right? So you don't have to look at all of it. Give you a summary of the service history. Also it can leverage external content, firmographic information, right? Their products and their business. And it all happens in an instant. You read the summary in five minutes. And then on top of it, if you know what the meeting is about, Generative AI can even help craft almost like a talk script, a self script. Or give you an agenda. Yeah, these are the things you should be discussing with and other stuff.

Michael: It even really is helping you with the idea that you're trying to discover and it knows what you know already, that's in the system. That's so then it can help you advance that position even more. Yeah, that's obviously a huge benefit

Volker: You put all the notes down it could automatically create a follow up email that if you're smart, you're going to review it before you send it out.

Michael: Yes I think human in the loop is still a good thing.  

Volker: And that's really an important thing. That's we don't want to replace sales reps. We want it to be a generative AI, be a copilot.

Michael: Yeah. Unfortunately, some of your competitors have glommed onto the term copilot, and I'm not sure that's the necessarily the most effective word. But I get it. I think that makes a lot of sense. I think conceptually. Yeah. And so what about the other thing? This is the one thing that I get from sales people all the time. They complain about the fact that they have to feed the system. Yes. So, could it help with that as well? Does it help you keep your data up to date and maybe keep your boss off your back?

Volker: It can help with a few of those things. And it can help you with again, maybe you need to put together a report for your boss, for example, right? It can do that. And it can leverage transcripts from the call that you have with the customer. It starts a zoom call, right? And then use that. It can even identify in the transcript for example, if you're talking about an order it could go and find that order number in the CRM system, for example. And literally connect the dots.

Michael: Got it. Yeah. So if, also from what you just said, if I stepped on the other side of the table and I said, I'm your prospect, how does that make it better for me? Because it does seem like there's some real benefit there too.

Volker: There's definitely a benefit because let's assume you're the prospect. I'm the rep. If I don’t come to you prepared, you will not have a good conversation with me.

Michael: And probably have a short conversation.

Volker: And maybe a very short one too. That's, so the more I know, the better prepared I am. And even critical things that maybe happened just recently. Maybe you're still waiting for an order for that should have arrived three days ago, right? If this wasn't in my summary I'm informed and then can have a better conversation since we're talking about also kind of customer service moving from prospect. Now you're a customer.

Michael: Yeah, I bought. So, we're good.

Volker: And then there's a lot of opportunities in generative AI for customer service, whether it's taking self-service and chatbots really to an entirely different level. And really have almost like a conversation with you as a customer and find the right answer. Even if it needs to put the answer together from different sources. And that's again the big difference between the generative AI because you basically can't take snippets from, different content sources and put it together. And it's a personalized answer specifically for your individual problem versus traditional.

Michael: Like an FAQ.

Volker: Yeah. Here's an FAQ. Or try this, if this doesn't help, try that.

Michael: Yeah. That seems like that would in fact enhance the experience a lot. And I know a lot of us have used sort of the old style chatbots. And the experience isn't really that great because it's just a logic tree, right? If you don't answer in its framework, it doesn't know what to do with you.

Volker: That's exactly right. It sounds like this moves to a more interactive approach with the capability to get really where you are and what you need. The chatbot becomes much smarter. Let's put it that way. Yeah. And then also can provide better answers. And that's really important because the biggest challenge with chatbots is that customer's already potentially in distress.

Michael: Exactly. You're already not happy. And then if the chatbot doesn't get what your problem really is frustration turns into anger.

Volker: It does, yeah. And really your customer experience turns sour immediately.

Michael: Yeah, I did a survey last month on AI adoption and one of the things that came out and actually a couple surveys I've done this year you can start to see that if you ask somebody, do you want to talk to a chatbot they're always going to say no. Yeah. But if you ask them, have you had any recent experiences with a chatbot where the result it got you to a resolution, that's actually pretty high. And the other thing I noticed was the idea that the hybrid approach people seem more comfortable with. Yeah, like I could deal with the chatbot and if it solves my problem, cool, but if it doesn't, then you get me to an agent. So is that how you guys are thinking about it?,

Volker: Yeah. Yeah. That that's how everybody should think about every company that wants to use chatbots or automate customer interaction. There always needs to be an easy way for the customer to basically switch from the chatbot to a human. And that's very important, From a customer experience perspective. But ultimately, customer experience, it's always going to, I wrote a book about customer experience.

Michael: I know you did.

Volker: It comes down to four things that really matter to customers. And if you do these four things right, they don't care whether it's a human being or a chatbot.

The first one is convenience. Make it easy. Yeah. I can even do it on my phone or, yeah, exactly.

Yeah. Number two, speed. Speed. Speed. I don't want to, wait, be put on hold, do all these things. So yeah, probably check. But then the other two is relevance better give me relevant answers and the fourth one is reliability, which means the answer should be the solution to my problem or the answer to my question.

Michael: Yeah, that's it. And so that kind of moving out of that logic framework, then that does help you get to the point where, because it can understand you, your data, your situation, and have an interaction, then it can get to a better, a much better resolution, much more quickly. Yeah. Makes sense. And in customer service, that's all that matters.

Yeah. Oh yeah, that's exactly right. Really all I want is you to solve my problem and I don't care if it's a person or a bot or whatever. Exactly. That makes sense. So I was thinking about this as I've, been here and I've watched where you guys are announcing and doing, and I think there's a lot of excitement there. So from a, from your product marketing hat, step back for a minute and tell me how does this differentiate SugarCRM in the marketplace. What does this do for you and your customers against your competition, which, we'll let them remain nameless, but I, but nonetheless, I'm just curious, how's this differentiating for you?

Volker: Yeah, I mean, a lot of vendors are obviously doing generative AI right now. I think one, one of the things we are trying to do is we're very focused on the business outcome. How does it help the sales rep, right? How does it help a customer service agent? How does it improve the customer experience, right? Is it faster? And we want our customers to basically look at it and see and get it immediately and say, yeah, I can see how this is helping my rep save time, be more productive or do that. So, one of the things we did with the launch is we actually released short videos that basically show what does that mean for a marketing manager? What does that mean for a sales rep? We have the use case earlier. I'm preparing for a sales call, right? Yeah. Or a call with a customer or a customer meeting. And these videos are awesome because you get immediately yeah this makes sense. Yep. Looks robust, it's easy and I can see the benefits right away.

Michael: Yeah, that makes sense. So it almost sounds and I guess this shouldn't surprise me that SugarCRM has this sort of attitude about it that it's what's the practical approach to Gen AI. So we're not talking marketing. We're not talking flowery. we're actually talking, how do you use this every day to make your agent or salesperson, your marketers life better and improve the experience for your customer?  

Volker: Yeah. Somebody was asking me the other day, how would you describe generative AI, and as I said easy, powerful pragmatic is probably the most important where I, of course, need to be easy. And yeah, you would expect that, right? Yeah. But, the. So it needs to be very pragmatic.

Michael: Yeah, that actually makes a lot of sense. And I do think that is differentiating in the marketplace from, at least from a lot of the conversations we've had so far. Yeah, that's good. Volker, I really appreciate it. I won't take up any more of your time today, but I really appreciate you joining me and joining us on the show. I know my listeners will really appreciate it. So thank you very much. And I appreciate the invite. I've had a lot of fun in London so far. All right, sounds good.

Volker: Thanks. Appreciate it. Bye. Bye.

Michael: Welcome, we're back at the London Sugar CRM Summit, Analyst Summit, and Connected event, which was yesterday. And I have the pleasure of having my, actually my dinner companion from last night too, Zac Sprackett, who is the Chief Product Officer at Sugar. And we're going to talk a little bit more about what you guys are announcing. And I know you've had a lot of this in private beta. So a lot of you've got some experiences behind some of that from your customers too. So I'd love to, to just get a bit about that, but let's start with just give me a bit about your background and what you do.

Zac: Sure. I'd love to. So again, my name is Zach Sprackett. I'm based out of Santa Cruz, California. I've been with SugarCRM for about 12 years now in a variety of different roles and it's been a pretty amazing ride at the company. I've just gotten a lot of different experiences, a lot of different options and I've gotten to the point where now somehow, I'm responsible for some of the product direction, which is amazing.

Michael: Yeah, that's great. I was excited about, I'm excited about Gen AI in general, most of my listeners already know that, but I think, as I looked at the way you started to put this together and I got a good picture of that from, from some of the things we talked about, I'd love to just understand first your overall approach to how you've used generative AI across the product suite.

Zac: Yeah. It's really interesting because, initially like everybody we started with kind of the gold rush of let's just go start typing stuff into boxes and see what comes back. All of us all of us have played with ChatGPT and what I think you quickly realize is, Whoa, whoa, like there's a lot of power here but actually I need to be, I need to be thoughtful about how I apply that power and so we stepped back at that point and took a really thoughtful approach of, okay, We know that there's a lot of benefit, right? We know that generative AI is clearly going to provide people with powers that they didn't have or optimizations that weren't possible previously. But we needed to build a framework that would allow them to do it in a way that was responsible and governed and that organizations could understand how to adopt. So that's the core of the design philosophy for us.

Michael: There's a lot of... There's been a lot of noise, I'll say, for the last, what, 10 months or so around this. But there's also a lot of practical ways that this does have a lot of benefit. And so I'm curious from the way that you've approached this in the product, How do you see this play across the modules? What's the general philosophy about how it's being applied to provide that benefit?

Zac: Yeah, look, there's a lot of different ways that you can get immediate benefits from this technology. It's incredibly good at summarizing information and taking information from different data sources, so the ability for, I don't know, a sales rep who's about to go into a meeting with a customer, maybe do a QBR to pull together information that exists in support cases, right? That sales rep is not going to be super effective reading through all of the details and the notes in the case. They might not have the skills or the background to be able to do that. But. Tools like large language models can really summarize at a level and in a language that enables that seller to have an intelligent conversation with the customer and it goes in the other direction as well.

Oftentimes as a support agent, when you're engaging with a customer, you might not have some of the context of what else is happening in your business, but imagine being able to quickly orient yourself as the call starts to. Oh, you know what? There's actually open opportunities here that we're working on.

Having that information presented to you in a time where it actually makes a difference is incredibly empowering.

Michael: So one of the things you just said really leads me to another place that I think is really interesting. And this is the sort of the ultimate problem that companies are having today is the fact that they're siloed across a lot of functions. And to do what you just said, that agent needs to have the capability to be able to have access to all that data, but in a logical, sorted way. That can be very overwhelming. How have you guys approached that from a data perspective?

Zac: That's the interesting part. We've been building towards this over the course of a number of years. As you're aware, right? We have the full platform marketing sales and service. And underneath them, we've been building this time aware data layer that sort of captures all of that information that's happening over time. And. When we introduced the AI layer a couple of years ago with our predictive technology it really started to harness all of that information that's available inside of the sugar platform.

And now as we're applying generative AI to it. We have the ability to go back and summarize all of those bits and pieces of interactions at different levels, right? So you can summarize a single piece of content, but then you can go and you can summarize multiple pieces of content and get that higher level overview, and you can build different windows and see the information in time horizons that are interesting to you.

Michael: Yeah, I mean that, the time dimension is a very interesting thing. Especially like from a support reps perspective, right? I want to know, what has happened to you historically? What's your evolution through whatever issues you've had and how have we helped you? Yeah, that makes a lot of sense actually. And a lot of talk about CDPs, but I’m not sure we've really talked enough about the fact that it, that time element is extremely important in the way you're looking at that data.

Zac: A hundred percent. Look, how do you understand what's happening in your business if all you have is a snapshot of what it looks like today, right? The, ultimately what we're really interested is... What's changing? Are the programs that we're putting in place? Are the things that managers are doing or are the programs that, that individuals are contributing to? Are they improving things in our business? And you can't see that without that time dimension.

Michael: Yeah, no, that's, that makes a lot of sense. One of the concerns that I hear and we can talk about several different pieces across the way you've implemented this, but one of the, one of the big concerns, sort of two things, actually, one is how do I make sure that I'm not sending the wrong things to the model? And then also, how do I make sure that the responses from the model are also in the format in a way that I want them and I guess my funny example has always been like when I call support, I'm generally not a happy person, right? And so I might actually use some colorful language to describe my problem, but it would be pretty bad if the chatbot gave me that back in the answer, so how do you stop that? And then also just from a personal information perspective, how do you guys deal with that?

Zac: Let me tell you how we're thinking about the problem. That's probably the right way to describe it because I think all of this is moving so fast and it's going to continue to evolve over the next little bit. But one of the very first things that we realized when we started down this path was that we needed to build a proxy that sat between the market sell and serve applications or the Sugar platform and the large language model for a number of different reasons, right? One we want to be able to cache the results. So we want to have a central place where things flow because we know we're going to need to plug in additional services in the future there as well. But some of the services that we knew we needed right off the bat were first of all, a grounding service, right? Being able to pull all of the different information that you want together so that a user doesn't need to, paste it all in.

Imagine having to go to something like ChatGPT and say Oh, here's all of my case information. Here's the orders that are happening. Here's some context about my, how we run our business or the tone that we use. That, that, that's not something that a user should have to do. We don't want users to have to be prompt engineers. So, the way that we've approached it is we want to build a system that's very interactive and people can ask questions and they can be inquisitive about things. But we're going to gather the appropriate data from all of the different sources that exist, whether it's inside of the applications or whether it's in third party data sources.

Pull all of that together and build, essentially, a prompt on behalf of the user that gets then passed through a second layer in the service. And this is a masking layer. So the cool thing about the masking layer is this is where we can look at the sum total of what we're about to send over to the large language model and figure out, are there things in there that maybe we don't want to send, that could be a personally identifiable information or it could be business confidential information. This would be a good place for that sort of filtering to be done and masking out. And it turns out you can do a lot of that masking and still actually get really good results from large language models. And so doing that centrally makes a lot of sense.

Michael: And you're using a different word here than I've heard from some other approaches because you usually hear the word filter. But you don't necessarily hear the word mask and I think that's actually really interesting. So maybe expand on that just a little bit. What do you mean by that and how? Is that a better approach?

Zac: Filter means, to me, at least, blocking, right? And ultimately, you're asking a question and you're hoping to get an answer on the other side of it. And obviously, you're looking for an answer that is accurate and helpful. Masking is about looking for the things that you don't want to send through and then finding ways to send information, whether it's placeholders or whether it's other metadata or other relative information that's maybe less sensitive through to the model, because ultimately you want to give it as much context as possible. Because the more context that we're able to provide the model, the better the answer that it's going to give to the user on the other side.

Michael: The other thing I get from that, is one of the like when I survey one of the biggest problems that companies mention is having employees with the right skills and then the second one is having partners with the right skills, which is essentially the same thing, right? So part of that is prompt engineering. So it sounds like what you just said was prompt engineering moves a layer back and the machine takes care of a lot of that for you.

Zac: Yeah, and that's the goal. Ultimately, if you think about it business is made up of people. People have skill sets. You want people to be focused on the things that they're good at. And I don't think that the world should have to become prompt engineers in order to benefit from technology. Yeah. That's the way that technology works, right? It's our job as vendors to simplify things and to make it usable so that average humans can benefit it on a daily basis.

Michael: And I've used the analogy before of, oh, when we first started using Google search, you had to learn how to search to use a large language model, you have to learn how to write prompts. But what you just said, I think is more akin to the search that in fact, it's a simple, more simple.

Zac: Yeah, the kinds of capabilities that we're trying to unlock with this are, the ability to configure your system without having to go through a bunch of different steps, but just describe the problem that you're after or to be able to. Get information back in the form of a report without having to know, Hey, I want to see all of these different fields or my data is stored over there. And if we force the user to use, to learn a different language, like here's how you write a prompt, we're not really solving the problem.

Michael: Now, and the other thing too in that is, and I know, I use the all the models a lot, is that you have to be very careful that you tell it how, what tone you want to use. And I would assume for most businesses, and I do this for my business. I have, there are certain things I want professional and conversational and blah, whatever. So that's built into the system then, and you could, as a business, define that, and then that's going to be consistent across everything.

Zac: Correct. Yeah, you can define that brand voice that, that, that is important to you, and you can tweak it and refine it over time.

Michael: When you thought about the evolution of CRM and where this, where we are, and we're moving obviously into a new era, I think, anyway talk a little bit about what how is that, how do you think that's going to evolve? And what does this mean for your customers?

Zac: That's a loaded question. As you're aware, right? Things are moving so incredibly fast right now. But I think the most important piece that we're looking at is the whole way that people interact with applications is changing as a result of this. So much of what we know is. Here's a list of steps that I need to follow. Here's a list of places that I need to go in the application. Here's a new list of fields that I need to fill out. And for eternity, that's basically been how you interacted with machines. But all of a sudden, we're getting the tools to be able to communicate with them at a different level.

And instead of taking out, taking care of each of those steps individually, we can ask for something and jump straight to the answer. That to me is incredibly empowering because you don't get that opportunity in, as a vendor, as a company who's trying to build software that makes people more efficient and helps them to do their jobs every day. You don't get those kind of opportunities to do that.  

Michael: And we've fought the system for years. We used to have screens that look like spreadsheets that we had to fill in just to get something to work and enter all the data, this is a showing my age and my nerdiness but I think of Star Trek and there's this one episode when they go to back to earth in the past and Scotty is like picks up a mouse and is talking to the mouse, and the system doesn't do anything, and he's very confused.

That's the leap, in a way. We're moving to this place where I can interact with the machine in a normal, natural way, and yet get back the things that I need for it to give me.

Zac: And you can refine things over time, and the Look, one of the challenges that we've always had is we want to give users back more information or more value than what they put into the system, right? And we've done a lot of things over the years to accomplish that, automated gathering of information from various sources. Wizards that help people to, to fill out fields and, BPMs and workflows that do things in the background. But the ability to go from just describing a problem to an implementation, think about it even the creation of that workflow that I was just talking about, system administrator sitting down and saying, Hey, I'm looking for a complex approval workflow, it's got three layers. Managers can approve up to this amount. The anything above this, it requires director approval and having the system be able to just configure that off to the races.

Michael: Yeah. Wow. Yeah. That is, that's a huge leap forward from the way we interact with the systems today. Now, when I interviewed Volker today we were talking a little bit about salespeople. And I know one of the, one of the sales force automation tribal myths, maybe, is that the data is almost always bad because you gave the salespeople a lot of work to do that they don't necessarily benefit from, that their manager benefits from or the CFO, or, and does this help that? Does this make the salesperson's life better?

Zac: I think it does. And it layers on some of the other things that we've been working on to make that salesperson's life better, data augmentation that we already have in the system those systems of acquisition, like connecting into people's email and calendar and facilitating the flow of information back into the CRM this now has the ability to look at those communications that are happening that the seller is choosing to put into the CRM through the rules that they've configured and decipher it. So instead of just, the dealing at an object level did you send an email? Yes, I sent an email. You can now think about Okay, we follow a sales process like Medic and have I actually identified the pain points? Or do I need to dig a little bit deeper, and the system can help you to recognize that hey Maybe you're not quite there yet.

Michael: Yeah. And the other thing too, it seems like you know again, when I talk to companies, I hear that it's difficult to get new sales reps up to speed, right? It's hard to bring somebody who doesn't really know your product, your offerings, the right questions, use cases, all those things. That helps with this too?

Zac: Oh, of course, yeah. Being able to generate scripts for various things. Being able to pull all of that information that you know about an account together. And put it in the hands of that rep and then some of the best practices And give them some suggestions of points they might want to walk through on the call with the customer in order to move things forward.

Michael: Yeah, that really would help you get that new seller much up to speed, much faster. I know the sort of average that I hear in the industry is it takes about nine months to get a ramp of sales rep up till they're really productive. Yeah, and that varies by industry, but that would cut the down, the time down significantly if I could give them tools that they could interact with.

Zac: Right, and if you think about the fact that yes, it takes that period of time. That's already problematic for a business because you've already got, you've always got people coming in and out of the business. But also, the leads that person is interacting with in that time period, are you losing leads that you would otherwise be able to win if they were handed to a more productive rep? It's so important to get people the knowledge they need quickly.

Michael: Lost opportunity is so hard to measure, right? And in fact, you probably, it's probably 3x what you think it is. Yeah that's actually really interesting. So I, we're starting to run out of time, but I just want to come back to this again and talk, just tell me a little bit about in general, what does this mean for your customers?

Zac: I think that for customers, this means that they're finally going to be able to get beyond just the data that exists in the CRM and get to insight and information being told Proactively hey here are the areas where maybe you don't know as much as you think you know about the account and here are some areas where you want to expand or being able to understand what's happening in different parts of the business with regards to the customer It's, to me, it's really interesting to see how different parts of the organization, how the knowledge that, that exists in those parts can be collided together. And then the kind of supernova that can happen when that that occurs there's a lot of insights that, that I think we're going to be able to deliver over the next little bit that previously were not possible.

Michael: It sounds like we're supercharging the system a bit to say we can make it a much more intelligent system that can really add a lot of benefit and change the way we interact, which is, yeah,

Zac: We believe that, we our tagline is let the platform do the work. And the thing that I love about that tagline on the product side of the business is it's easy to tell when the platform is not doing the work, right? And so it directionally. That's our marching orders in the SugarCRM product group.

Michael: Yeah, no, that, that makes a lot of sense. We're out of time, but I really appreciate you joining me and I won't take any more of your day because I know you have some other meetings that you have to jump off to. But again, thank you. I really appreciate it. And I know listeners are gonna really get a lot out of this because it is certainly novel and in a lot of ways, and I think you guys are really pushing the envelope here a bit forward by, by a lot of the things that I've seen over the last couple of days. So, thank you very much.

Zac: Thank you. And thank you for the opportunity to talk to your listeners. Appreciate it.