← B2B Agility with Greg Kihlström™: MarTech, E-Commerce, & Customer Success
B2B Agility with Greg Kihlström™: MarTech, E-Commerce, & Customer Success
#31: Getting the most out of AI with the right data approach with Emily He, Gong
00:30:33
To be truly successful with AI, capturing data is only the beginning.
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- [00:01]
Emily He
The Agile Brand.
- [00:08]
Greg Kilstrom
Welcome to the B2B Agility Podcast where we look at the factors that drive success in B2B marketing with a focus on the people, processes, data and platforms that make B2B brands stand out and thrive in a competitive marketplace. I'm your host, Greg Kilstrom, advising Fortune 1000 brands on martech, marketing operations and CX, bestselling author and speaker. Now let's get on to the show.
- [00:34]
Emily He
To be truly successful with AI, capturing data is only the beginning. You need to understand and contextualize in terms of your business in order to truly harness the power of AI and get real returns. Today we're going to talk about going beyond the hype of AI to get real tangible results and unlock benefits that approaching data in the right way can enable. To help me discuss this topic, I'd like to welcome Emily He, CMO at gong. Emily, welcome to the show.
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Thank you so much for having me, Greg.
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Yeah, looking forward to talking about this topic with you. Before we dive in, why don't you give a little background on you and your role at gong?
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All right. I have been in enterprise software for the last 20 something years and I live in the Bay Area, have always been Silicon Valley. I've been mainly in the application space. So I've done many years of CRM, some years of erp, some years of supply chain management, also many years of human Capital Management and B2B Marketing. I've been CMO in a number of companies including smaller companies, medium sized companies and really large companies. So I was actually at Oracle where I was serving as the CMO for their human capital management cloud. And this is during the pandemic. And one thing I noticed is when I was talking to customers they were using spending a lot of time using their collaboration and productivity tools while also trying to navigate their CRM ERP tools. This is during the pandemic where people are spending their time in Zoom or teams and there's this urgent desire for them to converge that experience because they're saying, hey, I have to toggle across 20 different applications to get work done. And this is when I went to Microsoft and my original intention was to integrate the experience across teams, Office and CRM ERP to give customers that more seamless experience. And that was the vision for Microsoft as well. And of course when I was at Microsoft, I joined the company in 2021 and in 2022 the watershed moment happened, which is OpenAI. So that started the AI momentum and consequently, consequently Microsoft also launched Copilot and I was A very big part of launching Copilot. So not only did Microsoft Launch Copilot for Microsoft 365, but we also launched Copilot for Steel's marketing service. And the whole idea is Microsoft Copilot is going to be this new inter, new user interface that people can use to navigate across all these different applications, including their productivity, collaboration tools as well as business applications. And as we were progressing the conversations with customers, I realized it's not that easy because for Copilot to truly work for employees in sales, marketing or service, all these different business functions, Copilot really need to deeply understand what the people do in these functions. More importantly, we need to capture a different type of, of data because AI is uniquely able to process large quantities of data. And this is exactly GONG does. I was hearing a lot about GONG so from our customers. So that's why I decided to leave Microsoft and join gong.
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Wonderful. Great. Well, yeah, it sounds like, I mean, you have some phenomenal experience and related to this, this topic here. So let's, let's dive in. You know, first I want to talk, you know, we certainly talk a lot on the show about AI and from, you know, a number of different perspectives. And you know, I've, I've written quite a bit about it. You know, there's, let's, let's acknowledge there's some hype about, about AI, but you know, there's also some real potential. And I think, you know, the, the real potential is why people continue to talk about it beyond the hype, but to kind of, to kind of start here. What are some of the biggest misconceptions that you've seen as far as, you know, how AI can be applied to business? What's the reason for some of the, the feelings of that there's a lot of hype here?
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Well, I think there are a couple of things. One is the, I mentioned OpenAI before. ChatGPT is just such a magical application that everyone could relate to. So that gives people inspiration about what AI can do. You can talk to AI, AI can author content for you. So that leads to a lot of hype. But I also think many companies are just adding AI to their marketing messages, although they don't really have the underlying data or AI technology. So that's leading to a lot of the hype or misconception of AI. And I would say at the highest level, when I talk to customers, there are a few things that I think are big misconceptions. The first one is AI is a panacea and can solve any business problems instantly.
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Right.
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And the implication of this misconception is AI can learn without proper data and can also understand the context and nuances effortlessly. That's just not true. In reality, AI can only be effective when it can access high quality, high large quantity of the right data. And also you have to train AI to understand the context and nuances of all these different business functions. The second misconception is AI can work autonomously without human input. Some people go as far as saying AI can replace skilled workers eventually, and I don't think we're quite there yet. In reality, AI can augment or amplify human efforts, and it can help you remove the mundane and automate some of the repetitive tasks. But human intervention and human oversight is very much needed. The third big misconception or assumption people make is AI can deliver ROI instantly. And that's not true either. Because AI is like any other technology. You need change management. You need to identify your priorities and goals, and you also need to go through the deployment and the implementation process, and you need to design your goals so you know what you're trying to achieve. So it requires a lot of organizational change to make AI happened and also to realize ROI you're looking for.
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Yeah, yeah. And so touched on maybe a couple solutions as you were, as you were talking through that as well. But I wanted to, in the three points that you made, I mean, there's. There's a few things I want to explore a little bit more. I mean, first of all, you know, I think, I think the obvious and, you know, I've. I've been seeing some of this relaxing a little bit. But, you know, the idea of augmenting versus replacing, you know, I think there's certainly a lot of people still, you know, about the future of their jobs or their roles or stuff like that. But I like that you use the word, you know, augmentation, and, you know, they call it copilot, not autopilot. Right. So it's, it's, you know, it, it kind of, it speaks to that. But, you know, for those managers out there that have perhaps anxious employees, could you talk a little bit more about, you know, how does, how does a leader manager, you know, kind of move past the hype when it comes to this concept of augmentation?
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Yeah, that's a great question. And I think that's the question. Obviously, as a technology provider, I would say the first one is you need to find the right business or right technology partner that is well versed in AI. Well versed in data. And there are a couple of things. One is AI needs to have the right data foundation. So you need to work with the vendor that is capturing the right data for your particular domain. Whether you're in the legal field or in sales or service. You need to capture a rich source of large quantity data that reflect the customer situation or intention. And then you also need to work with a vendor that is building the solution with AI at its core. And the solution needs to be AI native. There are a lot of solution providers out there that are using AI as a bolt on solution. The more I get into AI, the more I'm convinced AI is an opportunity for us to reimagine the user experience. All the workflows, all the insights. And you really need to start with data and turn that data into insights and then embed the insights into workflows. And today's applications are built with workflow first. So if you want to build a sales application, you start with understanding the workflow and then you pull the right data in to execute that workflow. That's not what AI does. So there's a new category or a new class of solution providers that are reimagining our business processes with AI data as the foundation. So make sure you work with those vendors first. And then the other thing is domain expertise. Whichever vendor you're working in needs to be well versed in your particular business function or particular business problem you're trying to solve. And this is no easy undertaking. It takes years of experience and testing and deploying solutions across thousands of customers for a solution to be proven and work. And that's definitely the case with gong. And then the third thing I would say is be prepared to go through change management because employees need to go through that learning curve to adopt AI. And before you can realize roi, you need to convince them or teach them how to work alongside Copilot or work alongside AI. So eventually you can reap the business benefits.
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Yeah. And so, you know, along along those lines of, of domain expertise and, and stuff. I want to, I want to talk in a second here about something really interesting at GONG the revenue AI. But first I thought it might be good to just explain a little bit more about who is gong's customers and what exactly does GONG do?
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Yeah, definitely. GONG has been on a really interesting journey. So if you think about the revenue organizations today, there are actually a lot of different players in the revenue organization. It used to be a primarily sales function, but now sales, service, product marketing are working together to much more collaboratively than before to achieve their revenue goals. And interesting, interestingly, right now many revenue teams still run their business on CRM. And CRM is a great tool, but it was built 20, 30 years ago when AI wasn't available or the AI technology wasn't mature. So if you really think about the data that's being captured in CRM, they're not really data directly from the customer's mouth, they're interpretive data entered by sellers. So, so typically a seller would need to, they're mandated by their manager to enter CRM data and they would summarize what's going on with the account, they will summarize what happened in the customer conversation. And typically, for example, if you have a customer meeting, the meeting goes on for maybe 30, 60 minutes, so you speak about 5,000 to 6,000 words. And when the rep enter this update into CRM, he or she will say something like, the conversation was awesome and the deal is going to close. Now, with that data, you can't really understand a whole lot about what's happening with the customer, what their concerns are, what their pain points are. So when GONG first started, this is way before the AI hype, the secret sauce for GONG was GONG managed to build a scalable process that captured all the sales conversations and the customer conversations. And as a result of capturing this rich conversation, now all of a sudden you are able to understand customers pain points, the competitive dynamics, any churn risks directly from the customer's mouth and the seller's mouth. So this helped uncover a brand new source of data. And from there we were able to turn that data into rich insights, whether it's your competitive information or forecast risk or the pipeline trends. And we're able to turn that insights into, embed that into workflows to help our sellers better engage with customers, help managers better identify what the sellers are actually doing and coach them to deploy the right deal strategy, help our sales enablement team to understand what the sellers are actually saying and guide them through messaging or sales methodology. So that's really the foundation of gong. And now we have built what we call the revenue AI platform with the data engine as the foundation and data AI as the AI layer. And with the AI layer, we're able to come up with insights that we then feed into the workflow, including our Engage application, Forecast application and Enable application. So that is what we call the revenue AI platform for go marketers.
- [13:51]
N/A
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Emily He
You highlighted some things that are lacking in a CRM, such as, you know, timeliness of entries and all those kinds of things. So yeah, this is, that's really, really interesting how that, that captures all of that stuff without relying again on manual input and making sure that some human summarizes an interaction accurately and all those kinds of things. And I just know from doing sales it can be difficult to consistently get some people to update their entries and all those kinds of things. So the help of AI, right. Can make things a lot more seamless. Right?
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Yeah. It's kind of interesting you mentioned the customer data platform because that was the rage for many years. That was supposed to be kind of the solution for this lack of understanding of customers. But what customer data platform does is it integrates data from different sources, whether it's your calendar data or your activity data. And sometimes it's the, what we call the intent data when customers are navigating across your website, their click throughs and what content they're downloading, but the issue with that data is still, it's not straight from the customer's mouth. So we call that inferred data because when you're downloading a piece of content, we infer you might be interested in a product, but we're not hearing from you directly. So in this regard, conversation data is super powerful because there is no ambiguity around what you're actually saying. And more importantly, if we listen to thousands and millions of customer conversations now, we all of a sudden have a different way of understanding trends. And you can use this insight to guide the way you design your messaging and positioning, guide the way you design your pricing and packaging, or your competitive materials or objection, handling because you are actually hearing thousands of conversations simultaneously by understanding the commonality and trends. And that's super powerful. And the reason the prior systems were able to capture that is because it's such a large quantity of data. Without AI you actually could not understand this much data. But now with AI, AI is uniquely able to synthesize large quantity of data. Actually, if you don't give AI enough data or the right data, AI is not as effective. So the conversation data is built perfectly for the AI technology and AI is also able to process all this data to finally get insights and the guidance to the different members of the revenue team.
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Yeah, and I think you mentioned, you know, AI is fundamentally a data strategy and you know a lot of what you're talking about. Also, not only does it take, you know, enough data and the right data, but also contextualized data. Right? Is that, can you, can you talk a little bit about, you know, data contextualization and the role that plays here?
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Yeah, definitely. This is a super important question because you can record a lot of conversation data, but if you don't correlate that data with the business context, including who's speaking. So if the two of us are speaking and AI doesn't understand who you are, are you a decision maker in a company, which account are you associated with and which opportunity are you associated with? Then AI can't make sense of this data and turn that data into insights specific to a contact or account or an opportunity or overall pipeline. So the first thing we do when we contextualize is to associate the conversation data with, with all the business contacts that are important for managing revenue and usually is your contact accounts and deals or the overall pipeline stages. And that's the only way we can turn the raw conversation data into something that's more actionable by sellers managers as well as the other members of the revenue team.
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I'm wondering if you could give maybe an example or two of how this works in practice. So, you know, how does revenue AI help revenue teams turn this, this contextualized data into some actionable insights?
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Yeah, definitely. So one of the more recent examples is we launched smart tracker as a capability and smart tracker, Smart tracker allows you to track keywords or signals when you are monitoring or assessing the seller's conversations. So you can tag a competitor or you can tag a particular phrase related to pricing and packaging. Or if I'm a marketer, I just launched my new messaging, I can tag certain words so I can see across thousands of conversations what people are saying, and I can identify those signals and pull out the conversations related to these keywords and help me understand the trends in either competitive dynamics or the reactions for pricing and packaging or how well the sellers are taking on messaging and the messaging and positioning. And with that information, I can embed that into new methodology. I can launch new initiatives to make sure I either tweak the messaging I have or I do something else to really reinforce the new messaging with the sellers. So this is a powerful example of how we're using AI to make sense of the large quantities of conversation data and make that actionable for all members of the revenue team, whether it's seller, sales manager, sales enablement professionals, revenue operations, or the marketing team. Another example is in our forecast process. Traditionally, when you do forecasts, you have kind of two ways. One is the sellers will Enter their best guess forecast number, whether they're committing where they're closing, and you'll roll up all their number and that's your forecast number. The second methodology is finance typically does some kind of historical trend analysis and they say, well, due to seasonality or from what we have seen in the last three, five years, this is where we predict the revenue is going to end. And both are not exactly accurate because the first one is very subjective by the sellers. And that's why so many companies are having issues with forecast accuracy. And with our forecast application, we actually have a third way to predict forecasts, which is to use AI to give you a forecast number. So now you can triangulate what AI is seeing versus what the sellers are saying versus what historical trends indicates. And that helps you really manage the forecast number based on what's actually happening in these conversations. And we've been told by customers it gives them much higher forecast accuracy rate.
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Yeah, yeah, I can imagine. I mean, you know, as even, even a very good experienced salesperson, I, I've experienced there's a bit of optimism there sometimes. So, you know, to, to that end, you know, it can be a little skewed or maybe someone's having a bad day. And so there's some pessimism mixed in there too. But yeah, that the AI seems like, it seems like a phenomenal way to kind of balance all of that subjectivity and predict as well.
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Yeah, and the other thing that impacts the seller's ability, I mean, some sellers, I think it's common knowledge that we have sandbagging going on depending on how you're rewarding them for pipeline generation or. But at the human level, there's a lot of turnover in the sales organization, so sellers don't always have the account history. And you might be brand new to this account. So how can you possibly predict what's going to happen to the account? And the great thing about having historical data in all the conversations is AI actually understand all the interactions different sellers have had with this account. So AI is in a much better position to string those interactions together and make more accurate predictions versus a human.
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Yeah, yeah. Well, one last topic I want to talk about quickly at least is kind of what, what we should expect in the months and years ahead. Certainly, you know, your gong is doing a lot of prediction as far as sales go. Want to do a little, a little looking at, looking out here. So, you know, you've mentioned before that we're still in the early innings of AI innovation. What developments do you see? You know or foresee in the, in the future of AI for revenue teams.
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Yeah, this is, I think the future is very bright when it comes to AI. We're only at the very beginning and in my career I've seen some major, major technology innovations. Whether it's, you know, moving the move into the cloud or mobile social. I would say AI is the biggest disruptive technology force I've ever seen in my career and the possibilities are endless. So when it comes to gong, GONG invented the category conversation intelligence. Now we're inventing revenue AI because we're turning that conversation intelligence into insights and to actions. And we really see the future as autonomous revenue generation and is the continuation of what we're doing now. But instead of AI identifying patterns and trends and turning that into action, we believe AI can make decisions. We're starting to make decisions on humans behalf. So you can train AI to identify a problem and string together a sequence of workflows. So in that sense, AI is indeed starting to act like an agent. And over time, AI will just do more sophisticated things, including decision making and predicting the revenue or pipeline trends. But having said that, I still maintain my belief that AI is here to augment humans instead of replacing humans, because humans are. We've proven in history that we know how to be creative and there are a lot of things we're doing right now we don't really want to do. Whether it's data entry or, you know, manually retrieving records from CRM or erp, nobody wants to do that. So once we're free from these repetitive, mundane tasks, I think we can do a lot more creative and strategic things and focus more time on engaging with customers and creating more of those human moments in the sales process. And that's what any company wants. And that will more positively contribute to revenue generation.
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Yeah, absolutely. Well, Emily, thanks so much for joining today. One last question before we wrap up. Just, you know, in your role as a CMO at gong, what do you do to stay agile and, you know, adaptable to change and you know, how do you, how do you find a way to do that consistently?
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That's a great question. And that's the fun part of my job. I think AI is so accessible now that you can ask AI to pretty much do anything. So oftentimes I go to, if I have a question, I go to ChatGPT or any of the AI tools for answers. I use ChatGPT a lot for content generation just to stay up to speed on what AI can actually do. And I'm expanding my repertoire. I started with content generation, but now I'm asking AI to write little apps for me or write code just so I can see what AI can really do. And things are changing so super fast. So I think the best way for anyone to embrace the future is by using the tools directly and then see for yourself what AI can do and use that to guide how you can do your work differently.
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Yeah, I love that. Great. Well again, I'd like to thank Emily Hay, CMO at GONG for joining us today. And to learn more about Emily and gong, you can follow the links in the show notes.
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Thank you so much for having me.
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Greg Kilstrom
Thanks again for listening to the B2B Agility podcast. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.b2b agility.com. that's b2b agility.com while you're there, check out my series of best selling agile brand guides covering a wide variety of marketing technology topics. Or you can search for Greg Kilstrom on Amazon. Until next time, stay focused and stay agile.
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Emily He
The Agile brand.