GTM Navigator: Signal Based Selling

GTM Navigator is our ongoing series where we break down the essential components of nailing the right go-to-market strategy. 

In this episode, Stephanie Perez, Managing Director at Fin Capital, speaks with Linda Lian, Co-Founder & CEO of Common Room. Linda brings a wealth of knowledge and unique insight into sales, revenue operations, and customer intelligence. Before founding Common Room, Linda spent time at AWS, Madrona Venture Group, and Morgan Stanley.  

In this episode, you will gain insights into how to capture & map buyer signals, the new role of RevOps, and defining Customer Intelligence Platforms (CIP). A big thank you to Linda for sharing her insights, we hope this episode of GTM Navigator enables you to deepen your understanding of potential customers to accelerate growth.  

Key Questions Discussed: 

• What problem is Common Room solving & who is the product built for? 
• How has the role of revenue operations evolved and how is it being impacted by AI? 
• Can you use AI to predict upcoming trends? 
• What are some of the signals that B2B enterprise companies should be tracking?
• How should B2B enterprise companies be thinking about scoring? 
• What is a Customer Intelligence Platform versus a Customer Data Platform? 
• How should companies think about signals from a PLG perspective vs a sales team perspective? 

Transcription (edited for clarity): 

Welcome to Go-to-Market Navigator by Fin Capital, our ongoing series where we break down the essential components of nailing the right go-to-market strategy. I’m Stephanie Perez, Managing Director of Business Development and Partnerships at Fin Capital. Today we’re diving into all things signal based selling with Linda Lian, Co-Founder & CEO of Common Room. Common Room is a customer intelligence platform for sales, RevOps, and marking teams. So looking forward to this conversation! 

Stephanie Perez (00:17): Linda, tell us about Common Room. What problem are you solving and who are you building for? 

Linda Lian (00:25): Hi Steph. So happy to be here. Common Room is a customer intelligence platform. We let go-to market teams capture every single buyer signal, and then we give sales and marketing teams superpowers with AI powered enrichment and automation to achieve ultimately what nirvana is for every customer facing team – what we all aspire to do, which is to reach the right person, at the right time, with the right message. We work with hundreds at the fastest growing B2B brands from early-stage startups like AirByte to growth companies like Some Grub, Notion, Zapier, and Grafana to enduring companies like Atlassian and AWS. 

Stephanie Perez (01:14): Great, those are some big names! Tell me, how has the role of revenue operations evolved and how is it being impacted by AI? It seems to be the topic everywhere. 

Linda Lian (01:24): Goals have turned to efficiency, which mandates that companies think more about how to make data-driven decisions. As a result, RevOps has more and more of a leading role to play. At Common Room, RevOps is one of our core buyer and customer personas, and we really think about it as jobs to be done. I think what’s exciting is, it’s not just where AI can increase productivity on previously manual or rote tasks, but more so, the sort of exciting piece is, can AI tell us what we don’t know already? I think this is particularly relevant for RevOps. When you think about RevOps, there’s three main buckets of their day-to-day responsibilities. There’s the first bucket: identifying, prioritizing, and creating those account lists. Second bucket: building those sales playbooks, making sure that the field is executing against specific strategies. And then that third bucket: is more so forecasting, planning, trying to understand predictability. 

In that first bucket, it’s easier than ever to use tools like Common Room to do things like find lookalike accounts, ML powered scoring across your entire TAM to prioritize best fit accounts, the right timing, what accounts in a rep’s book of business should they be focused on at any given time, etc. I think that’s a huge area where being data-driven, having all of the signals, starts to increase productivity and focus. On the sales playbook side, we’ve seen a massive evolution and innovation. Of course, at Common Room, we have a sales workbench and we focus a lot on – how do we let central ops come up with these very prescriptive segments or plays based on who they are. Asking who those prospects are, their title fit, the account fit, the specific signals or engagement that they’ve taken, etc. Then, all the way down to things like personalization of the message. We’re seeing that RevOps has more of a role to play, and centrally managing a lot of this, and making it easier and easier for reps to go from signal to action.  

This is where the ability to signal stack and create these high converting plays and then ultimately measure the results really comes in. Then I think of the last piece, which is forecasting. Planning and territory planning fit into this as well. It’s all part of that same flywheel, which is once you can really crisply define those accounts, and people that you want the field to focus on, once you can dictate precisely what those plays should be – personalized, high converting messages against those plays, measure what’s working – then you’re getting into what I think everyone is kind of early on. The promise of AI being able to suggest the next best action to suggest the plays that you should be running. I think we’ll get there soon. You need to collect a lot of the data first, but that’s where we see the RevOps role evolving. 

Stephanie Perez (05:06): That’s the wonder of AI, which is the ability to be able to synthesize the data, but also be able to predict trends from it as well. Right? 

Linda Lian (05:13): It’s interesting because many of our customers are, people will call them PLG, but you could also describe them as user-led brands. PLG, has a lot of fuzzy definitions, but at a baseline, these brands grow from users. A lot of our customers are thinking about, “can I use AI and ML to actually take historic data from the last 20 years of my PLG business and use it to predict what should be done and what actions should be taken?” and “What accounts are best fit accounts for the next 20 years of the business?” Which is likely going to be more outbounding, more sales led. It’s interesting that you can’t take that historic data and predict what’s going to happen in the future, because your business is going to be fundamentally different. There are limitations to a lot of this. It’s all conversations that are happening across our customer set in the market today. 

Stephanie Perez (06:22): I want to touch on PLG in a second, but if we dig into one of the things that you mentioned, which is signal based intent, what are some of the signals that B2B enterprise companies should be tracking and how should they think about scoring? 

Linda Lian (06:33): When you think about a sales team or a demand gen team, there’s really three dimensions that matter. When you think about your buyer journey, it’s who somebody is, what’s their title? Are they an executive buyer? Are they budget holders? Are they a practitioner within a buyer committee? Who is that person? Does their title help you understand how they fit into your buyer committee? What did they do? Did they take an explicit action? Did they like a LinkedIn post that your company just put out about a new feature? Did they sign up for a free trial with their email? Did they join your community and start asking implementation questions? Did they hit your website and look at a specific solutions page? Or maybe they’re looking at the pricing page, right? So, what did they do?  

Then the third aspect is where do they work? Is it the right account? Is it your ICP? Is it in your book of business? It’s who you are, what do they do and where do they work. We broadly bucket all of these into signals, and we define signals as first party signals, second party signals, and third party signals. First party signals are the signals that you own within the four walls of your organization. Think product signups could be with a company domain or Gmail account, visits to your website, that’s your address, that’s your property. Those are all first party signals. Second party signals are going to be engagements that’s happening with that prospective buyer or customer out there in the digital burst – on your LinkedIn post, following you on Twitter, maybe following your competitors on Twitter, commenting on an influencer who’s talking about your problem space, joining your community, starring your GitHub repost. If you’re an OSS company, these are all second party signals.  

Third party signals are really signals that are not explicit engagement but help to qualify timing. Things like an account you care about. For example, let’s say you sell to developers, this account is hiring developers way faster right now than any other account in your book of business. Not only are they hiring for developers faster, but within their job recs, they’re actually mentioning your tech stack or your competitor’s tech stack. That’s a huge signal around timeliness that this account is right for conversion. Maybe they’re talking about digital transformation, and the CMO or CIO is going on a speaking tour talking about key strategic initiatives. That’s a third party signal. Another really relevant third party signal is job changes. I think Gartner has a stat that, on average, an executive buyer will deploy 80-90% of their budget within the first six months of a new job. 

When you think about, are there past users, practitioners, or buyers of my software that have now moved jobs? People are moving jobs all the time, on average every 24 months. Think, have they moved jobs into a new target account where they’re not already a customer? And so, first party, second party, third party signals, we generate all of these out of the box. We’re the most complete solution in terms of depths and breadths of signals. But what do you do with these signals? That’s where the majority of the efforts and focus needs to be spent. Once you have all these signals in one place, then you go back to the jobs to be done – scoring across all of these signals. Most traditional scoring systems and marketing automations platforms only let you score across things like first party signals. 

When you integrate all of the signals and your scoring across title fit, ICP fit, account fit, timeliness fit, you’re going to see a 4-5x increase in propensity to buy. The accuracy of these signals around prioritization becomes really powerful. Then there’s the action piece. If you’re a rep, well, you have a book of business, let’s say there’s a hundred accounts in it, what do you do on Monday morning? Can you log into an interface that shows you all of the various contacts you care about? What activity has happened over the weekend across the entire universe of the internet? Whether it’s something that happens in the dark funnel like liking a post on LinkedIn or something that’s harder to see, like an email signup on your free trial? How do we bring all this intelligence to reps? Then that helps with the personalization of messaging. 

It creates the actionability of do I email them, do I call them? What do I do next? We call that the last mile of actionability. We focus a lot on consulting our customers, on investing in our platform to take signals to action. The last thing I’ll say is, most companies can overcomplicate some of this. Many times, the lowest hanging fruit, the way that you’re going to fundamentally affect your pipe gen numbers this quarter, are to just start simple. We find that across our customer base, simple means, “hey, executive buyers are hitting your pricing page, do your reps know about that?” Simple means “you have X thousands of email signups a week or a month on your free trial. Are you doing anything with those?” If the answers are no, that’s where to start. Simple. Start with low hanging fruit. 

Stephanie Perez (12:22): Oh, I’m so glad you touched that, because when I think about the signal-based world, just in terms of the amount of data that exists out there, there is so much noise. Understanding what to prioritize and where to start is really helpful to some of the folks in the audience. 

Linda Lian (12:38): A hundred percent, it is all about starting simple. 

Stephanie Perez (12:43): Tell us a little bit about where data lives. I noticed on your website, it was one of your blog posts, you were defining what a customer intelligence platform is versus a customer data platform. Tell us a little bit about that, because there’s obviously a lot of incumbent players in those spaces, and so I’m curious how you compare and work with some of those. 

Linda Lian (13:00): CDPs (Customer Discovery Platform) like Segment, Twilio and a lot of the ETL tools out there, they focus really on solving the underlying data problem that we know persists. This is that companies don’t have all of their buyer engagement in one place, and that creates a lot of siloed work streams, lack of visibility, and frankly poor customer experiences across your buyer journey. Most organizations, marketing is working off of marketing’s data and they’re doing workflows off of this data. While sales is working off a completely different set of data and they’re doing workflows off of that data. CS is working off of their own tools and their own data. And so, I think CDPs, which kind of came into popularity maybe a decade or so ago, was built to solve this problem. Where I think we’re seeing the next kind of revolution or innovation off of that concept, is the customer intelligence platform (CIP), which basically has a CDP at the fundamental data layer. 

But it’s again, not just that first party data. It’s not just the data you own. It’s not just, “hey, who’s using my product and who’s hitting my website and going to my meetups?” That’s all first party data. It’s also that second party data. We know the dark funnels getting darker. We know that people are more sales averse than ever before, because they’re digitally literate and they want to try out a tool on their own before they talk to a sales rep. How do you reach more into that digital sphere where they might be asking about competitors and looking at solutions and consuming your posts on social? That’s very different than a CDP. Then of course, that third party data around timeliness, job changes, accounts being ripe for conversion, that’s again, not really in the wheelhouse of traditional customer data platforms. 

I think the next big difference is that enrichment, the identity resolution that’s baked in, being able to anonymize all of these buyers against the signal that they’re taking. Then the workflows and the AI and the automations that sit on top. Most CDPs aren’t built for users, they’re not built for SDRs, they’re not built for sales reps. They’re built for data practitioners and operations practitioners, whereas the customer intelligence platforms of now in the future are really purpose built to bring that intelligence to the hands of the reps, so they can take action very quickly. 

Stephanie Perez (15:43): As you know, we’re B2B Fintech investors, and so many of our portfolio companies are selling into highly regulated spaces. Generally, we see a lot of sales led, AE led, sales engagements, and go-to market motions, but there are portfolio companies that may have, for example, an entry tier that feels a little bit like a PLG motion. Can you provide a little advice to our portfolio companies and how they should think about signals from a PLG perspective versus that of a sales led team? 

Linda Lian (16:11): I think the PLG perspective, that’s the one everyone understands, right? “Hey, I have a bunch of people hitting my free trial. I want to make sure that my sales team spends that human effort to build relationships with the right titles, with the right accounts that are coming inbound.” That’s a very obvious one. If you have a PLG motion, if you have a free trial, if you have a lot of users, you’re naturally going to have a lot of signals – people engaging with you on social, people coming to your website, looking at your documentation because they’re trying to figure out how to use the tool on more of the enterprise sales led side. I would say again, the goal for efficiency and conversion is, how do I make what used to be a cold outbound, a warm outbound? Cold outbounding is very, very hard today. 

You see kind of SDR replacement tools generating very generic messages that attempt personalization like, “hey, I see you’re from this city, Reese Witherspoon’s from the city, I love Reese Witherspoon. Let’s hop on a call.” And what we find is those messages are generally very low converting still. For enterprise selling motions, it’s signals like, are they hitting your website? Who are those executive buyers? Can we anonymize them and make sure that your sales team knows about it? Other signals that matter a lot are people that are changing jobs. Again, people are changing jobs on average every 24 months, whether you’re selling into an enterprise or not, you’re going to have executive buyers. You’re going to have users that are moving from job to job, and that’s going to be a warm entry into what used to be a cold account.  

I’ll say the other piece around cold outbounding into the enterprise, a lot of it happens to do with account fit, right? We have a feature called RoomieAI where you can do deep research on an account including using GenAI to actually scrape and return questions you may have about their strategy from their S1s and their public filings. You can look at certain executives and track what they are talking about. Are they talking about specific strategies that matter? There we see a lot of benefits to deep research and personalization around what’s going on in the account itself. 

Another great signal on the account level around timing is, again, hiring. You would be shocked at the amount of open recs that mention certain keywords that are applicable to your business. They could be describing a strategic project that makes it timely for you to go reach out to them. They may be describing certain competencies in that job rec that align with your technology and your product, or they may be even mentioning your competitors. If you’re a smaller company and you compete against a bigger incumbent and there’s a whole ecosystem of people who use that incumbent player, go look at the job recs and see, are they increasing hiring for this particular skill? All of that in Common Room, you can prioritize, you can aggregate those insights and create lead lists that turn your cold outbound into more informed and personalized warm outbound messages. 

Stephanie Perez (20:00): There’s always this debate about whether the function of the SD is dead. And I have to say, even with AI innovation, it seems like it’s much more process efficiency than being able to replace the actual intelligence and informed manner in which some of the SDRs do their job. I’ll give you a perfect example. The other day, I received an email, my name was wrong, it said that I am living in Miami, and so the weather must be amazing. It’s actually hurricane season, so it’s very rainy, and they’re also selling me a code development platform, but we’re investors, we don’t do any code development.  

I’m sure that coming up with the list and drafting the email was a lot easier than it was a couple years ago, but the accuracy rate and the use of data that’s already available out there was obviously not utilized correctly to prospect me.  

Linda Lian (20:47): It’s funny you say that. For SDR replacement tools, we’re seeing two major issues and challenges that you just called out. The first one is over generic messages, right? Like, “oh, you live in Miami, the weather must be amazing. Or Reese Witherspoon’s from your hometown.” Then the other one is hallucinations. You do something that you fundamentally don’t do. You did something that you fundamentally didn’t do. We see these two issues plaguing SDR replacement tools, which is why, again, that focus on signals, contacts, and depersonalization eliminates a lot of these risks. I will say that people will always buy from people. Sales will always be a human endeavor. What we find though, to your point, is that a lot of the jobs to be done on the backend, things like list building research, personalization, can be in a copilot manner, automated a way to make SDRs a lot more efficient. 

When that SDR efficiency happens, there’s two things that really benefit the organization. The first is that you can move SDRs deeper down the funnel so that again, they’re not cold outbounding. They’re reaching out with a personalized, contextualized and warm message like, “hey, I noticed that so-and-so on your team has fired up a free trial. I know you’re a budget holder; you were a customer at your last company, and you just hit our pricing page again, let’s hop on a call and reestablish this relationship.” That’s a very personalized, deeper in the funnel type of message. We’re seeing organizations want their SDRs to build these human relationships deeper in the funnel than clicking buttons at the top of the funnel doing cold outbound. The other efficiency driver we see is just the ratio of SDRs to AEs. Maybe you can have a smaller team that is producing more, and this goes into RevOps roles around capacity, planning, and forecasting. I think those are the two areas I’m excited about. Now, that being said, we do see that if you’re an early-stage company and the stakes are low, and you have no customers, you’re trying to get signals. You’re trying to do tests into the black box of the market as fast as possible. We’ll see more of those types of companies be okay with the risk profile of leveraging an SDR replacement tool versus when you’re talking to an enterprise, that have 20 years of customer trust built. I think there is really more around making SDRs more efficient with copilot and tooling. 

Stephanie Perez (23:39): A hundred percent. Well, with that, I wanted to thank you so much, Linda, so excited about what you’re building at Common Room. Please do check out their website and thank you so much for sharing your insights. 

Linda Lian (23:50): So great to be here. Thank you, Steph.