[00:00:00] Rosalyn Santa Elena: Welcome to the Revenue Engine podcast. I'm your host, Rosalyn Santa Elena, and I am thrilled to bring you the most inspirational stories from revenue generators, innovators, and disruptors, revenue leaders in sales, in marketing, and of course in operations. Together, we will unpack everything that optimizes and powers the revenue engine. Are you ready? Let's get to it.
PLG or product led growth is a hot, hot topic right now, but the PLG strategy and motion is very different from the traditional sales led motion and the requirements from an instrumentation and operational perspective, as well as from a data and insights perspective are very different
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[00:01:35] Rosalyn Santa Elena: In this episode of the revenue engine podcast, Momo Ong the co-founder of HeadsUp joins me to discuss the differences between product to lead and sales lead. And some of the things organizations should be really thinking about when either shifting to this motion or adding this motion.
So please take a listen to this data. Scientist turned founder to learn more. So super excited to be here today with Momo Ong, the co-founder of HeadsUp. HeadsUp is on a mission to help sales and revenue teams understand how companies are using their products and identify opportunities for revenue expansion.
So welcome Momo, and thank you for joining me.
[00:02:18] Momo Ong: Thanks so much for having me on in this podcast.
[00:02:21] Rosalyn Santa Elena: Love it, love it. I'm super excited to dive in with you. So let's talk a little about your background and your backstory, right? You've been in many different roles in product and research and in data.
And as you saw, you had a degree in physics from Princeton. So maybe, can you share more about your background and just your career journey, you know, prior to HeadsUp?
[00:02:40] Momo Ong: Yes, absolutely. So I was initially a data scientist, but I realized that I was actually much better or rather I wasn't a very good data scientist.
So I tried my hand at other things, specifically product and, and business. And I, and realized I was, I was much better at that. And so that, that led me to doubling down on product over my career. Yep. And so we're specifically my co-founder and I, we were early employees at this company called fiscal note.
B2B vertical SaaS company focused on the government affairs space based outta DC. And there I ran product that my co-founder Earl was responsible for setting up and owning analytics. And it's actually there where we saw firsthand the disparity in how the go to market teams were leveraging data compared to how engineering and product teams were leveraging data and saw an opportunity to build something six years down the road.
[00:03:33] Rosalyn Santa Elena: Wow. Wow. Well, that kind of leads into the next question, because I think, you know, oftentimes companies they get started right when founders are trying to solve a problem. So when you, and your co-founder decided to start HeadsUp, you know, was there. Specific problem that you faced that kind of led to that idea and maybe what was the original vision for the company?
[00:03:52] Momo Ong: Yeah. So, as I mentioned at fiscal note, we saw that the go to market teams, even though there was a very strong reason for them to leverage data, to make better business decisions were not doing that. Whereas the engineering and product teams were much more sophisticated in leveraging data. So that was the general overarching problem that we, we sought out to solve.
We knew there was an opportunity to help go to market teams, better leverage data, to drive revenue, to make better decisions. And so as we started running in that broad direction, and as we talked to more go to market and also data stakeholders, we saw that there were two themes that stood out. Number one was the rise of product led growth.
And number two was the modern data stack. So. You know, we realized that there was an opportunity to put the two together, right? How can we help product led go to market teams, which required more data than ever tap the modern data stack to make better decisions. And so that was the, the problem statement that led to the the founding of HeadsUp and our current iteration of the product and business.
[00:04:57] Rosalyn Santa Elena: Got it. Got it. Love it. Yeah. Yeah. Definitely seeing a lot of movement in this PLG space, you know, and with so many companies really moving to this kind of PLG strategy and motion, you know, we talk about P QLS now, right? There's not just M QLS and SQLs. Yeah. But now there's, PQL. Also which are really the future, right. They're very different, right. From a traditional marketing qualified lead, or even a sales qualified lead, at least from my perspective. So what are some of your thoughts on this? You know, how do you see P QLS, you know, versus the traditional, you know, MQL or SQL motion and what do you see as sort of the primary differences?
[00:05:33] Momo Ong: Yep. So first what are P QLS and MQL, right? MQL. Yeah. Just so that we're a hundred percent aligned. Are leads that are qualified because of marketing touchpoint or, or, or thanks to marketing touchpoints, like website views, signups, inbound, sign up webinar, attendances and product qualified leads, or P QLS are the product counterpart of MQL.
They index heavily on usage based signals so that companies can, so that companies can identify accounts that can be monetized or are under monetized and can be further ups. so both MQL and P QLS feed into a sales motion, right? Sales development qualifies them into SQLs or sales qualified leads, and then they're driven through the sales cycle.
Okay. So now that we understand what P QLS and M QLS are, how are they different? Well, number one, product usage is much more intentional than, than marketing signals. People are already using the product, right? So we've seen that P QLS convert at a much higher clip compared to MQL. It's not uncommon to see PQL conversion rates in the 20, 30% while MQL conversion rates are in the single digits single digit percentage rates.
The, the next difference is that there's normally one MQL score, right? That score can take various values. Okay. Is this lead qualified? That's basically the, the binary question that that marketers seek to answer. And then SDR seek to verify, right? Mm-hmm but when you look at P QLS, there's a plethora of P QLS when, and that's because when an account starts to use a product, there's actually a lot.
That you can do as a salesperson, as a customer success rep as a marketer to further unlock value and drive monetization and adoption, for example, you could upsell number of seats, upsell the plant tier nurture accounts to unlock further usage so that they get more value from the product. Right? So many different types of P QLS, one MQL.
[00:07:34] Rosalyn Santa Elena: Got it.
[00:07:35] Momo Ong: Oh, and. Now that that's today. Right? Now in the long term, I believe that the concept of MQL and P QLS will be blended together. So at the end of the day, you have some go to market objective, say upsell, cross sell trade mitigation, and you simply define logic based on usage signals, marketing signals, photographic signals, whatever signals mm-hmm that surface a set of leads.
That can be engaged against that specific goof market objective, right? And against that objective, you do something about it. So you route it to a specific channel, say sales, customer success marketing, and you could even cut that much more finely. So this view of the world where you have different types of leads that are defined by different types of by different criteria is what we at HeadsUp the company that I'm building.
And it's exactly what we're building towards. We're building towards a tool that allows you to define leads based on multiple sets of data, multiple data sources, select which specific channel you wanna engage, the set lead, and to complete the feedback loop by measuring, experimenting, and improving your segment definitions and the channels that you you use against each segment or each set of leads.
[00:08:56] Rosalyn Santa Elena: Got it. Got it. Yeah, that makes a lot of sense. I think you know, I think we're seeing. we're seeing more and more companies, you know, using this sort of PQL model. Right. And this product led growth model and then managing the P QLS. What are you seeing, I guess, in some of your research and kind of, you know, what you're building there with clients is like, what are you seeing organizations really doing wrong when it comes to defining M QLS and managing those?
[00:09:19] Momo Ong: Yeah, that's a great question. So let's start with defining, oftentimes we see. Companies leveraging rule based methods. So often, you know, the, the sales operations leader or the sales leader has some intuition about what usage behaviors lead to conversion. And so against that intuition, they define, they, they, they, they define a PQL right based on a rule mm-hmm
Now that works from an 80 20 perspective. But at scale, we find that it actually. Is relatively low signal compared to machine learning. So we, we find that there's opportunities to lower the noise and also not miss out as many leads if companies are, were to leverage machine learning to surface BQL.
So I think that's one thing that companies can generally improve on now. Number two. , I think there's a lot of emphasis on how to define a PQL using rule based methods or using machine learning. And there's less emphasis on how to operationalize these P QLS. So what do I mean by operationalizing P QLS?
Well, how do we get these P QLS in front of maybe the hundreds of sellers or thousands of sellers and CSMs that you might have in your organization? Right. And how do you make sure that there's compliance? How do you make sure that sellers. Leveraging these leads, understanding why these leads are surfaced and adopting their talk track as appropriate to maximize close one.
So that's actually a very, very hard process. Once you have a go to market team at scale lastly, Defining PQS and managing them is not a one and done effort. It requires persistent measurement and iteration. And I think that's what a lot of folks might not do yet. Right. Your product changes. The market evolves.
And initially your rule based criteria, or even machine learning based criteria might not be optimal, right? The channels you use to engage the leads might also not be optimal. You have to tweak and experiment. To make sure that you have to optimal go to market bullshit over time.
[00:11:35] Rosalyn Santa Elena: Yep. Yep. Yeah. I love that. I love that. I think that iteration and the not one and done is always so, so true. You know, even with MQL and traditional model, I'm always surprised by how many companies either don't do lead scoring or they don't continually. Update their lead scoring model based on learnings. So definitely I love that.
That's all great advice. Thank you. Let's talk about data, right? Because we all know how critical it is to have the right data at the right time, right. To the right people, but with so many organizations moving, you know, even to consumption based, right. And this usage based model, which a lot of times, you know, this.
This PLG motion, you know, supports, you know, having those deep insights into how your product is being used and that data is incredibly important. What have you seen, I guess, in terms of, you know, SaaS companies moving more into this product usage model and what do you see as some of the key challenges to really bubbling up the right insights?
[00:12:34] Momo Ong: Yeah, so I think. I think a key challenge is instrumentation. You can't bubble up the right insights if you're not logging the correct product usage events and marketing events. And a lot of times we see clients or prospects wanting to, for example, build a product led growth, go to market motion, adopt a product led sales tool without having.
In a prior sense adopt instrumented and locked appropriate events. So I'd say that's one of the key challenges and that's actually that's structurally as within an organization, that's a difficult challenge because it requires a lot of engineering and data engineering time.
So are you. Are you seeing this trend in the market, you know, in terms of folks moving to more of this, you know, product usage, data and product led strategies.
Right. Cause we're seeing a lot of companies shift right. From sales led to product led or kind of doing both right in their business model. And are you seeing this trend in the market, you know, kind of, where do you see or where do you think it's headed? Right. Do you have any predictions maybe as to how this might evolve over the.
You know, 12 to 24 months. Yeah, absolutely. So HeadsUp has been around for almost two years and in our existence, we've definitely seen the, the upswell of interest and adoption of a product led sales. When we first started the market consistently mistook us for yet another say customer success tool. But now there's definitely no question about that.
Right? Now the market appreciates and understands that there's definitely a need to leverage product users, data to drive better, go to market decisions. Now on a go forward basis. I think this trend will definitely speed up given a, the, the macro climate today and emphasis on profitability, right?
That's, that's a massive tailwind for product led growth because at this point, most companies and their boards appreciate the efficiency of a successful product led growth ocean. So yes, there's definitely. Looking backwards. We've definitely seen increased adoption over, over the, over the months and years and going forward, we expect there to be much more adoption as well.
[00:14:55] Rosalyn Santa Elena: Got it, got it. What about you know, from your perspective, like, what are some of the primary considerations you think businesses should really think about right before even deciding if product lead is right for them?
[00:15:06] Momo Ong: Yeah. So I think the key consideration. What is the product and who, who is the buyer and what does the sales process look like? Does the product lend itself to being, can, can someone sign up for the product very easily and get value? Right? Mm-hmm . So, if, for example, you require multiple stakeholders to be involved within a company. That's often a signal that it might be very hard for an individual to sign up and immediately get value.
Right. Data tools are, are one example of that. If you have to, if you're a marketer or a product manager who wants to use a data tool, and in order for you to successfully successfully get value from that tool, you need, you need to ask, say data analyst or data science to give you access to the warehouse.
That's oftentimes not a, a product led motion or rather it, it can be an impediment. So number one, What does the product look like? What does the sales motion look like and how, how fast does, how, how fast can, can someone unlock value from this product? The second consideration is the DNA of the company.
So I was actually just speaking to someone who, who, who ran a company that got acquired by Microsoft. Right? And so Microsoft, as we all know is very, very, it's. It's a, it's an enterprise sales machine. Microsoft basically said, Hey, you, you should, you should stop yourself. Serve ocean at this point. So certain certain organizations have different DNAs.
And I think that's very important because product let's growth and product led sales is multi multidisciplinary, right? You need, you need product, go to market teams ops to work together, understand the customer journey and push accounts at the right point. Towards later stages of the journey. If not everyone is bought into this thinking it's very hard for product led growth and product led sales to be properly adopted. So DNA matters.
[00:17:10] Rosalyn Santa Elena: Yeah. Yeah. Those are really great points. I think about for a product to lead motion, making sure that, you know, the product, like you said, is, is it something that somebody can singly, you know, or in their, by themselves basically sign up for the product? How quickly can they actually start to use the product?
You know, how easy is it basically from implementation perspective and then what, how quickly they can gain. Right. Which is exactly what you said. So I think that makes a lot of sense. It's not for everybody, for sure. And like you said, the company needs to really buy into it because it's not just, you know, one person.
Yes. It has to be the entire motion to support it. That's great. As I think about, you know, the revenue engine, I think about this podcast, I'm always hoping others will be able to learn, you know, how to accelerate revenue growth and really power. Right. The revenue engine. So what are the, you know, maybe top two or three things that you think, Hey, all revenue leaders should really be thinking about today that will have the biggest impact on revenue growth.
[00:18:06] Momo Ong: Yep. So I think the first thing is pricing and account tiering. This is a constant work in progress and something that I feel organizations consistently under invest in. We've seen so many cases where because of a pricing iteration, companies are able to unlock 10 to 20%, if not more gains in recurring revenue or at contract values.
And oftentimes it's not that difficult of an iteration. So I think pricing is oftentimes low hanging fruit for many organizations. Second, I think for sure if you're, if you're a product led growth company, figuring out how your go to market team. Since on top of your selfer motion is very important. Compared to enterprise sales, there's actually a lot here to figure out, right?
Enterprise sales is also very difficult, but oftentimes there's one process. And once that one process is nailed, it's repeatable. Right? Then you have, you have your sales team, you have your marketing team essentially work through those steps to, to consistently drive revenue. But there's so many different go to market motions in.
In, in PLS, right? In product led sales, you can there's sales serve there's sales assist. You can drive community, you can drive community leads, there's free or freemium accounts. And you have to think of ways to, for each of there's different interventions that need to be that need to be directed at different types of customers at different points in their customer journey.
And so it's as if you have to figure. Five or six concurrent go to market motions as opposed to just one. So I think revenue leaders definitely need to be thinking hard about that because a is definitely much more complicated and not as play bookable at least today compared to the traditional enterprise sales motion.
But if they successfully crack that a couple, if they successfully crack a couple of product led growth, go to market motions that can result in. Very efficient revenue growth.
[00:20:12] Rosalyn Santa Elena: Yep. I love that. I love that. What about, is there maybe one piece of advice that, you know, you would give to another founder, you know, sort of that one thing that really makes all the difference when starting your own business, you know, what would that be?
[00:20:25] Momo Ong: Yeah, I think what advice would be market selection deeply understand your market and the problem. and it's associated nuances before going ahead and building mm-hmm that's number one. And number two would be hiring. Make sure you, especially when you're an early stage company, and let's say you have a team of 10, every individual can be a game changer and can step change the company.
So hiring and identifying really, really strong stake individuals and team teammates, both on the engineering and non-engineering sites. I say hiring and also market selection are probably the two most important things at the early stage.
[00:21:06] Rosalyn Santa Elena: Yeah. Yep. I love that. Well, thank you so much for joining me. Today. Momo, as we wrap up and before I let you go, I always asked all the guests two things. So one, what is the one thing about you that others would be surprised to learn? And two, what is that one thing you want everyone to know about you?
[00:21:26] Momo Ong: Yeah. For number one, I think everyone would be surprised to learn that I'm actually based out of Singapore. So HeadsUp is a fully remote company. Earl. My co-founder is based outta San Francisco. We have folks on the east coast and west coast of yeah. East folks on the east coast and west coast, as well as in Europe and India. So we're fingers crossed. We want to be the success story of a company. Has been remote from day, day, zero, and was able to successfully scale to a very large and very successful size.
[00:21:57] Rosalyn Santa Elena: And what about one thing that you really want everyone to know about you?
[00:22:00] Momo Ong: Oh so my girlfriend recently got a rag doll and you know, I'm, I've, I've been obsessed with playing with it, so yeah. so I was definitely, that's definitely something that's it, it was very, it was definitely very unexpected. I did not. I did not find, I did not know that I would be a, a cat lover.
[00:22:19] Rosalyn Santa Elena: Oh, that's great. I love that. I love that. Well, thank you so much for joining me Momo, and I really appreciate your time and super grateful for, you know, sharing more about your story, more about what you're building and sharing a lot of your expertise around PLG, which is the hot topic right now. I think it's a hot, it's a hot and yet very impactful motion that many companies are definitely either interested in or already. Implementing, but can always do better. So thank you for joining me.
[00:22:46] Momo Ong: And thanks so much for hosting me. I really appreciate it.
[00:22:48] Rosalyn Santa Elena: That's great.
This episode was digitally transcribed.