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GTM Playbooks

A Playbook to go from Product-Led Growth to Product-Led Sales with Gabriel Madureira

This post by François Dufour is part of our series on Product-Led Growth and Marketing Playbooks. There, we share insights and advice from leaders who have built successful PLG businesses marketing and selling echnical products to technical audiences.

Gabriel Madureira, current Chief Growth officer at PlanetScale, knows the magic that can happen when a company can run both a Product-led and an Enterprise Sales-led motion concurrently. He’s seen that first-hand at MongoDB and played an active role in scaling both. He’s also seen the different “shades of Sales” - including Product-led Sales (PLS) - one we leveraged heavily at Twilio. While it is rare for a PLG company to go very far and very big without tacking on a powerful Sales - or at the very least a human-assisted “expand” - motion to their PLG playbook, the opposite is not as frequent: few companies with strong Enterprise Sales-led motions manage to scale a meaningful PLG and PLS motion.

My discussion with Gabriel highlights why that is so difficult and what steps he recommends one takes to create a PLG and PLS motion in an Enterprise Sales-led company. He shares learnings he accumulated across many companies he’s worked at, studied, and advised, from small startups to post-IPO companies.

Key Takeaways

  • A PLG motion differs in so many ways from a Sales-led one that you need not only different products but also different teams, tooling, metrics and cultures to run each one effectively
  • PLG is more data-intensive, real-time, and technical. Finding the right talent is difficult. You can’t hide imperfections behind star Sales reps. You can’t sell a roadmap or vision only
  • Handling product leads like marketing leads does not mean you are doing Product-Led Sales. It means you are likely to fail
  • To go from PLG to Product-Led Sales, inject humans where you know you have gaps or friction in your customer product journey, give them data and conversation starters, automate what works or build it into the product experience

Let’s first understand how Product-Led Growth (PLG) - and by extension Product-Led Sales (PLS) and Enterprise Sales - are different:

The characteristics of an Enterprise Sales Motion

  • Yields larger deals but typically with a higher CAC
  • Longer sales cycles. The cadence is normally quarterly and influenced by quarter-end
  • A team is buying instead of one user. Usually, one executive is involved who is not hands-on with the product or is not technical
  • You don’t just sell today’s product but also:
  • A future roadmap. Execs typically buy the future vs just current features  
  • The concierge approach (CSMs) and support you will offer
  • Professional services to help with implementation
  • The Channel and interaction mix is skewed offline and events-heavy with the potential attribution problem of tracking the impact of all the offline discussions between Sales and customers
  • The company’s Sales rep is the most important player. A whole team supports that player on the vendor side: SDRs, Sales Engineering, Sales Enablement, maybe Solution Consultants, partners, etc.  The better that salesperson, the more you have opportunities to hide your shortcomings. So Sales-led motions can be less efficient in the backend than PLG ones
  • “Get to give”: the GTM engine is designed first to collect data about the prospect to establish whether the prospect is “worth” meaningful interaction with Sales: name, company, need, budget, timing, etc. Prospects need to pay that tax even to discover the price or see the product
  • Salesforce Sales Cloud (or Hubspot Sales) is the tool of record: all analytics and records live inside of the CRM
  • Key data and info come from asking questions live - and you don’t get to ask many questions given time constraints
  • The motion unfolds in spurts: it’s OK  if you don’t talk to a prospect in a given week

The characteristics of a Product-led Motion

  • High scale and low touch: not many interaction points with your customers before they are in the product
  • “Give to get”: the company gives quick product access and a lot of self-serve information about the product, including the price, early on without getting much information or context back about the user.
  • Very data-driven but no humans can help you fill your data gaps (until you move to PLS).
  • You have a lot of usage data to leverage and can get good insights  
  • With many data behavior-based data points, you can use data science to establish correlations
  • The data needs to be real-time, for instance, so you can send an email about the action they just took in your product
  • But it can be difficult to figure out who the decision maker is or a person with access to the credit card among the users
  • You sell the product as it is today, not its roadmap
  • Great to capture an SMB/MM target audience or markets with a lot of users
  • You can experiment very fast and accurately:
  • More volume and faster answers
  • More control - you can set up A/B tests without human bias
  • As PLG leader, you lead a more technical and data-driven team: growth engineering, data analysts, and product designers - and not a team that knows Sales and their processes deeply
  • The tech stack is very different: typically composed of elements of “the modern data stack”, an email tool (e.g. Iterable, Braze, Sendgrid) with good trigger ability to send trigger-based emails rather than time-based emails mostly, product onboarding (e.g. AppCues, Intercom, Pendo), etc.
  • For analytics, you will use a BI tool like Amplitude based on your DHW data instead of Salesforce

The benefit of running both concurrently

One of the holy grails of Go-to-Market is to be able to run both a PLG at the “low” end and an Account-Based Sales-Led motion at the top of your customer pyramid, as well as the various shades in between.

By marrying the velocity and scale of PLG with the higher monetization of Sales-Led you will be able to capture more of your market potential - more deals each at the optimal deal size - while making both motions feed each other.

PLG: the misconceptions and challenges

People get it wrong if they think PLG is easy, and also if they think it is always cheaper than Sales-led. They may think about PLG as enhanced advertising where the self-serve - and especially the free - product does the work, replacing the human-delivered demo. They think you “just need” to point advertising dollars to product landing pages and that magic will happen.

“PLG is actually very hard. Getting it right is as hard as selling million-dollar deals on the enterprise side.”

PLG is a different dynamic. It requires a different skill set. And by the way, the right skill set and experience are very tough to find. There are few very good PLG leaders and they will be more expensive.

Getting your users to understand the product, adopt the right features at the right time and get value from them, and then expand to more users or use cases in the enterprises is a lot of work and far from easy.

A Playbook to go from pure Product-Led Growth to Product-Led Sales

Identifying the gaps

First, you need to understand your PLG motion very well.

Is it stable? Do you have the data? Do you understand and track the behaviors? Do you understand the levers that you can pull to move users down the funnel? Do you understand how those things vary across different geographies? Do you have a good experimentation culture and instrumentation? And especially, do you understand what are the friction points, gaps, or bottlenecks where users are stuck or not converting to the next stage? That's step one. More on that at the end of the section.

Testing Human intervention

Second, test where a human can usher users through specific moments to accelerate their adoption of new features, grow consumption, unblock certain things, or help onboard additional users from the same organization.

You should typically run this at the end of the initial activation or free trial phase, after companies already had some level of usage. Or when you detect that users work at a company that may prefer being invoiced rather than paying via credit card. You can then use that as an opportunity to engage the conversation.

“You’re really using humans - let’s call them members of the Product-Led Sales team - to advance users, cover for all the things that you have not yet figured out or built in the product experience, or assist users with tasks they don’t know how to do themselves.”

Your PLS rep will collect qualitative data to inform your product roadmap. That’s why it’s critical to keep that team close to Product or, at least, keep very good communications and voice-of-customer sharing channels between Product and PLS.

Equipping humans with data and conversation starters

This PLS team needs to be expert in the product. That’s critical. They need to have a lot of data available to them. And that data needs to be real-time, so they reach out to users at the right time with the right context.

“You should use product opportunities as conversation openers and offer assistance, especially around nearing consumption thresholds or the end of a free trial period.”

So here are a few examples to test:

  • “I see you are close to the end of your trial period, I can help you extend the period if you need a little more time or select the plan that’s right for you.”
  • “I see that you are close to some usage limits of your plan because this action is constantly consuming too much or because you are using this feature too much. I can show you how to optimize for that and/or give you a few more credits.”

You can also reach out to anyone who’s just upgraded to guide them through adopting the premium product.

Testing with enough scale vs. depth

Make sure that members of that small team work on multiple accounts at the same time with predefined playbooks and predefined cadences, instead of trying to narrow their scope and have them engage very deeply with each account. Cast a wide net to see, at scale, what's working and what's not, remove sample bias, and experiment a lot.

Make sure to use tactics that can be measured and that can be reproduced somewhat at scale.

Figure out what you can automate - or better integrate back into the product itself - to accelerate the time to solve a customer’s problem.

Creating and iterating on PQLs

From here, with enough data points, create and refine a Product-Qualified Lead (PQL) or Product Qualified Account (PQA) model. Use firmographic and technographic data plus behavioral data from outside and inside of the product (your docs, pricing pages, etc.). Merge all of that into a model and decide which ones you send to Sales. Iterate.

(Note: You can read more about PQL best practices from Atlassian and Slack here).

Avoid the common mistake of having that team spend time with free trialists who churned and never really used your product. You're going to lose 50, 60, or even 70% of your free trials who will never come back. Those are not the ones that you should be trying to engage at this moment. Engage the ones that are actually using the product, especially if you pick up on any signal that they:

  • Are stuck
  • Are likely to pay more

The profile of a successful Product-Led Sale rep

The ideal profile of these team members is a blend of a CSM and a Junior sales rep or a pre-Sale Solution Architect for a technical product. Not a traditional BDR, because a BDR never completes the sales cycle and their focus is on gathering information. Pick a profile that can be technical with a customer and can understand a commercial relationship. They're there to help the customer and they love talking about the product.

(Note: Twilio trained and assigned technical BDRs to do that work, Vercel created a team of Product Advocates. Both helped users, qualified them, and then passed them on to AEs and Sales Engineers in Sales if they picked up on a significant opportunity).

Scaling that PLS team and finding balance

You'll then start staffing up that function. Do that as long as you have the capacity to keep reps busy by feeding them PQLs and you keep seeing good close rates and higher ACVs than with pure self-serve. If that’s the case, keep hiring. But you may end up forcing your PLG motion and have to send too many PQLs to that team. That is going to unbalance your system. Productivity will start going down. You're going to see more churn in your self-serve customers because they're being annoyed by the PLS folks. So you want to grow your PLS team at the same pace as your PLG. Watch out for not overly crowding that team and draining too much of the self-service side to a point where your economics will not make sense.

Setting up the right systems for the right data

You really need a common and strong data foundation. Starting with the “modern data stack” is probably the way to go. You might need different analytics or BI tools on top of it. Amplitude is a great tool. You need a solution with which your team can do their own analysis ad hoc: PLG folks are data-savvy and should know SQL.

On the Sales side, you have to convince people off of Salesforce as much as you can, by providing more relevant dashboards. For instance, dashboards that better connect the end-to-end experience for them, or customer lifecycle dashboards with data from Product, Support, Sales, Marketing and so on.

Common mistakes in implementing PLS

Applying a traditional sales motion to your PQLs

“The companies who do it wrong take a product user lead - e.g. a signup - add it to Salesforce and then start their transitional SDR outreach cadence, the same one they use with leads from marketing or outreach campaigns.”

They don't look at product usage. They don't look at the behavior and the people that are around that particular lead that they received.

They use the wrong motion and that happens all the time. They see a lead as one more lead from marketing, a cheaper one because it came from the product. So they use the wrong tools, the wrong team the wrong content at the wrong time.

Scaling the PLS team too fast and ignoring the data

Another mistake Gabriel often sees is letting the Sales team decide what leads to engage. Don’t let them randomly pick “I want to talk to this five customers or this 10 customers, because I think they are fit for us”. You need to establish which ones to engage with data. Use machine learning and algorithms that will tell which customers actually convert better. Otherwise, you're not leveraging all the benefits of the data and telemetry that you built on for your self-serve motion and will end up making mistakes.


Gabriel shared these resources if you want to read more about these topics.

About PLS

About PLG

About the Tech Stack

Thanks Gabriel for sharing your insights. Best of luck in your new role at PlanetScale!