Alexa, CEO of Pocus, hosts PLS AMAs with product-led sales experts to share best practices, frameworks, and insights on this emerging category. These AMAs are an opportunity to ask PLS leaders any question - ranging from hiring to sales compensation to tech stack - in a low-key, casual environment.
The PLS AMAs are for members of the product-led sales community, the go-to-place to learn, discuss, and connect with GTM leaders at product-led companies. The goal of the community is to bring together the most thoughtful and innovative GTM leaders to build the next generation of sales together.
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Introducing Oran and Tom
Oran Akron is the Head of BizOps at Monday.com, where he runs sales / marketing / CS operations, business applications, and business analysis. He joined Monday.com just over three years ago to build the internal machine that provides data / insights to sales, CS, and marketing teams. Based on my hundreds of conversations with PLG companies, very few have as robust of an internal PLG engine as Monday.com.
Oran’s colleague Tom Ronen also joined us for this AMA. Tom leads Customer Success at Monday.com and has been with the company for over 5 years. Tom and Oran work closely together to ensure Monday.com users have the best experiences possible.
In this AMA, Oran & Tom discuss:
- Monday.com’s decision to build a sales motion on top of their entirely self-serve growth engine
- How they built the BizOps to 22+ people
- How they built the hyper growth revenue machine
TLDR of Monday.com
Monday.com is a platform to easily build, run, and scale workflows. When Oran joined Monday.com, the company had a successful self-serve motion due to a combination of great traffic acquisition and a healthy product conversion. This was the “PLG dream.”
Despite realizing the PLG dream, Monday.com knew they would hit a ceiling for growth and Oran’s team needed to break this ceiling in order to secure bigger deals. His main mission, when he joined, was to build a thriving revenue machine by leveraging the success of the self-serve user funnel.
In short, Oran was told: “Here is your ARR target. Now go and build the sales team.”
Although the vision was clear, accomplishing this was no easy mission.
Oran’s work included:
- Defining a framework for sales
- Picking the right KPIs for the sales team
- Building the right tools to scale (eg. CRM, operational database, data warehouse, business applications)
- And new business processes for the sales motion
This initiative required clear coordination with inside sales, finance, marketing, customer success, and product teams. Oran saw his mission as developing not only a sales machine, but also a business operations machine.
Over the course of about two years, Oran helped the company achieve a more balanced sales approach – moving from about 95% of their revenue coming from self-serve and 5% from sales, to a clean 50/50 split.
Our product-led sales community got a chance to ask Oran about these processes and his philosophy for building a sales team within Monday.com. Here are some of the questions and primary takeaways from that conversation.
When a self-serve user signs up, when should the sales team intervene?
Monday.com had a “rich problem” - they had a really good self-serve motion and they knew how to acquire traffic. Because their self-serve user growth was always growing, they didn’t even need traditional lead generation channels such as webinars, eBooks, and content marketing.
Oran knew that adding a sales function would need to compliment the bottom-up channel. Monday.com would never have a sales team outbound completely top-down. Instead, they would use the self-serve users from the bottom-up motion to inform the top-down strategy, and Oran’s team would bridge the gap between the two motions.
The challenge for Oran and team was finding the right benchmarks to hone in on the right self-users to target, because reaching out to every self-serve user would be inefficient and frankly impossible with such a small team.
To solve this, Oran and his team created a minimum benchmark for self-serve users to meet as the trigger for sales.
One of the benchmarks was a simple customer-fit threshold. Any user who signed up from a company with under 100 employees would not be a target for the sales team.
Tactically, Oran’s team began filtering signups, enriching data, and developing a lead scoring method to identify users at companies with 100+ employees. These leads were then funnelled to the right sales rep, and, as Oran said, “this is where our new business motion started.”
From a demand generation perspective, how does marketing approach that 100-employee benchmark?
Despite this 100-employee benchmark established by the sales team, the Monday.com marketing team didn’t go and reconfigure their marketing funnel to only bring in users who met that criteria. The mission at Monday.com was still to target their primary persona – which is essentially every knowledge worker.
The objective wasn’t simply to convert every user at a company with over 100 employees, but rather focus on where Monday.com could add value.
So, from a demand generation perspective, the marketing team focused on the granular aspects of user needs (such as whether they were looking for a CRM), rather than focusing on firmographic details or industry segments.
Because of the nature of product-led sales, it worked better to keep the marketing approach as a B2C play, with the end-user always being at the center.
As Oran says:
“It all boils down to the end-user and what they need the product to do.”
What systems did you use to measure account-level engagement?
Lead scoring is integral to creating a funnel. Oran and team relied on signals they defined to measure account level engagement, like:
- Relevancy of signups to sales (i.e. firmographic data like 100+ employees)
- Product data about usage and behavior (I.e. features used and frequency of use)
- Back office engagement (i.e. interactions with support)
The aggregate signals gave Oran and his team a way to prioritize the leads and score their readiness for the sales team or whether they should remain self-serve.
When analyzing data, Oran explained that it’s always a 50/50 split.
Yes, you want to look at firmographic data, such as job title, company size and geographic location. But, you also want to understand how the customer is interacting with the product, such as their level of engagement, how many other people they’re inviting to the product, etc.
A company with 1,000 people could only have one user, but a company with 200 people could have 10 users – and the latter is the type of company that would help Monday.com break the ceiling.
In other words, choosing the best business bets combined with product usage creates a baseline from which sales and marketing can work. There will always be a large number of users who don’t really need sales. So on the back end, looking at engagement with the product helps drive data that signals sales intervention.
How do you go from a top-down to bottom-up product if you have an API aimed at developers?
If you’re operating in a B2B space, it’s much harder to sell without a sales rep “touching” the customer. Self-serve acquisition channels simply aren’t as readily available. It doesn’t mean a bottom-up approach isn’t possible, it just means it’s trickier to make a play for the product to sell itself.
In these cases, offering a 14-day trial period can work well. It allows users – in this case developers – to explore the product and work through an integration. Through lead scoring, you can then determine if the customer is using the product in a desired manner, and trigger them to be transferred to the sales team.
How do you prioritize different lead signals?
If different user signals don’t align, working with this data can be a challenge.
Oran explained that they mostly analyze user event data (i.e. product usage), and the key has been to distill the most relevant signals. Once you hone in on these relevant signals, you can make predictions about the user’s future intent based on how they’re interacting with the product today.
The way they do this today is with an intent model that combines signals to predict a user's likelihood of conversion.
The monday.com intent model
Tom added that even before their sales team was created, the company had developed a machine-learning “intent algorithm” to determine how likely an account was to go from a free trial to a paid subscription within the self-serve motion. After the sales team came in, they only had to tweak the model to apply it to sales qualified leads.
Monday.com has always been a data-driven company, and this enabled them to develop their intent model from day one. Operationally, the model allows them to predict, within only 24 hours of a campaign launch, whether there are going to be high-scoring leads generated from that campaign based on product engagement measurements. Their marketing and product teams both live by this.
How long did it take the team to build this intent model?
An intent model isn’t a stagnant entity, but rather an iterative experimentation process that can take years to develop. It’s something that should always be reworked in accordance with behavioural signalling and sales funnels.
When Monday.com was just starting, they invested in hiring a BI developer. They’ve always valued data and BI systems, and they always knew they wanted to operate as a product-led company. Since then, the intent model has continuously evolved and the company sees it as a program, not a project. Taking regular measurements helps inform both the marketing and product teams, and strategies can be reworked as needed.
The process is also collaborative. For instance, if the product team changes a user signup flow as an A/B test, the intent algorithm needs to be adjusted to ensure it’s still providing accurate results. Conversely, if the model shows that collecting a phone number positively influences conversion rates, the product team is brought in to make the adjustments to the signup flow.
Final tips and takeaways
Once leads are prioritized, it can be a challenge to determine the best route to reach those leads, teasing out which leads are best, and which require hand-holding can compound the issue.
On this, Oran and Tom gave these few final tips:
Create something of immediate value to potential users. A 14-day free trial creates a sense of urgency. But this can also lead to new users feeling overwhelmed.
Tom recommends leveraging product engagement to inform sales messaging. In this case, taking measurements during that 14-day trial, such as which templates a user is gravitating towards or how they are using the product, can provide useful information. That information can then be used as a signal for sales to intervene.
Don’t talk to every single lead. Trying to reach as many users as possible is admirable, but not the best use of your time. Creating an iterative intent model should be a priority, because it will help sales identify which leads are more likely to convert to paid customers. From there, layering on data further supports lead scoring.
When approaching users, your strategy should be consultative. Sales strategies have changed. With product-led sales, who you approach in your marketing campaigns is just as important as how you approach your current users. Products like Monday.com sell themselves, but knowing when sales teams should approach users is crucial for building those relationships and, hence, sales. It’s about breaking through the barrier and leveraging larger potential customers.
For Oran, growing Monday.com has been all about building on the personal user experience. Whatever goals a product-led company has, it all boils down to the end-user, uncovering their needs, and delivering something valuable.
Join us for our next AMA with James Underhill (Sales Strategy & Operations @ MongoDB) on November 10 at 10 am PST/1 pm EST.
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