Product-led growth (PLG) has become one of the most widely discussed topics in enterprise software, and for good reason: customers love it, and companies who embrace it are the ones growing the fastest.
Introducing PLG into an existing sales-led GTM is anything but straightforward. We believe Pace will change that.
After joining MongoDB in 2015, Rez and I lived and participated in arguably the most successful transformation of an enterprise software company from entirely sales-led to a hybrid model combining both PLG and direct sales.
Getting there wasn’t easy. What is often missing from the PLG-related tweets and blog posts is that, in an efficient hybrid go-to-market, you incur an incremental cost each time customer-facing teams interact with users. Therefore, you must map out these touchpoints as part of the broader customer journey. Over-deploying resources like sales and customer success increases acquisition costs and decimates margins. On the other hand, failing to intervene at a critical moment of a user whose potential lifetime value is high can leave revenue on the table.
As a sales leader during the early days of MongoDB’s hybrid go-to-market motion, the new product-led funnel was starting to generate large quantities of new sign-ups. As a result, my team and I had to rethink how we prioritized our time. We spent hours combing through the admin panel of the product, building spreadsheets, and parsing Salesforce to find the most active self-service users. However, when we’d call into these seemingly equally-qualified accounts, we’d get widely different results. In some cases, my team would run end-to-end sales cycles only for the prospect to resume the same level of self-guided usage when we first engaged. In other cases, our involvement would coincide with massive increases in product usage and some of our first six and seven-figure deals.
Soon, there weren’t enough hours left in the day for this hit-or-miss approach.
Over time, we noticed that we were having more success with customers who had not yet launched their application into production or were gearing up to migrate their data to Atlas for the first time. In each case, the customer’s perceived risk was high. By corralling technical resources to assist with capacity planning and launch readiness and working alongside our champions to build trust with internal stakeholders, my team and I mitigated the perception of risk enough to unblock their further adoption of Atlas.
Moving forward, my team knew to qualify for business risk upfront and prioritize the customers who sought to benefit from our support, confidently leaving the lower risk customers to grow organically on their own.
This became the first of several playbooks we used to create a meaningfully differentiated sales-enabled customer experience that our users could not get without us.
By contrast, we experimented with inserting sales reps (sometimes called “sales assist”) into various points in the customer journey to improve free-to-paid conversion and grow existing self-service users. Unfortunately, this approach was ultimately unsuccessful because those individuals weren’t consistently delivering any value that the customer couldn’t get on their own. Every so often, they would catch a customer at the right moment and could fast-track a resolution to a problem. Still, more often than not, it was impossible to know which customers needed human assistance at any given moment, let alone what to offer them.
Years into MongoDB’s PLG transformation, we were still struggling to consistently identify the accounts and users that needed sales’ attention and who could be left to self serve.
There is a scene in Minority Report where Tom Cruise’s character can, with a simple flick of his wrist, conjure up and manipulate any information he needs so he can stop crimes before they happen. It’s admittedly very farfetched, but it’s striking how seamless the experience is. This image has become a recurring meme for us because it represents the complete opposite experience of what most sellers experience as their companies embark on a PLG transformation. While it may have started as tongue-in-cheek, the Minority Report has come to represent how we at Pace want sellers to feel: guided by data and effortlessly in control.
We have a long way to go before the desks of account executives at B2B SaaS companies look anything like the Department of Pre-Crime. However, we believe that if you can unblock revenue-generating teams and equip them with the insights they need, the result is explosive and sustainable growth.
Three new realities emerged when MongoDB introduced PLG and adopted a hybrid GTM.
After speaking to hundreds of sellers, sales leaders, and operations experts, we believe these are the primary obstacles to a successful hybrid GTM motion:
Fragmented or siloed data means customer-facing teams no longer have a complete picture of a user or account.
There are no reliable ways to deploy specific sales tactics based on where customers were in their adoption and buying journeys.
The lack of visibility means it was near-impossible to know the impact of any given tactic on the customer experience and drive repeatable results.
It all starts with the data.
As more of our customer journey moves into the product, we can better understand how best to support them using the data generated by their in-app behavior. However, more often than not, this product usage data is collected and stored by entirely different systems (the data warehouse and customer data platform) than where existing sales workflows are based (the CRM).
Having been a part of effective enterprise sales teams, we have seen the critical functionality housed in systems like Salesforce. We also understand that for any new solution to be viable for mature sales organizations, it must work seamlessly with the existing CRM-based stack.
However, we have also seen the immense value and potential of the insights contained in the data warehouse.
Merging these two datasets may sound simple in theory. Still, our own experience and conversations with other data and operations experts reveal that this is an expensive, error-prone, and complex undertaking.
Pace takes the complexity out of this process, integrating natively with your business's most important data sources. We then unify and transform this disparate data into a standardized format that makes intuitive sense for B2B sellers, and that can be easily synced and used by CRMs like Salesforce. (If you're interested in diving into the nitty-gritty of our data model, get in touch.)
PQLs are just the beginning.
It's hard to overstate how powerful a unified view of your users and customers can be. While no amount of product data will completely replace good sales discovery and qualification, a better understanding of where users are in their journey helps determine which accounts need additional attention. In addition, it can indicate what types of interventions will be most successful.
For years, the product-qualified lead (PQL) has been the de facto mechanism by which we flag self-service users of SaaS products for sellers.
However, for most complex enterprise software, there will be dozens of moments throughout the customer lifecycle where the assistance of sales or customer success is beneficial. In each case, the goal and tactics will differ based on the state of the customer.
Pace lets you build rules for all of these moments across the different product-led sales motions. But, more importantly, Pace aligns the state of the customer and desired outcomes with recommended actions and context. These are then delivered directly to the right team or person as insights.
You may want to overcome technical friction as a free user evaluates a paid plan or mitigate commercial friction as a self-service customer considers expanding their usage. In either case, Pace can help you orchestrate the right sales behaviors with the right person at the right time.
Double down on what works
Of course, the litmus test for any growth strategy is whether it can be scaled up. Many of our most successful playbooks start as a combination of intuition, conjecture, and luck. To replicate them across teams, regions, and verticals, they need to be measured, optimized, and codified.
With Pace, you can see which insights drive positive sales behaviors and, by extension, the outcomes you care about. So, you can build a set of repeatable sales motions and then scale them out across an organization in a matter of days, not months.
Join the beta
At Pace, our world-class team is working with incredible customers and design partners. We want to help you grow more efficiently by identifying where sales touchpoints can accelerate your product-led motion. So sign up for the beta, or let us know how we can help.
Justin Dignelli is the CEO and co-founder of Pace. Prior to Pace, Justin spent 6 years at MongoDB, where he first spent time as an inside sales leader before being promoted to Director of Cloud GTM. In that role, Justin supported the growth of MongoDB Atlas, including defining new sales motions and creating the playbooks around them, building the sales assist function, and directing the development of internal tools to increase sales productivity. Justin lives in New York City and comes from a family of entrepreneurs.
Rez Khan is the Chief Product Officer and co-founder of Pace. Before Pace, Rez spent nearly 4 years at MongoDB, where he was leading a team of product managers for Atlas, MongoDB's managed database service. In that role, Rez and his team were responsible for increasing consumption and retention of self-service customers by building products for observability, automated recommendations, search, and developer productivity. Prior to MongoDB, he spent 5 years at AppNexus (now Microsoft) building programmatic ad bidding technology. Rez is a proud immigrant who moved to the United States from Bangladesh at the age of 18 and is the first member of his family to go to college.