Product Led Growth: How Heap’s Matin Movassate is Building a Product that Sells Itself
“The typical sales conversation with an analytics provider revolves around things like mapping out a tracking plan and drafting an implementation timeline,” says Matin Movassate, co-founder and CEO at Heap. “We’ve tried to eliminate a lot of that implementation work. Instead of the traditional sales process — in which you try to figure out a long-term timeline after the prospect has committed money — we want prospects to gain insight during the sales process.”
This unique approach is an example of product led growth (PLG). While there are variations on what it actually looks like in action, the basic idea behind a PLG strategy is that product usage is the primary driver of customer acquisition and expansion, without requiring the intervention of a sales rep – it’s fundamentally different than the traditional sales-led approach that caters to economic buyers first and end users second. This is made possible by built-in product features that automate certain marketing, sales, and customer success functions.
Movassate founded Heap to eliminate the bottlenecks that prevent business users from turning data and analytics into real insights. Heap helps to bring data science to the masses by “automating the annoying parts of user analytics.” The product automatically captures all user actions in web or mobile apps – no event tracking required. This means that Heap customers can answer any user analytics question instantly with no engineering work required. And whatever question you ask, the data is already there because Heap tracks everything. They call it “instant retroactive analytics.” By delivering instant results, Heap is reducing time-to-value, or as Movassate describes it “time-to-insight,” a key differentiator against other analytics providers with long configuration processes.
This philosophy of delivering value also applies to Heap’s go-to-market efforts. “All of our marketing positioning stems from the problems we’re trying to solve in the product and the sorts of users we’re building the product for,” Movassate explains. By putting the product front and center, as opposed to leading with sales and marketing, Movassate and his team are able to create a situation where prospects come to Heap already being “pre-sold” on the product and the value it can deliver.
For Heap, the sales process has become something akin to proactive customer success. “Our best sales conversations are the ones in which we serve as a data scientist consultant,” Movassate says. “In these instances, the client has installed Heap (a process that only takes seconds), so on the first or second sales call we’re already able to share insights that we’ve uncovered. We’re able to suggest that they might want to take a different marketing approach, invest in the development of additional features, or focus on a particular segment of users that we can tell — based on the data — will be important for the customer’s business. That’s the most effective way we sell Heap.”
A Product that Sells Itself: Driving Growth through the Product
While unconventional, this product led approach to growth is clearly working for Heap. Well into its fourth year, the company is going strong and Movassate is only now beginning to build out a marketing team. “By and large, our biggest marketing channel to this point has been word-of-mouth,” says Movassate, validating the product’s ability to sell itself. And, while Movassate believes that marketing can play an important role in identifying new channels and creating content to educate the market, he also believes that there’s a high value in continuing to drive growth directly via the product.
“Heap is most successful when a higher percentage of a company’s workforce is using it,” he explains. “We have the opportunity to help improve adoption within an organization by adding in-product features and functionality that make it easier for people to share Heap-generated insights and onboard new teammates to the software. Using the product as a marketing channel is a growth engineering strategy that will help us increase usage and make our product more attractive to larger companies.”
So, what goes on behind the scenes at a company that is embracing product led growth? What kinds of operational and strategic elements have to be in play to make this kind of strategy viable? The details vary from company to company and are based on level of product maturation, but a closer look at how the Heap team moves forward provides some interesting insights that just might be universally applicable.
Intentional, Data-driven Growth Engineering
“In the product lifecycle and in people’s roles, we always try to make sure we use data,” Movassate says. “But today, our growth engineering practice is a distributed rather than a dedicated role. It’s sort of everyone’s responsibility.” You would expect as much from a company that is seeking to give more workers access to actionable insights from robust user analytics, and thereby democratizing data science. Indeed, one of the reasons they have so far been successful without this role is because Heap uses Heap.
Despite the distributed nature of Heap’s growth engineering practice, the team is very intentional about every move they make. “When we’re thinking about adding or building out a certain feature, we first try to get a clear sense of the problem we’re trying to solve,” Movassate explains. “We make sure we can identify which part of the funnel we’re addressing and the impact the feature will have on our business. If there is no compelling story there — no data to suggest that the effort is a high-leverage use of our time — we won’t do it. Essentially, the question to ask is not, ‘can we make this change,’ but ‘should we make this change?’ It’s about prioritization based on impact.”
On the back end, once a feature is built, the team is quick to solicit and analyze user response. “We expose users to each new feature as early as we can, and we use Heap to see how they’re using the feature so we can understand the paths they’re taking,” Movassate says. “And then, when a new feature is fully launched, we are disciplined about measuring the success metrics to determine if people are using it, whether it’s affecting retention or activation, and whether it’s something that’s coming up in sales conversations. All this data informs how we should refine and build that and future features.” This is what agile product led growth looks like in action. It’s letting the data inform what to do next, and not showing undue loyalty to pet features that simply aren’t working.
Well-defined Core Audience
For PLG to really work, it’s imperative that you have an accurate and deep understanding of your audience and their needs. “In terms of our buyer, we need to be very, very focused,” says Movassate. “We know that the most successful buyer persona for Heap is someone who wants to make their organization data-driven.” For Heap, this individual might be in any one of a number of titles from Data Scientist/Analyst to Product to CTO. The title may vary, but the persona is the same.
After honing in on the key persona, the Heap team then needs to consider the wide variety of technical and non-technical end users on the customer’s side, including developers, product managers, analysts, and marketers. “We start by identifying common use cases and then creating interfaces within our product to make it easy for people who don’t want to dive into writing SQL or running a mass-produced job to get the insights they need,” says Movassate. “Basically, we try to build intuitive tools that serve the needs of about 80% of our user base. The other 20% includes developers and data scientists who we serve through robust APIs, good documentation, and customer success.”
As Movassate and his team continue to embody product led growth, even while testing traditional marketing efforts, they’re keen to make sure the product delivers on the stated goal of unlocking insights through automation. “We need to think through the user experience from end-to-end and remove all the unnecessary barriers.” It’s this line of thinking that reduces time-to-insight for new users while continuing to deliver new insights to the existing 5,000+ customers.
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