What Primary Looks for in Early Stage Go-To-Market SaaS

At Primary we are always thinking about the future of the Go-to-Market stack. This is partially because we spend so much time—hundreds of hours a year—working with portfolio founders on the sales, marketing, and customer success functions. It’s also a space we know intimately well from our past lives in senior operating roles, where we were both economic buyers and end users of GTM solutions.

Cassie Young was Primary’s first GTM leader. She was previously the CRO at Sailthru and then joined its acquirer Marigold, where she became CCO and managed a $200+ million P&L and a team of 200+ people in the U.S., Europe, Australia, and New Zealand. In mid-2022 Jason Gelman joined us from Compass to further expand our GTM support; in backchannel calls, we heard he was “the best revenue strategy and operations leader in New York” several times over. And rounding out Primary’s GTM pod is Zach Fredericks, a principal on our investing team with rich product management experience on the operating side.

Our familiarity and excitement with the GTM space has left us with strong conviction that there are still many category-defining opportunities that don’t yet exist, and we believe the recent advances in AI will accelerate—though also complicate—their creation. 90% of sales leaders say that they plan to utilize AI solutions “often” in the next 24 months; Gartner predicts that sales enablement budgets will actually increase 50% by 2027—an increase much higher than we would have expected given overall budget compression.

We are specifically compelled by GTM SaaS businesses poised to offer a combination of integrated, ROI-rich workflows and data advantages, regardless of which comes first (but we believe the best businesses have plans to achieve both). We are also excited by companies focused on the "connective tissue" that exists between multiple GTM teams. Below, we’ll elaborate on these priorities, plus share insights from some of our notable friends and collaborators in the space.

In case this is not abundantly clear, if you are working on something in any of these areas, we want to speak with you.

The state of the sector

The GTM stack—also sometimes referred to as “RevTech”—refers to solutions that service the entire customer and revenue cycle.  The space is crowded. Really crowded. And generative AI will only compound that reality. Consider MarTech, which is just one sub-category of GTM tech: that vertical is estimated to have grown 7,000% over the past 12 years (not a typo!), even after plenty of industry consolidation, wind-downs, etc.. And the proliferation of “SalesTech” is quickly feeling like Martech 2.0. Buyers of these technologies are exhausted by the number of options available to them.

And while AI is making the GTM stack even more crowded, GTM executives are still shopping for new solutions, because technology advancements in this category are making it easier and cheaper to build and scale the GTM function. As Mandy Cole, Partner at Stage 2 Capital tells us, “One of the most significant changes we will see in GTM as a result of AI in the next year is improved efficiency and CAC payback because people will not only be able to do more in the same amount of time, but that improvement could mean less people to produce the same outcomes.” An Insight Partners survey supports this, indicating that 87% of surveyed GTM leaders anticipated that generative AI would increase efficiency by at least 16%. Companies are ready and willing to invest here.

Budget compression in the age of doing more with less

“Since 2015, the GTM tech space has gotten incredibly saturated,” says Max Altschuler, General Partner at GTMFund. At this point, “GTM tech is an area where CFOs are aggressively cutting budgets or just not spending. A space where tech is sold by seats and those seats are being eliminated via layoffs.” With that, “only the best one or two companies in each category will get funded. And they will have to have real proof points in order to even be considered a ‘need to have’ category.” Put another way, successful GTM solutions will address obvious and acute pains in an organization. We love The Challenger Sale as much as the next sales nerd, but GTM products that need to teach organizations why their solutions are relevant simply will have to work much harder to gain adoption and traction.

Feature vs. platform

The age-old “feature vs. platform” conversation is a common one in the GTM category. Today there are many GTM point solutions that are focused on a singular business application, and CFOs and most champions are eager to consolidate their vendor stacks. Moreover, businesses focused on a singular use case are inherently rate-limited on how big they can ultimately become. As such, the GTM category is ripe for both organic and acquisitive consolidation, as evidenced by Gong launching Gong Engage to compete with Outreach and ZoomInfo’s $575MM acquisition of Chorus.

How Gen AI is playing in so far

Generative AI has made things both exciting and challenging. Because the barriers to entry are so much lower with GenAI technology, the winners will be those who can think big. Now is not the time for point solutions.

GTM incumbents have embraced AI technology to launch countless new products. Salesforce recently launched AI Cloud, a set of GPT-powered tools supporting multiple business functions. 6sense, the revenue intelligence platform, launched a generative AI email writing feature. The list of established GTM tech companies launching AI products is seemingly endless.

And yet we’ve also seen waves of new startups and point solutions enter the market addressing GTM pain points. We’ve looked at more “AI-powered BDR'' businesses than we can count. The barrier to launching innovative pain-killing solutions with generative AI is very low, but in most cases, incumbents have a clear data advantage. It is difficult to see a path for a startup to win in the GTM category unless they are building something much bigger than a point solution.

In the startup landscape, we’ve been most excited about startups that are either attacking a high ROI, integrated workflow opportunity or setting themselves up to build a defensible data moat. Elaine Zelby, cofounder of Tofu and former GTM partner at Signalfire, summarized the opportunity with workflows: “In 5-10 years, humans will be solely focused on the creative (branding, messaging/positioning, etc) and the strategic (differentiation, relationship building, channel strategy, etc), but everything else from campaign/workflow creation and execution to measurement, optimization, and expansion will be done by AI.” A Salesforce survey reiterates this conviction—Seven in 10 marketers (71%) expect generative AI will help eliminate busy work and allow them to focus more on strategic work.

We regularly meet startups offering workflow innovation, but we’ve found it much more difficult to meet early-stage founders that have a clear path to a data advantage over incumbents. That said, these businesses certainly exist—Clay is a recent fan favorite in the ecosystem—and we have strong conviction that there are many parts of the GTM stack where generative AI could be used to ultimately develop a data moat.

So with all the challenges with GTM software/RevTech, why are we spending time here and which early-stage companies are we paying attention to?

Workflows to data advantages: Klaviyo

Companies like Gong, Outreach, SalesLoft, and more have built valuable workflows for GTM teams that also ultimately unlocked data moats/advantages. One other example we love is Klaviyo. Klaviyo’s early growth was spurred by its email service; they made it dead-simple for Shopify retailers to send targeted, automated emails at scale. The initial value proposition put Klaviyo on a breakout trajectory, growing from just 1,000 customers by the end of 2016 to over 5,000 by the end of 2017 and 12,000 by April 2019. By amassing a massive customer base and powering marketing workflows for all of them, Klaviyo ultimately built a marketing system of record and managed to achieve its long-term vision of building a Customer Data Platform (“CDP”) on the back of Shopify that enabled high-ROI workflows beyond email, which then allowed them to further expand their data moat, launch even more workflow automation, and so on.

Data advantages to workflows: 6sense

In the reverse direction, there is 6sense, which built an early wedge with a B2B buyer intent database that GTM teams would pay for to better understand which prospects were in buying cycles. With this data, 6sense’s customers were able to increase the reach and ROI of their top-of-funnel efforts without building workflow automation tools at the application layer. That said, 6sense did ultimately layer on a host of different workflow products ranging from display advertising for ABM to BDR email tools.

In either direction, data compounds over time

As Daniel Chesly of Work-Bench says, “The value of data is that it compounds over time. By leveraging historical data, insights and actions can be codified into the product and used as a moat. To create a data moat, the tool must own the atomic unit for which that company conducts business. For Gong, it’s sales calls, for Clari, it’s forecasting, for Dialpad it’s customer intelligence, for Zoominfo it’s prospecting information. Given the distribution moats for many growth-stage companies, emerging startups must attack an acute, but underserved pain point as their wedge. For startups, wedges are more important than moats. Wedges help you differentiate while moats help insulate you from competition” We agree with Daniel’s take, and it’s relevant regardless of whether a company begins with the workflow component or the data itself.

Opportunities in the GTM stack

We like to think of the stack broadly in two sections:

Acquisition tools (aka Sales/Marketing wedge products that help GTM teams contact, nurture and close new deals))

On the list of enterprise applications for generative AI, automating outbound sales is one of the most obvious, so there has been ample incumbent and startup activity in this space. Multiple companies in the most recent YC batch have launched products in this realm and incumbents have all said that they plan to release generative AI-based features in the near future. One company we admire in this space is Valley, which is building a product that automatically runs conversations between SDRs and prospects. Valley can look at a list of leads, write custom outreach, nurture conversations, and eventually set meetings for AEs. Users can drop a lead list link from LinkedIn Sales Navigator, upload information on their product, and then connect an AE’s calendar. From there, Valley runs the outreach and scheduling process on its own. This product is a wedge to Valley’s broader vision of building a sales and marketing data lake. The company believes that its wedge product will allow it to capture and store customer data in a vector database, identify patterns in that data at scale, and distribute insights on that data to their customers.

Leveraging AI for marketing automation is another common use case, but also a category that is ripe with point solutions. Tofu is an exciting new solution that is building a broader platform play by using generative AI to eliminate manual content creation for enterprise marketing. But in addition to simply offering a painkiller campaign creation tool, Tofu built its capabilities directly into the Marketo workflow, allowing them to automatically update content across each channel based on which campaigns and iterations perform best. This approach sets them up well for building a proprietary database of how and where certain leads respond to certain content and at scale, they should have a large database of lead behavior, which should make its products more valuable to marketing teams—a great example of the workflow-to-data advantage approach.

Retention tools (aka Customer Success wedge products that help existing business teams retain and grow their installed base of customers)

For this space, we bet on Lantern in 2022. To the naked eye Lantern may look like a customer success platform (similar to a Gainsight or Catalyst), but the core business is actually a CDP for B2B. Lantern built a novel approach to data ingestion that has allowed for an unparalleled single view of the B2B customer (a major barrier to adoption for the incumbents in the CS category). With this data intact, Lantern can offer a host of its own recipes and plays based on that customer data, and its early wedge is focused on customer success and account management teams (e.g. identifying expansion opportunities). Lantern is an example of the reverse approach of focusing on data to unlock workflow advantages.

Another trend that has us excited in the post-sales world is the power of LLMs focused on customer data. We’ve recently looked at businesses building LLMs to make it easier to action unstructured customer data—customer calls, tickets—for a variety of different use cases.

The more pervasive a software application is in an organization, the better the company is positioned to drive upsell and growth from its installed base of customers. So needless to say, regardless of workflows or data moats, the most powerful GTM tools will aid and abet more than one GTM team in an organization.

Finally, we believe that regardless of the initial direction—workflow or data—there is a big opportunity for GTM superapps: “Rippling for GTM,” if you will. This has been validated by several of the GTM stack incumbents (e.g. Outreach) already layering on new capabilities, and regardless of a startup’s initial wedge, we expect to see much more of this use case aggregation. The winners will be the businesses who build early momentum by picking the most compelling use cases for their wedge products.