HeadsUp helps PLG sales teams understand how and when to sell • TechCrunch

For many non-technical sales teams in product-development companies, sifting through rime data to determine the best way to connect with customers is difficult. That’s the problem HeadsUp, a product-led sales conversion engine, wants to solve. The San Francisco-based startup announced today that it has raised $8.5 million in seed funding led by 645 Ventures (also an investor in SaaS companies like Eatable, FiscalNote, and Panther), with participation from Wing Venture Capital, First Minute Capital, and Character. Other investors include the founders of Drift, Algolia and Crossbeam and senior go-to-market leaders at companies such as Asana, Amplitude and Miro.

Founded in 2020 by Earl Lee and Momo Ong, HeadsUp’s clients include unicorn SaaS companies. Li and Ong were early employees of Fiscalnote, a data tracking SaaS company that recently went public.

During their time at Fiscalnote, the two sales teams built an internal tool to provide visibility into how customers were using the software. But even with the new tool in hand, salespeople had to spend hours on accounts figuring out the best time to contact potential customers.

According to Lee, HeadsUp starts with PLG companies because they usually get a lot of usage signals before buying, but all SaaS sales teams want to understand when users are ready to convert to paid plans or upgrade their current subscriptions.

“Imagine you are a salesperson at a developer’s tool company. Engineers still hate selling the product when they’re mocking and testing it,” Lee said. “So vendors worry about annoying developers by shaping them before they’re ready to buy. At the same time, you don’t want to ignore them in the small window of time when they’re ready to engage with you to buy the device for their company.”

HeadsUp identifies that window by analyzing the vast datasets collected by SaaS companies and helping non-technical salespeople identify the best users to engage and when to engage them. For go-to-market teams, this includes finding users who are stuck on activation and identifying offensive opportunities or attrition risks.

The types of data that HeadsUp analyzes to increase conversion rates include usage data, billing and CRM data, and third-party data such as job titles and how much funding user companies have received.

All of this feeds into the HeadsUp machine learning model, which is trained on data from SaaS companies. The ML model allows customers to select an objective, such as conversion or crowding prevention, and then provides a score based on their accounts’ historical data.

Rather than showing salespeople all the usage and customer data that’s confusing, HeadsUp picks four to five data points that predict conversion, expansion and churn. For example, that might include the amount of time users spend in an app or the growth of seats in the past month. It also provides contextual information, including potential champions and senior executives and recent intra-product relationships, to help sales reps know how to approach prospects.

HeadsUp is particularly suited to SaaS companies looking for ways to monetize their user base with small sales teams and marketing budgets. Ong noted that SaaS companies may have hundreds of thousands or millions of users, but still only convert 1% to 2% into paid users. Pay for a sale or a smaller subset of sales, and the monetization process can take months, if not years.

Ong gave the example of a consumer-to-enterprise sales pipeline at one company. If an engineer is stuck on a feature, the sales team can send a marketing email that documents the usage. If they’re still stuck a week or two after the email, the sales team can call them and walk them through the process using the feature. Then when they get good results and use it for months and invite their manager to use it, their company may be ready to buy it.

But getting to that point requires understanding how the customer uses the product and coordination between multiple GTM teams. This means they need to access information and discuss when to hand the customer over to another team (ie from marketing, to sales assistant and product support, to corporate sales).

“PLG companies can have up to ten million users. Think about when you need to engage with tens of millions of users, and then coordinate that engagement between balanced and asymmetric ways,” said Ong. “This coordination and manual work would have been impossible without tools and analysis.”

HeadsUp helps coordinate customer touchpoints across sales, product, marketing and customer success teams, providing insights on exactly when and how to engage with consumers.

The startup will use the new funding to build its team. “We’re looking for strong Scala back-end engineers, as well as data scientists and machine learning engineers for our analytics and insights, specifically to work on our data processing infrastructure,” said Ong.