For many non-technical sales teams at product-driven growth companies, it’s difficult to sift through piles of data to find the best way to engage with potential paying customers. That’s a problem that NB, a conversion engine for product-driven sales. 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 such as Iterable, FiscalNote and Panther), with participation from Wing Venture Capital, Firstminute Capital and Character. Other investors include founders of Drift, Algolia and Crossbeam, plus senior go-to-market leads at companies like Asana, Amplitude and Miro.
Founded by Earl Lee and Momo Ong in 2020, HeadsUp’s clients include unicorn SaaS companies. Lee and Ong were early employees of Fiscalnote, a SaaS data-tracking company that recently went public.
During their time at Fiscalnote, the two built an internal tool to help sales teams understand how customers were using the software. But even with the new tool at their disposal, salespeople had to spend hours sifting through accounts to find the best times to engage with potential customers.
Lee said HeadsUp is starting with PLG companies because they often get a lot of usage signals before the buying process, but all SaaS sales teams need help understanding when users are ready to move to paid plans or upgrade their current plans.
“Imagine you are a salesperson at a developer tools company. Engineers hate being sold while they’re still tinkering and testing the product,” Lee says. “So sellers worry about annoying developers by pitching them before they’re ready to buy. At the same time, you don’t want to ignore them during the short time frame in which they are ready to get in touch with you to buy the tool for their business.
HeadsUp identifies that window by analyzing the wide range of data collected by SaaS companies and helping non-technical vendors figure out which users should be involved and when to engage them. For go-to-market teams, this includes finding users stuck in activation and identifying upsell opportunities or churn 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 many companies have raised from users in funding.
All this goes into HeadsUp’s machine learning model, which is trained on data from SaaS companies. The ML model allows customers to choose an objective, such as conversion or churn prevention, and then provides a score based on their account’s historical data.
Instead of showing sellers all usage data and customer data, which can be confusing, HeadsUp chooses four to five data points that most strongly predict conversion, expansion, and churn. This could be, for example, the amount of time users spend in an app, or the growth in the number of places in the past month. It also provides contextual information, including potential champions and senior executives, and recent interactions within the product, to help sales reps figure out how to approach prospects.
HeadsUp is particularly suitable for SaaS companies with small sales teams and marketing budgets who need to quickly find ways to monetize their user base. On notes, SaaS companies can have hundreds of thousands or even millions of users, but still convert only 1% to 2% into paid users. An even smaller subset pays for upsells or cross-sells, and the monetization process can take months, if not years.
Ong gave an example of the user-to-enterprise sales pipeline at a company. If a technician is stuck on a particular position, the sales team can send a marketing email directing them to documents about its use. If they’re still stuck a week or two after the email, the sales team can give them a call and walk them through the process of using the feature. After using it for months with good results and inviting their manager to use it, their organization may be ready to buy.
HeadsUp’s conversion page
But getting to that point requires understanding how the customer is using the product and coordination across multiple GTM teams. This means they must have access to data and be able to discuss when to transfer the customer to another team (ie from marketing, through sales support and product support to corporate sales).
“PLG companies can have up to tens of millions of users. Imagine having to understand when to communicate in front of tens of millions of users and then coordinate engagement between scaled and unscaled resources,” said Ong. “This coordination and handover becomes impossible without tooling and analytics.”
HeadsUp helps by providing insights into exactly when and how to engage with users, while also coordinating customer touch points across sales, product, marketing, and customer success teams.
The startup will use its new funding to build its team. “In particular, we are looking for strong array of back-end engineers to work on our data process infrastructure, as well as data scientists and machine learning engineers for our analytics and insights,” said Ong.
