PostHog has quickly become a favorite tool for engineers looking to improve their products through better analytics, session replays, and experimentation.
Founded in 2019, they quickly achieved product-market fit and a steady stream of self-serve users. PostHog is an all-in-one open source platform for building better software; offering tools which include product analytics, session replay, feature flags and data management.
Cameron DeLeone joined in 2021 as PostHog's first commercial hire in a Customer Success Manager role to support revenue growth from their expanding self-serve user base.
In charge of the company's entire revenue pipeline, Cameron's team takes on sales, support, and success functions. The team uses several tools to collect valuable customer and prospect data. To truly scale revenue, they needed an accessible way to analyze and act on it.
With Pocus, PostHog’s customer success team connected all of their customer data sources into a single source of truth, saving their Customer Success Managers 10 hours per week and leading them to uncover hidden revenue opportunities.
Here’s how they did it.
Two inbound paths: self-serve and sales
PostHog's Ideal Customer Profile (ICP) are product-oriented engineers who like to try before they buy — making self-serve the most attractive option. PostHog's robust self-serve flywheel includes a sales-assist motion with proactive outreach to the best opportunities on top of their free trial.
Running in parallel, the team also has a traditional sales motion for hand raisers — where prospects can talk to sales or request a demo before signing up.
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The challenge: Sifting signals from data noise to find the best leads
To optimize self-serve conversions, PostHog needed a scalable way to identify high-potential leads quickly. However, they faced a significant roadblock in linking product usage and billing data.
PostHog stores product usage data in both ClickHouse and Postgres databases, billing data in Stripe, and customer contact data in HubSpot. Joining all this data required queries in different systems. PostHog's metered usage billing model — meaning the more data customers push to PostHog, the more they pay — requires a strong forecasting component. Given the complexities, visualizing the forecasted revenue alongside product usage data for any given account was a challenge.
The missing link for the go-to-market team was connecting revenue forecast data with product usage data so that customer success could filter, sort, and analyze it to target outreach. To solve this, the team tried to build internal solutions, but the high volumes of data and, thus, the maintenance cost were not scalable. Plus, the dashboards created by the engineering team didn't allow customer success to segment cohorts of users or drill down for deeper usage insights without engineering support.
Easily visualize, analyze, and action data in a single source of truth
Pocus' flexible data model enabled PostHog’s customer success team to consolidate product usage, billing, and customer data into a single source of truth. Integrating data from multiple sources (HubSpot, Stripe, ClickHouse and Postgres) into a single platform gives the team a holistic view of each account's health and conversion potential without the manual work of pulling data from various systems.
By streamlining customer data analysis in Pocus CSMs save 10 hours per week.
"I want to understand which accounts have a high revenue forecast , are they engaged, which products are they using, and how many users they have. With Pocus I can answer all of those questions without having to hit five systems." — Cameron DeLeone
Repeatable workflows: Playbooks
Getting to a single source of truth is only half the battle. While it’s valuable for customer success to see health metrics and scores, the team also needed a clearer picture of what to do with that data.
With Pocus Playbooks, the customer success team knows precisely why a lead is being surfaced and the actions they need to take next, so they can consistently move those leads towards a goal. For example, knowing that a power user's usage recently spiked on an account up for renewal might be an opportunity to reach out personally. A high potential account entering a trial might be better suited to an automated email sequence.
PostHog created three main Playbooks to address this challenge and aligned them to their revenue goals: churn prevention, self-serve free-to-paid conversion, and self-serve account expansion.
The churn prevention Playbook helps PostHog take a proactive approach to account management. By analyzing historical product usage data in Pocus, the success team identified that retention largely correlates to a steady increase in usage of one key feature: insight analysis. Hence, the churn prevention Playbook surfaces accounts with a stagnant percentage of insights analyzed WoW. Surfacing these accounts quickly and before any significant decrease in product usage enables customer success to reach out before it’s too late.
The account owner gets a Slack alert with important account health information like total insights analyzed, the percent change of insights analyzed from last week, monthly events (engagement actions in the product), and lifetime revenue. Armed with this information, the CSM personally reaches out to provide assistance and collect feedback.
The free-to-paid conversion Playbook enables the customer success team at PostHog to not only surface the best opportunities for conversion, but also prioritize them based on revenue potential. They focus their outreach on the accounts that will most benefit from their product (and that will have the biggest impact on their revenue goals.)
This Playbook prioritizes Product Qualified Accounts (PQAs) based on forecasted MRR, product engagement milestones, and ICP fit.
For example, a key signal for an account to become qualified is if one or more users are senior engineers. Pocus surfaces these accounts through a composite score that weighs: job functions, usage, and revenue forecast.
Along with the PQA triggers— ICP fit, MRR, and product milestones reached— Pocus pulls relevant information about the account's activity and recent usage of PostHog. With these insights, success can build a convincing business case for economic buyers, highlighting the specific pain points PostHog solves for their team.
The free-to-paid conversion Playbook also surfaces new accounts with a high ICP fit, but low product usage in the first 14 days. Since these users can benefit from PostHog, the priority is to provide them with as much education as possible to increase adoption and get them to value.
But, a human touch point isn’t necessary at this stage — users are unlikely to have support or sales questions without spending some time on the product first. Pocus pushes out these leads for automatic enrollment in a HubSpot nurture sequence to educate new users on all the PostHog features that make their jobs easier.
The expansion Playbook is focused on upsell and cross-sell opportunities based on a weighted Pocus score that measures three metrics: feature usage, lifetime revenue, and forecasted MRR. This score alerts the customer success team on whether the opportunity is an upsell or a cross-sell of a new feature.
Data exploration: Lists
Cameron uses Lists to segment cohorts of users and quickly test his hypotheses around expansion and retention likelihood. Since PostHog tracks aggregate account data and individual user data in Pocus, Cameron can use Lists to filter users and accounts by products and features customers are using, revenue levels, sign-up date, and enrichment properties such as industry, job titles, or company size.
"Pocus really changed the game for us by having an agnostic data model which we can fit to our data . Since we have all our data feeding into one place, we can trust Pocus as the source of truth for information on any account." — Cameron DeLeone
Results
Time saved: 10 hours a week per CSM. Customer success can access and action customer insights in seconds instead of spending hours digging around for data.
Uncovering hidden revenue opportunities. With just a few clicks, customer success can spot correlations between product usage, revenue potential, and firmographic data leading them to discover new conversion signals and create more targeted Playbooks.
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