Pinpointing the right accounts for conversion
Clockwise has taken over corporate calendars everywhere. It’s a favorite among productivity hackers and every other professional who just wants to simplify their day. Clockwise’s freemium business model means they have created massively loyal fans of their product within an impressive list of companies.
That focus on creating a very happy and loyal base of end users has paid off, but deployments had gotten so large these accounts were certainly going to fail to convert from self-serve to paid plans. Head of Product-Led Sales, Melissa Ross, saw an opportunity.
“We had a sense that a self-serve revenue model was going to work in many cases, but, because we had been so focused on growing within companies before monetizing, there were really large deployments that we knew weren’t going to just self-serve and transact. They were going to need a lot of hand holding, a proper deal cycle, to support all of the organic usage.”
The Clockwise Customer Success team was overwhelmed with hand raisers asking questions about paid plans, upgrades, and pricing. It was time to adopt a new sales strategy to capture this demand.
If the Clockwise team could identify the best opportunities from their product usage, they could pinpoint the accounts ready to convert to paid plans. The problem was the sheer volume of data the team would have to analyze between what existed in their data warehouse, the CRM, and various analytics tools like metabase. The sales team didn’t have an easy or efficient way to synthesize the data and take quick actions.
Standing up Product-Led Sales at Clockwise
Clockwise implemented a Product-Led Sales approach that empowered their sales team to engage high-intent end users within large accounts to upgrade their plans.
The team took a phased approach to operationalize Product-Led Sales for their go-to-market team. The goal was to learn from what was already working in their PLG motion and complement that with a sales approach that would help them land those larger enterprise accounts.
Try this Playbook
What you’ll need
- CRM (like Salesforce or Hubspot) with firmographic customer data
- Data warehouse (like Snowflake or BigQuery) where you store product usage data
- A Product-Led Sales platform like Pocus (that can drive both insights and action)
- A sales email platform (like Outreach or Salesloft)
1. Understand and segment users through manual analysis
As a precursor to defining PQLs and operationalizing everything in tooling, the Clockwise team first wanted to deeply understand their existing users.
- Who are the primary user personas
- What type of companies sign up
- What are the use cases for Clockwise across departments
How did they execute on this research? Sales shadowed Customer Success calls and spent time with them to dive really deep on each customer account.
They then took this knowledge and used it to segment their customers and map them to personas. Clockwise segmented their GTM team by target market (SMB, Mid-Market, and Enterprise) and within those segments prioritized managers and above in Engineering, HR, Operations, and IT departments.
2. Prioritize accounts as a precursor to formal PQLs
Then they launched into an account prioritization exercise, which Melissa says was a precursor to their more official PQLs of today.
The prioritization work looked at different signals for each account like:
- How many users were active in each account
- How many conversations with Customer Success
- How much Focus Time Clockwise had created for the company
It was a mix of product data with internal Clockwise context. Because Clockwise is a product that works on your behalf, the team was not focused on logins or frequency of usage, they had to get more granular into specific features that signaled a truly activated user.
3. Clean up and unify customer and product data
Clockwise then set out to clean up their data. The team embarked on a migration to Snowflake as their data warehouse and Salesforce as their CRM. They then connected those two sources to Pocus to create a unified view of customer data.
Before getting started with Pocus, Sales was spending valuable time deep in manual data collection from various sources in order to find the best sales opportunities in their existing user base.
This didn’t just leave key info out of their lead picture, it was also becoming a very reactive model for sourcing pipeline — digging through data to find leads instead of being alerted of potential opportunities.
With Pocus, they’ve been able to seamlessly combine product and customer data into a single pane of glass view, showing Sales and the rest of the GTM team, granular insights into why accounts and users are good opportunities for outreach — so they can reach out at the right time, with the right message.
4. Set up PQL experiments and begin testing
From there, it was time to start experimenting with PQLs. The team built PQL scores in Pocus (without engineering support). What helped build trust in these models was the ability to drill into the ‘why’ behind the scores.
According to Melissa, on the surface, the raw number of users can look very attractive, “but you really need to layer on depth of usage on top of that,” she said. In Clockwise’s case, hundreds of active users doesn’t necessarily mean conversions. Sales needs to know what features they’re using, and how they’re using them, not just a black box score.
5. Drive sales behavior through Slack Alerts
With Pocus, PQLs are coming to reps directly to their Slack every morning instead of having to scout a CRM (or go in cold).. Reps can see a roll up of their top priorities, take action directly from Slack, or jump into Pocus to research further.
This proactive motion helps the team qualify these opportunities much faster.
The GTM team can see important info for each of their leads.
- Where are they in their Clockwise journey
- What their role is within their company
- Key insights into product usage data
“It is a lot easier to be hand-fed high-potential leads to reach out to than to go digging for the data ourselves in various tools. We would have gotten that info reactively before and now that info comes to us.” — Melissa Ross, Head of Product-Led Sales at Clockwise
The Results (so far)
- Generate new lead types: When Sales needs more pipeline,, they turn to Pocus to identify a new hypothesis for what a lead could look like and then experiment/operationalize it.
- Research quickly: Pocus provides a complete snapshot of each account and its usage, helping reps onboard into new accounts easily and without a ton of manual work.
- Proactive lead management: Alerts! Alerts! Alerts! PQLs are literally coming to reps instead of them having to go scout a CRM (or go in cold).
