Featured Event: Metrics in Motion: How Customer Success Evolves with Data Transformation
The only realistic way to make those [scaled] CSMs successful is to give them that data infrastructure and set of dashboards. They're effectively playing portfolio manager across a large set of accounts. And without that supporting infrastructure of signal, who do [they] pay attention to?
What we covered
In this session, Sanjeev Sisodiya, former VP of Customer Success, Support, and Sales at Postman, shared his insights on how to lead a GTM organization through data transformation. Sanjeev shared how Postman built a data-driven culture from the start. He also explained the challenges of building a data-driven GTM team, and provided tips on how to overcome these challenges
Postman Built a Data-Driven Culture from the Start
Postman began with product-led growth, which meant they analyzed data about their users to improve product development. Were users seeing value early in the product? Were new features adopted? Would people get stuck in onboarding? These were all questions that the product team had to answer.
To this end, Postman invested in data infrastructure from the very beginning. They made sure all systems sent data to a central data warehouse, and they hired data engineers and analysts to help them make sense of the data.
This investment in data infrastructure paid off. Postman was able to use data to identify trends in user behavior, which helped them to build customer-centric product improvements. As a result, Postman saw rapid growth and the company grew from 50-500 employees while Sanjeev was there.
Use Data to Identify Lead Signals
Almost every technology company understands the value of data-driven decision-making. But being data-driven in practice is the hard part.
At Postman, Sanjeev began using data to find lead signals from the support team. They started tracking the titles of people who wrote in to support. They also tracked the number of interactions that people had with the support team.
They found that senior-level employees were more likely to make expansion requests. They also found that people who had a lot of interactions with the support team were more likely to churn.
How to Build a Data-Driven Rev Ops Team
There are plenty of challenges building a data-driven team. It requires people skilled in data analysis, and people with a deep understanding of the business. For companies with limited resources, they may not be able to fill these requirements.
Teams should start by identifying the data that they need to collect and analyze. And start simple. There may be complex modeling and advanced statistical methods that may improve accuracy. But that requires resources, time, and testing, many of which are unavailable in the early days.
Rather, identify a few key, simple metrics that have a reasonable correlation to improve revenue, and start there. Overtime, as revenue grows, teams can invest in the tools and training that their rev ops team needs to be successful.
