Our work with Plain
Outlier partnered with Plain, a B2B SaaS customer support platform, to implement a modern data stack using BigQuery, Dataform, and Metabase.
The new infrastructure, along with ongoing fractional data support, has empowered Plain with clear product insights, streamlined decision-making, and enhanced fundraising readiness.
Overview
Plain is a B2B SaaS customer support platform on a mission to improve how technical product teams do support.
Founded in 2020, Plain offers a streamlined and consolidated alternative to more generalised support tools. With a team of about 10 people and having secured seed funding, Plain is positioning itself for rapid growth and a potential Series A round in the near future.
Situation
Plain's rapid growth led to an influx of new users, making it difficult for the team to understand their expanding customer base and product usage.
This challenge was particularly critical given that data reporting needed to be a core part of Plain's offering to their clients. The company's data infrastructure was not equipped to handle this growth or some of the new feature requests they were receiving.
Prior to working with Outlier their setup consisted of using Segment to collect and route event data from their application, which was then published to Mixpanel for analytics and reporting. They didn’t have a centralised data warehouse or a robust data modelling layer set up. This forced the team to rely heavily on manual data pulls and ad-hoc analysis.
This led to several key challenges:
Limited visibility and inefficient analytics: Plain struggled to gain clear insights into customer behaviour and product usage. Their existing setup (using Segment to publish events to Mixpanel) didn’t give them visibility into the current state in their backend, which made it challenging to answer even basic questions.
Lack of data infrastructure and expertise: With a small team focused on core product development, Plain lacked the resources and expertise to build and maintain a robust data infrastructure. This gap not only affected their internal decision-making but also hindered their ability to develop customer-facing analytics for their SaaS product.
Inadequate reporting for growth and fundraising: As Plain approached the possibility of a Series A round, they needed to significantly improve their data hygiene and reporting capabilities. Their current setup was insufficient to provide the comprehensive, trustworthy data required to support fundraising efforts and guide their rapid growth.
Working with Outlier has been great, they've effectively solved data for us and it feels like a huge weight has been lifted from our shoulders. We're no longer flying blind and have a solid data setup for every day operations, product and fundraising.
– Matt Vagni, CTO & Founder // Plain
GOALS
Plain partnered with Outlier to address these challenges and set up a scalable data infrastructure.
The key objectives of the project were:
Implement a modern, trustworthy data stack for clear visibility into product usage and customer behaviour.
Enable self-service analytics to reduce reliance on manual data pulls and specialised expertise.
Establish robust reporting capabilities to support fundraising efforts and customer-facing analytics.
THE SOLUTION
DATA STACK EVALUATION & IMPLEMENTATION
We started by conducting a comprehensive evaluation of data tooling options, considering Plain’s specific needs and constraints. We settled on BigQuery as the data warehouse and then helped Plain implement and configure the rest of the data stack:
BigQuery for data storage
Dataform for data modelling
Metabase as the BI tool for dashboards and queries
DATA MODELLING & INTEGRATION
We set up Dataform to model Plain’s Segment data, enabling the team to easily answer key questions about their business, such as:
Number of new customers acquired in a given week
Customers currently in trial periods
Feature adoption rates
Additionally, Stripe data was integrated to track important financial metrics like ARR, new customers, and number of paying customers.
DEVELOPING CUSTOMER-FACING ANALYTICS
We provided crucial support for Plain's project to build customer-facing analytics into their product. This involved working closely with Plain's engineering team on the implementation of Tinybird for real-time data APIs.
As a result, Plain was able to launch a dedicated reporting page, with near-real-time data, satisfying one of their most popular customer feature requests.
ONGOING FRACTIONAL DATA TEAM SUPPORT
After the initial implementation, Outlier transitioned to providing ongoing support as a fractional data team. This includes:
Weekly 30-minute office hours
Access to a shared Slack channel for ongoing communication
Assistance with ad hoc requests such as customer questions, financial modelling, report creation, and data API development
IMPACT
Setting up a modern data stack has led to significant improvements in Plain’s data capabilities and overall business operations.
Improved data visibility: Plain now has a clear understanding of their product usage and customer behaviour, enabling data-driven decision-making. For example, they can now easily track detailed activation metrics on each new workspace, which were previously very difficult to obtain.
Efficient reporting: The team can now quickly answer important business questions and create dashboards, fully transitioning from Mixpanel to Metabase.
Fundraising readiness: With improved data hygiene & reporting, Plain is well positioned for future fundraising rounds. They can now confidently present key metrics such as ARR, customer acquisition costs, and user engagement rates to potential investors.
Enhanced product offerings: The support for customer-facing analytics has allowed Plain to expand their product capabilities, increasing their value proposition to customers. A prime example is the new automated billing feature, which provides customers with real-time usage data, cost breakdowns, and forecasting capabilities.
Cost-effective data expertise: By leveraging Outlier as a fractional data team, Plain has access to high-level data expertise without the need to hire a full-time data team, which is more suitable for their current size and stage.
The data project has not only solved immediate data challenges but has also positioned the company for future growth and success. With a robust data infrastructure in place and ongoing support from Outlier, Plain is well-equipped to make data-driven decisions, improve their product, and confidently approach their next funding round.
