Using Pipefy, and a few simple integrations, we’ve built an experimentation system that costs virtually nothing and allows us to accelerate learning.

The system I describe in the following post can be adapted to fit the needs and goals of any team. Building it requires no advanced programming, takes only few hours to create, and can be broken down into 4 key steps:

Identify priorities based on your team’s strengths and weaknesses. Build the steps of your pipeline to establish a repeatable experiment flow. Create powerful reporting to help you learn and optimize the process. Set up simple automations and integrations to facilitate key actions.

By walking through each step individually with examples, my goal is to share a blueprint you can use to build a lightweight experiment system for your own growth team.

Step 1: Before you Build, Identify your Priorities

Many of the challenges of running an effective growth team are shared across organizations; there’s a reason the Reforge Growth Series event on “Building a Growth Team” is always standing room only. Even so, the existing pace of tests, approval process, and other unique aspects of your team may affect the pipeline you build, and may differ from the one described here.

The beauty of this system is that it can easily be adapted to address different needs, and it will undoubtedly evolve over time. For example, adding positive feedback loops via Slack and email is something we didn’t build into our system initially, but added later to further encourage idea contribution across the company, which increased the volume of ideas added to our prioritization queue by 2X.

We use the same growth principles that apply to building product to improve our process. We test against a few main KPIs including experiment speed, team adoption, and success rate, to measure how changes to the system affect those metrics.

We had a few main goals when we started - speed, collaboration, reporting, and price - all detailed below.

Speed

We wanted a system that empowers us to be fast and agile. Our previous system in Google sheets had become cumbersome as the number of experiments we ran increased. We wanted a system that can do most of the busywork and free us up to build more tests.

Collaboration

Like many companies, we have a talented and diverse group of people focused on very different areas of the product, from venue operation to performance marketing to sales, and the team members from each area have their own expertise. Our team is too small to have area-specific growth teams, so without a simple way of gathering experiment ideas, we relied on weekly meetings to generate ideas. To facilitate idea collaboration without adding more meetings, we wanted the company to be able to create an experiment in our queue directly from Slack (which they always have at their fingertips).

Reporting

Moving experiments through a series of Google sheets made it cumbersome to create reports on testing pace and accuracy, which means reporting happened far less than we wanted. Layering onto the issue, we also weren’t spending enough time on post-mortem analysis of our projects. We wanted to create a pipeline that would force us to follow best practices and require retrospection before any test could be marked as “complete.”

Price

We took a few calls with SaaS experimentation management platforms, and the price seemed unjustified given our stage and the efficiency gains we expected. Happily we were wrong about the latter, but given how easy this system was to build I’m glad we decided to create something on our own.

As a team, we discussed the features that would help us accomplish the goals outlined above. From there, we created a spec sheet before investigating tools to create the system. Even with this guide, I’d recommend following a similar process before beginning the build steps.

Step 2: Building the Experiment Pipeline

The experiment pipeline is similar to any board system, such as Trello or Jira, in which you move cards from left to right as the task progresses from start to finish. Along the way we add different fields (some of which are required before a card can move forward), which triggers automations and reports.

Our pipeline has the following steps: