Segment
A customer data platform used to collect and route event tracking.
Segment began as a classroom confusion tool, then a Mixpanel competitor, and became a hit when users said they only wanted the JavaScript that routes events. It later moved pricing from number of APIs, which buyers did not understand, to monthly tracked users. Lesson: build what users actually use, and pick a price metric buyers grasp. Its hub role helps switching tools but is expensive.
- 1Track eventsRise's app events go through Segment
- 2Land in warehouseSegment feeds the data warehouse
- 3GrowthBook reads warehouseTest statistics sit on the clean data
- 4Standard engine picks winnersNo spreadsheets, no homegrown statistics
From Rise's experimentation stack, Segment page
What was said about Segment
Rise tracks events in Segment and tests on the warehouse with GrowthBook. Rise uses Segment for event tracking, Amplitude for product analytics, and GrowthBook for test statistics. GrowthBook sits on the data warehouse that Segment feeds.
Rise's advice is to use a standard statistics engine rather than have product managers judge winners in spreadsheets or build their own, because of pitfalls like sample ratio mismatch. Metrics such as trial start rate need clean definitions, since real user journeys are non-linear with reinstalls and returns. GrowthBook was Rise's biggest move toward standardizing experimentation, and it is cheap if the data is already organized. Quote: "never write your own statistics engine"
How Rise Turned “Sleep Debt” Into a 10M+ User Sleep App
Rise uses Segment for event tracking, feeding the data warehouse that GrowthBook analyzes.
Rise tracks events in Segment and tests on the warehouse with GrowthBook. Rise uses Segment for event tracking, Amplitude for product analytics, and GrowthBook for test statistics. GrowthBook sits on the data warehouse that Segment feeds.
Rise's advice is to use a standard statistics engine rather than have product managers judge winners in spreadsheets or build their own, because of pitfalls like sample ratio mismatch. Metrics such as trial start rate need clean definitions, since real user journeys are non-linear with reinstalls and returns. GrowthBook was Rise's biggest move toward standardizing experimentation, and it is cheap if the data is already organized. Quote: "never write your own statistics engine"
Lessons from 1,000+ YC startups: Resilience, tar pit ideas, pivoting, more | Dalton Caldwell (YC)
It started as software for students to tell professors they were confused, then became a Mixpanel competitor. Users said they only wanted the JavaScript that routes events, which became Segment.
Reflections on a movement | Eric Ries (creator of the Lean Startup methodology)
It began as a classroom lecture tool, and something built for that first idea proved more valuable. Its Hacker News post blew up.
The ultimate guide to Martech | Austin Hay (Reforge, Ramp, Runway)
Its tracking drops a cookie with an anonymous ID, then swaps it for a known user ID at login. It also offers reverse ETL features.
The ultimate guide to adding a PLG motion | Hila Qu (Reforge, GitLab)
Recommended as the data hub in a starter PLG stack, since it lets you switch analytics tools easily, though it is expensive. Some companies skip the hub and go straight to an analytics tool.
The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher)
Moved its price metric from number of APIs, which marketing buyers did not understand, to monthly tracked users. That was a fairer measure of value.
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