Plausible.io

Problem: A European SaaS company wants to scale experimentation but legal and security teams block tools that rely on cookies or extensive user profiling, stalling their CRO roadmap. Solution: Convert runs the experiments while Plausible receives experiment and variation data as privacy-safe custom events, using no cross-site cookies and minimal data collection. Outcome: Legal approves the stack, tests launch faster, and the team grows conversions while staying fully aligned with GDPR and internal privacy policies.

Problem: A growth team runs A/B tests in Convert but must switch between multiple analytics tools to understand impact, causing confusion, duplicated metrics, and slow decision-making. Solution: Convert sends experiment and variation assignments into Plausible as custom events, so test exposure and performance appear alongside traffic, referrers, and behavior metrics. Outcome: Stakeholders see experiment results in the same Plausible reports they already trust, reducing friction, speeding up analysis cycles, and improving confidence in test decisions.

Problem: Marketing has carefully defined Plausible goals for signups, trials, and key actions, but experiment reporting lives in a separate system with different goals and naming. Solution: Convert passes experiment and variation details into Plausible custom events, which are mapped to existing Plausible goals so tests use the same KPIs and goal definitions. Outcome: The team compares variations against familiar goals, avoids misaligned metrics, and can quickly see which experiments move the same numbers they already report to leadership.

Problem: A small marketing team lacks engineering bandwidth to build complex analytics pipelines for every new test, so many experiments go untracked or under-measured. Solution: They add the standard Convert and Plausible scripts plus a simple JavaScript snippet that automatically pushes experiment and variation names into Plausible events. Outcome: Marketers can independently launch and track tests with accurate analytics, increasing testing velocity while keeping implementation light and maintainable.

Problem: Product wants to understand how different variations affect downstream behavior—like scroll depth or feature usage—but can’t easily segment Plausible data by test exposure. Solution: Convert sends experiment and variation IDs as event properties to Plausible, enabling segmentation of all existing Plausible reports by which variation users saw. Outcome: Teams uncover deeper behavioral insights per variation, identify why a winner works (or fails), and design follow-up tests grounded in real user behavior patterns.

Problem: A digital agency serving privacy-sensitive clients juggles separate tools for analytics and A/B testing, leading to fragmented reports and difficult client communication. Solution: They integrate Convert with Plausible so all experiment data flows into a single, privacy-first analytics environment, using Plausible’s custom event goals for each test. Outcome: Clients receive unified, easy-to-understand reports in Plausible, the agency reduces tool sprawl and onboarding time, and can confidently pitch experimentation to privacy-first brands.