Clicky

Problem: Teams run A/B tests in Convert but analyze traffic and engagement in Clicky, forcing them to jump between tools and manually reconcile data to understand experiment impact. Solution: Enable the Convert–Clicky integration so each visitor’s experiment name and variation are sent into Clicky’s custom data on every pageview, surfacing test data directly in Clicky reports. Outcome: Marketers see experiment performance alongside traffic, bounce rate, and time-on-site in a single Clicky dashboard, speeding up analysis and reducing reporting errors from manual exports.

Problem: You know which variation wins on conversions, but you lack insight into how each variation changes visitor behavior across sessions, pages, and events in Clicky. Solution: Use Convert to push variation assignments into Clicky’s visitor properties and keys cookie, attaching experiment data to each visitor session for granular behavior tracking. Outcome: Teams can see how each variation influences scroll depth, engagement, and navigation paths at the visitor level, enabling more informed hypotheses and higher‑impact follow‑up tests.

Problem: Clicky segments show overall performance, but you can’t easily isolate metrics for visitors exposed to a specific A/B test or variation without complex tagging or filters. Solution: With Convert integrated, experiment and variation labels appear in Clicky’s Custom section, allowing you to build segments and filters directly on those properties. Outcome: Analysts can instantly compare metrics like bounce rate, goal completions, and engagement by variation, uncovering nuanced performance differences and validating winners with confidence.

Problem: Analysts export data from Convert and Clicky, then stitch it together in spreadsheets to report on experiment impact, wasting hours and risking inconsistent definitions. Solution: Activate the integration once in Convert so experiment data automatically flows into Clicky Pro’s custom data, removing the need for separate exports or custom tracking hacks. Outcome: Reporting becomes a repeatable, low‑effort process inside Clicky, freeing teams to focus on insight generation and allowing stakeholders to self‑serve experiment views in the tool they know.

Problem: When launching new tests, teams worry whether experiment tracking is correctly configured, but only discover issues after days of data collection and lost traffic. Solution: After enabling the Clicky integration, marketers visit a test page and check Clicky’s Custom section to confirm experiment and variation data is appearing as expected. Outcome: Teams validate tracking within minutes, avoid broken or incomplete experiment datasets, and confidently roll out new tests knowing Clicky is capturing accurate visitor‑level assignments.

Problem: Stakeholders rarely log into Convert, so experiment insights stay siloed and optimization decisions are delayed or based on partial data from the analytics tool alone. Solution: By streaming Convert experiment data into Clicky, all key metrics and test results live in the same interface stakeholders already use daily for performance monitoring. Outcome: Experiment data becomes part of regular analytics reviews, increasing adoption of A/B testing insights, accelerating decision cycles, and improving ROI from ongoing optimization work.