Use this funnel analysis tool to run a conversion funnel analysis on your site. Share your URL and get drop-offs by stage, channel splits, and ranked fixes.
Share your website URL and the conversion path you want to examine. Juma connects to your analytics accounts, maps every funnel stage, and returns a branded PDF with drop-off rates, channel splits, and prioritized fixes. No analytics account connected? Upload a CSV or share screenshots and the Flow handles it from there.
Segment the funnel by splitting the default view into separate mobile and desktop paths. This shows exactly where the conversion gap is largest between devices. The team leaves knowing whether the drop-off is a funnel-wide problem or an experience issue specific to one device.
Compare two date ranges - such as this quarter versus last quarter - to show whether funnel performance is improving, declining, or holding steady. Changes in the data connect to specific events like redesigns, new campaigns, or pricing updates. This step turns conversion funnel analysis into a trend and builds the foundation for ongoing conversion funnel optimization.
Rank landing pages by the gap between sessions and conversion rate to surface which pages attract traffic but fail to move visitors forward. The funnel shows the overall drop-off, but this step finds where the problem concentrates at the page level. The team leaves with a prioritized list of pages to fix, ordered by potential impact.
Turn funnel findings into a prioritized action plan by assigning each recommendation an expected impact, an effort level, and a suggested timeline. The plan sequences changes from highest to lowest leverage, so the team builds momentum with quick wins before committing to larger projects. Each recommendation traces back to a specific funnel stage and data point, making it easy to get stakeholder alignment and measure results in the next analysis.
Teams build one Juma project per client and add context over time. Every flow the team runs for that client pulls from the same project. If a project already exists, adding funnel context means each analysis starts from the client's own definitions and targets instead of inferring them from the data.
Add a short description to each knowledge item in the project's info field so Juma knows what each file contains and when to use it. For example:
A conversion funnel analysis maps each stage of the funnel with real numbers: entries, exits, drop-off rates, and the gap between adjacent stages. Results are split by traffic source and device type to reveal which channel-device combinations convert and which ones pull the overall rate down.
The shape of the funnel becomes visible at a glance, so the conversation shifts from "conversions feel low" to "we lose 68% of visitors between the product page and checkout." Paid search visitors, organic blog traffic, and direct visitors often convert at very different rates, and the device breakdown surfaces whether mobile or desktop drives a specific drop-off. Without those splits, a blended average describes no user group accurately. When multiple analytics sources are connected, Juma cross-references event counts to flag tracking gaps before any optimization decisions are made.
If no analytics account is connected, upload a CSV export or share screenshots from your analytics platform and the Flow runs the analysis from that data. Most tools including GA4, PostHog, and Mixpanel support CSV exports of funnel reports. Either format provides enough data to map stages, calculate drop-off rates, and identify the largest gaps.
Live connections to Google Analytics, PostHog, or Google Ads return deeper results. With a live connection, the analysis pulls segment breakdowns automatically, cross-references event counts across platforms, and detects tracking gaps that would not appear in a static export. Manual uploads reflect data at a single point in time and require a fresh upload each time the analysis runs. For teams running the Flow monthly, connecting an analytics account removes that manual step and makes re-running the analysis as simple as starting a new session.
Yes. Whether the funnel covers a marketing path such as landing page to trial signup, or a product flow such as signup to first action completed, the analysis runs from the funnel definition the team provides. Specifying the start and end stages keeps results focused on the conversion path that matters most.
Website funnel analysis works best when the team maps funnel stages explicitly before running the Flow. For a marketing site, that means defining which page views or events mark the beginning and end of the conversion path. For a product flow, it means identifying the GA4 events or PostHog steps that correspond to each stage in the user journey. The more precisely the funnel is defined, the more accurate the drop-off rates. Specific event-to-stage mapping produces the granular breakdown that turns a funnel report into an actionable plan.
