Get a funnel diagnostic with stage-by-stage drop-off rates, charts, and prioritized recommendations showing where visitors are lost and how to fix each leak.
Share the website or product and describe which conversion path to examine. The flow returns a branded PDF with funnel visualizations, channel and device breakdowns, and flagged tracking issues that might be disguising the real drop-off points.
The team gets a clear picture of where visitors are lost and where to focus. The report walks through each stage of the funnel with real numbers: how many people entered, how many reached the next step, and how large each drop-off is. A chart makes the shape visible at a glance, so the conversation shifts from "conversions feel low" to "we lose 68% of visitors between the product page and checkout."
The analysis splits the funnel by traffic source and by device, because a single conversion rate often hides very different stories. Paid search visitors might convert at 4.8% while organic blog traffic converts at 0.1%, and a funnel that works on desktop may fall apart on mobile. Without those splits, the team optimizes for a blended average that describes no one accurately. When data from multiple analytics sources is available, event counts are cross-referenced to catch cases where a steep drop-off is actually a broken tracking event, not real user behavior.
Each recommendation is tied to a specific funnel stage and cites the data behind it, so the team knows what to fix, why, and in what order.
No analytics account connected? Upload a CSV export or share screenshots, and the analysis works from that. Live connections are faster and deeper, but the flow handles both.
A site-wide conversion rate averages wildly different behaviors into a single number. Paid search traffic might convert at 4.8% while organic blog traffic converts at 0.1%. Blending them produces a rate that describes neither group accurately and points the team toward generic "improve the funnel" work instead of the specific fix. The same pattern appears across devices: mobile paid traffic can outperform desktop paid traffic on both bounce rate and conversions, a counter-intuitive finding that only surfaces when the funnel is segmented. Segmented analysis reveals which combinations of channel, device, and landing page actually convert, and which ones are pulling the average down. That specificity is what turns a funnel report from a dashboard screenshot into a prioritized action plan.
Mobile and desktop visitors often convert at very different rates, but the default funnel view blends them together. This separates the funnel by device type to show whether the problem is the funnel itself or the experience on a specific device.
Break down the funnel by device type. Show mobile vs. desktop conversion rates at each stage and flag where the gap is largest.
A single funnel snapshot shows where people drop off today. Comparing two time periods shows whether the problem is getting better, getting worse, or staying flat, and connects changes to specific events like redesigns, new campaigns, or pricing updates.
Compare the conversion funnel for the last 30 days against the previous 30 days. Show which stages improved, which got worse, and what might explain the shifts.
The funnel shows the overall drop-off, but the problem might concentrate on specific pages. This identifies which landing pages get traffic but fail to move visitors to the next step, so the team knows exactly which pages to fix.
Show which landing pages get the most traffic but the fewest conversions. Rank them by the gap between sessions and conversion rate so we see the biggest opportunities first.
The analysis surfaced the problems. This turns findings into a prioritized list of changes with expected impact, effort level, and a suggested timeline so the team knows what to tackle first.
Turn the funnel findings into a prioritized action plan. For each recommendation, include expected impact, effort level, and a suggested timeline.
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.
Funnel Stage Definitions
Which events define each stage of the funnel, mapped to the analytics platform (GA4 event names, PostHog events, or custom events). When this exists, the analysis uses the client's actual funnel rather than inferring stages from the available data. Especially valuable when the funnel includes custom events that are not obvious from page views alone.
Conversion Targets and Benchmarks
Target conversion rates by stage, acceptable drop-off thresholds, and historical baselines. With this in the project, every analysis measures against the client's own goals instead of relying on industry averages.
Past Funnel Reports
Previous funnel analyses from earlier periods. When present, the analysis compares trends over time and tracks whether past recommendations moved the numbers. If analytics accounts are already connected, Juma can pull historical data directly, so this file becomes optional.
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:
