Get a side-by-side funnel comparison across paid search, organic, direct, and referral traffic with per-channel conversion rates, landing page analysis, and budget recommendations.
Name the website and the channels to compare. The flow returns a branded PDF with side-by-side funnels, device splits per channel, campaign-level breakdowns for paid traffic, and a strategic verdict on where each channel wins and where it leaks.
The team gets a side-by-side view that answers one question: is the problem the funnel, or the traffic being sent to it? Each acquisition channel gets its own funnel with sessions, engagement, bounce rate, and conversion metrics, so the team can see exactly where paid and organic visitors behave differently. If paid search bounces at 52% but organic bounces at 36%, that points to a landing page mismatch on the paid side, not a site-wide problem.
A device split per channel adds another layer. A channel that "underperforms" might actually have a mobile-specific problem: paid mobile visitors bouncing less than paid desktop visitors is a counter-intuitive finding that only surfaces when both dimensions are visible at once. For paid traffic, the report goes deeper with a campaign-level breakdown showing which campaigns earn their budget and which ones burn it. A landing page comparison shows where each channel sends visitors and how those pages convert, because the same product page can produce a 5x conversion rate difference depending on whether the visitor came from a Google Ad or a blog post.
No analytics account connected? Upload a CSV export or screenshots from each platform, and the comparison works from that. Live connections pull all channels at once and make the analysis faster to repeat.
A funnel that converts 3% overall might convert 5% from organic desktop and 0.3% from paid social mobile. The blended number describes neither group and leads to generic optimization advice. When the funnel is split by channel, the diagnosis gets specific: paid search might have a landing page mismatch (high bounce, low engagement), while organic traffic converts well on the homepage but poorly on blog pages. The channel comparison reveals whether the problem is the funnel itself or the traffic being sent to it. That distinction changes the fix: a funnel problem means redesigning the page, a traffic problem means changing the targeting or the landing page assignment.
The channel comparison showed paid search underperforming. This goes one level deeper to find which campaigns and ad groups are dragging the average down, so the team knows exactly where to cut or scale.
Break down the paid search performance by campaign. Show which campaigns convert best and which are wasting spend, with cost per conversion for each.
Different channels often land on different pages. This shows which pages each channel sends traffic to, how those pages convert, and whether routing traffic to a better-performing page could close the gap.
Show the top landing pages for each channel side by side. Compare sessions, bounce rate, and conversion rate per page, and flag any pages where traffic is high but conversions are near zero.
The data shows which channels convert and which ones leak. This turns findings into a specific reallocation plan with dollar amounts, expected impact, and a rationale for each shift.
Based on the channel comparison, build a budget reallocation plan. Show how much to shift between channels, what we'd expect in return, and which landing pages to change for paid traffic.
The channel comparison showed overall performance by source. This adds a device layer to reveal whether mobile or desktop traffic behaves differently within each channel, because a channel that "underperforms" might have a mobile-specific problem, not a channel-wide one.
Add a device breakdown to the channel comparison. Show mobile vs. desktop performance for each channel and flag any channel where the device gap is largest.
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 channel performance context means each comparison starts from the client's own targets and campaign history.
Channel Performance Targets
Target metrics per channel: CPA, ROAS, conversion rate, or cost per lead. When this exists, the analysis measures each channel against the client's own goals instead of comparing channels only to each other.
Campaign and Budget Context
Active campaigns, budget allocations, and any recent changes (paused campaigns, new ad groups, landing page tests). This gives the analysis context for interpreting performance differences rather than diagnosing from numbers alone.
Past Channel Reports
Previous channel comparisons from earlier periods. When present, the analysis tracks whether shifts in channel performance are trends or one-time anomalies. If analytics accounts are already connected, Juma can pull historical data directly.
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:
