Standard Definition
A funnel analysis systematically examines the drop-off rates between the steps of a conversion funnel. Defined funnel stages (e.g., landing page view → product page → shopping cart → checkout → confirmation) are quantified with sessions or users per stage. From this, drop-off rates between stages emerge — where do how many users lose the path to conversion. Funnel analyses are conducted in tools like GA4, Mixpanel, Amplitude, Heap. Prerequisite: consistent event tracking at each funnel stage. An extended form is the cohort-based funnel analysis, which compares funnel performance over time or per user segment — very useful for seasonalities or marketing campaign evaluation.
What this means in mandate practice
Funnel analyses are the diagnostic tool that precedes every A/B test — and is often skipped.
First, economic lever identification happens in funnel analysis, not in the test. Those with a funnel showing 80 percent drop-off between product page and shopping cart should optimize there — not at the landing page. Those with 5 percent drop-off between checkout and confirmation should optimize there — even if the optimization hypothesis would be more exciting for the landing page. Funnel data prioritizes the optimization sequence by economic lever, not by intuitive interest.
Second, „normal" drop-off rates vary strongly by industry. E-commerce funnels typically have 60-80 percent drop-off between product page and shopping cart. B2B lead funnels often have 40-60 percent drop-off between demo request and actual demo appointment. SaaS trial funnels see 70-90 percent drop-off between sign-up and first login. Those without an industry benchmark don't know whether a 70 percent drop-off is „normal" or „optimization-worthy". Calvarius works with industry databases and comparable mandate profiles — which operationally offers a substantially stronger lever than isolated funnel analysis.
Third, the „non-linear funnel" is the most common reality complication. Users don't move strictly through funnel stages — they switch between product pages, jump back, return over multiple sessions. Classical linear funnel analyses underestimate this reality. Extended methods — multi-touch attribution models, cohort-based returner analyses, path analyses in Mixpanel/Amplitude — paint the more accurate picture. Practice note: with funnel analyses, always also look at the „non-linear" paths, otherwise the most important economic improvement levers are overlooked.
End of EN file — 15 Tier-1 detail pages (Stage 3b-ii).
Status after this file: All 25 Tier-1 detail pages produced.