01 · The Funnel
Where do users drop off?
The session-based funnel tracks users from viewing a product → adding to cart → completing a purchase. The pattern is unambiguous: 91.5% of sessions that include a view never add to cart. Once a user does add to cart, 59% convert to purchase — a strong, healthy rate. This immediately tells us where to focus: not checkout, not pricing — the add-to-cart step.
91.5% drop at View → Cart is the single biggest lever. Every 1pp improvement in cart rate drives outsized revenue gains.
Cart rate shows a slight upward trend; purchase rate is stable at ~57–61%. The bottleneck is structural, not seasonal — a temporary fix won't solve it.
02 · Category Deep-Dive
Not all categories are equal
Top 3 categories — Computers (~36%), Unknown (~27%), Electronics (~19%) — account for most traffic. But their conversion behaviour differs sharply. Cart rate varies from 4% to 13% across categories; purchase rate is consistently 55–65%. The entire performance difference lives at the add-to-cart step.
Cart rate varies widely. Purchase rate stays in a tight band. The message is clear: conversion is a discovery and decision problem, not a checkout problem.
03 · What Drives Cart Rate
Brand, price, and the hesitation effect
Drilling into Electronics — the highest-traffic, lowest-converting category — reveals two structural drivers of low add-to-cart rates.
Premium Brands = More Hesitation
Sony (~$313 avg) and LG (~$218) show cart rates of ~0.7–0.9%. Samsung (~$72) achieves ~4.3%, Sirius (~$23) hits ~10%. Higher price → more comparison browsing → lower cart rate.
Sub-category Variation
Telephones convert at ~7% cart rate. Clocks and video products fall to 2–4%. Differences reflect purchase urgency and decision complexity, not demand.
Time of Day: Not a Factor
Cart rates are remarkably flat across all hours of the day. Time-based ad targeting will not move this metric — budget is better spent on product and UX fixes.
"Unknown" Category Problem
27% of traffic lands in an unclassified category — weak product metadata and poor discoverability. These users can't find what they're looking for.
Clear inverse relationship: as average product price increases, cart rate drops. Premium brand users are browsing and comparing — not committing.
04 · Revenue Impact
What does fixing this mean in revenue?
Cart rates were improved toward the top-performing category benchmark across three scenarios. Even the conservative case produces meaningful gains. The moderate scenario — a realistic 12-month improvement target — delivers approximately 15% revenue uplift.
Conservative (30% gap closure) → ~9.2% uplift. Moderate (50%) → ~15.3%. Aggressive (full benchmark) → ~30.6%. Even modest cart rate improvements compound into significant revenue gains in high-volume categories.
06 · Recommendations
Reducing hesitation, not manufacturing demand
The core finding: users aren't failing to want products — they're failing to commit to them. Every recommendation targets a specific friction point.
| Action | Supporting Insight |
|---|
High Priority Fix the Unknown category metadata |
27% of traffic has no category assigned. Better product classification and tagging removes a structural barrier — users who can't find their category can't convert. |
High Priority Add comparison tools for Electronics |
Premium brand users (Sony, LG) are comparison shopping. Side-by-side specs, ratings, and value-for-money signals reduce evaluation burden and accelerate add-to-cart decisions. |
High Priority Use pricing cues for high-price products |
Cart rate drops sharply as price rises within Electronics. Instalment options, "best value" labels, and discount cues reduce perceived risk and lower hesitation at the critical add-to-cart moment. |
Medium Priority Promote high-converting sub-categories |
Telephones convert at ~7% within Electronics — well above category average. Ranking these higher in search and featuring them in navigation would immediately lift overall Electronics cart rate. |
Deprioritise Don't optimise for time-of-day |
Temporal analysis shows flat cart rates across all 24 hours. Time-based campaigns will not move this metric — every hour of resource spent here is better directed at product and UX improvements. |
