Where Rent the Runway Loses the Sale: Auditing a Real Funnel Report

Marketing Academy · Field Work●Analytics & Attribution

MiniAudit· 30 minutes

Rent the Runway

Objective: Given a real 5-stage funnel report (browse, item page, add to bag, checkout start, order complete) with per-stage, per-device user counts, calculate drop-off rates and name the single leakiest stage.

You're the growth analyst at Rent the Runway reviewing the rental checkout funnel after a seasonal traffic spike. Leadership wants to know exactly where the extra visitors are being lost.

Calculate drop-off rate at every stage, then segment the worst stage by device before recommending a fix.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets

FreeCalculate drop-off rate across the 5 stages from the CSV export

Free, works directly from the exported report

Google Analytics 4

FreeSegment the worst stage by device category

Free, and Funnel Exploration supports device segmentation natively

The process

2 steps

Step 01 of 02

Calculating drop-off rate at each funnel stage

The lesson defines drop-off rate as (users who entered, minus users who completed) divided by users who entered, times 100. The stage with the biggest drop-off is the biggest opportunity.

The 5-stage report shows: browse 10,000, item page 6,200, add to bag 2,800, checkout start 2,050, order complete 1,240. Which single stage has the worst drop-off rate?

Google Sheets— Import funnel-report.csv, add a drop-off-rate formula column.

Procedure

Import funnel-report.csv with the 5 stage counts
Calculate drop-off rate for each stage transition using (entered - completed) / entered x 100
Rank the 4 transitions from worst to best drop-off rate
Flag the single worst transition as the priority stage

Sample output

DROP-OFF RATES BY STAGE
  browse -> item page:        38% drop-off
  item page -> add to bag:    55% drop-off  <- worst
  add to bag -> checkout:     27% drop-off
  checkout -> order complete: 40% drop-off

Healthy

The team prioritizes fixing item page -> add to bag, the 55% drop, before touching the checkout flow that has a smaller but louder problem.

Unhealthy

The team redesigns checkout first because it's the closest stage to revenue, while the biggest leak (item page to add to bag) keeps losing over half of engaged visitors.

What this means

The biggest number, not the closest-to-revenue stage, is where the fix has the most leverage.

So what do I do about it?

Symptom	Action	Effort
Checkout redesign shipped but overall order volume barely moved	Re-run drop-off rates on the item-page stage before the next roadmap cycle	30 min

YouYou can do this yourself, no engineering access required.

Step 02 of 02

Segmenting a funnel by device to find masked problems

The lesson warns that an aggregate funnel number can mask a device-specific problem, splitting by device type is one of the most underused diagnostic techniques.

The item page -> add to bag stage shows 55% drop-off overall. Split by device, desktop drops 31% and mobile drops 68%. What does that tell you about where to focus the fix?

Google Analytics 4— GA4 Funnel Exploration, segmented by device category.

Procedure

Open the same funnel in GA4's Funnel Exploration report
Add a device-category segment breakdown to the item-page -> add-to-bag transition
Compare desktop drop-off against mobile drop-off
Check whether mobile traffic volume is large enough to explain most of the aggregate 55% figure

Sample output

ITEM PAGE -> ADD TO BAG, by device
  Desktop: 31% drop-off  (2,400 of 3,900 users)
  Mobile:  68% drop-off  (2,100 of 6,100 users)
  Aggregate: 55% drop-off (masks the mobile-specific problem)

Healthy

The fix scope narrows to the mobile add-to-bag flow specifically, likely a tap-target or size-selector issue, instead of a generic 'improve the item page' project.

Unhealthy

The team ships a desktop-only redesign because that's where the design system is easiest to update, and the real 68% mobile leak goes untouched.

What this means

A 37-point gap between desktop and mobile at the same stage means the aggregate number was hiding a mobile-specific defect, not a general page problem.

So what do I do about it?

Symptom	Action	Effort
Item page 'improvements' shipped but mobile add-to-bag rate didn't move	Pull session recordings filtered to mobile only at the item-page stage	30 min

YouYou can do this yourself, no engineering access required.

Final deliverable

A one-page funnel diagnosis memo naming the single leakiest stage, the device segment driving it, and a recommended fix scope.

See a reference example

Sample output

Casper Sleep, funnel diagnosis memo (excerpt)

WORST STAGE: product page -> add to cart, 61% drop-off aggregate
DEVICE SPLIT: mobile 74% drop-off vs desktop 38% drop-off
RECOMMENDATION: scope the fix to the mobile product page, not a full-site redesign

Success criteria

You're done when you can:

Correctly identifies item page -> add to bag as the worst-performing transition
Uses the device split to narrow the fix to mobile, not the whole item page