Free tool for store owners

Conversion Funnel Calculator for Online Stores

From visit to purchase: enter your sessions and the rate at each stage to see where customers drop off, how many orders and how much revenue you make, and which stage to improve first for the biggest gain.

  • Five-stage funnel
  • Where customers drop off
  • Data-driven priorities

Funnel data

Each rate is from the previous stage, not from total sessions.

%
%
%
%
SAR
pt

In percentage points, to compare the impact of improving each stage.

Funnel

Enter sessions and the four stage rates.

Developed by Ahmed Elshamy All free tools

How it works

Three simple steps

  1. Enter your sessions

    Sessions for the period from Google Analytics or your store analytics.

  2. Enter stage rates

    Product view, add to cart, begin checkout and purchase.

  3. Find your priority

    The tool compares the impact of improving each stage and tells you where to start.

Why look at the funnel, not just conversion rate?

Overall conversion rate is one number that hides the story: do visitors fail to reach products, view them but not add to cart, or abandon at checkout? Each stage has completely different fixes.

The tool calculates the effect of raising each stage by the same number of percentage points, because improving the weakest stage usually brings the most extra orders for the same effort.

FAQ

Good to know

Where do I get the stage rates?

From the purchase journey reports in Google Analytics 4 if e-commerce events are set up, or from your store platform’s analytics dashboard.

How is overall conversion rate calculated?

By multiplying the stage rates together, or dividing orders by sessions. Example: 60% × 10% × 50% × 60% = 1.8%.

Why does the weakest stage give the biggest gain?

Because one extra point on a small rate is a bigger relative increase. Raising a stage from 10% to 11% lifts orders by 10%; raising one from 60% to 61% lifts them by under 2%.

Should I use sessions or users?

Use the same base for every stage. Sessions are the most common in analytics; what matters is consistency when comparing periods.