---
title: "How to increase your ecommerce conversion rate"
author: "Performetic Ekibi"
url: "https://www.performetic.com/en/blog/how-to-increase-ecommerce-conversion-rate"
published: "2026-08-09T08:00:00.000Z"
updated: "2026-10-05T01:24:56.879Z"
---

# How to increase your ecommerce conversion rate

> You increase an ecommerce conversion rate by measuring it correctly and then finding exactly where shoppers drop off. Divide orders by sessions, read mobile and desktop separately, break the funnel into steps, fix the step with the biggest loss first, and validate every change with a test before moving on.

## How do you increase an ecommerce conversion rate?

You increase an ecommerce conversion rate by measuring it properly, then splitting it by device, channel and funnel step until the leak becomes obvious. A single average never tells you what to fix. Find the step where you lose the most shoppers, solve the problem behind it, test the change, and only then move to the next step.

This guide turns "our conversion rate is too low" into a list you can actually work through. We deliberately skip industry averages. Two stores with different price points, traffic mixes and product types will never have comparable rates, so the benchmark that matters is your own store last month and last year.

## How do you calculate conversion rate?

The common definition is: conversion rate = orders / sessions x 100. Three details decide whether that number is useful.

- **Denominator:** sessions or users? GA4 reports both. A session based rate is usually lower because it counts repeat visits. Pick one and use it everywhere.
- **Numerator:** orders or purchasers? If one person places two orders in a day, the two definitions diverge. For most stores, orders are the more practical reference.
- **Source of truth:** the conversions an ad platform reports are not the same as orders in your store admin. Calculate the rate from store or GA4 data, and read platform numbers separately for campaign optimization.

**Example:** a home textiles brand gets 48,000 sessions and 768 orders in August. The conversion rate is 768 / 48,000 x 100 = 1.6%. On its own that figure says very little. The useful information sits in its components.

## Why should you segment conversion rate?

An average blends what works with what does not. A store converting at 3% on desktop can look like a 1.5% store overall simply because most of its traffic is mobile. In that case the problem is the mobile experience, not the product page in general.

### How do you read it by device?

Track mobile and desktop separately. If the gap is large, start with mobile speed, mobile forms and the payment step. For the speed side, our article on [Core Web Vitals and conversion rate](/en/blog/core-web-vitals-conversion-rate) is a good starting point.

### How do you read it by channel?

Organic search, paid search, paid social, email and direct traffic carry very different intent. Someone who sees your product for the first time in a social ad will not convert like someone who searched for your brand name. Compare each channel with its own history, not with the other channels.

### How do you read it by funnel step?

This is where the real diagnosis happens. With GA4 ecommerce events in place you can see the pass rate between each step:

| Funnel step | GA4 event | If it is weak, look at |
| --- | --- | --- |
| Product view | view_item | Category pages, site search, ad targeting |
| Add to cart | add_to_cart | Product page: images, price, shipping info, reviews |
| Checkout start | begin_checkout | Cart: surprise costs, coupon field, trust |
| Purchase | purchase | Checkout: form length, forced accounts, payment options |

**Example:** in the store above, 9% of product viewers add to cart, 45% of those start checkout, and 40% of checkout starters buy. Product page to cart looks like the weakest link, but confirm it against the previous period and the device split before you act.

## Which actions should you prioritize?

Score every idea on three questions: how many users does it affect, how strong is the evidence, and how easy is it to ship? The order below is a sensible starting framework for most stores.

1. **Fix measurement first.** If purchases are missing or double counted, every other analysis is wrong. Start with a tracking audit.
2. **Show the total cost early.** In Baymard Institute research, once "just browsing" is excluded, the most common abandonment reason is unexpected extra costs such as shipping, taxes and fees, at 40%. Show shipping cost and delivery time on the product page.
3. **Simplify checkout.** In the same research, 18% of shoppers abandoned because the site wanted them to create an account and 17% because checkout was too long or complicated. Guest checkout and fewer fields are often the fastest win.
4. **Treat mobile as its own project.** Tap targets, keyboard types, a sticky add to cart button and digital wallets make the difference on phones.
5. **Answer product questions on the page.** Size, dimensions, materials, return terms and genuine reviews should be easy to find before anyone has to ask.
6. **Improve speed and stability.** web.dev recommends an LCP within 2.5 seconds, INP of 200 milliseconds or less and CLS of 0.1 or less. Pages beyond those limits can lose shoppers in the first seconds.
7. **Match the ad message to the page.** The price, offer or product promised in the ad must be visible immediately on the landing page.
8. **Win back those who leave.** Cart and browse reminders recover part of the traffic that did not convert. Our [ecommerce email automation guide](/en/blog/ecommerce-email-automations) covers the flows in detail.

## How do you know a change worked?

Shipping a change and checking the rate the following week is misleading, because seasonality, promotions and traffic mix shift results on their own. If you have enough traffic, validate changes with an A/B test. If not, run a controlled before and after comparison that matches weekdays and channel mix, and log the outcome as "likely" rather than "proven".

Also, never make conversion rate the only goal. Discounts lift the rate easily while cutting average order value and margin. Always read conversion rate next to revenue per order and profitability.

## How do you find the reason behind the numbers?

Analytics tells you where the loss happens but rarely why. Pair the quantitative data with qualitative methods:

- **Session recordings and heatmaps** show where people hesitate, what they tap repeatedly and where they give up.
- **Site search reports** reveal zero result queries that point to gaps in your range or your product naming.
- **Customer service logs** surface repeated questions such as "when will it arrive?" or "does this run small?", which tell you exactly what the product page is missing.
- **One question exit surveys** on checkout give you direct answers instead of guesses.

Collect these findings in a single hypothesis list and note which funnel step each one affects, so the whole team prioritizes from the same table.

## What are the most common mistakes?

- Comparing yourself to an industry average instead of your own trend.
- Leaving bot traffic, internal visits and test orders unfiltered.
- Shipping several big changes at once and never knowing which one worked.
- Confusing ad platform conversions with actual store orders.
- Averaging mobile and desktop together.

If you would like a second pair of eyes on your funnel data, you can book a free growth analysis with the Performetic team through our [contact page](/en/contact).

## Key takeaways

- Calculate conversion rate as orders divided by sessions, from one consistent data source.
- Do not act until you have segmented the rate by device, channel and funnel step.
- Start where the loss is biggest: surprise costs, checkout friction and mobile UX are the usual suspects.
- Validate every change with a test or at least a controlled before and after analysis.
- Read conversion rate together with average order value and margin.

## FAQ

### What is a good ecommerce conversion rate?

There is no universal good rate. Price point, product type, traffic source and device mix all move the number significantly. The most reliable comparison is your own store over time, using the same definition and the same channel and device breakdown for every period.

### Should conversion rate be based on sessions or users?

Both work as long as you stay consistent. A session based rate counts repeat visits and is usually lower than a user based rate. Choose one definition for all reports and compare periods and channels using only that definition.

### What is the first step to improve conversion rate?

Make sure tracking is accurate, because missing or duplicated purchase events invalidate every analysis. Then review funnel steps by device and focus on the step with the biggest drop. Extra costs, checkout friction and mobile experience are usually at the top of the list.

### Do discounts increase conversion rate?

They often do in the short term, but they can also lower average order value and margin. Conversion rate should never be the only goal. Read it alongside revenue per order and contribution margin to check whether a discount actually produced profitable growth.

## Sources

- [Baymard Institute: Cart Abandonment Rate Statistics](https://baymard.com/lists/cart-abandonment-rate)
- [web.dev: Web Vitals](https://web.dev/articles/vitals)
- [Google for Developers: Measure ecommerce (GA4)](https://developers.google.com/analytics/devguides/collection/ga4/ecommerce)