Find Why Conversion Dropped on Your Shopify Store

Overview

Conversion fell this week, and everyone asks the same question: was it the site, the traffic or something else? eCommerce managers, marketing teams and developers each look at their own reports, and each sees only part of the answer. A conversion dashboard shows that the rate moved, not why. The cause can be a payment provider that times out, a theme release that slows the product page, or a campaign that brings shoppers who were never going to buy. Each needs a different team and a different fix. GermainUX answers the question from the data. It compares the period with its baseline, ranks the segments that moved with it, overlays the releases and related signals on the same timeline, and rules out what stayed normal. Then it shows you the sessions behind the drop, so you see what shoppers saw. This page uses a Shopify store as the example. The same approach works for any online store that GermainUX monitors.

Questions It Answers

What GermainUX Watches

GermainUX collects the signals that explain a conversion change in one place, so the analysis can compare them with each other.

Find the Cause

The investigation follows the same path every time, whatever the cause turns out to be:

1. Open Aggregate Analysis on the conversion or checkout KPI, for the period when it dropped. GermainUX compares the period with a baseline from the time before it.
2. Read the Leading Factors. A segment classified DEGRADED shows where the drop is concentrated. A segment classified BETTER shows who wasn’t affected.
3. Check Relationships and Related KPIs for the signals and releases that changed at the same time.
4. Rule out the signals that stayed normal. They tell the team what not to fix.
5. Open one affected session in Session Replay as evidence.

The four examples below come from simulated store data, and their numbers belong to the example. Each one ends at a different kind of answer.

Example: A Payment Gateway Starts Timing Out

One morning, the payment provider’s API starts timing out. In this example, 40% of started checkouts convert that day, against 72% over the previous days. The fix belongs to the payment provider. Once it’s in place, run the same analysis on the following hours to confirm that conversion and the payment API’s response time are back at their baseline.

Example: A Theme Release Slows the Product Page

A storefront theme release rebuilds the product page. The deployment succeeds and nothing errors, so no health check fires. But the page gets slower, the layout shifts, and fewer shoppers add products to the cart. Fix or roll back the changed files, then compare the days after the fix with the days after the release.

Example: A Campaign Brings Traffic That Doesn’t Buy

The marketing team retargets a paid social campaign at a broader audience. Its traffic keeps arriving, but these shoppers browse without adding to the cart. In this example, store conversion falls from about 10.4% to 8.0%. This answer goes to the marketing team, not the developers. Adjust the campaign’s audience, then compare its conversion with the other channels again.

Example: A Checkout Fix Brings Conversion Back

For weeks, shoppers in some countries were told to “enter a valid ZIP code” when their address was correct. The team ships a checkout release that fixes the validation. The same analysis that finds a drop shows whether the fix worked. Share the result with the team that shipped the fix, and keep an alert on the checkout errors so a regression shows up quickly.

Alert and Act

Each example points to a signal you can watch from now on:

Set It Up

The analysis needs the store’s data, the checkout funnel, and relationships that tell GermainUX which signals belong together.

Additional Resources

Get Help

Every store has its own funnel, providers and release process. The GermainUX team can help you set up the checkout process, the KPI relationships and the alerts that fit your store.

The content of this document is proprietary to GERMAIN SOFTWARE.