Plenty of visits and few purchases is not a specific enough problem to fix. A store can lose interest at the product page, when requesting an address or when revealing delivery costs. A funnel helps locate a transition worth investigating. It does not explain a visitor's motivation by itself, nor prove that a particular visual element caused the abandonment. Use it to narrow the question before deciding on a solution.
Define the journey you want to observe
Start with a few stages: product view, add to cart, checkout start and purchase. Google's ecommerce documentation describes events including view_item, add_to_cart, begin_checkout and purchase. Installing Analytics does not guarantee that your store sends these events correctly. Before designing a chart, follow a test purchase in an appropriate environment and check with your technical team what is recorded at each stage. Include payment-return paths and page reloads in that review.
Decide who enters and what order counts
Specify whether the analysis requires entering at the first stage and whether steps must occur in sequence within a chosen window. A customer can return another day, open several tabs or arrive directly at the cart. Those choices change who appears in your funnel. Do not divide separate event counts and label the result a people-based conversion rate: one person can generate several events. Document the unit and the inclusion rules beside the analysis.
Read a transition using sample figures
Imagine 1,000 people included at the first stage, 150 at cart, 90 at checkout and 60 at purchase. In this hypothetical example, checkout-to-purchase completion is 60 divided by 90, approximately 66.7%. That proportion is not a universal target. It gives you a baseline for comparing the same defined journey with its own history. Confirm that all four numbers use the same definition and period before assigning work based on them.
Segment before redesigning
Inspect mobile and desktop separately, then channels or landing pages where volumes support the analysis. If the issue appears on one device, test that journey: difficult fields, errors, unexpected costs or missing payment methods. Compare the finding with payment-system incidents too. Falling measured purchases with stable payments may suggest an instrumentation issue rather than lost buyers. Treat that as a hypothesis and check specific operations before declaring that the tracking or the checkout is broken.
Make a change you can evaluate
Choose a small intervention, such as explaining delivery earlier or repairing a field that fails. Record its release time and how you will assess the outcome. Avoid promising a percentage improvement before measuring it. ClawStud can help surface broad changes in traffic and sales; detailed funnel explorations and event validation belong in GA4 and the store implementation. Keeping those responsibilities clear helps route each investigation to the appropriate tool and person, with a defined follow-up rather than an open-ended redesign.
Official reference: Google Analytics: Measure ecommerce.
Read next: How to share store metrics without sharing passwords.
