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To measure ecommerce customer experience, track the shopping journey from product discovery through purchase, combine funnel and technical data with customer feedback, and investigate why shoppers leave before changing the experience. Conversion rate and cart abandonment show outcomes, but neither explains the whole experience—or, on its own, why a shopper did not buy.
What ecommerce customer experience metrics should you track?
Use several kinds of measures together. Transaction and journey metrics show what shoppers did; performance metrics reveal technical friction; customer feedback and downstream outcomes add context about how the experience felt and what followed.
| Measure | What it can show | What it cannot establish by itself |
|---|---|---|
| Purchase and revenue outcomes | Whether visits result in purchases and how much revenue is recorded. | Why a shopper did or did not buy, or whether the experience was easy. |
| Journey-stage events | Where shoppers move from product views to adding items, starting checkout, and purchasing. | Why a particular stage has a drop-off. |
| Core Web Vitals | Real-world loading performance, interactivity, and visual stability. | Whether shoppers understood product information, trusted the offer, or found checkout usable. |
| Customer research and feedback | What shoppers say or do when asked to complete tasks; possible points of confusion or dissatisfaction. | How often an issue occurs across all customers, unless paired with broader behavioral evidence. |
| Repeat purchase, returns, and support contacts | Downstream signals that may indicate loyalty, product or expectation mismatch, or added support burden. | A single definitive cause for a change in those outcomes. |
Google says, “Core Web Vitals is a set of metrics that measure real-world user experience for loading performance, interactivity, and visual stability of the page.” These are useful technical experience signals, not a complete customer-experience score. Google’s documented “good” thresholds are Largest Contentful Paint (LCP) within 2.5 seconds, Interaction to Next Paint (INP) under 200 milliseconds, and Cumulative Layout Shift (CLS) below 0.1. See Google Search Central’s Core Web Vitals guidance for definitions and measurement guidance.
Keep the measurement definitions consistent
Choose what counts as a product view, add-to-cart, checkout start, and purchase, and keep the definitions and reporting windows consistent when comparing periods. Check that events are present and not duplicated before interpreting a change. A missing purchase event can make performance look worse than it is; a duplicated event can make it look better.
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How do you instrument the ecommerce journey?
Start by defining the key stages shoppers should be able to complete. For a typical purchase journey, measure product views, add-to-cart actions, checkout starts, and purchases. Add other events only when they answer a specific question about your store—for example, whether shoppers reach a shipping step or encounter a payment error.
Google Analytics does not automatically send ecommerce-specific interactions such as purchases, add-to-carts, and product views. Merchants must implement the ecommerce events they want to measure. Follow the current setup instructions in Google Analytics Help’s ecommerce documentation, then validate that each event fires at the intended point and carries the information needed for reporting.
- Map the journey. Write down the stages a shopper can complete, from product discovery to purchase. Include only meaningful stages for your store.
- Choose an event for each stage. Define what action counts—for example, whether a product view requires a product detail page to load, or whether an add-to-cart counts only after the cart updates successfully.
- Implement ecommerce events. Configure your analytics implementation so the relevant actions are sent. The exact implementation depends on your analytics setup and store platform.
- Test the event stream. Complete a test journey and confirm that each event appears once, in the correct order, with the expected product or transaction details.
- Build a funnel and review it over a consistent period. Google Analytics’ Purchase journey report displays steps and drop-off, subject to the report’s setup and available data. See Google Analytics Help’s Purchase journey report guide.
Do not treat a funnel as reliable until its underlying events have been checked. An implementation change, a missing event, or duplicate firing can create an apparent change in shopper behavior that is really a measurement problem.
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How do you find where shoppers drop off?
Use a purchase funnel to locate the stage where the journey loses shoppers, then decide what evidence to collect about that stage. A funnel tells you where to start investigating—not why the drop-off happened.
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Look at movement between product views, add-to-cart, checkout starts, and purchases. Prioritize a stage where the number or share of shoppers moving forward falls sharply, especially if the stage carries meaningful business impact. Then compare useful segments, such as device, traffic source, product, or customer type, to see whether the pattern is concentrated rather than store-wide.
If the largest change appears during checkout, make the investigation specific. Ask whether shoppers can see shipping costs before committing, whether an account requirement blocks progress, whether forms work on the affected device, and whether payment controls respond correctly. These are hypotheses to test, not causes proven by the funnel.
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Separate expected non-purchase from avoidable friction
Not every abandoned cart represents a broken checkout. Baymard Institute reports that 42% of US online shoppers in its latest quantitative study abandoned a cart in the prior three months because they were “just browsing / not ready to buy.” The finding, described in an article updated February 2, 2025, is a reminder that some shoppers are not yet trying to complete a purchase. It does not show what share of any particular store’s abandonment is intentional. Read Baymard’s article on reasons for cart abandonment for its study context.
Baymard’s research overview reports a 70.19% global average cart-abandonment rate and says it has tracked that global average for 14 years. Treat this as Baymard’s cross-site benchmark, not a target for an individual retailer or a diagnosis of its checkout. Store context, measurement definitions, and shopper intent matter. See Baymard’s ecommerce cart and checkout usability research overview.
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Pair analytics with direct evidence. Analytics is good at counting actions and locating changes in a journey; customer research can reveal confusion, hesitation, or interaction problems that event counts cannot explain.
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- Observe representative shoppers completing tasks. Ask participants to find a product or complete a purchase, and watch where they hesitate or get stuck. A task-based session can expose unclear labels, difficult form entry, or controls that do not behave as expected.
- Review customer contacts and feedback. Look for recurring questions or complaints related to product details, shipping, checkout, payment, or account access. These reports can suggest what to investigate, but do not necessarily establish how widespread a problem is.
- Audit checkout usability. Inspect the actual steps, content, form behavior, and controls. Baymard’s checkout research explains why an analytics report alone cannot reveal every usability failure.
- Compare qualitative findings with funnel patterns. If a suspected issue appears in customer feedback and the affected journey stage also shows a drop, the combined evidence gives you a stronger basis to test a change than either signal alone.
How do you decide what to improve first?
Prioritize issues by customer impact, how many shoppers they may affect, severity, and strength of evidence. A severe payment-control failure deserves attention even if it affects a narrower segment; a small wording concern seen in a few sessions may need more evidence before a broad redesign.
| Evidence or symptom | What to investigate | Potential next step |
|---|---|---|
| Shoppers drop at shipping information or cost disclosure. | When shipping costs become clear, whether delivery options are understandable, and whether the displayed information matches checkout. | Make relevant costs and delivery details easier to find, then measure movement through the affected stage. |
| Shoppers start checkout but do not complete forms. | Required fields, account requirements, error messages, keyboard or device behavior, and whether form entry is difficult. | Remove avoidable complexity or clarify form instructions, then check completion and error signals. |
| Checkout controls appear to fail. | Buttons, validation, payment interactions, and whether the issue occurs on a particular device or segment. | Fix the verified defect and confirm that the intended interaction works before assessing conversion impact. |
| Page experience is slow or visually unstable. | Core Web Vitals and the affected pages or devices. | Address the measured loading, responsiveness, or stability issue, then recheck the relevant technical metric and journey outcome. |
| Abandonment is high but evidence of friction is weak. | Whether shoppers are browsing, comparing options, or waiting to buy, alongside feedback and observed sessions. | Collect evidence before treating all abandonment as a checkout defect. |
Baymard estimates a 35.26% potential conversion-rate increase for the average large ecommerce site from checkout design improvements. This is a modeled research estimate based on Baymard’s checkout usability work, not a forecast or promised uplift for an individual store. The same research overview summarizes the potential as 35%; neither figure establishes the result a particular retailer will achieve.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you verify that an improvement worked?
Make a focused change whose intended effect can be measured. When feasible, use a controlled experiment so you can compare the changed experience with a concurrent comparison group. If you instead compare before and after, account for seasonality, changes in traffic mix, and other changes that could have influenced the result; the timing alone does not prove that your change caused it.
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- State the problem and expected effect. For example, if shoppers cannot find delivery costs before checkout, specify which information will change and which journey stage should respond.
- Choose a primary measure. Pick the outcome closest to the problem, such as movement from checkout start to purchase—not a convenient metric unrelated to the suspected friction.
- Set guardrails. Check relevant signals such as payment errors, returns, or support contacts so a gain in one measure does not conceal a new problem.
- Make one interpretable change or run a controlled experiment. Avoid changing many unrelated parts of the journey at once if you need to understand which change mattered.
- Recheck the outcome and the experience. Review the same event definitions and reporting window, inspect relevant segments, and use customer feedback or task observation where the numbers do not explain the result.
What makes a good online shopping experience?
A good experience lets shoppers understand what they are buying, make informed choices, move through the journey without avoidable obstacles, and complete the transaction with confidence. Its quality is broader than a completed sale: technical performance, clear information, usable interactions, customer-reported satisfaction, and downstream outcomes such as repeat purchase, returns, and support needs all provide different evidence.
There is no single metric that captures all of these dimensions. Use transaction outcomes to understand results, journey events to locate friction, performance measures to assess the technical experience, and customer research to understand the human reasons behind the behavior.
Frequently Asked Questions
Is a high cart-abandonment rate proof that checkout is broken?
No. Some shoppers abandon carts while browsing or before they are ready to buy. A funnel can identify where to investigate, but you need additional evidence to determine whether checkout friction is the cause.
Does Google Analytics automatically track product views and purchases?
No. Ecommerce-specific interactions such as product views, add-to-carts, and purchases require implementation; validate the events before relying on funnel results.
Are Core Web Vitals a complete measure of ecommerce customer experience?
No. They cover loading performance, interactivity, and visual stability. Pair them with journey outcomes, customer feedback, and relevant downstream measures.
Should a store use Baymard’s abandonment benchmark as its target?
No. Baymard’s 70.19% figure is a reported global cross-site average, not a target or diagnosis for an individual store. Compare your own consistently defined data and investigate the causes behind it.
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