Why Shoppers Drop Off and the eCommerce Data Tools That Fix It

eCommerce teams aren’t short on data. What’s missing is clarity. When revenue stalls or conversion drops, the real question isn’t “What happened?” It’s “Where are shoppers getting stuck, and what should we fix first?” In our recent webinar with Webeyez Founder and CEO Uri Strauss, we explored how friction detection helps you pinpoint the exact moments shoppers abandon their intended actions, connect those moments to lost revenue, and prioritize the changes that drive measurable impact.

What “Friction” Means in an eCommerce Funnel Most eCommerce journeys follow a familiar structure. Shoppers land on a homepage, product page, or campaign entry point. They move into browsing, search, and category pages, often navigating large SKU catalogs. Then they reach checkout. On paper, it looks straightforward. In reality, friction can surface at every stage.

Landing pages fail to load fast enough, or calls to action do not respond. Middle-funnel journeys become difficult to manage across hundreds or thousands of SKUs and varied discovery paths. Checkout breaks down with payment errors, validation issues, or failed order placement. And the cost is not small. 78% of users are unlikely to return after a bad experience. When the technology feels unreliable, trust drops. Not just in the site, but in shipping, returns, support, and even the product itself.

Business Friction vs. Technical Friction Friction can be grouped into two categories:

Business friction : usability and journey issues (login, discounts, checkout validations, search results) Technical friction : performance and reliability issues (page load metrics, JavaScript errors, failed API calls) The truth is, 100% of brands lose money due to friction. The question is: how much? Brands commonly lose 25% to 30% of revenue due to poor user experience and friction.

How Lost Revenue Gets Attributed to Friction Lost revenue can be measured by comparing what happens after a friction event. If a shopper encounters an issue but still progresses to the next funnel step, the impact is different than when that shopper drops off entirely. When friction is followed by abandonment, the revenue risk becomes measurable.

For example, if the conversion rate after a successful experience is 21% but drops to 11% after a failure, that gap represents attributable revenue loss. By tracking success-to-failure rates at each step, teams can quantify the financial impact of friction and prioritize fixes based on real revenue exposure rather than assumptions.

Most Common Business Friction Points (and Key Fixes) Business frictioncan be grouped into four categories:

Login and registration Place order Discounts Other (checkout validations, cart issues, search with no results) 1) Login and registration failures A friction point does not necessarily indicate a bug. For example, a forgotten password is not a system failure, but it becomes friction when it occurs at scale.

Common contributors:

Forgotten passwords Technical login errors (server/service issues) Registration errors (smaller share) Fixes that reduce login friction:

Social login (Google, Facebook) One-time passwords Magic links (email link that logs the shopper in) Rewards programs (increases likelihood of returning and logging in successfully) Technical stability still matters: login services must work correctly, but when they do, the login experience design becomes the main lever.

2) Place order failures (checkout “holy grail”) Order placement failures occur at the most critical point in the funnel. Common failure types include:

Card transaction declined Technical errors Invalid shipping address Actions that reduce place-order failures:

Add payment options (a declined method can be replaced with another) Offer buy now, pay later Open chat/support at the moment of failure Use fallback mechanisms (avoid reliance on a single payment provider) Reduce false positives in fraud detection Fix overly strict validations, especially for shipping address formats A recurring issue: address validation rules can be overly strict (e.g., state abbreviations, character limits, formatting differences). Fixing validation and adding fallback validation sources reduces unnecessary checkout failures.

3) Discount and coupon code friction Discounts are difficult because coupon scraping and trial-and-error behavior are common. Even so, the impact is measurable. Conversion decreases by 20% when shoppers fail to apply a successful coupon code.

Primary fixes:

Better error messaging “Invalid coupon” is insufficient. Messaging needs to explain the reason (minimum spend, product requirements, shipping constraints). Validate coupon setup and distribution Expiration issues Typos in sent codes Influencers using incorrect codes Offer a small fallback discount in repeated failure scenarios A cited approach: if repeated coupon attempts fail, offering 3%–5% off can be preferable to losing the transaction entirely. 4) Other business friction: validations and search failures Common issues:

Checkout page validations that block progress Cart activity issues Search returning zero results Actions:

Fix validation rules and add fallback mechanisms Improve search by: Adding synonyms Adding keywords and descriptions to titles Reviewing failed search queries (including non-product queries such as “return policy”)

Most Common Technical Friction Points (and What to Prioritize) Technical friction can be categorized into three major areas:

JavaScript errors (largest share of lost revenue in the technical category) Core Web Vitals / load-time metrics Failed API calls (to first-party and third-party services) JavaScript errors JavaScript errors are difficult to eliminate completely. Not every error is worth fixing; prioritization depends on:

Where the error happens How often it happens Whether it affects shopper actions Observed categories:

Internal script errors Internal type errors Third-party errors Actions:

For third-party errors: send the script error, stack trace, and session evidence to the vendor. For internal errors: Remove unused scripts (especially those added via tag managers) Monitor errors by funnel stage Start with checkout, then move up the funnel (cart → PDPs → PLPs → home) Core Web Vitals and load-time metrics Core Web Vitals affect user experience and influence traffic channels that rank sites based on these metrics.

Key issues include:

Page movement while clicking (CLS) Interaction delay (INP) General load-time problems Optimization is ongoing rather than a one-time fix. Practical actions include:

Use caching (CDN/server; multiple caching approaches exist) Defer images and load them as needed during scrolling Avoid oversized images and heavy videos Ensure correct sizing and positioning for assets across mobile and desktop Prioritization approach:

Start with top 10 pages by page views Ensure metrics are below relevant thresholds Failed API calls API failures tend to split between:

Calls to first-party servers Calls to third-party servers Actions:

Monitor failing calls and prioritize by shopper-facing impact A broken action matters more than skewed analytics data. Remove unused code and review third parties monthly Open support cases with vendors when 400/500 errors appear, including evidence of the failure Operating Model for Ongoing Friction Detection and Resolution A repeatable process prevents friction work from becoming reactive.

The four-step loop Track Track friction points, not only aggregate KPIs. Use anomaly detection and real-time awareness rather than discovering issues after conversion drops. Quantify Measure magnitude: affected users, where it happens in the funnel, and revenue impact (or a proxy if revenue attribution is unavailable). Alert Set alerts so issues are addressed when they begin, not after they escalate. Early alerts make it easier to correlate issues with releases, campaigns, and channel changes. Resolve Prioritize fixes based on revenue loss, magnitude, and user experience impact. Maintain a clean benchmark and close the loop after fixes. Cadence for teams High-performing teams manage friction on a clear cadence, often in two- to four-week sprints. They review friction data, prioritize issues based on revenue impact and frequency, and assign fixes across product, eCommerce, and technical teams. Critical issues still move faster. When revenue is at risk, response happens immediately, not at the next sprint review.

Why Funnel Drop-off Data Alone is Insufficient Standard funnel reporting can show where drop-off occurs (for example, a 25% drop between product pages and cart), but it does not explain why. Without visibility into friction events, such as payment failures, out-of-stock behavior, JavaScript errors, or API failures, drop-off remains a metric without a cause.

How to Get Started The highest-leverage improvements are rarely random. Start where revenue is most exposed: checkout failures, payment errors, and false fraud declines. Remove login friction that blocks repeat purchases. Reduce coupon fallout with clearer validation and smarter fallback logic. Address JavaScript errors and performance issues where they interrupt the funnel. Monitor API failures that silently break key shopper actions.

Friction reduction is not a one-time cleanup. It is a revenue protection practice. Track, quantify, and resolve it consistently. That is how data turns into growth.

Watch the webinar on demand to see how leading eCommerce teams are identifying friction, prioritizing what matters, and protecting revenue with measurable, repeatable action.

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