Funnel Analysis in eCommerce: How to Find Drop Offs and Fix Them

There is a moment in every store where the magic either happens or slips away. 

A visitor hovers over the add to cart button, pauses, and then vanishes like a ghost. You check revenue and feel that familiar twist in your stomach. Something is leaking in the funnel. The good news is that funnel analysis is not a mystery. It is a method. It is a set of steps that turn confusion into a map you can follow.

Funnels are series of events

In eCommerce, a funnel is just a sequence of events that describe how shoppers move from first visit to completed order. You define the key steps, measure the conversion between them, and study where people drop off. That sounds simple. It gets tricky when real customers behave like cats with credit cards:

They browse on phones at the gym, return on desktop at work, and the session boundaries never line up neatly. This is exactly why a disciplined approach pays off. 

I have been there, staring at the chart, drinking stale coffee, and whispering to the checkout page to please behave.

Its not fun, at all…

Setting up the stage

Let us set the stage. Think of your funnel as a path that usually includes landing page, product page, add to cart, checkout start, shipping details, payment, and order confirmation. Your store may add or remove steps, but the logic is the same. Each step has a numerator and a denominator. 

How many people reached it, and how many continued. Your job is to measure those transitions with clean events, slice the data by context, and then test better experiences where the flow slows down. 

The results can feel dramatic once the leaks are identified and sealed.

What funnel analysis tells you that averages never will

Averages hide the story. Funnel analysis reveals it. You do not just ask how many people converted. You ask where people stopped, on which device, under which traffic source, and after which friction. Funnels show compounding losses. A small failure early can crush the final rate. For example, a product page that fails to load variant options on older phones might shave two points off add to cart. 

That loss then echoes through the rest of the steps and suddenly your week looks worse than it should. I once chased a conversion dip for days and discovered a broken size chart link that looked harmless but killed intent.

Good funnel work also answers questions about momentum. How fast do shoppers move between steps. Where do they hesitate. Which fields they abandon. Which error appears before rage clicks. I like to pair funnels with supporting views like form analytics, session replays, and heatmaps. 

A good tool for this can be prettyinsights, which is very affordable.

The building blocks you need before you start

You cannot analyze what you did not measure. Your event schema is the foundation. Name events clearly and persist user identity across sessions where privacy rules allow. 

Define everything clearly

Define at least these core events. Page viewed, product viewed with product ID, add to cart with quantity and price, checkout started, shipping information submitted, payment attempted with method, order completed with value. 

Add properties such as device type, traffic source, campaign tags, currency, country, coupon code, and experiment variant. Keep names stable. Future you will thank present you.

Data quality wins always

Data quality wins the boring award, and it wins the conversion race too. Check that events fire once and only once. Confirm that the checkout form fires a success event only after the server acknowledges payment. If you measure client side, include server side verification for the money events. Avoid clocks that drift between systems. Align time zones. I once shifted a dashboard to local time and thought conversion fell. It was a timezone mismatch. Yes, I felt very clever afterward.

How to frame the funnel for eCommerce

Start with the most common journey and add branches later. A good baseline might be:

1. Landing page viewed 
2. Product page viewed 
3. Add to cart 
4. Checkout started 
5. Shipping submitted 
6. Payment submitted 
7. Order completed 

Measure the step to step conversion rate, the absolute counts, and the time between steps. Run the same funnel for the last thirty days and for the last seven days. Compare mobile with desktop. Compare new visitors with returning visitors. Compare organic search with direct and with paid social. If a rate shifts more than a few points week over week, that is a flag. If mobile breaks while desktop holds, hunt for layout or input problems. If returning visitors convert and new visitors bounce, look at landing page clarity and message match.

The usual suspects that cause drop offs

Certain problems appear in many stores. Here are patterns that often create leaks, along with quick checks and fixes.

• Slow product pages cause hesitation and bounces. Audit largest elements and image sizes. Lazy load what you can. 
• Confusing sizing or shipping rules reduce confidence. Add prominent size guides, delivery estimates, and return policies near the call to action. 
• Cart friction pushes people away. Remove surprise fees. Show taxes early. Make discount codes forgiving. 
• Checkout forms demand too much. Remove optional fields. Offer guest checkout. Auto detect city and state from postal code when possible. 
• Payment errors stop momentum. Offer at least two methods. Provide clear error messages with next steps. 
• Mobile keyboards fight input. Switch input types to match the field. Numeric keypad for phone and card number. Email keypad for email. 
• Pop ups and modals collide with native browser UI. Test on a handful of real phones. Emulators miss rough edges. 

Each bullet sounds small. Together they define the shopper experience. You fix a few and the funnel begins to relax.

Choosing a funnel analysis tool

Choosing your funnel analysis tools can be a tedious task, especially with all these options available on the market, but i have a solution:

A useful funnel tool should meet these criteria.

• Event based tracking with custom properties, not only page views
• Retroactive funnels so you can define a new step and analyze past data
• User stitching across devices where privacy allows, with clear identity rules
• Filters and breakdowns by device, source, campaign, country, and experiment variant
• Form analytics for field time, error rate, and abandonment
• Ability to view sessions for context when a step shows heavy drop
• Real time or near real time updates for fast debugging
• Alerts for sudden changes, not just daily emails
• Data retention that matches your buying cycle
• Export or API access so you can back up raw events and run custom queries
• Privacy controls and cookieless options where required by regulation
• Honest pricing that does not explode when you grow traffic
• Allows robust event tracking 

Pick the smallest set that fits your team today and can scale with you tomorrow. Fancy features are great, but the basics must be rock solid.

How to use your tool to find drop offs fast

Open your tool and build the baseline funnel with the steps defined earlier. Use a clean date range, such as the last four full weeks. Make sure the funnel excludes bots and internal traffic. 

Now apply the breakdowns:

• Device first. 
• Source second. 
• New versus returning third. 

Those three slices uncover most big problems within minutes. If desktop holds and mobile collapses at checkout, you know where to look. If organic converts and paid social vanishes at product view, revisit message match and creative promise.

Next, zoom into the worst step. If checkout start to shipping is weak, open form analytics for that step. Look at fields ranked by average time to complete and error rate. 

You will usually find one or two culprits. Phone number validation fails. Apartment field is hidden. Postal code rejects valid formats. 

Fix those. 

Then review sessions around that step to confirm what the field metrics suggest. I once found that a country dropdown loaded below the fold on smaller phones, and shoppers thought the page froze. The fix was a simple layout tweak. The conversion impact felt like magic.

Quantify impact and prioritize fixes

Funnel work turns best guesses into measurable bets. Estimate the impact of each fix by modeling the step uplift. If ten thousand shoppers started checkout and only six thousand reached payment, lifting that step by two points adds two hundred extra payment attempts. With your usual payment success rate and average order value, you can forecast revenue upside. Prioritization becomes much easier when you attach numbers to the work. 

Keeping a simple table

I like to keep a simple table with issue, hypothesis, estimated impact, effort, owner, date, and result. It keeps the team honest and motivated.

Also, treat experiments like a muscle. When a step shows drop off, try two or three fixes in sequence rather than a big redesign. Shorter cycles give faster feedback and reduce the risk of making things worse. Keep wins in a playbook so you can repeat them on future launches and seasonal campaigns. The boring habit of documentation outperforms sudden bursts of inspiration.

Connect funnel analysis with creative and product strategy

Funnel charts reflect the story your store tells. They show whether the pitch lands and whether the product answers the need. If paid social sends curiosity but not intent, your creative might be entertaining but poorly aligned with the offer. 

If search brings intent that dies on the product page, your page may fail to show trust signals or make benefits obvious. Pull designers and copywriters into your funnel reviews. Bring engineering for form and performance issues. Invite support to share customer questions that hint at hidden friction. The best improvements come from cross functional eyes on the same chart.

I remember a brand that sold beautiful shoes and hid the return policy in a tiny footer link. Mobile add to cart looked healthy, but checkout start cratered. One line near the button solved it. Free returns within thirty days. Conversion rose and returns did not spike. Shoppers wanted safety, not excuses.

A practical weekly routine you can copy

You do not need a giant project. You need a rhythm. Here is a routine that works.

• Monday. Check the main funnel for the last seven days and compare with the previous week. Flag steps with meaningful shifts. 
• Tuesday. Deep dive into the weakest step with breakdowns and supporting context. List problems with evidence. 
• Wednesday. Ship small fixes for the top one or two issues. Update the playbook. 
• Thursday. Review performance of recent experiments. Archive wins and sunset losers. 
• Friday. Share a one page summary with charts, insights, and next actions. Celebrate one bright spot. 

This pattern keeps the team focused and stops the funnel from drifting out of sight.

Advanced moves once the basics work

When your baseline is healthy, try more strategic cuts. Build funnels by product category to spot weak merchandising. Compare first purchase funnels with repeat purchase funnels to find loyalty friction. Add a step for post purchase engagement like account creation or review submission. Use cohort views to see if changes help new customers more than returning ones. Feed funnel results into your ad platforms by passing conversion values that reflect actual checkout completion rather than earlier micro goals. Your budget will thank you.

You can also tie funnels to inventory and pricing events. If a variant goes out of stock, watch the add to cart step and the rage click rate on the size selector. If you run a coupon, track whether it improves add to cart or simply delays checkout while people hunt for codes. The point is to connect funnel movements with real business changes. Do that, and your analysis stops being a report and becomes a steering wheel.

Common pitfalls to avoid

A few traps catch many teams. Do not declare victory after a single good day. Seasonality and campaigns create noise. Give changes enough time and use clear holdouts where possible. Do not compare funnels across radically different traffic mixes without normalizing. A spike of new visitors from a viral mention will usually depress conversion rates. Do not obsess over a tiny step if the upstream leak is obvious. Fix the biggest pipe first. And please do not chase vanity metrics that do not move revenue. I learned that lesson with a gorgeous new carousel that nobody asked for.

Bringing it together with a sample workflow

Let us walk a quick example. Imagine your funnel shows a sharp drop from checkout start to shipping details on mobile during the last seven days. You break it down by device and confirm that desktop is stable. You check form analytics and see phone number errors spike for one country. You open a few sessions and watch people type numbers that your validator rejects. You patch the rule to accept various formats and adjust the input to show a country code helper. You retest on real phones, deploy, and set an alert on the step rate. Over the next three days the step improves by three points. You update the playbook and thank past you for measuring properly.

That is the job. Measure, observe, hypothesize, ship, learn, repeat. It is not glamorous. It works.

Conclusion

Funnel analysis gives you clarity where intuition fails. It turns hand waving into specific problems you can fix with specific changes. 

Your store already records the story. You simply need the right events, the right views, and the discipline to ask better questions each week. When you do, drop offs become road signs rather than dead ends. The team gets faster, calmer, and much more confident.

Choose a good tool

Choose a tool for its fundamentals, not its buzzwords. You for example can go with PrettyInsights which is a product analytics type of tool, and an excellent funnel analytics tool.

You want clean event tracking, strong breakdowns, retroactive analysis, useful context like forms and sessions, privacy controls you can defend, and a price that scales without heartache. Then use that tool like a craftsperson. 

Build the baseline. Segment. Investigate. Fix. Document. Share. Over time the funnel becomes a living system that supports design, marketing, and product decisions with evidence rather than opinion.

And if everything fails, check the size chart link. Trust me, I have scars.

One last line to make your day. If conversion were a pet, it would be a cat that only listens after you fix the litter box.

Wether you have a shopify, or woocommerce type of store or any kind of ecommerce or a website that involves payment from the customers, you need to analyze your funnels.