How To Improve Customer Experience In eCommerce in 2026: Fix the Windows That Actually Decide Repeat Orders

Quick answer: Most advice on this topic points at the storefront, which is the part online retailers already invest in most heavily. The larger opportunities sit either side of it: the certainty problem before checkout, the unowned window between order confirmation and delivery, and the returns process that decides whether somebody buys from you a second time. Fixing those three costs less than a redesign and moves more.

I should say upfront that I find most writing in this category unhelpful. It recommends personalization, mobile optimization, and faster support, none of which is wrong and none of which tells you where to start. So this piece is organized around where the evidence says the losses concentrate rather than around a tidy list of eight strategies.

What Experience Means When Nobody Meets You

The three windows that matter

Online retail has no shop floor, no assistant reading a face, no chance to recover a bad moment in person. What you have instead are three windows where a customer forms an opinion:

1. Deciding, from arrival through checkout, where doubt kills more sales than price does
2. Waiting, from confirmation to the parcel arriving, which nobody in the organization owns
3. Resolving, covering returns, faults, and complaints, where loyalty is either earned or lost outright

Everything else is decoration. Useful decoration sometimes, but decoration.

Worth noting how differently these three are resourced. The first gets a team, a budget, and weekly reporting. The second gets an automated email nobody has read since it was written. The third gets a policy page and a shipping label. That imbalance is where the opportunity sits, because the parts nobody works on are the parts where small changes still produce visible movement in customer experience.

A useful exercise is to ask which of the three your organization could describe in detail from memory. Most teams can walk through the storefront funnel step by step and go vague somewhere around dispatch. That asymmetry in attention is usually mirrored exactly in the results.

Why site polish is the least of them

Storefronts get the budget because they are visible, measurable, and fun to work on. Nobody presents a redesigned returns portal at an all-hands meeting.

Yet the pattern in the data is stubborn. Cart abandonment has hovered near 70% for well over a decade despite enormous investment in checkout technology, which suggests the constraint is not aesthetic. A prettier site does not resolve a shopper’s uncertainty about total cost, delivery date, or whether they can send the thing back easily.

Prioritize functionality over aesthetics, and you will feel like you are neglecting the part everyone notices. Do it anyway.

This is not an argument that design does not matter. A store that looks untrustworthy loses orders on sight, and visual credibility does real work in a category where shoppers hand over card details to a brand they may never have heard of. The argument is narrower: past a threshold of looking legitimate, further visual refinement returns very little, while clarity about cost, timing, and returns keeps paying.

Before Purchase: A Certainty Problem, Not a Price Problem

What the abandonment data actually says

The Baymard Institute maintains the most-cited figure in this field: an average documented abandonment rate of 70.22%, drawn from a meta-analysis of 50 separate studies. That number has barely moved in a decade.

More useful than the headline is the breakdown of reasons. Among people who abandoned for a reason other than simply browsing, the leading cause is extra costs appearing at checkout, followed by mandatory account creation and a process that runs too long or feels too complicated.

Read those together and a pattern emerges. Not one of them is really about price. They are about surprise, obligation, and effort. A shipping fee revealed on the final screen is not expensive so much as it is a broken expectation, and the reaction is to whatever you concealed rather than to the amount.

Certainty beats polish

Practical changes that address certainty rather than appearance:

• Show total cost, including delivery, as early as the product page allows
• State the free shipping threshold everywhere, not only in the cart
• Give a delivery date, not a shipping speed, since nobody translates “2-5 business days” into a calendar
• Allow guest checkout, and earn the account afterward when there is a reason to create one
• Show the return policy before checkout, because it removes the biggest unspoken risk
• Reduce form fields to what you genuinely need, and stop asking twice

None of that is glamorous. All of it removes a reason to leave.

A test worth running on your own checkout: hand a phone to somebody who has never used your store and ask them to buy something, without helping. Watch where they hesitate. Five minutes of that tells you more than a quarter of analytics, mostly because you will see them pause at things you stopped noticing years ago. The pauses are where doubt lives.

Where search and findability fit

Internal search deserves a mention because it is consistently under-resourced relative to how much revenue depends on it. A shopper who types a query has declared intent; failing them is worse than failing a browser.

Check what your zero-result queries look like this month, since that report is sitting in your analytics untouched in most businesses. You will typically find misspellings you could handle, product names customers use that you do not, and whole categories people expect you to stock. That list is a cheap roadmap, and reviewing it monthly costs an hour.

The Post-Purchase Gap Nobody Owns

Now the part I think most operations get wrong, and the reason this article exists.

Why “where is my order” dominates your contact volume

Between the confirmation email and the parcel arriving, who owns the relationship? Marketing has moved to acquisition. Logistics owns the parcel, not the person. Support hears from them only once something has gone wrong. The customer sits in a gap between departments with no visibility and no obvious owner.

That gap produces the single largest category of inbound contact in most online retail operations: people asking where their order is. Those conversations cost money, add nothing, and arrive with mild irritation already attached, because ringing you was not the plan for their afternoon.

The compounding effect is what makes it expensive. A customer who feels uninformed checks tracking repeatedly, contacts you once, and then treats your next brand communication with slightly less patience. None of that shows up as a complaint. It shows up eventually as a customer satisfaction reading nobody can explain and a repeat rate drifting downward for reasons the dashboard does not capture.

Worth being precise about the cause. The customer is not impatient about the delivery. They are uncertain about it, which is a different problem with a much cheaper solution.

A proactive update rhythm

Fill the gap deliberately:

• On order: confirmation with a delivery date, not a range, and what happens next
• On dispatch: carrier, tracking, and a revised date if it moved
• Day before: a reminder with a delivery window
• On any delay: the delay itself, before they notice, with a new date and an apology that does not read as legal boilerplate
• On delivery: confirmation, plus how to start a return if the item is wrong

The fourth is the one that matters and the one most retailers skip. Telling somebody their parcel is late feels like admitting failure. Letting them find out by checking a tracking page five times is worse, and it converts a logistics problem into a trust problem.

Use whichever channel the person prefers. A text message suits most delivery updates; email carries detail better; messaging apps work well in markets where that is where people already are. Our notes on scaling support cover the operational side of running this at volume.

Who should own this window

Somebody has to. My view is that it belongs with customer support rather than logistics, because the skill required is communication rather than transportation, and because the support team is the one carrying the cost when the window goes unmanaged.

Give that team the tracking data, the authority to compensate within limits, and a target that reflects prevention rather than handling. If their target is calls answered quickly, they will get very good at answering calls that should never have happened.

The tools for this are mostly ones you already own. Carrier webhooks, a messaging platform, and a rule that fires when a promised date slips. What is usually missing is not technology but ownership, which is a management decision rather than a procurement one. I have watched operations spend six figures on new tools while nobody was accountable for whether a late parcel produced a message, and predictably nothing improved.

Returns Are the Second Purchase Decision

What the returns data shows

The National Retail Federation, working with Happy Returns, put the 2025 online return rate at 19.3% of sales, against 15.8% across retail as a whole. Roughly one online order in five comes back.

Three findings from that research matter more than the volume:

• 82% of consumers say free returns are an important consideration when shopping online, up from 76% the previous year
• 71% say they are less likely to shop with a retailer again after a poor returns experience, up from 67% in 2024
• Four in five will tell friends and family about that bad experience

Sit with the second figure. A return is not the end of a transaction; it is an audition for the next one, and the majority of people who fail you here do not come back.

Designing a return people forgive

Returns get treated as a cost center because the accounting says so. The customer relationship data says otherwise, and I would treat the process as a retention program with a shipping line attached.

What works:

1. Publish the policy plainly, before purchase. Ambiguity here suppresses first orders as well as repeat ones.
2. Make starting a return self-service and immediate. Nobody should need to speak to anyone to send back a jumper that did not fit.
3. Refund on scan, not on receipt. Holding money for the ten days a parcel spends in transit is the single most resented practice in the category.
4. Ask why, in one field, optional. That answer is your product quality data and nobody else collects it.
5. Route the exceptions to a person quickly. Faulty, damaged, and high-value returns need judgment, and the automated path handles them badly.

One caution about generosity. Free returns raise conversion and raise return rates simultaneously, so the net effect varies enormously by category and margin. Test it rather than assuming, particularly in apparel where bracketing behavior is common.

There is a strategic reading here that goes beyond operations. A brand competing on convenience cannot afford a returns process that feels grudging, because the whole promise it makes is that shopping with it is easy. A brand competing on craft or specialism has more latitude, since customers accept more friction from a business they believe is genuinely careful. Match the returns experience to the promise the rest of your brand makes, and the awkward cases become considerably easier to decide.

Personalization: Where It Works, Where It Annoys

Ranking and merchandising

Personalization earns its keep quietly, in ordering rather than in address. Showing a returning customer the categories they actually browse, remembering their size, surfacing product recommendations based on genuine behavioral signals: all of that reduces effort and nobody notices it happening, which is the point.

Use real-time data for this where you can, since a session-level signal about what a customer is looking at right now beats a profile assembled last quarter. Individual customer behaviors observed in the moment are simply better evidence than demographic inference.

The intimacy problem

Where it goes wrong is when personalization performs a relationship rather than removing friction. Personalized experiences that address someone by name in every subject line, or reference a purchase in a way that reads as surveillance, produce discomfort rather than warmth.

The line I use: tailoring should make the shop feel organized, not make the shop feel like it has been watching you. Tailoring communications to what somebody has actually asked for stays on the right side of it. Inferring intimate detail from what somebody bought last month does not.

Retargeting the item somebody already bought remains the most common own goal in ecommerce marketing, and it happens because the systems doing the advertising and the systems recording the order have never been introduced.

My honest position, and I go back and forth on it, is that most stores would gain more from spending personalization budget on the previous two sections. Recommendation quality is a real lever at scale. Below a few hundred thousand orders a year, the delivery and returns experience almost certainly matters more, and it is cheaper to fix. Reasonable people in this industry disagree with me about that, particularly the ones selling the software.

What to Fix First

Two observations about that ordering. The top three are configuration work rather than development work, which is exactly why they get deprioritized in favor of things that look like projects. And the eighth item is where most budgets actually go first.

A third observation, offered with less confidence. The sequence above assumes a reasonably healthy business with steady traffic. If your problem is that nobody arrives in the first place, none of this is your bottleneck and a customer experience strategy is the wrong project this quarter. Fix acquisition, then come back. Experience work compounds on top of traffic; it does not substitute for it.

Measuring It Without Fooling Yourself

Conversion rate alone will mislead you, because it improves when you spend more on acquisition quality and worsens when you widen the top of the funnel. Watch these instead, and prefer the ones a customer would recognize as describing their own experience:

• Contact rate per hundred orders, split by reason, since status enquiries falling is the clearest early signal
• Repeat purchase rate at 90 days, which captures the post-delivery and returns effect that conversion misses
• Return rate alongside return satisfaction, because the two move independently and only tracking one is misleading
• Checkout completion by step, which localizes the problem rather than reporting that one exists
• Effort scores at delivery and at return, which our guide to customer effort covers properly
• Time from problem to resolution, measured from when the customer first noticed rather than from when they contacted you

Run changes one at a time where you can. Ecommerce teams tend to ship five things in a sprint and then attribute the result to whichever one they liked best, which is not measurement so much as storytelling.

Where Live Help Still Earns Its Cost

Self-service handles most of this, and should. Two categories genuinely benefit from a person, though, and the growth potential in both is routinely underestimated.

High-value orders are the first. Somebody hesitating over an expensive order converts materially better with a short conversation, and the economics justify staffing for it even at low volume. Offering a call-back option on a high-value cart is one of the few interventions in this space with an obvious payback.

Recovery situations are the second. A customer whose order arrived broken during a gift-buying period does not want a form. They want somebody to fix it now, and how that goes determines whether you keep them. Peak periods concentrate these, which is why our peak season guidance treats staffing for exceptions separately from staffing for volume. The distinction matters because exception handling needs judgment and authority rather than throughput, and the people good at one are not automatically good at the other.

Keep the route to a human visible rather than buried. Hiding it saves less than you think, since determined customers find it anyway and arrive annoyed by the hunt.

Two related points on loyalty, since it comes up whenever anyone discusses repeat orders. A formal points program is not the same thing as loyalty and frequently substitutes for it, buying repeat behavior that stops the moment a competitor discounts harder. Genuine loyalty in online retail tends to come from a boring reliability nobody advertises: things arrive when promised, problems get fixed without argument, and nothing about the experience requires effort. That is harder to build than a points scheme and considerably harder for a competitor to copy.

Frequently Asked Questions

How much does improving this actually affect revenue?

Honest answer: it varies enough that any single figure would mislead you. Baymard estimates around $260 billion in recoverable sales across the US and EU through checkout improvements alone, but that is a market-level number rather than a promise about your store. A more useful approach is to size your own gap: multiply your abandonment rate by average order value, then assume you can recover a modest share. Measure the actual result and revise.

Should small stores bother with personalization?

Not first, and possibly not at all in the sophisticated sense. A store with a few hundred products and modest traffic lacks the data volume for recommendation engines to outperform sensible manual merchandising. Spend that effort on clarity, delivery communication, and returns instead. Once you have enough repeat customers for behavioral signals to be meaningful, revisit it. Buying a platform before you have the data to feed it is a common and expensive sequencing error.

What is a reasonable target for cart abandonment?

Compare against your own vertical rather than the global average, since the spread is enormous. Grocery runs around 60%, mainstream retail in the high sixties to mid seventies, and travel and finance considerably higher. Your structural band is set by what you sell; checkout work moves you a few points within it. Chasing the headline 70% figure as a target is meaningless if your category naturally sits at 85%.

How quickly do these changes show results?

Delivery communication changes show up within weeks, since they affect contact volume immediately and the mechanism is simple. Checkout improvements take a full buying cycle to read reliably, usually a month or two. Returns changes take longest, because the payoff is a second order and you cannot observe that until people either come back or do not, which means six months minimum before you draw firm conclusions about whether anything worked.

Does mobile really need separate treatment?

Yes, and more than most teams allow for. Mobile carts abandon at meaningfully higher rates than desktop across every benchmark network, driven by form friction, small tap targets, and payment steps that assume a keyboard. Treating mobile as a scaled-down version of the desktop layout is the usual mistake. Test the checkout on an actual phone with one hand, outdoors, on a poor connection, which is how a substantial share of your orders are placed.

What should you do about negative reviews?

Answer them, publicly, without defensiveness, and quickly. Prospective shoppers read the replies more attentively than the complaints, and a measured response showing that problems get fixed does more for your brand than the rating average does. Fix the underlying cause too, since one complaint appearing repeatedly is telling you about a product fault or a broken process rather than about an unlucky order. Never remove criticism you simply dislike, because a flawless review page reads as fake to anyone paying attention.

How do you spot emerging friction before customers complain?

Watch leading indicators rather than waiting for feedback. Zero-result searches, checkout step drop-offs, repeat contacts about one order, and sudden changes in a single product’s return rate all surface problems before anyone writes in. Complaints arrive late and represent only the small minority of shoppers who bother to write in at all. A weekly look at those four signals catches most issues while they remain small enough to fix cheaply.

Ready to close the gap nobody owns?

Voiso runs voice, SMS, and messaging apps on one platform with shared conversation history, so proactive delivery updates go out on the channel each customer prefers and whoever handles the reply can see everything that came before. Our ecommerce solutions page has the detail. Talk to the Voiso sales team about what share of your inbound volume is people chasing an order, because that number usually surprises people.

Sources

• Baymard Institute, cart abandonment rate statistics (70.22% average across 50 studies; leading fixable reasons including unexpected extra costs, forced account creation, and overlong checkout): https://baymard.com/lists/cart-abandonment-rate
• National Retail Federation and Happy Returns, 2025 Retail Returns Landscape (19.3% of online sales returned; 82% weigh free returns; 71% less likely to shop again after a poor returns experience): https://nrf.com/research/2025-retail-returns-landscape