Inclusive eCommerce: Best Practices to Make Your Site More Accessible

As the world increasingly moves online, eCommerce retailers face an important challenge – and opportunity. Accessibility isn’t just a buzzword; it’s the key to unlocking your store’s full potential. By making your website more inclusive, you can reach a broader audience, improve user experiences, and demonstrate your commitment to creating a welcoming space for all.


This guide will walk you through the essentials of eCommerce accessibility, offering actionable steps to ensure your online store is usable by everyone, including people with disabilities. Let’s dive into why accessibility matters, the principles of inclusive design, and practical strategies to implement it effectively…


Why eCommerce accessibility matters


Accessibility isn’t just a nice-to-have; in fact, it’s often a legal requirement. Laws like the Equality Act in the UK and the European Accessibility Act mandate that websites and digital platforms accommodate users with disabilities. Non-compliance can result in lawsuits, fines, and damage to your reputation.


Beyond compliance, accessibility opens your site to a much larger audience (which makes good business sense). Did you know that over one billion people worldwide live with some form of disability? By making your online store more accessible, you’re opening your business to a vast, often underserved customer base. At the end of the day, accessibility improvements enhance the user experience for everyone, not just those with disabilities. 


Think of closed captions on videos – not only helpful for people with hearing impairments but also for those watching in noisy environments. Many accessibility improvements, like image alt text and semantic HTML, also overlap with search engine optimisation (SEO) best practices, making your site easier for search engines to crawl. This can ultimately help your site rank higher in the search results, boosting your brand’s online visibility and driving more traffic to your store.
 

Accessibility 101 

 

The UK’s 2010 Equality Act requires businesses and organisations to make their websites accessible to everyone, including people with disabilities. This means removing barriers that could make your site difficult or impossible for some users to navigate.


Website owners are obliged to make “reasonable adjustments” to accommodate disabilities, such as adding text-to-speech compatibility for visually impaired users or ensuring keyboard navigation for those with motor impairments. If these adjustments aren’t made, this could be considered discriminatory.


The Act applies to all sectors, from online commerce to government services. While it doesn’t lay out specific technical rules, it often references the Web Content Accessibility Guidelines (WCAGs) as a benchmark for what constitutes a reasonable level of accessibility.


The principles of inclusive eCommerce design 

 

To create an inclusive eCommerce experience, your site should follow the four guiding principles of the WCAGs.


Perceivable


All users, regardless of their abilities, must be able to access the information presented.

  • Provide text alternatives for images and multimedia (e.g., alt text, captions)
  • Offer alternatives for audio and video content (e.g., transcripts, subtitles)
  • Ensure content is adaptable and easy to view, such as by allowing zooming without losing content clarity
  • Use sufficient colour contrast for readability.


Operable


Users must be able to interact with your site using a variety of tools, such as keyboard navigation or assistive technologies like screen readers. 

  • Make all functionality accessible via keyboard (not just a mouse)
  • Give users enough time to read or interact with content
  • Avoid design elements that can trigger seizures, such as flashing images
  • Provide clear, consistent navigation aids (e.g., menus, search functions).


Understandable


Make your site’s content and navigation intuitive.

  • Use simple language
  • Provide clear instructions
  • Ensure web pages behave in predictable ways (e.g., no unexpected pop-ups)
  • Provide clear instructions for filling out forms or correcting errors
  • Error notifications should be easy to interpret.


Robust


Ensure your site is compatible with a wide range of assistive devices and adaptive technologies, both now and in the future (as technology evolves).

  • Use clean, semantic code for screen readers and other assistive tools
  • Test compatibility with a range of devices and assistive technologies.


Simple steps to an inclusive shopping experience 

 

To create an inclusive eCommerce experience, your site should follow the four guiding principles of the WCAGs.


1. Use alt text for images


Alt text provides a textual description of an image that can be read by screen readers, enabling visually impaired users to understand the content even if they cannot see it.  For instance, instead of saying "image of shoes," a better description might be, "Red high-heeled shoes with a glossy finish." For decorative images that don’t convey essential information, using a null alt attribute (e.g., alt="") ensures that screen readers skip these images, reducing unnecessary clutter.


2. Optimise keyboard navigation


Test your site by navigating it without a mouse. All essential functions, such as navigating menus, accessing the shopping cart or completing checkout, should be operable with keyboard-only input. Use visible focus indicators (e.g., a highlighted outline around the currently selected element) to show where the user is on the page.


3. Add ARIA labels


Use ARIA (Accessible Rich Internet Applications) attributes to enhance screen reader compatibility. ARIA labels provide additional context for interactive elements like buttons and links, making it easier for screen readers to interpret their purpose. For example, a button that simply says “Click here” could be enhanced with an ARIA label that specifies “Click here to add this item to your cart.”


4. Ensure colour accessibility


Users with visual impairments, including colour blindness, may struggle with low-contrast text or interfaces that rely solely on colour to convey information. Choose colour combinations with high contrast and use patterns or text labels to supplement colour-coded indicators. For instance, instead of using only green and red to indicate availability, add text labels like “In Stock” or “Out of Stock” to ensure all users can interpret the information.


5. Create accessible forms


Label all form fields clearly and use placeholder text sparingly. For example, "First Name" should be a visible label, not just a placeholder. Error messages should also be descriptive and provide guidance on how to correct issues. For example, instead of a generic “Invalid input” error, a more helpful message might say, “Please enter a valid email address in the format name@example.com.”


6. Design for screen readers


Use semantic HTML to structure content logically and ensure that screen readers can interpret your content effectively. Headings (<h1> to <h6>), lists, and tables should follow a clear hierarchy and avoid overly complex layouts or elements that may confuse or mislead screen reader users.\


Best practices for specific eCommerce features 

 

Accessible eCommerce features can significantly improve the user experience. Product pages, for example, should include detailed descriptions that go beyond marketing copy to provide essential information in plain language. High-resolution, zoomable images are also beneficial for users with low vision. Your site’s search functionality should also be flexible and intuitive. Predictive text and autocorrect features can help users find what they need, even if their search terms aren’t exact matches. 


For mobile accessibility, ensure your site is optimised for smaller screens, with larger buttons and adequate spacing to accommodate touch interactions. And during the checkout process, simplicity is key. Reducing the number of steps and providing clear instructions at every stage minimises barriers for users with cognitive or physical disabilities. Descriptive error messages are particularly important during checkout, as they guide users to resolve any issues quickly. 


Data validation is an oft-overlooked aspect of accessibility. Fetchify's tools, like address and phone number validation, streamline the user experience by reducing the risk of input errors during checkout. This benefits all users, including those with cognitive or motor disabilities, by minimising frustration during checkout. For example, Fetchify’s address auto-complete automatically completes addresses in real-time, reducing the effort required from users. This is especially beneficial for individuals with motor disabilities or cognitive challenges who may struggle with typing lengthy information accurately.


Accessibility isn’t a one-time project


Accessibility is an ongoing commitment. Regularly audit your site, stay updated on WCA guidelines, and prioritise user feedback. Inclusive eCommerce not only demonstrates social responsibility but also strengthens your business by fostering loyalty and trust among all customers. By following the steps outlined in this guide, you’ll be well on your way to creating a shopping experience that’s welcoming, inclusive, and enjoyable for everyone. 


Ready to start your journey toward accessibility? Explore how Fetchify’s tools can help you build a more inclusive eCommerce site today.

About Fetchify


Fetchify’s address lookup and data validation platforms cover more than 250 countries, and increases customer conversion with the fastest, most accurate customer data capture. Fetchify’s flagship products – Address Auto Complete and Postcode Lookup – reduce friction at the checkout, and also significantly increase the number of successful deliveries. Founded in 2008, Fetchify processes millions of data transactions every day for clients ranging from startups to established high-street names, and offers a full suite of data validation tools, including phone, email and bank, too.

The Silent Churn You Never See

By Fiona Paton • June 18, 2026
How data decay is quietly removing your best customers before they ever decide to leave. Somewhere in your CRM right now, there is a customer you think you lost. They stopped buying about eighteen months ago. They went into a lapsed segment, got a couple of reactivation emails, did not respond, and were eventually written off. The assumption was that they moved on. What actually happened, in a surprising number of cases, is much simpler. They moved house. The reactivation emails went to an inbox they no longer check. The direct mail went to a flat that has a different tenant. The customer was not gone. They were just unreachable. And because the database had no way of flagging the difference, they were counted as churn. This is how data decay works. Not in dramatic failures, but in a steady accumulation of records that have quietly stopped being accurate. Around 30% of customer data goes stale every year, not because anything went wrong, but because people move, change jobs, switch email addresses, or get married. Left unaddressed, that figure compounds. A database that has not been properly maintained for three years may have a third of its records either partially or wholly unreachable. The problem is that it is almost invisible until it is already significant. A handful of bounced emails does not raise an alarm. Neither does a slightly elevated returns rate. The metrics look broadly normal because the volume of bad data is not yet high enough to distort them. By the time it is, the damage is done. The churn you cannot account for Most businesses have a reasonable handle on the customers they actively lose. Cancellations are tracked. Lapsed accounts are flagged. Retention programmes exist precisely to address the customers who stop buying. What those programmes cannot reach is the customer who never formally left. They sit in the CRM as a lapsed record. They count toward the database size. They get included in reactivation segments. They cannot receive the communication because the address on their record is no longer valid. The downstream effect is real. A repeat customer whose address changed after a house move never receives the offer that would have brought them back. A lapsed member does not see the renewal reminder and lets the subscription quietly expire. In both cases, the organisation records an attrition event. In neither case did the customer actually decide to leave. A customer who moved house is not the same as a customer who left. That distinction tends to matter quite a lot when you are trying to work out where your retention budget should go. Why reactivation campaigns underperform When a win-back campaign comes back with poor results, the instinct is to interrogate the campaign. The subject line gets tested. The offer gets more aggressive. The timing gets adjusted. All of that is reasonable. None of it helps if a meaningful share of the list cannot receive the email in the first place. A lapsed customer segment typically contains three types of contact: people who genuinely disengaged and are unlikely to respond, regardless, people who might respond to the right message, and people who would respond, but the email never arrives because the address has changed. The frustrating thing is that you cannot easily tell these groups apart from the outside. Low open rates and low click-through rates look the same whether the cause is disengagement or data decay. Email is only part of it. Physical address decay affects direct mail and delivery. Phone number decay affects SMS and outbound calling. Each channel erodes at its own rate, and most organisations are not tracking the accuracy of their data across all of them. 30% of customer database records become inaccurate within 12 months, without any action by the customer. What changes when the data is clean A data cleanse does not just improve deliverability, though it does that. It changes what the numbers actually mean. When ghost records are removed from a lapsed segment, the remaining file is smaller but more meaningful. Reactivation revenue from that cleaned list is real revenue, not a percentage improvement calculated against contacts who were never going to respond. The churn figure, once recalculated without the unreachable records, is often more positive than expected. Some of what looked like permanent attrition turns out to be recoverable. There is a GDPR dimension too. Article 5(1)(d) requires that personal data be kept accurate and, where necessary, up to date. The ICO can issue fines of up to £17.5 million for data accuracy failures. Most organisations are not at serious risk of enforcement, but most organisations also have not checked how their database holds up against a standard they are legally required to meet. The more common consequence is commercial rather than regulatory. Marketing budgets applied to an inaccurate list simply do less than they should. The same spend, against a validated file, produces measurably better results. Not because the campaigns improved, but because the contacts can actually receive them. The practical starting point Addressing data decay does not require a significant IT project. For most organisations, the starting point is a cleanse of the existing CRM: matching records against current address databases, identifying email addresses with persistent bounce history, removing duplicates, and flagging phone numbers that are no longer in service. Done once, it resets the foundation. Done regularly, and combined with validation at the point of data capture, it prevents the drift from accumulating again. The customers in those unreachable records did not all decide to leave. Some of them are still out there, still buying in your category. They just moved. Improve your data health and protect your business today. Reach out to our team below for a free data health check.

From the Alexandria hills to Fetchify: meet Jay, our Tech Lead

By Fiona Paton • June 15, 2026
Jay’s career has never followed a straight line. Electronics engineering. Automotive systems. A social app for hostels that was about to launch just as COVID closed every hostel in the world. A pivot into web development. And eventually, Fetchify - where he now leads the team building the technology that keeps millions of data lookups running accurately every day. Looking back, the route makes perfect sense. Jay has always been drawn to what’s next. To faster feedback. To building things that work and seeing them work quickly. Software gave him all of that in a way that automotive engineering, for all its complexity, eventually stopped doing. The long way round Jay studied electronics engineering and came out of university specialising in embedded systems. By 2015, he was working on automated parking systems - the kind built on sensors and split-second decisions - and for a while, he found it genuinely interesting. But something was missing. “I wanted to see results faster,” he says. “With embedded systems and automotive work, the feedback loops are long. I wanted to build something and see it working.” So, he pivoted. He taught himself mobile development and from there, a startup building a social app for hostels and hotels - a platform that matched guests by shared interests, so someone travelling alone could find other guests up for the same activities. It was a genuinely good idea, with a handful of places trialling the beta version. Then 2020 arrived, the hospitality industry stopped overnight, and the timing simply couldn’t have been worse. Most people would have counted it as a setback. Jay counts it as part of the story. Finding something that fits He joined ClearCourse, initially working on the membership CRM side of the business. When a role came up at Fetchify, he knew it was the one. Tech Lead. A team to run. Real scope to build, improve and innovate - and enough space to do it properly. “What I love most about my job is the chance to be innovative and improve the quality of the software - and the opportunity to keep learning. There’s always something new.” His approach to leading the team reflects the same values. He talks about trust a lot - giving people the space to do things the way they think makes sense, rather than prescribing the path. The team checks in daily, whether that’s to swap ideas, talk through a problem, or join a scrum call. It’s not just his immediate team either: the wider Fetchify team, and within the ClearCourse group, there’s a culture of helping out. Of people being willing to lend a hand when it’s needed. “Software development can feel like a solo job, but actually the team here is solid, and we enjoy working together.” The thing he's most excited about Ask Jay what he’s most passionate about right now, and the answer is immediate: AI. Not in an abstract, trend-chasing way - but with a specific and considered view of what it actually means for software developers and the organisations they build for. “AI is raising the bar for what developers can produce. But I see it as a two-way collaboration - a helping hand to do the grunt work, while the ideas, the creativity, the innovation still come from people. It should help people achieve more in less time. Not replace the thinking.” His long-term goal is to help other ClearCourse businesses integrate AI into their products - starting, naturally, with Fetchify. For a company built on data accuracy, the intersection of clean data and AI capability is not an abstract future conversation. It’s already the direction of travel. Beyond the screen Jay grew up in Egypt, and travel is still one of the things he values most. He heads home to family a couple of times a year, and fits in city breaks wherever he can - somewhere new, with good food and different people and things to explore. His ideal off-duty scenario involves a beach, good conversation, and absolutely no particular agenda. The gym, friends and music round it off - time away from the screen that, for someone whose working life involves building technology that processes millions of data points a day, seems like a fairly sensible skill. When he imagines the distant future - the looking-back version - he pictures a career of creation, innovation and the willingness to embrace whatever comes next. That, and a beach somewhere warm. We’re very glad the winding road brought him to Fetchify.

From blocked to booming - how a data cleanse changed everything

By Fiona Paton • May 28, 2026
“Fetchify turned what felt like a crisis into a straightforward fix - and in just a couple of days. We went from not being able to contact anyone to generating four new client applications from a single send. The data cleanse didn't just fix a problem - it opened the door again.” – Marcel Stirling, Phoenix Insolvency

Why AI makes bad data more dangerous, not less

By Fiona Paton • May 26, 2026
There is a lot of enthusiasm right now about what AI can do for ecommerce and CRM teams. Personalisation at scale. Predictive analytics. Automated outreach that learns and adapts. The pitch is compelling, and much of it is real. But there is a foundational question that almost nobody is asking loudly enough: what happens when you run AI on bad data? The answer is not that the AI fails gracefully. The answer is that it fails at scale, confidently, and in ways that are harder to trace than a simple spreadsheet error. This is not a theoretical risk. It is already happening inside the organisations that have moved fastest to adopt AI-driven tools without first addressing the quality of the data those tools run on The assumption nobody questions Most organisations treat AI as a layer that sits on top of their existing data. Feed in the CRM, connect the customer database, and point the model at the transaction history. The assumption is that AI is smart enough to work around imperfections. It is not. AI systems are pattern recognition engines. They find what is consistent in the data and treat it as a signal. If your data consistently contains errors - outdated addresses, duplicate records, lapsed contacts still marked as active - the AI learns those patterns as the truth. It bases its predictions, segments, and recommendations on a foundation that does not reflect reality. B2B contact data decays at 30% per year. For a database of 100,000 records, that means 30,000 entries become inaccurate every 12 months. When an AI personalisation engine is drawing on that data to decide who to target, when to contact them, and what to offer, it is working with a picture of your customer base that is one-third wrong AI doesn't fix bad data. It amplifies it. What this looks like in practice The problems that emerge are not dramatic. They are quiet and cumulative, which makes them harder to catch. Automated email sequences reach the wrong people or the wrong addresses, generating hard bounces that damage your sender reputation and, in serious cases, trigger blocks from email service providers. Personalisation that references a customer's last purchase or location draws on a record that has not been updated in two years. Predictive models identify high-value customers to target for retention campaigns - but a portion of those customers moved, changed roles, or lapsed long ago. Each of these is a cost. Collectively, they represent a significant drag on the performance of tools that were supposed to be driving efficiency. The irony is that AI makes these problems less visible, not more. A human reviewing a list might notice that an address looks wrong. An AI processes it at speed and acts on it. A case study: what happens when AI meets dirty data A professional services firm recently experienced this directly, who work with our sister company FLG for lead management. The team began bulk emailing an existing database through their email marketing system - a reasonable use of automation for a business trying to re-engage contacts at scale. The data, however, was old. Hard bounces accumulated quickly, and their account was flagged and blocked from sending. Fetchify cleansed the data. Contact information was standardised, and inactive or undeliverable entries were identified and removed. When they resumed outreach, the results were immediate - higher engagement, no delivery issues, and the kind of performance the automation was always supposed to deliver. The AI-driven outreach did not fail because of the tools. It failed because the data had not been maintained. Once the data was clean, everything else worked as intended. The AI readiness question organisations should be asking As AI becomes a standard component of ecommerce and CRM operations, the conversation around data quality needs to change. It is no longer just a compliance issue or an operational nicety. It is a prerequisite for AI to function as intended. Before deploying any AI-driven personalisation, automated outreach, or predictive analytics tool, the right question is not 'which AI platform should we use?' It is 'is our data clean enough for AI to learn from?' For most organisations, the honest answer is no - not without first running a data cleanse. The good news is that this is not a complex or expensive process. It is a one-time exercise that resets the foundation, followed by ongoing validation to prevent decay from accumulating again. What clean data actually enables Organisations that address data quality before deploying AI achieve fundamentally different outcomes. Personalisation engines draw on accurate records and produce recommendations that reflect the real customer base. Automated outreach reaches real inboxes and generates real responses. Predictive models identify genuine opportunities rather than ghost records. The regulatory dimension is worth noting, too. The ICO can issue fines of up to £17.5 million or four per cent of global annual turnover under UK GDPR for data governance failures. AI that acts on inaccurate or out-of-date data does not protect organisations from that exposure - it amplifies it, at speed and scale. Clean data is not an enhancer of an AI strategy. It is the essential prerequisite that makes an AI strategy viable. The organisations seeing the best results from AI aren't necessarily the ones with the best tools. They're the ones with the cleanest data. Start with a free data health check and find out where you stand.
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