Check Your Ecommerce Site for Errors Before Ads Go Live

An ad campaign can be ready to launch while the site it points to still has problems. The desktop home page might look fine, for example, while the mobile menu is broken. Products may also appear available when they are sold out.

In a recent article, Practical Ecommerce describes ways merchants can use AI and site-checking tools to find potential issues before a promotion goes live. The key is to treat AI as a helper, not a substitute for checking the site yourself. Read the Practical Ecommerce article.

For a business owner, this is a practical campaign-preparation task. Review the pages your ads will send people to, check what shoppers can actually do there, and give the person fixing problems clear evidence.

Start with the pages your campaign touches

A broad request to check an entire website can produce a lot of findings without making the most urgent problem clear. Instead, start with the pages affected by the campaign. These could include category pages, product pages and checkout.

That focus matters when your store has many products. It may not be practical to inspect every detail manually, but you can begin with the items you intend to promote. In the source article’s example, an online clothing store advertises 30 garments, and AI could help spot which are sold out before the promotion starts.

Think through the path a shopper will take from the ad. Does the destination page show the advertised product? Is the availability information consistent? Can the shopper find the relevant product details and move towards checkout? Treat these as questions to investigate, not assumptions about what the tools can verify.

If your campaign sends visitors to a focused page, include that page in your checks too. EmmaForYou’s landing page and sales funnel service is one relevant option for businesses planning a dedicated campaign destination.

Give AI a narrow task and ask for evidence

AI is more useful when you give it a specific job than when you ask it to “find everything wrong.” Ask it to review particular pages for particular issues, and request evidence for each finding. Evidence might include the page URL, a screenshot or a relevant section of page content.

The source article says ChatGPT and Claude can analyze screenshots and page content, and can simplify technical reports. It also provides a prompt businesses can adapt:

Review the attached URLs, screenshots, and crawl reports from our site. Look for broken links, conflicting offers, misleading availability, missing product information, and mobile layout issues. For each issue, provide the page URL, the evidence, and the likely impact on shoppers. Flag the findings you cannot verify. Don’t claim clicks, form fills, or checkout steps unless you have actually completed those actions. Don’t edit the site.

Add the products, policies and campaign details that matter to your review. Keep the instruction not to edit the site. The goal is to produce findings for a person to validate, not to let an AI system make changes or claim it completed actions it did not perform.

You can also ask AI to group findings by potential customer impact. That can make it easier to distinguish a possible checkout failure from a minor layout issue. Still, verify the findings yourself before deciding what to fix.

Combine page review with technical checks

The source article outlines several tools for collecting page content or checking a site. Firecrawl can gather content and links from public pages. Tavily can search and pull page content. Both offer free monthly credits and have web playgrounds for non-coders, although they are designed for developers. You can paste in a page URL, run a scrape or search, then download the results to share with ChatGPT or Claude.

For technical checks, Google Lighthouse is available for free in Chrome DevTools. It flags speed, accessibility and search engine optimization elements. Screaming Frog can crawl up to 500 URLs for free and can catch broken links and missing metadata, such as title tags, canonicals and image alt text.

These tools provide different kinds of material for a review. Page content and links can give AI something to examine, while a crawl report can highlight technical issues. A report still needs interpretation: a flagged item is a reason to investigate, not proof that a shopper has encountered a problem.

If you need help with the store itself, you can review EmmaForYou’s online store development information. For a general first look at your site, the website audit tool may also be useful.

Test the parts that tools cannot confirm

AI can help assess screenshots and page content, but the source article stresses that people are best placed to check whether interactions work. Open the site on both your phone and desktop. Check the menu, select a product, choose a size or colour where relevant, and add the item to the cart.

Then place a test order. Check whether the process functions correctly and whether the sale is recorded correctly in the ad platform reports. These checks matter because a screenshot or crawl cannot, by itself, show that you completed a customer action.

Keep track of what you actually tested. If you did not click through a form or complete a checkout step, do not present an AI-generated finding as proof that the action works or fails. Ask AI to flag what it cannot verify, then test that part yourself where appropriate.

A useful review should end in an action list, not an unwieldy report. The source article recommends gathering affected screenshots and URLs so the person fixing an issue knows where to start. Record the issue, the page, the evidence and the next action in a way your team can follow.

Prioritise problems by customer impact

Not every issue deserves the same urgency. The source article gives a clear comparison: a checkout failure matters far more than a minor layout tweak. Use likely impact on shoppers to order the findings, then validate the important ones with a human.

For example, first investigate whether advertised products are actually available and whether shoppers can use the path to purchase. Then review issues such as broken links, conflicting offers or missing product information. Keep smaller presentation changes on the list, but do not let them distract from a problem that could stop a customer completing a purchase.

This approach can help an owner turn a technical report into decisions. It also helps the person making fixes understand which pages are involved and why the issue matters to the campaign.

What to do next

Before your next campaign, use this checklist:

1. List the campaign destination pages, including relevant categories, products and checkout.
2. Collect screenshots, page content, links and any available crawl report for those pages.
3. Give AI a specific review task. Ask for URLs, evidence, likely shopper impact and anything it cannot verify.
4. Test the mobile menu, product selection, cart and checkout on your own phone and desktop. Place a test order and check the ad platform record.
5. Group findings by customer impact, confirm them yourself and give the fixer the affected URLs and screenshots.
6. Repeat the same checks before every campaign.

The aim is not to create a long technical report. It is to find problems that could matter to shoppers, verify them and give your team a clear list of what to address before the promotion goes live.