A shopper is looking for a desk which costs under $300, having a solid wood top and enough space to work in a small apartment; our store carries one of these desks. However, the listing for it is marked ‘Premium Work Desk’ and gives no measurements.

Because that information is not available, the customer has fewer things on which to base a comparison. Moreover, the shopping systems are left with less evidence showing that the desk meets the request.

Improving and maintaining the product information that is sent to online shopping platforms involves, in the case of AI shopping, the use of accurate identities, giving useful specifications and providing details that correspond with what customers actually are able to buy.

What should you fix first in your product feed?

Start with errors that might lead someone to the wrong product or to an offer they can’t afford, in the case where the price is incorrect, delaying the creation of a more descriptive title.

For a first audit, check:

• Each record shows the correct item, variant, bundle or multipack.
• The prices and stock status correspond to those of the product chosen on the website.
• For the destination, the category and the country, the required fields are included.
• The link for the product and the image refer to the variant that was submitted.

The specification for Google’s product data sets out the attributes that are required. Since other destinations might accept different attributes, a Google export that works should not be regarded as a universal feed.

Store the approved facts in a single catalog record and adapt them to fit the format of each channel; otherwise, a correction made in one export will cause both the website and another shopping channel to continue displaying the outdated information.

Can the platform identify the exact product?

Records that look similar require close examination; for example, one item, a replacement part and a pack of two, may have a lot of their description in common yet refer to different purchases.

When a GTIN has been assigned by the manufacturer use that one; in some cases no GTIN is assigned, and in such instances you should follow the destination’s identifier rules rather than making an assumption. Google also treats the individual item ID as being different from the item group ID which links the variants.

When carrying out the audit, open the links that have been submitted. A blue medium shirt should then appear with the option available and accurately displayed. Compare the image and price of the shirt with those in the feed record. If the connection is not clear, ask the catalog owner to sort it out before rewriting the description.

Which details help answer a buying question?

In the case of the desk, the relevant facts are its dimensions, the material it is made from and its price. The statement ‘perfect for modern living’ doesn’t really show whether the desk will fit next to a bed.

If the specifications are checked, an example of a title could be: “Brand Model Writing Desk, with a Solid Oak Top, 42-Inch Width, and a Black Frame.”

The details will vary depending on the category; furniture buyers will need the assembled measurements, and a person who is buying an electronic accessory will need documentation showing the compatibility, since this cannot safely be deduced from the connector alone. A USB-C connection does not mean that all the functions of the device are supported.

Before adding a claim it is necessary to check the supplier’s documentation; if the answer is not available then a question should be left for the product owner. Although AI can assist in drafting the copy from the approved information a plausible answer is not verified until it has been checked by someone.

Where does conversational product information belong?

Some questions need an explanation that would make a title unwieldy. Assembly requirements or package contents may belong in product copy or a supported question-and-answer field.

Google’s optional question and answer attribute is intended primarily for conversational experiences such as AI Mode. It has specific limits: answers should concern the product, exclude time-sensitive prices and dates, and avoid repeating information already submitted in other attributes. Google also says not to submit this attribute alongside its document link attribute.

Ask customer support which questions keep coming up. Merchandising can verify the answers, then the channel owner should confirm that the connector supports the field and sends it in the required format. Keep a reference to the supporting documentation so the next editor can check the answer too.

Does the live page tell the same story?

Even if the feed is corrected the page may still be out of date. When checking the specifications, the chosen variant and the structured data, remember to also look at the image that the customers see first.

Google suggests that both product structured data and Merchant Center feeds should be provided in order to increase the range of eligibility for its shopping features and to enable it to understand and verify the information; separate implementation checks are required for each.

Make sure that the key specifications are easy to read on the page, and when carrying out technical reviews that focus on Google, use the Rich Results Test and URL Inspection to examine any relevant markup and indexing issues.

Individual offers also require attention since the information should be checked to make sure it is up to date, that delivery is covered and that the terms of purchase are appropriate for the target market. A business buyer should be able to tell from the amount shown whether it buys one unit, a case or a sample. The minimum-order requirements or conditions which are based on a postcode should remain in place and not be changed into general promises.

How do you keep prices and inventory reliable?

Decide who investigates a failed update before the next promotion goes live. Otherwise, a feed may continue sending yesterday’s price while the storefront has already changed.

Google’s guidance on current product data recommends coordinating submissions with website updates. Automatic item updates can help with some mismatches, but they do not replace accurate, current submissions.

Set the update schedule around stock movements and price changes. After a change, confirm that the destination processed the intended values, then check the customer’s selected offer through checkout. Producing an export file is only part of that check.

If the same promotion causes mismatches repeatedly, investigate the source mapping and timing. Another manual correction may clear today’s error without resolving why it happened.

How should you measure the results?

Select a pilot category that includes important products and with a few well-known exceptions. Before carrying out any changes, note the missing fields, the rejected items and the offer mismatches. Assign a responsible person to each issue and establish a clear check for acceptance.

After the release has occurred, compare the relevant figures with the time it took to resolve the failed updates and examine the traffic and sales separately, observing any changes in advertising, promotions, and stock that may have affected the results.

Manual checks involving AI shopping can help in identifying incorrect product information. When testing realistic buying questions, it is important to save the prompt, the date, the market and the response. Simply one instance does not show that the visibility will be lasting or prove that the sales were caused by the feed changes. Even accurate data cannot ensure that the recommendations are valid.

How Alacrity can support the work

Alacrity E-Commerce Solutions offers ecommerce development, website design, digital marketing and SEO; these services apply whenever changes to the catalog also involve work on the storefront templates and search presentation.

Before you begin, you Before you start, you must clarify the scope of the implementation by deciding which catalog records are to be looked at, by determining who is responsible for mapping the fields, and by agreeing on the method that the team will adopt for keeping the records up to date after they have been handed over. The product specifications, prices and claims will still have to be approved by the brand.

Make the first category reliable before expanding

Begin with a category your team can review thoroughly. Correct its records, test the published offers and assign responsibility for future updates. That gives you a repeatable process for the rest of the catalog. Discuss your catalog priorities with Alacrity to assess where development and marketing support could fit.

FAQ‘s

Does optimising the product feed ensure that AI recommendations are produced?

By no means does it make product information clearer and help with eligibility, since submitting it and ensuring the data is accurate does not guarantee that placement or recommendations will result.

Does it have to be the case that each product has a GTIN?

If a GTIN has been assigned by the manufacturer then use it; otherwise, follow the identifier rules which apply to the destination. Do not produce a number to fill the field.

Can artificial intelligence compose product titles and answers?

AI can produce content using approved facts, but a person must check the specifications and claims, as well as make sure that the channel’s requirements regarding content generated by AI have been met.

How frequently should product feeds be updated?

The matches need updating in response to any changes in price, inventory or offer, and the destination’s requirements have to be met. When stock is changing quickly, more frequent updates will be necessary than is the case when the stock level is stable. Nevertheless, the number of failed submissions should still be kept under observation even if this number is small.

Should the feed and product page contain identical wording?

The words used might differ since the formats of the channels are different, but the basic facts must be identical, covering the variant, the specifications, the price and availability as well. The structured data should also correspond to that on the visible page.

Tagged product feed optimization, ai shopping, product data optimization, ecommerce feeds, merchant center, ai ecommerce, product visibility, gtin, product variants, structured data, product schema, inventory optimization, pricing accuracy, ai product discovery, ecommerce seo, alacrity ecommerce solutions