Ecommerce presence which in turn is defined before any product page is viewed. Search engines, shopping directories, marketplaces, recommendation engines and AI based search solutions require detailed product info to know what sellers offer and at what time those products should be put forward. That in turn puts product feeds at a very important point in the ecommerce structure.
A product feed is a technical file of the past which now serves as a live database of product info. We see them transform into dynamic sources of intelligence which merchants can update in real time. As digital discovery automates what it reports, those who have put in full and consistent product details will outdo competitors which still report incomplete or which are out of date.
Product Info is Becoming More Structured.
In today’s world shopping is done via search results, shopping tabs, market places, social commerce settings, comparison engines, and AI tools which means that we are only as good as the product information we put out. We require solid details in order to put forward a proper item to our customers.
- Proper titles help platforms determine what you are selling.
- Attributes in detail better target customer search terms.
- Present pricing which causes checkout issues.
- Availability info which in turn reduces exposure of products that don’t sell.
- High quality images improve visual discovery and product recognition.
Feed quality plays a role beyond that of advertising. It may determine if products are properly interpreted, matched and presented across many different discovery platforms.
Product Info is no longer the sole source of truth.
Traditionally ecommerce teams have put great focus on product pages. While those still are important elements, also of importance are feed systems, structured markup, merchant data and marketplace attributes.
This is an issue of wide scale optimization. An in depth product page does not make up for sparse info which is found in other places. As it stands merchants report that they are looking for the same base facts to be present in their web stores, structured data and external feed reports which in turn is what platforms present to customers.
Attributes that determine which products make it into the consideration set.
A general product title may put out the basic item type, as goes into detail attributes which describe size, material, compatibility, color, condition, gender, intended use or other relevant info. As search trends increase in specificity so do the value of those details.
Platforms which have a very in depth knowledge of an item are better able to determine if that item is relevant to a given request. Also feed optimization is turning into in part an information architecture issue as opposed to what was mainly a concern of keyword placement.
Feed Recency May Also Be a Competitive Indicator.
Ecommerce info is in a constant state of change. Prices fluctuate, inventory goes up and down, promotions are introduced and passed, new products are put out and old ones retired, out of stock issues are a regular thing. If we don’t see these changes in our feed in real time, the info that our discovery platforms present is out of date.
Inventory Accuracy Improves Customer Experience Visibility.
Presenting out of stock items may get a click but creates a poor shopping experience. Also we see that putting up old prices causes confusion when customers get to the site.
For which platforms aim at consistent shopping experiences, we see that data freshness is key. Those merchants which update inventory, price and promotion info in a timely fashion are able to reduce discrepancies and put out more reliable product listings across external channels.
AI which is into Shopping has Machine-Readable Product Context.
AI powered shopping experiences put forth another issue of improved feed quality. In the case of conversational systems they may have to compare many products according to very specific criteria instead of just returning pages with matching keywords.
In order to support such interactions machines should have clear product info. We see that out of dimensions, tech specs, compatibility info, materials, features and other structured data which together present a rich picture for recommendation and comparison systems.
Five types of vulnerability which may silently decrease reach.
As data sets grow in importance we see that small issues of data can in fact create large scale visibility gaps across many channels.
- Generic names make it hard to tell products apart.
- Attributes which are missing reduce relevance for in depth product searches.
- In which some categories do not agree which in turn causes issues for platform classification systems.
- Out of date stock or pricing info hurts listing reliability.
- Duplicate and contradictory info causes uncertainty across channels.
These issues are very much so because they scale across an entire catalog. We see a repeating attribute issue which may affect thousands of SKUs and thus report much broader issues than a single page problem.
Catalogue Management is a Focus in Our Search Strategy.
This change is in the air. What we are seeing is an end to the independent operation of SEO, merchandising, paid media, development and catalogue management which previously may have had little to do with each other as they all put out info on the same product.
For instance merchandising teams will handle product names, developers will work on feed generation, marketing will do better campaign attribute optimization, and SEO’s will look after structured data. Better communication will help to prevent different systems from publishing conflicting products info.
Smaller Retailers may compete by better using information.
Large ecommerce players of course have it easy with their catalog size, brand recognition and advertising budgets. In terms of product discovery however, structure of the info is what sets them apart.
A small player which has their specs right, great product descriptions, reliable inventory and in depth product info will in fact put large competitors to shame which have poor catalog management. Thus feed optimization is a technical solution for better search visibility which in turn leads to more clicks rather than just depending on outspending the competition.
Visibility is a Goal for each SKU.
In the growing ecommerce discovery space competition may play out at the product level. Each SKU is a set of variables which platforms will use to determine if a product should show up in a particular search, recommendation or compare results.
This is that ecommerce teams will have to include feed completeness in addition to traditional SEO and advertising metrics. What we see as issues of missing identifiers, poor descriptions, wrong categories, or in full product specs should be classified as issues of discoverability instead of just admin errors.
As technology in shopping interfaces advances more in automation, what we will see play out is that which does best will be that which although designed for machines still puts customer accuracy first. For clear product info, consistent category structure and regular updates which will enable merchants to maintain relevance in a broken discovery environment.
End result.
Product data is at the core of what we see in ecommerce today search engines, marketplaces, shopping platforms and AI powered search are at large using structured info to index products and put them in front of right audiences.
In the coming months we will see which players come out on top in the war for ecommerce visibility via catalog accuracy, attribute depth, feed freshness and cross channel consistency. Those merchants which treat feeds as a base of strategic discovery rather than just data exports will be better off as product search shifts to become more automated, contextual and machine driven.
Frequently Asked Questions.
What is an ecommerce product data feed?
A product feed is a structure which contains info for many products that includes product titles, prices, images, availability, identifiers, categories and attributes for use by external platforms.
Do product feeds play a role in ecommerce visibility?
Yes. Product data feeds which in turn make shopping and discovery systems’ performance better by the accuracy of product representation which in turn improves how well related products are put forward in search and recommendation.
What products should we pay the most attention to in terms of which fields we fill out?
Titles, descriptions, prices, availability, images, categories, identifiers and product specifics are of great importance though they do vary by platform and product category.
Why is feed freshness important?
Fresh data which has been updated to reduce the difference between what external platforms display and the merchant’s own site, especially for products which see great price, promotion, and stock fluctuations.
Will AI play a key role in product feeds?
AI which is an element in shopping systems’ structure may put more value on the use of structured product info as it makes products easy to compare, classify and recommend.