Even a product that seems simple at first glance, such as a T-shirt or a sleep pillow, can have dozens of attributes beyond color, size, and materials.
All of these characteristics matter to a potential buyer, who of course does not want to risk money. So to increase the chance of a purchase, the seller needs to take care of a number of details.
Keep product information up to date.
If something changes in the product attributes, the corresponding updates must appear immediately in the product record across all sales channels.
Provide the most complete information possible.
Sometimes even a basic product description requires several hundred parameters: dimensions, weight with and without packaging, color, size, primary and secondary materials, production parameters, seasonality, included items, and so on.
Adapt product information for different sales channels.
Different marketplaces have different requirements for how information is organized, for example the format of images or how dimensions are written. This also needs to be taken into account.
Eliminate errors and inaccuracies in data
For example, in units of measure and other quantitative indicators, manufacturer data, etc.
Doing this manually is difficult and time-consuming. First, because of the company's established data storage practices.
Let's return to SportS: different product attributes are stored separately.
This storage setup emerged at the start of the business.
Production information is stored in 1C, photos in the corporate cloud, fabric care requirements in spreadsheets, descriptions in Google Docs, size charts in the sewing workshop files, and so on. Some information exists only in employees' heads and in correspondence.
The more possible sources of information there are, the longer a manager spends looking for it, especially if they have only recently joined the company and have not yet learned the shortcuts to where certain information is stored.
This is inconvenient and exhausting, but to bring together data on all products, even more time has to be spent aggregating, updating, and rechecking the data. Second, product listings on marketplaces also have to be assembled manually. That increases the risk of errors or typos in product characteristics, missing important fields, and confusion in names and images.
And despite the huge amount of time and effort spent, there is no real quality improvement - the domino effect still kicks in with the following chain: product descriptions on the website and marketplaces are incomplete, not always high-quality or up to date; customers are forced to request additional information, clarify details down to shades, compatibility with other products, millimeters in dimensions, etc.; this intensifies
the flow of inquiries going to quality control and/or support teams, chatbots, and the seller's hotline; the incoming line is overloaded, handling follow-up questions as well as complaints related to incomplete product information across sales channels; at the same time, frustration arises over the overload of customer support and the slow resolution of issues where such help is genuinely needed.
Result: the seller's reputation suffers, and sales do not grow even when market conditions are favorable.
