Comersus includes many features specifically designed for retail and wholesale online clothing stores. In fact, Comersus was implemented at its early development stages for major European textile companies.
The sale of clothing online shows some unique features. Firstly, the need for flexible management of variations over each product, since clothes are offered in an array of sizes and colors. Other variations may be present, such as different prints and sleeve lengths.
For some stores it may be enough to handle this scenario as only one product, and consider variations just as useful information at the time of preparing orders. For other stores a detailed management of stock, with independent tracking of each variation, will be necessary.
Consider the case of a store which doesn’t need to track stock for each variation of an item. If its shopping cart is prepared for variation-specific stock tracking, it is likely that a lot of work will be needed during product loading and stock maintenance. Therefore the store’s administrators will waste considerable time in loading unnecessary data. Since the company’s internal paperwork does not maintain stock by variations, they will be forced to load the data randomly or assign the whole stock to one variation. This will result in misinformation, false low stock alerts and constant need for manual inventory correction.
Comersus allows the creation of products with variations without defining or tracking stock. Variations can be grouped and assigned to different items. For example: it is usual for a clothing store to offer Small, Medium and Large sizes. Comersus allows grouping these variations and assigning the same group to the whole catalog.
Those clothing stores which need to track stock for each variation of an item are actually treating each variation as a separate item. The first impulse might be to load one product for each variation. However, this approach is impractical since it involves repeating product attributes such as description and category. Moreover, customers performing a search will find themselves with a long list repeating the same item with different occurrences for each size, each color, and so on.
Comersus allows the management of items with variations and stock through the feature Bundle Products. In this case a parent product is loaded with the attributes shared by all variations (for example: cotton t-shirt) and then a product is created for each variation (for example: 1- Red, Small; 2- Red, Medium; etc.). When performing a search or browsing a list, customers will see the details of the parent product including all of the item’s common attributes, but when they click on the details view they will see all possible variations and choose an item from all possibilities displayed on the screen at once, each with their unique prices and pictures.
Another important factor for clothing stores is a supplier integration system. The manufacturing of clothes can entail a lengthy cycle, transport delays and other circumstances which produce a time gap between the need to restock and the placing of an order. Comersus allows the setting of a minimum stock for each item. When this critical stock level is reached, the shopping cart notifies the supplier about the need to restock.
With respect to the termination of an order, we’ll mention the shipping quote process. Comersus supports defining the weight of each item including variations, in order to provide all necessary data for an accurate shipping calculation at checkout. Comersus can apply to this information a set of predefined rules (shipping type and fee based on weight, price, quantity and destination) or use real-time quoting systems such as DHL, UPS, USPS and Fedex among others. It is even possible to quote through several shipping companies and produce a mixed quote with no sensible delay for the customer.
Lastly, it is important to note that the sale of clothing involves rather short product life cycles, usually comprising two seasons per year. It is therefore vital to have a good grasp of sales history in order to examine the customers’ likes. However, this is not enough: sales history must be mapped into the future to accurately predict future sales before engaging in further orders. Comersus includes a sales forecast report using least squares mathematical theory. This report produces a monthly sales graph, analyzes its structure, and provides a prediction for future sales. This tool, until now only available with expensive additional systems, produces valuable information for decision-making.