Introduction
With a significant increase in the amount of data being produced and stored, automation has become a huge part of how companies are organizing and handling such big data. One of the most crucial tasks is to achieve high fill-rates efficiently. But simply achieving high fill-rates without looking at consistency across all attributes can be self defeating.
So, for this entire chain to function correctly, there needs to be a logical consistency across the different attributes of any given product. For example, if the ‘Shade’ attribute of a handbag contains the value ‘Teal’, its ‘Master Color’ attribute cannot be way off, such as Red or Black.
Why can this occur?
- Failure of Completeness Audits: A completeness audit is simply concerned with whether the value is present or not. The audit will be passed regardless of the values in cell A and cell B, as long as a value is present. Whether these values coincide logically cannot be confirmed through this process, which can ruin filtering, and thus, cause customer dissatisfaction. Therefore, the relationship between data points is something that needs to be understood by the system, for it to function well.
- A Break in the ‘Web of Logic’: All the attributes pertaining to a product must add up to a complete and accurate description of it. For instance, If Net Weight is N and Packaging Weight is P, then Gross Weight must be G = N + P. An inconsistency here can lead to unreliable descriptions, and the customer loses trust in the eCommerce site.
Further Consequences
- Broken Search Filters and Discovery: If, for example, a customer is shopping for jackets, and selects the ‘Material= Genuine Leather’ filter, they would expect clothes that are as per the filter applied. However, an inconsistency, such as a product’s title saying ‘Leather’, while having a ‘Composition= Polyester’, will be unreliable, and the company will lose its sale, and a potential customer.
- Ruined Customer Experience: All of the examples above detail how any miss in the logical consistency of attributes will have a direct impact on customer experience. Such experiences can be detrimental to businesses, especially ones who rely on eCommerce.
- Costly Returns and Logistical Errors: Inconsistency in attributes such as Gross Weight, and Package Length can lead to multiple errors related to shipping, storage in warehouses, most of which are expensive ordeals, and lead to delays in all processes.
Therefore, it is important to look beyond fill-rates and ensure that the data is also reliable and accurate.
