eCommerce Data Standardization
Get clean, consistent and standardized data across your eCommerce store!
At SunTec India, we understand that the success of your eCommerce store depends on clean, consistent data. Not only that, standardized product data also carries a substantial long-term advantage for online retailers, in the sense that it improves their competence in building, importing, and managing the product data, ensuring a higher level of consistency and data quality. However, most eCommerce stores distrust the data available in their systems as they face serious problems with the quality of product data.
With ground-level experience of nearly a decade and a half offering data support to eCommerce stores globally, covering every type of industry, PDM professionals at SunTec are familiar with just about every data inconsistency possible, and have successfully untangled virtually all kinds of data disasters.
To ensure data quality – eCommerce data components, for instance abbreviations, URLs, attribute tags, meta-information like title, keywords, and price formatting, etc. – require proper standards in place. A multi-step process, product data standardization with us involves the following:
- Parsing of product data
- Classification of product data
- Normalization of product data
- Standardization of product data employing reference data
- Augmentation of product data
eCommerce data standardization is indispensable in optimizing many business processes, including product data upload, order management and fulfillment, customer relationship management, etc. Your product data is often unoptimized and lacking, with contradictory inputs and poor integration of data from different sources for product codes, brands, manufacturing, models, catalog numbers, unique product identifiers, etc. Your customer data is usually not updated and straightened out for names and addresses of your consumers, making your data unsuitable for your business needs.
Unless you take solid action to protect your eCommerce data, given the many chances for confusion, scrambled and mixed-up data is a close possibility.