Ecommerce Data Categorization in 2024: Guide & Best Practices
The speed of digital acceleration yields larger and more complex data sets across all industries. A report by Seagate and IDC predicted that by 2025 the amount of data created, captured, and replicated globally would rapidly increase, with most of it being unstructured (See Figure 1).
Figure 1 The global datasphere expands
In the e-commerce sector, unstructured data yields higher value than structured data since it offers more diverse insights into the business. Click here to learn more about structured and unstructured data. However, to prepare unstructured data for any kind of value creation, it must first go through data categorization and classification.
This article explores data categorization, how it’s done, why it is important for your online business, and what are some best practices to consider.
What is data categorization??
Data categorization and classification are interchangeable terms. A business generates various types of data; data categorization is done to identify those types by picking them from the pool and classifying them into separate levels. This process can be done manually, can be outsourced or crowdsourced, or can be automated.
Data categorization aims to organize and manage structured and unstructured data for further use, such as business analytics, improving the online store, or fueling AI-enabled solutions.
How to categorize data?
The following simplified steps can be considered as a roadmap to data categorization for your online business.
- Conduct the risk assessment for confidential data
- Determine the classification criteria
- Identify all the sources of the data and the storage location
- Classify data manually through outsourcing/crowdsourcing or an automated tool.
- Update the data categorization process as the policies and business needs change
This process, however, can vary from business to business.
E-commerce data can be categorized into the following basic levels:
Why is data categorization important for your online business?
There are millions of products available for sale on Amazon. Now imagine if all those products are laid out on a single webpage. It would become impossible to find the item you are searching for.
But a successful data categorization makes navigating through the products easier (see image below).
Data categorization makes the purchasing process easier for the customer by providing the data necessary to improve the website, which is one of the most important components of an online business. For instance, through data classification, customer feedback data regarding the website can be separated from data from other customer communications and can be used to improve the website.
Similarly, purchase history data can be classified and fed into a product recommendation system to provide relevant product suggestions to the customer as he/she navigates the online store.
In other words, it enables easy access to the data required by the business to improve how it offers products/services and how the customers buy them.
What are some best practices of data categorization for e-commerce?
This section identifies some best practices you can follow to ensure proper data categorization for your online business.
Use hierarchical categorization
The terminologies used to categorize the data should be aligned with the structured data hierarchy. For instance, “customer payment method data” should be categorized under “customer transactional data.”
Keep the data simple and clear
The data should be clear and consistent so it can be easily found in multiple classifications. For instance, the product “blue boots” can be both in the shoes category and also discounted products or newly added products categories.
Avoid excess categorization
Online shoppers like to save time. Therefore, too many categories can demotivate them from navigating the website altogether. It can also make searching and sorting the data difficult for the company’s analysts. Make sure that the categories are of an appropriate number and simple to sort.
Further reading
- Crowdsourced AI Data Collection Benefits & Best Practices
- Quick Guide to Data Collection
- Quick Guide to Datasets for Machine Learning
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