Bad product data can severely hinder the effectiveness of your eCommerce operations, impacting search, product discovery, customer satisfaction, and sales. In fact, according to Gartner, every year poor quality product data costs organizations an average of $12.9 million.
The reality is, many retailers are grappling with the issue of poor-quality product data, but most aren’t aware of just how bad their data actually is. To help you evaluate the quality of your product data, here are 6 metrics you should be measuring:
Think of accuracy as the truth serum for your product data. This metric measures the presence of errors – typos, incorrect measurements, or outdated information. Imagine a customer looking for a "16GB memory card" but yours is listed as "16GB RAM." Not an ideal first impression! It’s important to identify and rectify these inaccuracies before they impact your customers.
Completeness asks whether all the essential information about your products is present. Missing details like size charts, material information, or warranty details can leave customers confused and hesitant to buy. Filling these gaps ensures a smooth and informative customer experience.
Consistency means your data adheres to a set of established standards. Imagine having weight listed in kilograms for some products and pounds for others. Inconsistent formatting creates confusion. It’s important to standardize your data, including attributes like size, capitalization, units of measurement, and date formats to ensure consistency.
Validity ensures your data adheres to defined business rules and industry standards. For example, a product category might only allow for whole numbers in stock quantities. Data validation can catch entries that violate these established norms. With valid data, you can be confident that your product information plays by the rules, reflects reality, and is easy for customers to understand.
Uniqueness identifies and eliminates duplicate product entries. Duplicate entries not only clutter your database but can also lead to inaccurate stock levels or inconsistent information. It’s important to regularly check for and eliminate duplicates to ensure a clean and streamlined data set.
Timeliness refers to how recent your product data is. Outdated information about prices, availability, or product descriptions can seriously mislead customers. We recommend establishing a regular update schedule to ensure your data is always fresh – reflecting the current state of your products.
Once you’ve evaluated the quality of your product data and identified areas in need of improvement, AI-powered data enrichment can help you clean, standardize, and enrich your product data at speed and scale.
GroupBy offers Enrich AI, which delivers unparalleled data enrichment, enabling retailers to optimize product information, enhance customer experiences, and drive significant revenue growth.
To learn more ways to unlock the full potential of your product data by identifying and addressing common data issues, register for our upcoming webinar with retail experts from RETHINK Retail, SADA, and GroupBy titled, Bad Data, Big Trouble: How To Turn The Corner On Poor-Quality Product Data With the Help of AI.
