‘Tis the Season for Product Data Clean-Up: Boost Sales and Improve CX

E-commerce is predicted to be the primary driver of holiday retail sales this year, with the National Retail Federation (NRF) forecasting that shoppers will spend between $979.5 billion and $989 billion in November and December of 2024. As the holiday shopping season kicks off, retailers face increasing competition, pushing them to leverage every advantage to capture sales. One often-overlooked but critical factor is the quality and accuracy of product data. Clean, enriched product data enhances discoverability, improves search results, and ensures a seamless shopping experience, making it a game-changer for retailers.

Why Product Data Quality Matters

For years, product data issues have plagued retailers. Inconsistent SKU information, missing specifications, and errors from different sources can damage backend systems and impact customer-facing platforms. As online shopping continues to rise, poor product data directly affects discoverability and sales. Missing or inaccurate data makes it difficult for shoppers to find what they're looking for, which can lead to lost revenue, especially during the peak holiday season.

Poor product data also causes significant issues with customer experience (CX). It hinders search and navigation, with incomplete or inaccurate details frustrating shoppers and potentially leading to lower conversion rates. Additionally, inconsistent data can undermine product recommendations and the overall shopping experience, damaging customer trust. The need for accurate, well-structured product data has never been more critical to online retail success, particularly as the volume of digital shoppers increases each year.

Leveraging AI for Data Enrichment and Standardization

Thanks to advancements in artificial intelligence and machine learning, product data management has evolved. AI-powered solutions, particularly Catalog-as-a-Service (CaaS) tools, enable retailers to clean, organize and standardize product data more efficiently, significantly reducing the time and resources traditionally required for data management. Automation simplifies the process of correcting errors, enriching data, and transforming disorganized catalog information into a consistent, searchable and optimized resource for both internal teams and customers.

AI-powered data enrichment solutions improve product discoverability by making product information more relevant and easier to search. Automated systems quickly identify and correct issues, enhancing overall data quality and helping retailers maintain high standards as they scale. This leads to better search engine optimization performance, more targeted recommendations, and a smoother shopping experience — crucial during the holiday season.

Steps to Holiday-Ready Product Data With AI

To maximize the effectiveness of AI-powered data strategies, retailers should take a structured approach. Here’s how to build a strong product data governance strategy and optimize product data for the holidays:

• Audit your product data. Begin by assessing your product data to identify errors, inconsistencies and missing information. AI tools can automate this process by using pattern recognition to flag discrepancies like typos or outdated information. This audit ensures that only accurate and relevant data populates your catalog. Focusing on high-impact categories will help you see quick results in sales and customer satisfaction.
• Standardize product data. Uniformity across product details — e.g., size, color, and brand names — improves usability and product discoverability. AI algorithms using natural language processing (NLP) can help analyze product descriptions and convert data into consistent formats, eliminating language variations. Additionally, AI can apply local sizing or currency formats based on the user’s location, providing a personalized experience that enhances relevance and usability.
• Enrich product data. Once your data is standardized, enrich your product listings with comprehensive details, images and descriptions. AI tools can analyze visuals and text, tagging additional product attributes that improve search relevance, SEO, and customer understanding. Enhanced product descriptions help customers make informed purchasing decisions, reducing returns and boosting satisfaction.
• Leverage AI for continuous data enrichment. As the holiday season progresses, automation ensures that product data remains accurate and up-to-date. AI-driven solutions continuously monitor data quality, correcting errors in real time. This constant iteration is crucial during periods of high product turnover and seasonal changes.
• Run A/B tests. Measure the impact of your data clean-up efforts through A/B testing. This helps quantify the return on investment of your improvements by testing key performance indicators such as conversion rates, search engagement, and revenue. A/B testing also provides insights into which changes have the most significant impact on your customers’ shopping experiences, helping refine future data enrichment strategies.
• Engage stakeholders across teams. Successful data governance requires collaboration. Assign a project champion and involve teams from marketing, customer service, and operations. This ensures alignment and fosters company-wide support. Engaging stakeholders from different departments helps maintain consistent data standards and ensures that improvements are implemented across the entire business.
• Monitor key metrics. Track metrics like customer satisfaction, engagement and revenue to assess the success of your clean-up efforts. These metrics provide insights into the effectiveness of your data management strategy and help you identify areas for further improvement.

The High Cost of Poor Product Data

Bad product data not only affects customer experience but also leads to inefficiencies in backend systems. The time spent fixing errors, updating data, and dealing with incorrect information increases costs, delays time-to-market, and reduces revenue opportunities, especially during the holiday season. The consequences of poor data extend beyond missed sales — retailers may face customer dissatisfaction, increased returns, and a damaged reputation.

Unwrapping Holiday Success With Clean Data

A well-organized, accurate product catalog is no longer optional. It’s a competitive advantage that drives both sales and customer loyalty. During the holiday season, a systematic approach to data clean-up — leveraging AI to audit, standardize, enrich and automate data — can help retailers meet the demands of shoppers. By addressing long-standing data issues, retailers can improve customer experiences, enhance product discoverability, and ultimately increase sales.

With the holiday shopping season in full swing, focusing on product data clean-up can lead to immediate gains in revenue, customer satisfaction, and operational efficiency. By implementing AI-driven solutions and maintaining strong data governance, retailers will be well-equipped to face the challenges of the season and beyond.

Roland Gossage is the CEO of GroupBy, a product discovery platform powered by Google Cloud Retail AI.