The challenges of data quality in the Living range
Data quality is the backbone of modern retail. Those responsible for product data management, e-commerce and marketing know how important error-free information is for customers' purchasing decisions. But what happens when data is implausible or incorrect? A leading European furniture retailer shows how AI-based plausibility checks help to optimize data integrity and data quality - especially in the lighting and carpets categories.
Objectives of the XXXLutz initiative: Efficient product data verification for home textiles and lighting
The product range of furniture retailer XXXLutz extends from inexpensive furniture to exclusive designer brands. Those responsible for master data and product data management are faced with a challenge: standardized checking processes detect basic errors, but leave linguistic and content-related inconsistencies undetected. In order to improve product data quality, a pilot project for AI-supported plausibility checks was launched in the lighting and carpets categories.
The goals of the initiative were defined:
- Evaluate the feasibility of an AI-based plausibility check.
- Quantify error rates in the product categories and develop solutions.
Structured approach: Step by step to better data quality
- Data transformation: The supplied JSON data was converted into a processable structure using rule-based procedures.
- Plausibility check - texts: AI-supported text analysis was used to identify linguistic errors, such as spelling mistakes or contradictory formulations.
- Plausibility check - content: AI models checked for content errors, such as implausible technical values or contradictory material specifications.
- Error report: A scoring system evaluated the results and created structured error lists.
The AI analysis provided reliable error reports for lights and carpets
- Any anomalies identified were listed in a defect report.
- The scoring provided information on the criticality (high, medium, low) of the individual anomalies.
- Binding work instructions are available for subsequent correction.
Importance of plausibility and data integrity in retail
- In the digital retail world, precise and consistent product data is an indispensable success factor. Those responsible for marketing, e-commerce and product data management must ensure that customers receive correct information in order to promote trust and purchasing decisions.
- Customer expectations and trust: Contradictory or implausible information leads to uncertainty and can break off the purchasing process. Once disappointed, customers often do not return.
- Returns and costs: Incorrect product data increases returns, which not only results in financial burdens but also has a negative impact on the environment.
- Competitive pressure: High-quality master data creates competitive advantages by improving internal processes and promoting a positive customer experience.
- Regulatory requirements: Legal regulations require accurate and transparent data. An AI-supported plausibility check helps to fulfill such requirements.
Recommendations for action in practice
- Utilize automation potential
- Integrate AI models into existing content workflows.
- Regularly update AI models with industry-specific knowledge.
- Supplement specialist checks
- Involve product experts in the review of complex cases.
- Strengthen cooperation with suppliers to clarify implausible data.
- Optimize processes sustainably
- Early error detection during data entry.
- Conduct employee training on data quality and AI solutions.
Our conclusion: increasing efficiency through AI
The pilot project at XXXLutz has shown that AI-based plausibility checks can significantly improve data quality. Automated processes save time and costs, while manual intervention is limited to complex cases. The introduction of such systems strengthens the company's competitive position in the long term and increases customer satisfaction.
Follow XXXLutz's example and optimize your product and master data with AI