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Companies face significant losses when dealing with poor-quality product data, resulting in various consequences. According to Gartner, these deficiencies incur an average annual cost of $15 million for businesses. Similarly, MIT Sloan reports that underestimating the importance of data quality can lead to losses ranging from 15 to 25% of total revenue. Product data quality remains a major challenge in various sectors, with the retail sector particularly concerned due to the volume and diversity of data it manages.

In this context, the process of controlling product data quality becomes more complex. Key Performance Indicators (KPIs) used to assess product data quality vary based on specific goals, such as improving regulatory compliance, operational efficiency, or customer experience. It is crucial to clearly define these objectives before identifying relevant KPIs.

Key Performance Indicators (KPIs) to evaluate product data quality for regulatory compliance

Distributors bear the responsibility for product data as distributors and face fines and penalties for non-compliance. Penalties for non-compliant labeling can quickly result in significant costs for the retail brand. For example, the INCO regulation stipulates criminal penalties and fines of several hundred euros per point of sale for each non-compliant label. Other regulations in force in Europe, such as the CLP regulation for cleaning products, the REACH regulation ensuring the safety of chemical substances, or Regulation (EU) No. 1007/2011 regarding fiber designations, corresponding labeling, and marking of the fibrous composition of textile products, also impose strict requirements.

Due to the risks associated with non-compliance, it is imperative that the regulatory compliance rate approaches or reaches 100%. The Key Performance Indicators (KPIs) used to improve regulatory compliance depend on various factors related to the sector or product category. Once the quality standard is defined, it becomes essential to determine how and when to measure this quality. Here are some KPIs that can assess quality in terms of regulatory compliance and facilitate decision-making:

  • Regulatory compliance rate.
  • Comparison of regulatory compliance rates before and after implementing measures, such as rules in product data management.
  • Error rates by product category.
  • Frequency of these errors.
  • Monitoring the number of error reports and notifications.
  • Tracking the number of modifications made.

Key Performance Indicators (KPIs) for product data quality to improve operational efficiency

Product data encompasses various aspects such as technical characteristics (weight, dimensions, composition), marketing elements (descriptions, benefits), multimedia content (photos, pictograms, videos), commercial aspects (barcodes, EAN, unit prices, VAT-inclusive prices), logistics information (substances, manufacturing, stock management, logistics, and transportation), Corporate Social Responsibility (CSR) data (scores), and more broadly, product life cycle characteristics (sales, recycling, reparability). As a result, data quality control covers these diverse aspects, involving teams that use and consume this information.

The volume of data increases by approximately 56% each year, directly impacting the multiple teams involved in product data management. These teams dedicate a significant portion of their time to data verification and correction. Here are Key Performance Indicators (KPIs) that can assess data quality to improve the operational efficiency of teams:

  • Number of corrections per type of product data.
  • Time dedicated to correcting product data.
  • Supplier response time.
  • Time required for request processing.
  • Number of interactions between two teams.
  • Speed of creating a product page.
  • Speed of validating a product page.

Key Performance Indicators (KPIs) to evaluate product data quality for an enhanced customer experience

While manufacturers are experts in the product data they offer, it is the distributors who face the consequences of an unhappy consumer. If product information is incomplete or inaccurate, the dissatisfied buyer returns the product, resulting in a loss of sale for the supplier through that distribution channel, potentially in favor of a competing product. Product data quality can be decisive in the success or failure of a sale. In France, 46% of consumers abandon their shopping carts if they do not find relevant product information.

Here are some Key Performance Indicators (KPIs) that can measure product data quality and contribute to an improved customer experience:

  • Speed of validating a product page.
  • Speed of bringing a product to market.
  • Most viewed information on a product page.
  • Comparison between available information and the most frequently asked questions in FAQs.
  • Tracking keywords used by customers during information searches.

Before implementing these indicators, the organization must define its governance model, as defined by TechTarget as "the process of managing the provision, ease of use, integrity, and security of data in business systems, based on internal data standards and policies that also control data use." Data governance relies on three elements: the people who collect, use, and consume data; processes that must apply across the organization and be easily understandable; and technology, which must be equipped with analysis and decision support features.