×

Find out what Vue.ai can do for you.

To get started, fill out the form. Our experts will get back to you shortly.

I understand that Vue.ai may reach out to me with relevant communication. By clicking "Request a Demo", you agree to our Privacy Policy.

Diesel enhances product data with AI-powered automated tagging

Diesel improves productivity and enhances product discovery on-site with AI-powered product tagging.

The big picture

22,300

Tags generated

130

Batches uploaded

eCommerce success for luxury brands revolves around how effectively they bring the offline shopping experience to online shoppers. A big part of delivering this online experience lies in the nature and quality of product data on-site. An AI-powered solution must go beyond generic tags and basic image descriptions to provide business-specific intelligence with rich, consistent, and accurate data.

Diesel started using Vue.ai’s AI-powered automated product tagging solution, in 2020 to streamline onboarding, tag their products, build custom solutions, and save time in the process.

The need

Diesel had new products every season that needed to be sorted, styled, photographed, and then sent to the team for attributes to be tagged manually. The challenges of the manual process include slow onboarding time and increased effort due to inconsistency in the catalog due to a lack of standardization.

To solve these issues, Diesel needed a tool that could:

Automate the tagging process with the capability to integrate with their existing PIM tool.

Recognize and identify specific attributes like 'iconic' logos that are present in the products.

Standardize the values and attributes for use across the organization & save time by streamlining the process end-to-end.

Diesel chose Vue.ai since it catered to their needs to optimize process efficiency and build customer-specific taxonomies while enhancing the quality and reliability of product data.

The solution

Summary: AI-Powered Tagging Solution

Vue.ai's Automated product tagging saves 30 hours of time per week per person.

Diesel implemented AI-powered instant tagging to get their products to market faster. It ensured:

  • Product tagging is automated across attributes and categories, reducing the time teams spend on tagging.
  • Products are tagged as soon as the images are available, rather than waiting for a significant portion of the collection.
  • Review processes and feedback on tags are faster and more quickly done through the tool - helping reduce go-to-market time significantly.

Automated product tagging generates tags with standardized information - reconciling and developing content. Product data is developed with a combination of computer vision and NLP.

Images are from the Automated product tagging dashboard and may not represent the Diesel catalog

450M product tags and attributes served with Automated product tagging

Tags extracted by the tool helped Diesel enrich their catalog data.
Downstream benefits included:

  • Powering filters on the website to assist user journeys
  • Enrichment of catalogs from other vendors, and
  • Using the extracted tags for analysis and forecasting

By shifting efforts from manually extracting product data to a streamlined workflow - teams now focus on informed decision-making. Diesel teams can review predicted tags and send their feedback back to Vue.ai networks, improving predictions' accuracy over subsequent iterations of uploads.

Images are from the Automated product tagging dashboard and may not represent the Diesel catalog

121 types of attributes recognized; 1043 different attribute values available with automated product tagging

Standardizing data across the catalog is essential for unifying the shopper experience across channels. Vue.ai:

  • Identifies missing data, incomplete metadata, or duplication of images across sellers.
  • Helps reconcile visually and textually extracted attributes.
  • Highlights inconsistencies and boosts the confidence and accuracy of attributes based on contextual cues from different sources.

It takes into consideration the information based on historical product data or data uploaded from various sources. This helps standardize the information stored in the database about that product.

Images are from the automated product tagging dashboard and may not represent the Diesel catalog

The solution

Summary: Customer-Specific Solutions

Retailers can integrate automated product tagging in several ways - from the dashboard to APIs and even integration with their existing PIM system.

Diesel integrated with Vue.ai via API with their incumbent catalog management & PIM system. A critical aspect of the integration was the standardization of product data.

  • Once integration & testing were completed, Diesel could automate tagging of all images seamlessly and hassle-free.
  • The integration process included the ability to customize & map the values of attributes to precisely what the Diesel team wanted.
  • This included training the Vue.ai networks to recognize and identify logos & patterns that the Diesel team considered 'iconic.'

Automated product tagging’s AI can be customized with new tags, and the retailer defines the tags based on their business goals and priorities.

As a globally recognized brand - Diesel has many iconic logos. A vital part of the automation process was automatically detecting these logos on products in the system.

  • Diesel used automated product tagging to build their custom taxonomy and trained networks to identify the required particular tags.
  • The system understands both text and image-based input from Diesel - identifies the specificities, and tags the products in a consistent & easy-to-export format.
  • A simple QA process allows Diesel to correct incorrect tags while forming a feedback loop, improving the network iteratively.

Key takeaways

Post-implementation, Diesel observed:

  • Faster product digitization process with less time wastage & shorter go-to-market period.
  • Decrease in the effort taken to tag products manually and improvement in the efficiency of product onboarding.
  • Enhanced search and detailed filters for better product discovery due to the depth of product data.
  • It reduced upfront data cleaning work often needed to leverage and gain deeper insights from structured and unstructured data.
  • Better understanding of the volumes of data in the catalog and shoppers' interaction with the products with granular tags.
  • Optimized pages on the site that help teams make better merchandising decisions and inventory planning.