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Oracle Retail Attribute Extraction

Improve Data Quality with Machine Learning

Increase analytical potential and streamline the customer shopping experience with clean and complete attribution using machine learning-enabled attribute extraction.

Extract attributes from item descriptions

Extract attributes from item descriptions.

Building a Solid Foundation for Analytics

Building a Solid Foundation for Analytics

  • Use supervised machine learning to extract, normalize, and generalize product attributes from structured and unstructured sources
  • Improve results of retail science processes, including forecasting, customer decision trees, and demand transference, through increased model quality
  • Organize and optimize assortments through the lens of the customer
  • Streamline the customer shopping experience with clean and complete attribution

Related Solutions

  • Oracle Retail Advanced Clustering

    Evaluate sales performance, customer penetration, and market share across stores, geographies and markets to plan and compete in an evolving landscape of pure play, general merchants, and traditional competitors.

  • Oracle Retail Customer Segmentation

    Manage merchandise and sales strategies in a targeted way by effectively identifying and grouping customers (by channel) using multiple methods (category purchase behavior, demographics, etc.) and transaction data.

  • Oracle Retail Affinity Analysis

    Gain actionable insight into your shoppers' behavior across channels and maximize the potential of your data through persona-based workflows and dashboards. Affinity analysis identifies similarities, halo, and cannibalization effects, and shows how store clusters perform relative to each other.

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