No results found
Your search did not match any results.
Oracle Retail Innovation Workbench
A fit-for-purpose machine-learning and artificial-intelligence platform that empowers business analysts and data scientists to perform ad hoc analyses and then operationalize the results in a secured workspace using a wide selection of open source tools and languages.
- Video: Retail Science in Action (2:09)
- Blog: Accelerating Advanced Analytic Strategies with Retail Science
- Blog: The Benefits of a Hybrid Software Solution
- Infographic: Using Machine Learning to Optimize Retail Performance (PDF)
- Guidebook: A Case Study Collaboration with MIT Researchers on Pricing and Demand Forecasting
Operationalize the Art of the Possible
- Access cleansed and structured data aggregated at different levels and across several dimensions, as well as various cutting-edge tools for machine learning, optimization, and data analysis
- Focus on implementing models and state-of-the-art algorithms and spend less time on data preprocessing
- Scale to retailer’s data, data science projects, and data science team growth
- Increase productivity by saving time and effort running machine-learning pipelines and creating new code
- Import external data and use a wide selection of open source tools and languages like R, Python, and Gurobi
- Prototype, package, and operationalize science with application builder and flexible integration adapters
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 Attribute Extraction
Remove data quality issues from the equation with machine-learning to enable attribute extraction.
-
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.
-
Oracle Retail Customer Decision Tree and Demand Transference Science
Create customer segment-specific decision trees using available transaction level data and understand the incremental value of all items with an unbiased view of manufacturers.
-
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.