Your search did not match any results.
We suggest you try the following to help find what you’re looking for:
- Check the spelling of your keyword search.
- Use synonyms for the keyword you typed, for example, try “application” instead of “software.”
- Try one of the popular searches shown below.
- Start a new search.
Oracle Retail Science Platform Cloud Service
Take your retail business to the next level with a proven suite of retail science applications, purpose-built and field tested for specific retail use cases.
-
Evaluate sales performance, customer penetration, and market share across stores, geographies, and markets.
-
Gain actionable insight into your shoppers' behavior across channels and maximize the potential of your data.
-
Define and execute local market assortments, improve conversion of traffic into sales, and increase customer satisfaction.
Advanced Retail Science at Scale
- Empower Business Users: Advanced retail science applications that leverage AI and machine learning can bring big business impacts, but can also be intimidating to business users. With Oracle Retail Science Platform, business users can leverage the latest advancements without specialized skillsets.
- Inspire Analysts and Data Scientists: For retail teams that have the luxury of in-house analysts and data scientists, the Innovation Workbench provides full access to data and algorithms most relevant to modern retail challenges, so team members can run ad hoc analyses such as customer churn models, offer redemption rates, and more.
- Create Space for Innovation: Competition in retail is fierce. Without advance retail science and analytics, it’s tough to keep pace. Oracle Retail Science Platform levels the playing field and gives your team the requisite tools to innovate at scale.
Retail Science Platform Features
-
Big Data (PDF)
Retailers can acquire, model, prepare, and serve structured, semi-structured and unstructured data for advanced use with the Oracle Retail Science Platform and Oracle Retail Insights Suite.
-
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.
-
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.
-
Affinity Analysis
Gain actionable insight into your shoppers' behavior across channels and maximize the potential of your data through persona-based workflows and dashboards. Identify product affinities, halo and cannibalization effects, and shows how store clusters perform relative to each other.
-
Attribute Extraction
Remove data quality issues from the equation with machine-learning to enable attribute extraction.
-
Customer Decision Tree and Demand Transference Science
Create customer segment-specific decision trees using available transaction level data and understand incremental value of all items with an unbiased view of manufacturers.
-
Innovation Workbench
Empower business analysts and data scientists to perform ad hoc analyses and then operationalize the results.
-
Profile Science
Accurately predict sales with a high degree of granularity using machine learning techniques to refine assortments based on purchase behavior, down to attribute level, such as size and style.