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Increase the accuracy of retail merchandise and sales plans by effectively identifying, grouping, and targeting customers by channel using multiple methods, including category purchase behavior, demographics, and transaction data. Drive customer-centricity and equip your data science teams with advanced retail science models and tools to enhance localized assortments, targeted offers, and personalized shopping experiences.
Review customer distribution across attributes.
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.
Remove data quality issues from the equation with machine-learning to enable attribute extraction.
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.
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.
Empower business analysts and data scientists to perform ad hoc analyses and then operationalize the results in a secure workspace using a wide selection of open source tools and languages.