Innovative solutions, intuitive results

Q Analytics translates data into profit

Q Analytics are a suite of cloud-based software services designed to address specific inventory challenges with strategic, data-driven solutions scaled to retailers of all sizes. Using technology from Quantum’s acclaimed inventory management platform, Q Analytics distills those solutions into powerful, individually packaged subsets of functionality targeted to particular retail problems.

Like our more comprehensive solutions, the Q Analytics products synthesize consumer demand and other retail metrics to deliver an insightful view into your most pressing market challenges. These products are designed to work effectively either as stand-alone solutions or in conjunction with the comprehensive Q platform. Q Analytics scales the power of Q to fit your business needs, resources, and goals

True Historical Demand

Generates a body of data that reveals the true potential of products by location and channel, identifying any missed sales opportunities and providing a superior reference point against which to make future decisions.

Size Optimization

Aligns stock and allocation with real-time selling patterns by determining how each product type sells across all sizes, locations, and channels, allowing you to optimize inventory in each and every store.

Pack Optimization

Determines which prepack configurations will most cost-effectively move product through your supply network and enable purchasing and replenishment decisions to evolve according to demand patterns.

Network Flow Optimization

Identifies the most cost-effective path for moving inventory from supply network to consumer, reducing lead times and minimizing handling costs so you can be more responsive and more profitable.

Promotional Flow Effectiveness

Captures data on promotional product flow to identify the ideal cadence for moving inventory into place prior to promotions in order to maximize demand and improve efficiency during promotional periods.

Promotional Demand Effectiveness

Measures historical performance of a product during promotional periods in order to determine whether promotions are an effective method of meeting sales goals and to understand how customers respond to different promotional models.

Historic Buy Optimization

Uses true historical demand functionality to evaluate the performance of short life product, translating that into strategy for how the product can be better positioned to capture demand and improve future sales performance.

Continuous Clustering

Enables retailers with many stores to simplify the order planning process by identifying in-store product groupings that obey similar demand patterns and consolidating them into clusters that follow synchronized lifecycles.

Assortment Rationalization

Analyzes historical or planned assortments to determine whether they will be too broad or too narrow based on customers’ reactions to additional choice, thereby proactively validating assortment decisions to optimize revenue and minimize markdown.