Business Analytics

At ReaderLink, we highly value discovering what works and changing the things that need improvement. With our unique systems and algorithms, we can forecast store distributions and support our partners with the latest data and insights. By applying technology, we help drive sales, increase operational efficiencies, and lower supply-chain costs by reducing returns.

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Meet Reader Demands

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Reduce Excess Inventory

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Leverage Industry Data

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Gain Shopper Insights

Proprietary Product

Our systems allow all our decisions to be insight driven. This lets us ultimately deliver and merchandise the right titles, in the right quantities, to the right stores, at the right time.

ReaderLink’s proprietary Vendor Management Inventory (VMI) System utilizes machine learning algorithms to predict the demand in each store. Based on actual historical sales patterns, we look at major bestsellers and new releases. Designed and developed by ReaderLink, VMI produces benefits in sales and sell-through. It also serves as the cloud-based platform for reporting critical supply chain information to our retailers and supplier partners.

ReaderLink’s trailblazing business analysis platform, Insight, also provides detailed historical business analysis from macro to granular data levels. Valuable data collected (consumer, customer, category, channel, format, genre, and title) combined with our ability to use that data helps us uncover patterns and create actionable business insights. This data gives our retailers and supplier partners a powerful competitive advantage.

Through the latest technology of cloud-based software, ReaderLink supports category management efforts, identifies and improves financial developments, monitors return rates and trends, identifies better merchandising strategies, and improves logistics efficiency.

Our industry-leading database maintains over 3 billion records of sales data, which we constantly leverage to optimize and improve efficiencies.

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Products introduced to the market through Title Submission Portal
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Machine learning algorithms calculate store/item predictions using historical data, attribute matching, and category trends
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Curated titles are selected for 40,000+ planograms using advanced assortment optimization technology
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Products are procured and distributed to each store based on model output and corresponding planograms
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Planograms uploaded to merchandisers’ handheld devices to set shelves to distribution quantities, along with data alerts to drive activity
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Algorithms refresh daily for 20 million+ store/item locations based on POS to determine appropriate replenishment strategies
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Custom-built web portals for both retailers & publishers for category/sales insights and supply chain performance
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ReaderLink creates proprietary publishing IP based on data / insights