In Retail, Prediction Means Profit

Knowing what your customers want means profit

By using the vastly increased amounts of customer data available today retailers can build a better picture of what their customers want. This big data alone is not enough however; retailers also need a analytics platform that can allow them to discover truly actionable intelligence from the masses of data available. A robust platform allows all manner of data streams to feed into a predictive analytics system, ultimately providing unprecedented accuracy when forecasting buyer behavior. For retailers, this means profit.

Knowing when your customers will shop means profit

When it comes to marketing and merchandising, timing is everything. Knowing exactly when your customers plan to shop allows for targeted marketing at the time it will influence them the most. A discount coupon sent by email just before a lunch-break shopping spree, or sending a sneak preview of the new collection before your customer plans their new season’s wardrobe.

“When?” is a powerful question for retailers, and big data can provide answers. Using powerful data analytics platforms retailers can accurately predict the shopping patterns of their customers. Knowing when means a retailer can maximize profit by telling potential customers what they need to know at just the right time.

Knowing what your customers want and when is the ultimate retail intelligence

By unleashing the power of big data, analytics platforms are creating the competitive advantage in the modern world of retail. Adopters of RFID and analytical technologies are offering the right products at the right time to capture their share of this dynamic retail market. Those who choose not to adopt such technologies are being left behind.

Ultimately, in modern retail, prediction means profit.

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Jim Donaldson

Jim is the Sr. Director of Corporate Communications at Mojix, Inc., a global leader in item-level intelligence solutions for Manufacturing, Supply Chain and Retail. Jim has more than 30 year's experience working for both start-up and public technology companies.

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