Rubikloud’s latest artificial intelligence-enabled solution helps retailers optimize promotions to maximize sales and reduce stockouts

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The latest in Rubikloud’s lineup of AI-enabled solutions increases the effectiveness of promotions, optimizes category spend, improves forecast accuracy and saves valuable time

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The solution will help retailers and CPG manufacturers experiment, simulate and plan more effective promotions to meet business objectives, maximize margins and drive profitability.

Rubikloud, the maker of world-leading artificial intelligence (AI) and machine learning technology, is increasing its capabilities to help retailers and consumer packaged goods (CPG) manufacturers clearly understand and automate decision making across the forecasting lifecycle, improving financial results and increasing the ability to predict customer demand. Early tests have indicated that retailers can achieve more than 10% incremental sales and 13% margin improvement versus their baseline approaches and existing legacy systems.

Inclusive of the full omni-channel ecosystem, including in-store placement, e-commerce, mobile, third party applications and alternate fulfillment options such as click-and-collect, Rubikloud's Optimize module uses retail centric algorithms that constantly learn and improve, to generate early forecasts, address data scarcity and improve forecast accuracy. It also centralizes the overall promotional planning effort in one location, reducing organizational silos and miscommunication.

The solution will help retailers and CPG manufacturers experiment, simulate and plan more effective promotions to meet business objectives, maximize margins and drive profitability. It transforms a slow and manual task, previously informed through disparate systems and outdated information, into a seamless process that utilizes data-driven decision making and constraint-based plan optimization including cannibalization, halo effect, cherry-picking, pantry loading and missed cross-selling and upselling opportunities.

For example, one key activity for merchandising teams at retailers is building a promotional calendar for their mass-market promotions, a task that typically takes multiple weeks and heavily relies on last year’s performance to predict the performance of future plans. With Rubikloud’s Optimize module, each category manager can run what-if scenarios that simulate an expected outcome by changing various demand-driver levers such as promotion mechanics, store placement, digital placements, flyer and vendor/trade funding. The solution generates granular forecasts and integrates them into downstream supply chain systems. From there, the individual plans are consolidated into one view where the business as a whole can clearly and easily understand and communicate the projected performance of the upcoming months.

“We understand firsthand the challenges retailers and CPGs are facing, as retail is the core focus of our business,” said Waleed Ayoub, CTO, Rubikloud. “Merchandising decisions, marketing and promotions planning, supply chain, retail operations – they’re all connected, and we’re proud that we are helping retailers solve their toughest challenges through comprehensive intelligence, automation, and integration for every area of the business.”

More information at: https://rubikloud.com/promotions/

About Rubikloud Technologies Inc.
Rubikloud uses AI to deliver intelligent decision automation with the world’s leading machine learning platform for retail. Its full-stack, cloud-native platform, and flagship applications automate targets mission-critical business problems by analyzing large amounts of data from multiple legacy sources and new online and offline systems to empower retailers to take tangible actions with powerful results. Rubikloud is a private company with investors that include Intel Capital, Horizons Ventures and Salesforce Ventures, and Inovia. The company has raised $45M in venture financing to date. More information can be found at http://www.rubikloud.ai.

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Christina Dela Cruz
ARPR on behalf of Rubikloud
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