Millions of dollars are spent each month by businesses to roll out thousands of campaigns, but it is not always clear which ones were the most successful. When the discounts are broken down, the vast majority of them did nothing to boost sales for the business.
Kuona, a software as a service firm located in Mexico, uses machine learning to analyse all of these promos, revealing which ones are successful and automatically optimising product pricing and inventory for consumer packaged goods companies and retailers.
With experience in data science, AI, and consumer packaged goods (CPG) revenue and pricing, Chema Sanroman and Agustn Magaa founded Kuona in 2017 to address the industry’s pricing and promotional issues.
Just follow these steps to see how it all comes together: To foresee demand and monitor user activity, Kuona built data-driven solutions. The solutions use neural networks for simulations and real-time integration with corporate data to boost ROI on promotions while maintaining or growing sales.
“Our platform helps them identify what’s working and not,” CEO Sanroman told TechCrunch. They can see what has worked in the past, the present, and the future.
Kuona has just launched an office in Brazil in addition to its existing operations in the United States, Mexico, Peru, and Ecuador. It has two solutions available: one that optimises prices and discounts, and another called Perfect Order that helps retailers cut down on customer returns and stockouts.
The business is already profitable after two years of rapid customer and revenue growth. It has around 15 to 20 regular clients, including multinational corporations like Coca-Cola and retail chains like OXXO.
They have announced a $6 million initial funding round headed by Cometa and featuring Seaya Cathay Latam and FEMSA Ventures. After this round of fundraising, Kuona will have raised around $7.2 million.
Sanroman plans to use the fresh capital on recruiting and expanding its staff in Latin America and the United States, as well as building a team in Europe, where it already has links.
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