Pecan, an Israeli AI-based predictive analytics startup, announced today that it has raised $66 million in a Series C round led by Insight Partners, with participation from GV (formerly Google Ventures) and existing investors S-Capital, GGV Capital, Dell Technologies Capital, Mindset Ventures, and Vintage Investment Partners.
The latest financing round of a $35 million round was completed in April 2021. The company has raised over $100 million in the last 12 months alone.
The funding will be used to expand Pecan’s global footprint and expedite the company’s research and development of the industry’s first low-code predictive modeling and data science platform.
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Founded in 2018 by CEO Zohar Bronfman and CTO Noam Brezis, Pecan’s low-code predictive modeling and data science platform, which sits at the intersection of AI and data analytics, enables business intelligence (BI) analysts to forecast revenue-impacting risks and outcomes.
Users can transform enormous amounts of raw transactional data into precise forecasts of vital key performance indicators (KPIs) that have a direct impact on sales and profitability.
Pecan’s annual recurring revenue has more than tripled over the last year. This increase is being powered by increasing customer adoption across finance, insurance, retail, consumer packaged goods, mobile applications, and consumer services.
These organizations selected Pecan to achieve transformative improvements in customer acquisition, retention, lifetime value, demand forecasting, supply chain optimization, resource allocation, and pricing and packaging.
“We believe that any company should be able to deploy AI-based predictive analytics, even without data science resources on staff. This new funding will help us scale Pecan further to overcome the data science scarcity gap, enabling our customers to move beyond outdated data-mining techniques that offer little value in predicting future outcomes. We are on a mission to unlock the potential of business intelligence and analytics through the power of AI.”
“With the commoditization of data ingest, the majority of businesses are struggling to extract insight from the oceans of data they manage,” stated George Mathew, Managing Director of Insight Partners. “Today’s legacy platforms are still struggling to properly democratize AI; paying armies of data scientists to construct predictive models is out of the question. Pecan is a category-defining platform in its application of artificial intelligence to advanced business analytics in order to foresee a wide variety of outcomes – from strategic global demand forecasting to the performance of everyday marketing initiatives, and everything in between. We look forward to assisting the Pecan team as they grow,” Mathew will join the Pecan board of directors.
Pecan’s patented AI algorithms refine and train prediction models in real-time to provide the most effective results imaginable. It applies advanced data science approaches and next-generation data science automation to an ever-growing variety of predictive use cases. Pecan transforms SQL-trained business intelligence analysts into knowledgeable and effective data scientists capable of building accurate forecasts in days by simplifying data preparation, automating feature engineering, and refining models for high performance in specific use cases.
“We are still in the early stages of integrating artificial intelligence skills into everyday business value,” Crystal Huang, Partner at GV, explained. “We are ecstatic to collaborate with Pecan on their objective to democratize access to AI-powered predictions.” Huang will serve as an observer on Pecan AI’s board of directors.
“Pecan has increased the efficiency and effectiveness of our demand and supply chain forecasting teams,” said Neil Ackerman, Johnson & Johnson’s Head of Supply Chain Innovation Hubs for the Americas, Europe, the Middle East, and Africa. “Within weeks, we saw a return on investment,” Thomas Dickey, Director of North America Supply Chain at Johnson & Johnson, explained. “With Pecan’s solution, our forecasting accuracy improved significantly, particularly in our most difficult consumer groups, and we were able to quickly deploy our models to production.” We now have a detailed grasp of the elements impacting consumer preferences and can adapt our production and distribution to eliminate supply chain variability. We look forward to further expanding our cooperation as we work to satisfy the consumer.”