The big data analytics train keeps moving, but there are still problems to be solved in implementing it in the business world. Data science expertise is required to build and maintain a big data infrastructure that can rapidly transform large data sets into actionable insights. There is also a skills gap between data scientists, analysts, and business users, and while several low-code or no-code platforms have aimed to solve this, certain use cases add complexity. is left.

One company looking to bridge the gap between business analytics and data science is Pecan AI. The company says its no-code predictive analytics platform is designed for business users in sales, marketing, and operations, and the data analytics teams that support them.

“Pecan was built under the assumption that the demand for data science far exceeds the supply of data scientists. We wanted to help bridge the gap with our platform,” said Zohar Bronfman, CEO of Pecan AI. Data Nami in an interview.

The Pecan AI Platform allows users to connect various data sources through no-code integration capabilities. A drag-and-drop, SQL-based user interface allows users to create machine learning-ready data sets. Pecan’s proprietary AI algorithms can build, optimize, and train predictive models using deep neural networks and other ML tools, depending on the needs of your specific use case. With less statistical knowledge required and automated data preparation and feature selection, the platform removes some of the technical barriers BI analysts can face in leveraging data science.

“Interestingly, in most data science use cases, as a data scientist, you spend a lot of time and effort getting the data right, extracting it, cleansing it, matching it, structuring it, and many other basic tasks. We define data science use cases, and now that it can be automated, analysts who have never done this before can do it,” Bronfman said. says.

Additionally, the platform continuously analyzes data for more accurate predictions, prioritizes features according to their changing importance over time, and monitors model performance via live dashboards. It provides a monitoring function for

Bronfman noted how customer behavior patterns can change in response to factors such as inflation and supply chain disruptions, and the current model has been scrapped. To continue to provide accurate predictions, the platform automatically looks for changes in patterns in the data and, when identified, feeds new data into the algorithm to retrain and update the model, said Bronfman. Adapt to newer patterns.

Example Pecan AI dashboard showing expected churn rate. Source: Pecan AI

Bronfman and co-founder and CTO Noam Brezis founded Pecan AI in 2016. The two met in graduate school while working on their PhD in computational neuroscience, and their work led them to explore recent advances in AI, including its ability to automate data mining and statistics. . process. Brezis has become a data analyst focused on business analytics, but his data science know-how has been relegated to highly specialized teams and businesses that can most benefit from the predictive potential of data science. I’m surprised he’s often isolated from analysts. Bronfman and Brezis saw an opportunity to build a SQL-oriented platform that could harness the power of data science for BI audiences while eliminating much of the manual data science work.

Pecan AI addresses a variety of use cases including sales analytics, conversions, and demand forecasting. Bronfman is particularly enthusiastic about his Pecan’s predictive analytics capabilities on customer behavior, an area he believes has three main pillars. The first pillar is acquisitions, when companies are looking for ways to acquire and engage with new customers. lifetime value model. “These models ultimately give a very good estimate of how well a campaign will perform from the marketing side before things actually happen. You can wait a few days and say, “My friend will stop investing in a month or three, so I should double my spending.” In this campaign,” or in other cases, “Further investment should be refrained.”

The second pillar of customer behavior is the monetization pillar. Businesses may ask how they can provide a better experience for their customers in order to encourage their continued engagement. [or] Whatever your brand, you need to optimize both what you offer and when you offer it. [it]Again, our forecasts can tell at the customer level when to deliver what to whom,” Bronfman said.

Finally, the third pillar is retention. Bronfman says it’s much more economically efficient to retain customers than to acquire new ones. , is the churn prediction. Churn rate is a very interesting area of ​​data science because it is notoriously difficult to predict. This is a classic case where, if you don’t do it right, unfortunately, the prediction is not accurate even though it is. has no effect. ”

Pecan AI co-founders: CEO Zohar Bronfman and CTO Noam Brezis.

Bronfman says time is of the essence when predicting churn. Case, to change their minds. But if we can predict churn in advance, that’s what we specialize in, and this is an opportunity to pre-emptively engage with customers to provide a better experience, better price, and better retargeting. There’s still a tight time frame left. Whatever it is, put in the effort and increase your retention. ”

Both investors and customers seem interested in what Pecan has to offer, and the company is experiencing significant growth. So far, the company has raised a total of $116 million, including its latest Series C funding round of $66 million in February, led by Insight Partners. and GV and existing investors S-Capital, GGV Capital, Dell Technologies Capital, Mindset Ventures, and Vintage Investment Partners.

Pecan recently announced that it more than doubled its revenue in the first half of the year, increasing annual recurring revenue by 150%. Its customer numbers grew 121% from his, with mobile gaming companies Genesis and Beach Bum and wellness brand Hydrant joining its roster that includes Johnson & Johnson and his CAA Club Group. The company also expanded its workforce to 125, a 60% increase from his.

Bronfman said Pecan’s growth stems from strong tailwinds from two factors: But we’ve also found that business people love being able to drive fast and effective data science without necessarily needing data science resources. ”

Related products:

Pecan AI announces one-click model deployment and integration with popular CRMs

Foundry Data & Analytics Survey Reveals Investments and Challenges for Business Data Initiatives

Narrowing the AI-BI gap with exploratory analytics