Obviously AI Rolls Out Natural Language Platform for Predicting Customer Outcomes

Obviously AI, a no-code platform for machine learning and analytics, launched today to enable anyone to access crucial information and data predictions simply by asking questions in natural language.

To use Obviously AI's no-code tool, users upload their datasets from CSVs, databases, or CRM systems and then get a Google-like search bar to ask a question in natural language. For predictive questions, such as "Which customers are likely to cancel their subscriptions?" the platform will understand what the user is asking, find the right data, and build a machine learning algorithm on the fly. It also shows exactly which factors drove results. Similarly, the platform can answer analytical questions that look for existing patterns in data, such as "What is the average daily foot traffic for my retail stores?"

"We realized that business users truly cared about getting decision-making insights about their customers, products, and usage. This often meant writing frustrating SQL queries and waiting on web engineers who would try to figure out machine learning algorithms," said Nirman Dave, co-founder and CEO of Obviously AI, in a statement. "That's why we have been on a mission to make data science effortless just by asking questions. It's amazing what marketers, salespeople, and other non-technical business users can learn when they use our platform."

To make predictions, Obviously AI uses natural language processing to break apart a question, interpret it, and find the right data. Then it runs hundreds of machine learning algorithms in parallel and evaluates each one for accuracy to find the right algorithm for the dataset. Finally, it identifies top attributes that impact the outcome and delivers reports in under a minute.

Obviously AI can address questions like the following:

• Which users are most likely to make in-app purchases?
• How much is a customer willing to pay for my product on Wednesdays?
• Which customers are most likely to commit fraudulent activity in February?
• Will Tim Smith miss his appointment today?
Which users are likely to buy again?
• What's the age and location of users paying more than $500?

The platform can predict churn, lifetime value, returning customers, conversion key performance indicators, and in-app purchases. In retail, it can optimize assortments, predict inventory burn, and forecast staffing. For healthcare companies, it can predict and prevent hospital readmissions, predict which patients are less likely to adhere to prescribed drug regimens, and predict appointment no-shows.

Other use cases include insurance, banking and gaming.