Loops Launches Scout: An AI Analytics Agent that Redefines Self-Serve Analytics for Product Teams

Empowers teams to ask questions & get insights, automatic KPI alerts and summaries in Slack, Teams, & email - freeing analysts for deep, growth-driving research

Loops, the causal analytics platform trusted by modern product and growth teams, announced the launch of Scout, the industry’s first AI Analytics Agent. With Scout, anyone can ask a question and instantly understand why a KPI changed, where users are dropping in a funnel, or what happened in a recent experiment. Scout doesn’t wait for your questions, it pushes automatic insight summaries and alerts - all delivered directly in Slack, MS Teams, or email. Unlike typical Gen AI tools that correlate or hallucinate, Scout delivers accurate, causal insights you can trust. For years, “self-serve analytics” has meant an explosion of dashboards. But in reality, teams are overwhelmed by data, underwhelmed by insights, and stuck waiting on analysts to find answers. Yet, around 70% of business team questions could be handled with accurate AI automation. “With Scout, we’re redefining what self-serve analytics actually means,” said Tom Laufer, CEO and Co-Founder of Loops. “Scout is like ChatGPT for product data, but it empowers every stakeholder – from product managers to marketers – to get trusted, causal answers instantly, without needing to wait on an analyst, nor find and decipher a dashboard. And just as importantly, it frees analysts to focus on deep research and uncovering high-impact growth opportunities. This is the future of product analytics.”

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Highlights of Scout:

• Users can ask Scout anything in plain English and instantly understand why a KPI changed with its detailed, root cause responses.
• Scout gives business and product teams faster, more accurate insight into product performance by pushing alerts, trends, and insight summaries with root cause analysis
• Users are better prepared for Business Reviews with less effort with Scout’s automatic daily, weekly, and monthly Insight Summaries - each with a PDF they can forward to the team.
• Scout adds greater value over time as users train it on their businesses by uploading context, terminology, and synonyms.
• Users better understand experiment performance with Scout’s automatic A/B test and Loops Release Impact summaries.

“AI data analysis alone isn’t enough - without business context, it’s just noise. What makes Scout compelling is that it brings context into the loop (pun intended), making insights more relevant, accurate, and actionable. It’s a strong first step toward truly intelligent analysis. We’re excited to see how it helps our teams at Wahi move faster and make better decisions,” said Simon Trudeau, Head of Analytics and Optimization at Wahi. SOURCE: EIN Presswire