Resources

How to leverage causal inference models. Understand causal insight accuracy versus A/B tests. Learn how to make impactful decisions with less traffic, time, and cost.

The ultimate playbook for understanding your product KPI changes and avoiding loss.

Wahi has always had a focus on product analytics, but quickly determined that their correlation-based approach of formulating hypotheses, trying to optimize campaigns and the product, and waiting weeks to months on A/B tests trying to identify causation, was not optimal.

Using Loops, Atera’s analysts detected that a particular modification was the primary cause of a KPI decline. Armed with this newfound insight, Atera’s team was able to adjust their views about the success of a new feature release, ultimately implementing a third round of updates in the same product area. These subsequent changes resulted in a substantial boost in Atera’s funnel conversion rates. Thanks to the insights provided by Loops, Atera’s team rapidly learned from their previous iterations and could react accordingly until achieving success.

Generating hypotheses is a critical aspect of the data analysis team’s work at monday.com. However, assessing the worthiness of a hypothesis can be overwhelming, particularly for a team that conducts hundreds, if not thousands, of analyses daily.

In this session, we asked Casey Winters, the former CPO at Eventbrite, about what opportunity he sees in Loops for product and data teams. Casey shares a lot of valuable tips about how to approach experimentations, how to align product managers and data teams, and shares from his vast experience working in companies like Eventbrite and Grubhub. Enjoy.

When 18Birdies started working with Loops, the platform presented an opportunity to improve on 18Birdies’ pre-set KPIs with the “Goal Driver” analysis. This analysis automatically identifies the features that maximize a company’s KPIs using causal inference models, which enable you to get significant results with minimal traffic.

In this article, Iyar Lin, data science lead at Loops, goes through the “behind the scenes” of one of our most used types of analyses: Release Impact.

In this session, we asked Adam Fishman, the former VP Product/Growth at Patreon, about what opportunity he sees in Loops for product managers, and to share some tips about experiments and alignment between product managers and data analysts. Enjoy.

A partnership between the firms aimed to identify more product growth opportunities by validating uDocz’s hypotheses using Loops’ unique causal inference models and analyses.

Hi, I’m Tom Laufer, and together with Ido Adiv, we founded Loops, a revolutionary analytics tool that helps product and data teams crush their KPIs by finding impactful growth opportunities hidden across their data.

Taboola is one of the leading ad-tech platforms in the industry, serving over 360 billion content recommendations each month.

Whether you’re looking to make product feature updates, increase customer retention, enhance the conversion rate of a particular stage of the customer journey, or penetrate an entirely new market segment, the “how” will start with a hypothesis. Just like other fields of scientific studies, product management and growth requires data to back up your hypothesis and ensure the insights gathered are accurate and yield the best decision.

There’s no doubt that in today’s data-driven digital landscape, the amount of raw data a product manager encounters on a daily basis is enormous. Analytical product managers and their team members alike spend hours upon hours, staring at dashboards analyzing data, as they pursue their “never-ending quest” in discovering valuable insights about how users interact with their product.

In today’s forward-thinking, customer-focused organizations, product teams are constantly evolving and changing. New teams and functions are created as a way to empower the specialized expertise that’s leveraged to establish a competitive advantage. As the issue of acquiring and retaining users is critical to a product’s success, the evolution of product growth teams that focus on moving the needle on critical success metrics of their products should come as no surprise.

The explosion in the use of data has upended the way product and growth professionals make critical decisions about how they build their products. Product teams do their best to leverage near-infinite amounts of data to discover insights that help them develop better products. They’re on a mission to gain clarity regarding how users interact with their products and more specifically, discover the features and flows that delight users to the point that they become ambassadors that drive viral growth.