Data Science

A Monetization Strategy highlights reel from industry expert panelists in the B2C Subscription Growth webinar.

“If you torture the data long enough, it will confess to anything" said British Economist, Ronald H. Coase. One of the things that really separates "good" versus "great" analytics teams is their ability to deal with data biases, in particular Confirmation bias.

The Loops KPI Tree helps you understand how each of your KPIs affects others. You can create a map of your KPIs and discover the biggest levers that move each one.

Tired of staring at dashboards, drilling for insight? Loops is different. Play with Loops yourself in our self-serve demo environment.

Tom Laufer talks about a sad reality in analytics today: delaying insights until the data is perfect. And when is it actually...

Loops CEO, Tm Laufer, shares his perspective on defining Activation metrics, comparing two approaches.

Product Analytics has changed so much in recent years. This blog explores integrating Product Analytics systems with Loops causal insight, today.

Identifying the specific user journeys that explain why users convert, exit the funnel early, or get stuck is hard. Traditional methods, like Sankey diagrams, don't actually work, nor provide actionable insight. Learn what does...

Analytics teams today are expected to deliver insights that affect the KPIs. The goal is to maximize KPIs, but traditional methods don't actually work, nor provide actionable insight.

KPI drops are unwelcome surprises, This checklist will help you focus your efforts tin trying to determine why the KPI dropped.

Measuring the full impact of a new feature or product version has traditionally required a proper A/B test.T he problem is - they require traffic, time and resources. Now, there's a true paradigm shift...

Announcing Loops automated, Gen-AI Insight Summaries and Alerts that Loops can push to you and your team via Slack, MS Teams, and email

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.

It’s 2024 and analysts are still wasting time figuring out why their KPIs dropped. We know this from our interviews with 40 growth analytics leaders. They all said the same thing

Moments I call “change-led growth” ;) When Ido and I founded Loops, it wasn't to change the world, at least not all of it at once. We saw a specific problem.

The simple answer is yes. The not-so-simple answer is that Loops’ "Release Impact" offers a proven alternative to A/B testing. Let me elaborate.

For data and product professionals, one of the significant challenges we often face is conducting meaningful experiments when product traffic is relatively low, a known scenario in B2B products.

Until recently, AI was perceived as almost imaginative, only reserved for particular usages. But in a very short time, and with the emerging generative AI, we have an AI solution for almost anything - including Product and Growth teams.

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.