Case Study – Closing the Loop on Hypotheses Prioritization


About (MNDY) is a work operating system (Work OS) where organizations of any size can create the tools and processes they need to manage every aspect of their work. The Work OS is a low code-no code platform that democratizes the power of software so organizations can easily build work management tools and software applications to fit their every need. The platform intuitively connects people to processes and systems, empowering teams to excel in every aspect of their work while creating an environment of transparency in business.   The platform also boasts automation capabilities and supports integration with other work apps. With over 1,600 employees worldwide, the company is recognized as one of the premier work operating softwares globally.

Why chose Loops

The primary advantage of Loops for us lies in the preliminary stage before execution – identifying hypotheses with a higher likelihood of success. Loops enables us to improve the success rate of product changes and allows us to concentrate on what truly matters.”

Noa Kind, Senior Growth Product Manager,

The Hypothesis Validation and Prioritization Challenge

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

Prior to adopting Loops, data analysts could spend an entire day validating a single hypothesis. Multiply this by the number of analyses conducted on a daily basis and the result is a significant amount of time devoted solely to hypothesis validation. 

New Efficiencies Are A Cause for Celebration

After the data team started using Loops, validation time decreased from a whole day for some hypotheses to less than an hour. Key to this significant improvement in efficiency are Loops unique causal models. The team now also uncovers hidden insights that would have otherwise been very challenging, if not impossible, to discover previously on their own.

Idan Lupo, product analyst works on the growth team as part of sales CRM’s conversion to paid users. Idan explained a typical scenario in his work.

Suppose there’s an area I want to explore to gain a better understanding of conversions or to determine if a feature can improve retention. Before diving deep into understanding things, I prefer to establish a direction. So, I utilize Loops to explore different directions and then decide where to focus and prioritize my efforts drawing on Loops impact analysis.” 

Idan Lupo, Product Analyst,

Today, the growth team can validate multiple hypotheses and discover actionable insights concurrently in a fraction of the time previously required. Loops’ unique causal inference models not only help achieve quicker results they also empower greater confidence in the actions and outcomes. Newly empowered, the analysts deliver more robust insights that directly affect the company’s top-line metrics.

Other Capabilities Empowered with Loops

Other capabilities empowered with the application of the Loops unique causal models allow the team to find the causal drivers to improve conversion, activation and retention. The models enable simulation of how the adoption or alteration of a specific feature affects key performance indicators (KPIs). By understanding which interactions lead to improved performance, the team can make informed decisions in designing executions to achieve maximum performance enhancement and, in turn, reach their goals.

Empowering Analysts

A final word from Tom Laufer, Loops CEO,

“It’s humbling that companies as prestigious and technologically advanced as choose to partner with Loops. We set out to solve the core frustrations confronting product growth and analytics teams. It is deeply satisfying that our solutions are making such a profound impact on the lives of our users and the companies they support and helping analysts make tangible impact on their business with Loops.”

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