The Hypothesis Validation and Prioritization Challenge
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.
Prior to adopting Loops, monday.com 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, monday.com product analyst works on the growth team as part of monday.com 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.”
Today, the monday.com 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 monday.com 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.”
