AI will disrupt entire software categories, in the same way that cloud and SaaS did in the past. We call this new class of software “cognitive apps,” and believe that the next generation of product analytics will be part of it.
The problem is clear. Product teams are drowning in the vast amount of raw data and dashboards at their disposal, but still struggle to find actionable ways to improve their business metrics. This problem is compounded by the fact that many companies lack the resources or expertise to extract conclusions. As a result, product teams find themselves overwhelmed by data without being able to use it to their advantage. Enter Loops to the rescue.
We’re thrilled to announce that Scale is leading a $14M Seed round in Loops. By using the Loops platform, product teams can cut through the noise and focus on insights that help them maximize their most important business metrics. Loops proactively identifies growth opportunities, which Product Managers (PMs) can implement to drive growth, engagement, and retention targets.
Drowning in Data
Companies collect vast amounts of user activity data and enrich it with other data sources all in the name of making informed product decisions. While this is a great start, this process makes it overwhelmingly difficult to ideate and test hypotheses to drive growth.
The answer to “what works” may already exist in the data warehouse, but it’s difficult to uncover. This can have adverse consequences: the causes of underperformance can go unnoticed, resulting in a disappointing user experience that leads to poor conversion, low activation, or even churn. When faced with this crunch, product teams resort to gut and intuition, sacrificing a data-based approach.
What’s the answer to the data flood? Increasingly, it can be found in causal inference models, which help companies identify opportunities backed by data to improve their KPIs. These machine learning models are significantly more sophisticated and complex than run-of-the-mill correlation analyses.
Loops abstracts this complexity into a product that simplifies the task of going from data to insights. It becomes an intelligent layer sitting on top of the company’s data warehouse. With a simple integration, PMs can start deriving insights from data, saving hundreds of hours spent ideating and carrying out experiments that often don’t work out. Instead, statistically-relevant insights are automatically surfaced and PMs can focus their attention on how to implement them in the product. This not only speeds up iteration cycles, but also surfaces recommendations that would otherwise not have come up in a less systematic brainstorming process.
A New Era for Data Science
Tom Laufer, co-founder and CEO at Loops, is intimately familiar with product growth challenges, having led the growth and analytics team for EMEA at Google. His experience developing best-in-class data science methods sparked the motivation to start Loops. With a productized solution, the same analyses that have driven the growth of top tech companies can now be adopted by everyone.
Our diligence uncovered customers who were skeptical at the initial value proposition, only to be later surprised and impressed by the quality of recommendations that the product surfaced. We’re eager to partner with the Loops team to continue to scale the business.