Partnering to Identify Product Growth Opportunities
Auke van Deutekom, the former Chief Growth Officer at uDocz, recognized an opportunity to grow the uDocz business through a collaboration with Loops, the leader in causal product analytics. 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.
Leveraging the Loops platform, uDocz quickly gained key insight benefits, including:
- Identifying features that causally enhance user retention.
- Understanding the optimal timing and audience to drive sign-in rate.
- Maximizing retention and conversions by discovering the most effective user journey.
- Identifying the right content categories in which to invest
Actionable Insights
After setting up initial KPIs, Loops rapidly identified numerous actionable insights that could most effectively influence the uDocz KPIs. Notably, Loops highlighted three critical areas for uDocz to focus on, which Auke van Deutekom referred to as “eye-openers:
- The Onboarding experience was a key opportunity to show students relevant learning materials. However, Loops identified specific segments that were underperforming here, partially because some users didn’t provide career information during onboarding and the uDocz content recommendations subsequently missed the mark.
- Exposing several “Aha Moments” not currently being presented to users in the first session could significantly increase both retention and conversion metrics.
- The impact of sign-in is very significant for the Retention KPI, enhancing the platform to improve the user experience could drive long-term retention.
Leveraging Loops Causal Inference Models
To originate these actionable insights, Loops ran various causal inference models focused on the KPIs chosen by uDocz’s team:
- Goal Drivers Analysis: Determined features that had a causal effect, and which should be emphasized to improve 2nd Week Retention and Conversion To Paying.
- Causal Journey Analysis: Identified the optimal activation journeys, the sequence of experiencing features, and amount of content consumed that lead to long-term retention.
- Root Causal Analysis: Discovered hidden segments that underperform in Retention and provide the specific reasons for the trend.
Loops Causal Inference Models Surface Retention-Related Drivers
uDocz subsequently implemented Loops’ recommendations, focusing on their user experience and optimizing the display of the ‘log-in’ screen based on usage patterns:
Gains in Signups and Logins:
uDocz added restrictions, such as ‘login’ or ‘register to keep reading’ after a specific number of documents were read. To test the Loops insight, the restriction was first made in just a few countries. As a result, uDocz saw a 20% lift in signups and 34% in logins. Once Loops’ insights had the desired impact, uDocz deployed the changes across the entire platform and user base.
A Product Tour Speeds Adoption:
Further based on a Loops recommendation, uDocz created a Product Tour that not only shortened the time to value of adoption of core features by 25%, but notably also drove a significant impact in retention.
User Details Drive Usage and Retention:
uDocz also encouraged users to provide more information, such as topics of interest, during the onboarding process. This new information enabled uDocz to provide users better content recommendations, leading to an increase in the number of documents read and retention.
Summary:
Analytic Insight Efficiency, Growth Success, and Team Empowerment:
Loops not only assisted uDocz in validating hypotheses and focusing their efforts they also helped identify key user segments for retention. With hundreds of thousands of documents and a diverse student base, uDocz could now pinpoint the specific content and features crucial for maintaining healthy retention rates.
With Loops, the uDocz data science team was empowered to focus on the insights driving the biggest impact on top-line metrics. They no longer needed to sift through endless data points (KPIs, segments, features, different documents, etc.) to try to find what casually drives KPIs.
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