Activity-Based Segmentation With Automated Flags.
Objective
Unpick the irregular patterns of investors by modelling their trasnactions.
Obstacle
Fresh analytics infrastructure needed to be set up.
Outcome
Activity-based segmentation with automated flags.
Background
Hugosave is a digital financial companion designed to empower people of all generations to spend, save, and invest with confidence and ease. Recognising the limitations of their existing tools, they turned to 173tech to establish a robust and scalable data infrastructure that would serve as the backbone of their analytics-driven approach. As a startup, they knew that unlocking the power of app usage analytics would be critical to driving their growth, and were looking for a cost-effective and quick way of getting started with data.
Challenges
Impartial Expertise: Although Hugosave had some familiarity with data tools, they were concerned that internal biases, shaped by past experiences, might influence their choices. They needed a partner with extensive experience across a wide range of tools who could provide objective, well-rounded advice.
Uncovering Key Trends: Hugosave was dealing with a vast volume of transactional data, making it difficult to pinpoint meaningful trends that could guide product improvements. While they had visibility into general activity levels, they lacked a clear understanding of how these activities translated to customer value or impacted retention rates.
Solution
Designing Infrastructure: When designing Hugosave’s data stack, scalability was at the forefront of our approach. Knowing that the large volume of transactional data associated with investments could lead to rapidly increasing costs, we worked hand-in-hand with their team to structure their analytics in a cost-efficient manner. Our focus was on maintaining affordability while delivering actionable insights, including metrics on investments held, user queries, log-ins, and customer lifetime value.
Activating Data: With the infrastructure firmly in place, we turned our attention to creating accessible and actionable tools. Using Metabase, we built a series of intuitive dashboards that provided Hugosave with a holistic view of their operations. To enhance responsiveness, we implemented automated alerts directly into Slack.
The Proof Is In The Numbers...
12
>$200
12
Implementation
Unique Usage Patterns: Hugosave presented a unique challenge compared to many other apps we have worked with. Their users tended to engage with the platform at sporadic intervals, often tied to investment cycles, making it more difficult to establish consistent usage patterns and calculate metrics like activity levels and churn. Understanding these irregular behaviours was key to delivering actionable insights.
Segmenting User Behavior: To address this, we conducted an in-depth analysis of their transactional data to segment users based on their activity patterns. This allowed us to define typical behaviours for each group. For example, one segment consisted of users who managed their investments annually. By identifying the lead-up behaviours, such as researching options in advance, we were able to detect when these activities were missing and set up automated flags.
