Gold standard models across user engagement, revenue and profit
Objective
Implement best-in-class data modelling across revenue, retention, and user engagement.
Obstacle
Rapid growth had left Smitten reliant on fragmented reporting and siloed data sources, limiting consistency across key metrics.
Outcome
A modern, automated analytics stack, delivering clarity, scalability, and confidence in every decision.
Background
Smitten is a fast-growing dating app redefining online dating through shared experiences and authentic connections. With rapid product innovation and an expanding user base, the team needed to make faster, smarter decisions backed by reliable insights.
While they had strong in-house analytical talent (including a data analyst and growth lead) the lack of a scalable data infrastructure made consistent, trustworthy reporting a challenge. That’s where 173tech came in: to design and implement a modern data foundation that would empower Smitten to move faster, dig deeper, and reduce manual reporting effort.
Challenges
Ad-hoc Analysis: Despite having a data-savvy team, Smitten struggled with fragmented reporting tools and a lack of a centralised, reliable view of performance. Metrics such as revenue, churn, and retention lived in multiple systems; Amplitude, Adapty, Postgres, each with its own inconsistencies and definitions. Teams frequently lost time reconciling figures or working with outdated snapshots.
Thinking Ahead: With increasing user complexity, features like Duo Mode & multiple subscription tiers, it was clear the team needed more than just better dashboards. They needed a foundational rethink: how data was modelled, defined, and operationalised across teams.
Solution
Data Dictionary: The project began by aligning all stakeholders on a shared KPI framework. 173tech collaborated with Smitten to produce a robust data dictionary, covering definitions across revenue, subscriptions, retention, DAU/WAU/MAU and app usage metrics. This served as the blueprint for everything that followed.
Data Pipeline: Our team built a modern data stack on Snowflake, provisioned with Terraform and connected to all key sources. dbt was used to develop layered, modular data models with reusable macros, incremental logic, and robust CI. Staging models were optimised to reduce compute load, and event data from Amplitude and the server was reconciled to create a unified source of behavioural truth. Everything we did during implementation was aimed at long-term cost reduction of analytics and utmost scalability, as we knew from our bumble days just how quickly dating data could scale.
Impact
Clarity in Revenue & Retention: Smitten’s first major win was visibility into how their product was monetising. Before the project, revenue performance was difficult to track across plan types and cohorts. With a clean, consistent foundation in place, the team could finally see how different subscription tiers were converting, what retention looked like over time, and where potential drop-offs were occurring. This empowered smarter pricing decisions, cohort-specific strategies, and a deeper understanding of lifetime value.
User Engagement: The second breakthrough came from behavioural data. Smitten’s app features both solo and duo experiences, and user activity was scattered across server and third-party sources, making it nearly impossible to understand the full picture. Through a unified and deduplicated view of in-app behaviour, the team uncovered rich insights into how users were exploring features, how usage patterns varied across cohorts, and what engagement loops drove retention.
Cross-Team Visibility: All of this data came to life through intuitive, journey-based dashboards built in Metabase, covering acquisition, activation, engagement, and monetisation. With training and documentation in place, Smitten’s team can now self-serve insights without relying on manual reporting or engineering.
Creating Value For Smitten...
A massive 90% reduction in reporting time,
As well as an 8 month saving vs doing the work in-house,
That centralised all customer behaviour data in one place.
