BorrowMyDoggy - 173tech

Customer Behaviour

Using data to boost subscriber retention by 39%

Objective

Centralise and model customer behaviour to uncover key drivers of acquisition, engagement, and retention.

Obstacle

With mountains of unstructured data, the team needed to pinpoint the small insights that could deliver big impact.

Outcome

A streamlined analytics pipeline and customer behaviour model that drove a 39% increase in subscriber retention.

Background

BorrowMyDoggy, the UK’s most loved dog-sharing platform which connects dog owners with trusted local borrowers to help share the joy of dog companionship.

As the community grew, the BorrowMyDoggy team wanted to deepen their understanding of how users engaged with the platform, from first sign-up to long-term loyalty. They sought to uncover what attracted new members, what kept them active, and how subtle changes in the user experience could improve retention.

To do this, they partnered with 173tech to centralise their data, model customer behaviour, and translate complex datasets into actionable insights that could guide growth.

Challenges

Too Much Data, Too Little Clarity: BorrowMyDoggy had years of rich transactional data, covering sign-ups, likes, searches, and subscriptions, but no clear way to interpret it. The challenge lay in connecting millions of data points into a coherent story that revealed why users stayed, left, or converted.

The ‘Why’ Behind The ‘What’: The team needed deeper insights into what attracted new users, kept them engaged, and encouraged long-term retention. They wanted to identify the key drivers behind acquisition and determine what features or activities boosted engagement and loyalty. Improving the user experience and conversion rates was also a priority, requiring a better understanding of pain points and opportunities to streamline the customer journey.

Solution

Choosing the Right Tools: Building on BorrowMyDoggy’s existing infrastructure, 173tech evaluated a range of analytics tools to design an end-to-end data pipeline optimised for speed, flexibility, and scale. The solution integrated seamlessly with their operational systems, ensuring reliable and continuous data flow.

Automated Data Pipeline (ETL): We implemented a fully automated ETL process to extract, transform, and model data from the operational database. Raw events were turned into structured datasets that reflected user journeys, behaviours, and conversion triggers, forming the foundation for strategic decision-making.

Accessible Insights: To make insights actionable, we developed clear, intuitive dashboards in Metabase, visualising the customer funnel, engagement metrics, and retention drivers. These tools empowered every team, from product to marketing, to explore and use the data directly.

Impact

Empowering the Team: Once the new analytics pipeline was live, we focused on enablement. The BorrowMyDoggy team received tailored training on Metabase, giving them the confidence and capability to self-serve insights and explore behavioural data independently.

Delivering Quick Wins: Our deep-dive uncovered seven actionable strategies across CRM, marketing, and platform experience, from improving search visibility to refining communication timing, that immediately boosted engagement and retention. These quick wins drove rapid ROI while establishing a strong data foundation for continued growth.

Understanding User Behaviour
With a centralised dataset, we segmented users based on behavioural and transactional patterns. This analysis revealed clear differences between high-value and at-risk subscribers, guiding more targeted retention and reactivation campaigns. This helped to increase retention by 39%.