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Customer Behaviour

Detailed product analytics identified key features.

Customer behaviour was modelled and allowed us to segment based on activity groups.


Hugosave is a digital companion empowering any generation to spend, save and invest confidently, effortlessly. As a startup they understood that the sooner they could leverage usage analytics on their app, the easier it would be to create and promote new features and understand which behaviours led to higher value customers.


When we design data stacks, it is always with the next 3-5 years in mind. Scalability is everything and we knew that with the large amount of transactional data associated with investments, that the cost could spiral for Hugo. As such we worked closely with the team to try and structure the analytics in such a way to keep costs down whilst delivering the key info on investments held, queries, log-ins and lifetime value.


As an app, Hugo is a little different then most we work with, in that users want to look at their investments at more sporadic intervals and so it was harder to establish the usage base and patterns to determine metrics such as activity and churn. We grouped users into different categories depending on their habits and then cohorts of value. So that for instance, if you had one segment of users who typically handle all their new investments once a year and had a high value, where typical actions such as research were not carried out, the team at Hugo could intervene.


“Great attitude as group. Generous with time, listened to our requirements and gave us a range of solutions. Looking forward to getting into the real smarts of data usage and analytics!” Co-Founder

Tech Stack

AWS, AWS Lambda, Redshift, Metabase, Custom Python Extractors

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