Snapshot: Machine Learning Gives a Fintech Unicorn a Marketing Makeover

Successful marketing teams need efficient data matching and pairing processes to succeed. This was a big challenge for one of our FinTech clients, who was aggregating data from various sources and running advanced analytics models on local machines.

Altimetrik created a machine-learning model that provided better data in a timely matter, ultimately making our client more agile and nimble with more robust data, deeper insights, faster ability to take action, and better feedback loops.

Fast Facts

Increased Revenue by More Than £1,000,000+

Reduced New Model Development Timelines by 70%

Improved Efficiency of Direct Marketing Performance

Higher Incremental Annual Originations

Creating a Seamless Approach to Collecting Robust Data and Analytics

Aggregating data from various sources without a reliable collation mechanism and running advanced analytics models on local machines led to a cumbersome data matching and pairing process for this Fintech marketing team. Building out data models took 6-10 months and even the most basic uses of the data – list management and analysis – were manual, complex, and prone to error.

With a Big Bang approach, a significant amount of money is invested in building a large data lake project. The roadmap planning team knew that it might take months or years to realize any true business insights or results. The Altimetrik team took a lean approach ­– creating a machine-learning model with just enough data to allow the team to seed the data lake while further enrichment would enable the build-out of additional models.

Elements of the makeover included:

  • Building a cloud-based (AWS) data lake for ingesting the data from various sources
  • Crafting a resilient architecture from the ground up
  • Designing the data pipeline to simplify the future addition of new data sources
  • Employing a well-conceived battery of Agile (Scrum and Kanban) teams who delivered work in line with stringent compliance/audit requirements
  • Using the new data lake to create Machine Learning models

Robust data and deep insights that are relevant to current / near-future business cycles, and better feedback loops provided improved ROI and dramatically increased revenue.


Jayaprakash Nair

Head, Analytics CoE

Decisions – small and large – are made with a combination of analytics drawn from data and augmented by the business experience. Decision-makers now have the ability to gather deep insights and create predictive tools and analytics that drive real business outcomes.

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