Turning marketing data into predictive growth at Kuda Bank
Objective
Centralise and optimise online advertising to drive efficient customer acquisition.
Obstacle
No marketing data infrastructure or central source of truth.
Outcome
Campaigns optimised with performance predictions from day one, driving significant efficiency gains.
Background
Kuda Bank is a fintech on a mission to make financial services more accessible, affordable, and rewarding for every African. While they had a strong internal data team, the team’s efforts were focused on core banking operations, leaving marketing analytics underserved. This created a gap: customer, communication, and acquisition data were fragmented, making it difficult to evaluate campaign performance or scale growth effectively.
Challenges
Busy Team: Kuda’s internal data team was stretched thin with product and banking priorities, leaving little time to provide marketing insights. This meant decisions were often delayed or based on incomplete data, hindering campaign effectiveness.
New Infrastructure: Most of the data systems in place were to aid Kuda’s day-to-day banking operations. There was some debate as to whether adding marketing into these systems would be optimal, we suggested that would not be the case. 173tech have a lot of experience when it comes to growth related analytics, and so we were the best people to take this on.
Solution
New Infrastructure: Instead of retrofitting banking systems, we built a separate, purpose-built data infrastructure for marketing. This eliminated risk to core operations and ensured marketing had the flexibility and speed it needed.
Data Modelling: We centralised, modelled and activated data from across Kuda’s advertising and social media channels creating one source of the truth. With this setup, Kuda’s team gained a detailed and actionable understanding of which campaigns, channels, and messages were driving the highest conversions.
Impact
LTV & CAC: One of our key focuses was gaining a clear understanding of customer lifetime value (LTV) to determine the payback window from marketing. By accurately calculating LTV, we were able to project how long it would take for the initial acquisition cost to be recovered and how much could be reinvested back into scaling customer acquisition efforts.
Activation & Churn: By analysing customer behavior and engagement patterns, we were able to pinpoint what drove users to fully activate and adopt the product, as well as the key reasons for churn. This allowed us to design targeted interventions to improve activation rates and reduce churn, ultimately enhancing customer retention
Predictions: Beyond developing an omni-channel reporting system to track and analyse marketing activity, we linked this data directly to customer value, enabling highly accurate lifetime value (LTV) forecasts. Within just days, Kuda’s marketing team was acting on predictions that proved to be 89% accurate, allowing them to make confident, data-driven adjustments to campaigns from the very start.
Creating Value For Kuda Bank...
We created models that were 89% effective in predicting ROI,
Which reduced CAC by 34% across all platforms,
As well as creating a whole new pipeline.
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