34% lower CAC through predictive marketing analytics
Objective
Reduce customer acquisition costs and predict marketing ROI across all channels.
Obstacle
Marketing decisions were based on incomplete data, with the internal team stretched on banking priorities.
Outcome
34% reduction in CAC, with ROI predictions that were 89% accurate from day one.
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, their efforts were focused on core banking operations, leaving marketing analytics underserved. With customer, communication, and acquisition data fragmented, evaluating campaign performance became guesswork, putting pressure on growth targets as the company scaled across Africa.
Challenges
Stretched Resources: Kuda’s internal data team was focused on product and banking priorities, leaving little capacity for marketing insights. Without timely data, the marketing team risked overspending on underperforming channels while missing high-value opportunities.
Infrastructure Risk: Kuda’s existing data systems were built for day-to-day banking operations. Adding marketing data risked destabilising core infrastructure, but building a separate stack from scratch needed to happen fast to support growth targets.
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 meant the marketing team could move fast without waiting on engineering approvals.
Data Modelling: We centralised, modelled, and activated data from across Kuda’s advertising and social media channels, creating one source of truth. For the first time, Kuda could see exactly which channels, campaigns, and messages drove the highest-value customers, not just clicks.
Impact
LTV & CAC: By mapping customer lifetime value across channels, we identified which segments delivered the fastest payback and which were draining budget. This insight drove a 34% reduction in CAC by reallocating spend towards campaigns acquiring high-LTV customers.
Activation & Churn: By analysing behaviour and engagement patterns, we pinpointed what drove users to activate and what caused them to drop off. Targeted interventions based on these insights improved activation rates and reduced early-stage churn, directly increasing the return on every acquisition pound spent.
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,
And built a full marketing data pipeline from zero in weeks.
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