How Aprila Bank Increased Applications by Over 37% with AI-Driven Marketing Mix Modeling*
Company: Aprila Bank is a fast growing Norwegian fintech company offering innovative digital banking solutions for small and medium-sized enterprises (SMEs). They provide fast, flexible, and tailored financing options to help businesses optimize cash flow and drive growth.
Challenge
Cross-Channel Budget Allocation
The budgeting process balanced brand building and performance activities, with the latter utilizing last-click attribution and channel-specific reports. Some channels showed signs of diminishing returns.
Exploring different budget scenarios
Estimating accurate sales impact with different budget allocations across channels was highly challenging, especially due to increased competition and distrust in channel specific reports.
Building Reliable Models Internally
Aprila has a data driven marketing team, and wanted to start leveraging modern MMM models. Still, developing effective models internally was time-intensive and strained limited data science resources.
Cross-Channel Budget Allocation
The budgeting process balanced brand building and performance activities, with the latter utilizing last-click attribution and channel-specific reports. Some channels showed signs of diminishing returns.
Exploring different budget scenarios
Estimating accurate sales impact with different budget allocations across channels was highly challenging, especially due to increased competition and distrust in channel specific reports.
Building Reliable Models Internally
Aprila has a data driven marketing team, and wanted to start leveraging modern MMM models. Still, developing effective models internally was time-intensive and strained limited data science resources.
“By leveraging MMM in Odins.ai, we’re able to identify the optimal mix, and make informed decisions about budgeting and ROI.”
Nicolai Lenouvel Hansen.
CMO, Aprila Bank
Solution
Aprila Bank utilized the Odins platform for
Online and offline marketing data collected and structured
is unified in Odins, and years of historical data was structured in two days. Marketing data is structured in real time, and flows back to Aprila’s own data warehouse.
Marketing Mix Models suggested allocating marketing budget to more profitable channels, and identified saturated channels at current spend levels.
Decisive and open to experimentation, Aprila Bank was able to take action and experiment quickly, leveraging the insights to the fullest.
Figure: Using AI-driven Marketing Mix Models in Odins.ai, Aprila Bank identified overinvested channels and reallocated budgets to channels with a better marginal return.
Online and offline marketing data collected and structured
is unified in Odins, and years of historical data was structured in two days. Marketing data is structured in real time, and flows back to Aprila’s own data warehouse.
Marketing Mix Models suggested allocating marketing budget to more profitable channels, and identified saturated channels at current spend levels.
Decisive and open to experimentation, Aprila Bank was able to take action and experiment quickly, leveraging the insights to the fullest.
Figure: Using AI-driven Marketing Mix Models in Odins.ai, Aprila Bank identified overinvested channels and reallocated budgets to channels with a better marginal return.
"Odins.ai has been instrumental in helping us optimize our budgets and tackle the complexities of modern marketing. They’ve built a model that sharpens our understanding of resource allocation, ensuring more reliable decision-making across the board. Their expertise has made a real difference."
Nicolai Lenouvel Hansen.
CMO, Aprila Bank
Results
With AI-driven Marketing Mix Models for channel allocation, Aprila Bank increased total loan applications by over 37%*.
This data-driven approach helped Aprila Bank enhance marketing efficiency, gain deeper insights into channel performance and marginal returns, and make more informed decisions when budgeting and planning for the future.
* Period: 07.10.24 - 26.11.24
Comparison: Year over year Adjusted for spend changes, accounting for ~13% increase.
Time savings
Allowing the team to focus on strategic priorities.
Cost efficiency
Reduce cost and time setting up and maintaining data integrations.
Improve data quality
With ongoing collection, more granular data and new channels.
Time savings
Allowing the team to focus on strategic priorities.
Cost efficiency
Reduce cost and time setting up and maintaining data integrations.
Improve data quality
With ongoing collection, more granular data and new channels.
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