We focus on Business Intelligence and improving our products. We get to the bottom of things, gain knowledge and insight about our customers and the driving forces of our loan comparison platform. Our Business Intelligence Department is divided into 4 sub-departments:

Business Analytics, Web Analytics, Data Science and Data Warehouse Development.


We are all responsible for the compilation of business analyses, prognoses and ad-hoc assessments in the area of online performance, finance, transactions, data interfaces and risk management.

Testimonials

Our tech stack

Our sub-teams

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Business Analytics

Business Analytics

Our main tasks revolve around defining KPI’s, business logics, analyzing data and reporting. We provide insights and recommendations for both operative and strategic developments to all teams within smava. We use state of the art technology, like Tableau, to visualize and process our data. On a daily basis we use PostgreSQL for data querying. We are also responsible for maintaining the consistency of our database with external data providers.
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Web Analytics

Web Analytics

We dive deep into the day-to-day trends of our customers to optimize conversion rates and thus Marketing’s ROI. We are the in-house experts in customer journey and customer lifetime analyses. Planning, testing and measurement of conversion effects on our platform as well as constant improvement of our tracking and reporting architecture are our daily tasks. To do that, we use tools like Google Analytics 360, Kissmetrics, Google Tag Manager, Firebase, etc.
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Data Warehouse

Data Warehouse

As data warehouse engineers we work collaboratively with our business analytics and IT team to implement new ideas & initiatives according to our stakeholder requirements. We provide data-driven solutions & easy implementation with the latest technology. Our major task is to build and maintain our Enterprise Data Warehouse using Data Vault 2.0 methodology. Our challenges are maintaining, extending and optimizing our data warehouse so that business analytics and stakeholders can make correct decisions. We always appreciate new ideas and suggestions that take our data warehouse forward.
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Data Science

Data Science

Our statistical models create the foundation for the best matching solutions between the customers and the bank partners. Therefore, in the close cooperation with our Technology and Product Teams, we enable the best possible credit offers for our customers.

We acquire insights from data sets, insights which are the foundation for critical decision making. We work closely together with the Product and IT teams, supporting our customers in finding the best matching offers from our partner banks. For our models and algorithms, we are not bound to a specific data science tool kit (R, Python, etc.), but rather use what is most efficient to achieve our goals. At the end of the day, we see ourselves as 40% scientist, 30% software engineer, 20% hacker and 10% hippie.