Tailored product offers increase customer satisfaction – strengthening long-term loyalty.
🚀 Customer SatisfactionWhat is Cupulis for Banks?
Customer Purchase Analysis
Every year, billions of transactions take place in the financial sector. In this immense volume of data, it is difficult for banks to identify specific patterns, regularities, or the individual needs of their customers. Traditional analysis methods quickly reach their limits here. Cupulis is an innovative analysis model that not only evaluates transaction data mathematically but also creates an individual psychological profile for each customer – based on transactions, master data, and behavioral patterns. These profiles are compared with reference groups to precisely identify desires, life situations, and potential needs. From this, Cupulis derives concrete actions for banks.
From Need to Solution – Through Data-Driven Actions
The identified desires – such as the need for a bicycle, car, or insurance – are directly translated into targeted marketing measures. Cupulis enables banks to efficiently turn individual offers into concrete actions:
Personalized credit suggestions achieve high acceptance – directly impacting loan volume and margins.
💹 Revenue GrowthThe combination of credit, specific products, and discounts increases repayment likelihood and reduces default risk.
⚠ Risk ReductionPrecision-targeted marketing minimizes waste and lowers acquisition costs.
💰 Cost EfficiencyAttractive terms and exclusive discounts attract new customers while boosting sales with existing ones.
🤝 New CustomersHow Does It Work in Practice?
- Data Analysis & Profile Creation: Customer data is analyzed in a GDPR-compliant manner and anonymized into needs-based groups. Your data never leaves your bank.
- Product Matching: For the identified desires, Cupulis searches for suitable retailers and service providers. Based on aggregated demand, they submit a concrete offer.
- Example – Desire: Bicycle: Cupulis identifies 100 customers who want a high-quality bicycle. A retailer then offers a special model for €5,000 (instead of €6,000) – exclusively via the bank.
- Marketing Action by the Bank: The bank sends these 100 customers a personalized offer: an instant loan for €5,000 (depending on creditworthiness) plus an individual discount code for the bicycle.
- The Result: The customer gets their desired product at special conditions, the retailer generates sales, and the bank increases both customer satisfaction and loan volume.
All steps not only include a data protection-compliant analysis, but also the involvement of all relevant departments of the bank such as IT, risk management, data analytics, payment transactions, marketing, etc. for double security.
About the Model
Big Data is manageable today – what’s missing are clear metrics to describe customer behavior. We have developed a new metric that, combined with specific products, strategically supports lending.
- Credit Usage Propensity (CUP)
-
The Credit Usage Propensity (CUP) describes the statistically measurable tendency of a person or population group to actually take out a loan for consumption or investment purposes – despite having sufficient creditworthiness. It is based on psychological, social, and behavioral factors and serves as a KPI to quantify the gap between theoretical borrowing capacity and actual loan usage.