An Australian bank had issues with its Credit Card Products:
- Declining customer acquisition
- Ineffective marketing spend. Not delivering the right credit card product offer to the right customer
We grouped customers based on their product purchasing behaviour, using the following data
- customer data for the 12 months prior to credit card purchase
- up to 300 variables including customer geo-demographics, bank relationship, transaction details etc
We identified the four main drivers of product choice:
- Age of customer
- Annual income
- Use of ATM outside of home state
- Average transaction amount
The bank was able to improve its rate of customer acquisition by offering more tailored credit card products that better aligned with the needs of new customers.
The approach used the power of contemporary Data Science methods and was transparent, repeatable, scientifically valid and accurate.
Data Science, also referred to as Advanced Analytics or Predictive Analytics, is an analysis approach that provides businesses with accurate What-If scenarios and evidence-based proactive decision-making tools.
- It is based on predictive analysis of domain-specific organisational data. If an outcome of interest to the business can be measured, then Data Science methods can determine which factors influence it and to what extent – and based on the delivered insights, suggest the call to action.
- It has been proven and pressure-tested globally across many industries. It has been a key to the success of Google and Amazon. It is used by leading banks, insurers, telcos, retailers, manufacturers, utilities and governments to gain insight into how to efficiently improve business outcomes including:
- improving customer value and customer retention
- marketing campaign improvement
- loan default reduction
- customer churn prediction and reduction