Analytical models determined best customers to target for cross-selling and funding opportunities
The Business Challenge
This client recently launched a new type of account to improve the penetration of a new product within its existing customer base in Australia. They asked Fractal to help them identify the customers most likely to open this new type of account as well as fund it. The client did not have an analytical solution to help drive its cross-sell activities.
Fractal’s engagement had two objectives:
- Develop a model to determine the likelihood of an individual customer opening and funding the new type of account.
- Segment the bank’s retail base to effectively target prospective customers for cross-sell activities.
Fractal Analytics developed a “vintage-based” predictive solution framework to address the bank’s two-fold objective:
- For retail banking customers who had been with the bank for less than six months, a decision tree identified the customers most likely to fund their recently opened account.
- For retail banking customers who had been with the bank for more than six months, a logistic regression model identified the customers most likely to open and fund the new type of account.
Fractal’s client now has an analytical solution to help drive its cross-sell activities, based on a customer’s length of relationship with the bank. The client expects to generate an incremental fund value of $15-20 million (Australian) and cost saving of 25%-30% in FY 2014-15 alone.
Customers have a varying propensity to fund their account depending on the length of their relationship with the bank. The likelihood of funding increased the longer a customer had been with the bank, resulting in a length of relationship (vintage-based) strategy for cross-selling and funding.
By targeting the “Top Priority” customer segment for cross-selling, Fractal determined that this client can generate an incremental fund value of $15-20 million (Australian) in the current fiscal year.
Targeting only the “best segments” of the bank’s customers who had been with the bank less than six months can lead to a cost savings of 25%-30% every month.
Fractal’s “vintage-based” analytical framework helped our client devise different cross-sell strategies based on a customer’s length of relationship with the bank.
Fractal developed a predictive model that eliminated any variations due to seasonality, market fluctuations, marketing campaigns or other initiatives.