Matching customer service skills with customer needs

Jun 2018, Sydney

Eamonn Barrett

Delivering Call Center efficiencies through clustering and customer analysis

A large Australian Mortgage Broker was looking for ways to speed up their applications process and increase the number of successful new loan applications. Remi AI used advanced clustering methods and network analysis to uncover trend and information in the Applications Process.


Remi AI developed a clustering algorithm that placed over ~20,000 previous applicants into 80 different buckets based on their individual behaviours during the application and loan process. Remi AI simultaneously ran a separate clustering algorithm over the call centre team to understand the how call centres buckets were best placed to help provide information and aid in helping their customers find a suitable loan. By comparing the results from the two clustering algorithms, Remi AI was able to find the most suitable Applicant Team Member for future Loan Applicants. This approach was rolled out across their applications process, so that new Loan applicants were allocated a specific Team Member from the most suited Cluster.


The Remi team was able to reduce the number of contact points between new applicants and the applications team by 17%, and increased the rate of successful loan creation by 4%​