Our client is an American multinational financial services firm specializing in the credit card, charge card, and traveller’s cheques businesses. In fact, a whopping 30% of all credit card transactions in the US are attributable to their credit card business.
Why they came to us
The client’s Corporate Credit business identifies companies across the globe who could become potential clients. This identification process was still extremely archaic and manual. Our client realized that they would have to use an intelligent process automation system to shorten their sales cycle and find the right corporate clients.
The sales team was using B2B databases and third-party websites to find company data. They would then manually scan this data and collect it in spreadsheets, before cleaning and feeding it into a common database.
Due to the archaic nature of their process:
- The sales cycle became long and unwieldy, resulting in many lost opportunities
- There was no way for our client to identify which prospects were more likely to convert than others
Based on discussions with the client, our business-tech experts, at Valiance, decided to zero in on one geography, to begin with — Switzerland. That’s because we realized that any effective solution would need to have 4 critical components:
Data collection layer
This would have web crawlers to gather data through geography-specific websites. The programs would also need to be customized for each website.
This would check the scraped data for validity and consistency before storing it in MySQL.
Machine Learning Algorithms
These Machine Learning algorithms would identify not only the prospects that are most likely to convert but also the best products to offer them.
Seamless user interface
An intuitive and visual tool that would greatly reduce the learning curve for the sales team.
- We worked with the client to figure out the most appropriate sources of data collection along with 50+ data points that we wanted to capture for each prospect.
- We then created customized, automated web crawler to collect, clean, and consolidate this data and store it in MySQL.
- Our data scientists used Likelihood Modelling to create a Propensity Score. This used historical customer acquisition data provided by our client. The Propensity Score identifies not only the best prospects in the database but also which products to pitch to them.
- The Intelligent Process Automation for Switzerland went live in 3 short months.
- We were able to reduce manual intervention in the data collection process by 50% in the first month itself.
- By the third month, the manual workload was reduced to 0 and the process became entirely automated.
- Based on the success of the Switzerland model, we implemented the same model for the client’s Middle East market.
- Our Propensity Score had an accuracy of as high as 75%
Book your free analytics consultation with business experts, at Valiance! We help fintech firms scale their businesses by putting gathered data into an effective process. Contact us, Now!