Overview
For B2B enterprises, client classification focuses on putting a customer into a specific category or bucket. Equity crowdfunding company Crowdcube identified DataSpartan early in their journey, as they wanted to identify whether or not a business on their platform was likely to “hit” their crowdfunded target amount raised. Crowdcube also wanted us to inform them of which variables were most important in terms of increasing the likelihood of a raise.
Approach
Historical data was extracted, cleaned and standardised to allow this analysis and three variables were singled out which determined the probability of a successful raise. A supervised learning model was then created which could classify these leads based on their likelihood of conversion. Python was used for this portion of the data analysis and libraries such as NumPy and SciPy were leveraged to ensure rapid computation and fast model prototyping.
Results
Our solution has resulted in a significant decrease in the number of man hours used as well as a 6% increase in the number of companies that successfully raise. Further work is being done to automate some of the manual processes such as KYC (Know-Your-Client) document verification and to improve the data pipeline to ensure that the data collected is being stored in the appropriate format for analysis.
