For B2B enterprises, client classification focuses on allocating a customer into a specific category or bucket. In this specific scenario, DataSpartan worked with a crowdsourcing startup who wanted to identify whether or not a business on their platform was likely to “hit” their crowdfunded target amount. The client also required us to analyse for them which variables were most important in terms of increasing the likelihood of a raise.
A large proportion of the onboarding process was done manually and it was extremely time consuming for the startup to fully onboard each client only to have them not complete a successful raise. Standardized data was collected about the size of the company, the number of followers they had in social media as well as financial performance, their team, industry and competition as well as exit potential.
DataSpartan’s quant team was assigned to work on and analyze different data sources and match them with the historical funding success of different companies in the platform. The team developed both the ingestion processed for these data sources and the forecasting and feature selection algorithms of the final model.
This research project highlighted the importance of some unanticipated aspects of funding projects in the process of completing a raise in a crowdsourcing platform. These results were materialized in a proprietary algorithm that significantly pushed up the success rate of selected companies and increased the efficiency of the marketplace.