RentoMojo is using machine learning to create credit profiles of users
Bangalore-based RentoMojo, which runs an online rental marketplace for furniture, home appliances and bikes, is using machine learning to analyse the credit profiles of its customers, a top company executive told TechCircle.
It also uses predictive analysis for stock or inventory management. “This is one of our core practices and it helps us maintain optimum inventory so that we don't retain most of our assets in the warehouse, or in another case, customers can't avail a product in spite of it being listed on the platform,” Geetansh Bamania, co-founder and chief executive, RentoMojo, told TechCircle.
These technologies have helped the firm streamline its operations, making it more efficient and has also allowed it to offer its customers better products, he said.
The company, which recently allowed users to own its rental products, considers itself more as a fintech lending startup than a direct e-commerce company.
"We are more in the lending space than being in e-commerce. Our lending is targeted and shaped in a way so that young customers can avail the services hassle-free. Other [companies provide EMI options making] sure that you finish the lending contract but what we offer is flexibility with the option of moving out of the contract or later owning the product itself," Bamania explained.
To determine the credit profile of a user, RentMojo rates a customer based on his or her CIBIL score and the individual’s history on the platform, among other parameters.
“Because we are in the lending business, we have to verify or analyse a customer's profile. Giving someone $1,000 or a product worth [that amount] is [the] same. We have to make sure that the customer's RentoMojo score is above a certain [level] before we can pass off the rental product," Bamania said.
When customers close a rental deal, the company asks them to submit information about themselves which is later used to derive the score.
"We also keep track of the customer or the product even after the rental by checking whether the EMIs are coming in on time. If they are defaulting, then what is the reason behind it," Bamania further explained. He added that his startup was working with banks and non-banking finance companies at the back-end to get certain data for the credit profiling using its machine learning tools.
As part of the predictive analysis approach, the co-founder said that his core team of five engineers keeps track of data coming in from different geographies. Data sets can vary based on the demand of the categories, particular sets of products, and the exact time when the demand shows up, among other things. He also said that the company's historical rental information helps it to maintain the data.
According to the co-founder, activating new technologies on the RentoMojo platform has enhanced the startup's business, which has grown four-fold since it last raised capital in July 2017. It raised $10 million (Rs 64.3 crore) in a Series B round of funding led by Bain Capital Ventures and Renaud Laplanche, a French-American entrepreneur.
The company had earlier raised $2 million in a pre-Series A round from Accel Partners and IDG Ventures India in November 2016.
RentoMojo, which was founded in November 2014 by IIT Madras graduates Bamania and Ajay Nain, currently offers its services in Pune, Mumbai, Bangalore, Delhi, Noida, Gurgaon, Chennai and Hyderabad and claims to have more than 25,000 subscribers on the platform.
Last week, the company partnered with online marketplace for pre-owned furniture and appliances Zefo to provide their customers with an option of rental monthly instalments to pick the product of their choice.