Churn data analysis of 91SpringBoard with respect to meeting room usage

Abstract

In the times where all new businesses are trying to make their business as physical infrastructure-light as possible, 91springboard has entered with a business model which demands leasing properties, and this requires require high capital investment. 91springboard is a working space rental start-up. Their primary offering is to create a coworking community for start-ups, freelancers and business owners, which are in the initial stages of development. 91springboard was officially launched in January 2013 and today it operates out of five major cities – Delhi NCR, Gurugram, Bengaluru, Hyderabad, and Mumbai. In addition to working space, 91springboard’s subscription plan also involves usage limits for shared services such as meeting rooms. Before October’19, although 91springboard had an allotted limit of meeting room usages for teams, they didnt regulate the usage of meeting rooms. More often, some of the teams ended up overusing the permissible limits of the meeting room hours. This resulted in scheduling challenges and loss of productivity. And hence, 91springboard decided to monetise overusage of meeting rooms. But the management was worried about the adverse effects of monetisation in terms of increased churn rate. Hence we went about testing the following hypotheses in relation to this: 1. Churn users ramped up their usage of meeting rooms few months before leaving 91springboard 2. Average utilization rate of churned users is greater than that of Active users over the period of 2 years from Jan,2018 to Dec,2019 3. Only few teams were consistently overusing meeting rooms 4. Average meeting room usages are different for teams in different segments, team sizes and plan category type In order to test these hypotheses, we received following data sources from 91springboard: 1. Meeting room usage sheet: It specifies the team which booked the meeting room along with duration of booking, person who has booked apart from the joining, leaving and cancellation dates. In addition to that, plan type, segment and average team sizes were also mentioned. We found 374 unique teams booking meeting rooms. 2. Churn Data sheet: It specifies the current state of the teams with respect to Active or churn. We found 317 unique teams for which churn data is available. 3. Audio recording of a conversation between Professor Sreelata and 91springboard wherein we received qualitative insights about the usage behaviour of the teams.