This project is intended to determine the profitability of App based taxi companies considering their current pricing methods. Taxis are commonly used in towns due to their convenience and comfortability. Unlike other public means of transport, they can be hailed at any time of the day and night and clients can be dropped off at any place. This convenience has caused an increase in the demand for taxi services. As a result, many taxi companies have sprung up. However due to their high costs, taxis were limited to corporations, business individuals and emergency trips. The entry of UBER in Kenya in January 21st 2015 and subsequent App-based Taxis such as MONDO, TAXIFY and LITTLE CAB brought the prices down by almost half. They also brought the base fare down to Ksh 150 per trip. This means you can travel short distances with App-based taxis without the risk of incurring high costs. In addition, they offer promotions that range between ksh300 - Ksh500 especially on the first ride. This in turn boosted the taxi market and it grew from business based to individual based. Now, most people use taxis for daily activities such as shopping, visiting friends as the price is very friendly. Assuming that the investment in the Taxi business is funded by debt (i.e. they buy the cars using a loan) we have calculated the investments’ IRR, NPV and PI of the four commonly used taxis: UBER, MONDO, TAXIFY and LITTLE CAB and seen that after the four years of paying for the funding UBER and TAXIFY will be making a profit unlike their counterparts, LITTLE CAB and MONDO. This is mainly because UBER and Taxify have a large customer base and many drivers who work in their companies. We therefore recommend more marketing be done for MONDO and LITTLE CAB to enable them increases their rate of return.


Message From the Director

Workshops and Seminars

Community Outreach

Contact Us

School of Mathematics, CBPS College.

P. O. Box 30197 - 00100
Tel: 254-02-4445751.

Mobile no :0780-834766.




UoN Website | UoN Repository | ICTC Website

Copyright © 2019. ICT WebTeam, University of Nairobi