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Kai.Ba Making your daily commute simple! http://kai.ba
Kai.Ba Making your daily commute simple!
http://kai.ba
Problem 1. Overcrowded public transportation, taxi shortages and heavy
traffic lead to uncomfortable and long transit times for residents.
2. The same problem repeats on the same route twice a day, five days a week for months or years.
High demand
Long transit
Large market
But why? 1. There are just not enough rides for everyone. There is a
large difference in peak and non-peak demand. Most commutes take place during peak periods but increasing the number of vehicles on the road only leads to redundancy and massive traffic jams.
Non-peak
Peak
Solution and Product Kai.Ba is a ridesharing application that predicts and recommends rides based on past travel habits. We focus making the daily commute to and from work more convenient! • Save the hassle: Kai.Ba makes it easy for passengers to find neighbours
in the same way because we run a shuttle service leaving on regular timings.
• Save money: Kai.Ba is 80% cheaper than a regular cab ride the fare is split among all passengers.
Share
Passengers
Kai Ba = Rental Car
+ Professional Driver
Book
The start of predictive sharing…
To download this app, please go to http://kai.ba/download (available on iPhone and Android)
Data collection of our users Home / Office locations and regular travel times. (all new users)
Tap to book (no typing required)
To download this app, please go to http://kai.ba/download (available on iPhone and Android)
Red Tags indicate Kai Ba “Stations” (Only clustered points are offered enable easier sharing)
Next functional improvement
快速: 家 公司 Kai.Ba will automatically detect is there is a route running and allow users to jump straight to available shuttle timings.
Drivers receive bookings via their APP.
To watch a DEMO video, please go to http://youtu.be/5auXgw4Tmn0.
Baidu API shows congestion info. Not shown: drivers get a voice notification when they receive a new booking.
Validation - Over 4,000 Yuan in revenue within 2 weeks of launch (and minimal
publicity). - Test bookings served: Over 200 - Customer Loyalty: 80% of our users are repeats. They use our APP for
their daily commute to and from work.
@superlana: Today, I booked a car via Kai.Ba (from Wang Jing to Wu Dao Kou). The driver was early. He was dressed in a shirt with a company badge – and had a great attitude! I am the only passenger thus far – let’s hope Kai.Ba will grow and thrive!
Market
We’re targeting white collar professionals and trips between home and office.
Market Segment
China has more than 20 cities with a population size above 5 million, and a whopping 160 cities with more than 1 million residents. It the single largest urban transportation market in the world, as well as the largest taxi market (with more than 5 million taxis, and growing). It is an ideal market to experiment with new, innovative models of transportation. *There are 70,000 taxis in Beijing serving 3 million passengers daily. Our targeted demographic are the 20% of passengers use taxis to commute daily to and from work.
Investment bankers, lawyers (Transport subsidy, limousines)
Targeted Audience
Lower middle class (subways, public buses)
Targeted: Executive level employees earning
between 10,000yuan – 30,000yuan a month.
(E.g. Programmers, middle managers)
Business Model 1. Cost per ride
› 50% of regular taxi ride in Beijing (after fares increase on 1 June 2013)
2. Profit margin per booking › 2 passengers –10% › 4 passengers – 40%
*A Profit Share of 10% for every passenger
We earn a revenue USD 3,000 / month per car (or more)
› Daily revenue: USD 100 (USD 12.5 / trip X 8 trips) › Less daily costs: USD 40 (car + driver) › Less fuel costs: USD 30 (150 km)
Marketing Channels
Large
Medium
Small / Localized
Marketing Channels Publicity Channels Size
• Phone Manufacturers • Telcos • Payment gateways
• Media
• Apartment Buildings • Offices • Factory areas
• Tie ups • Localized banners • Corporate
programs
• Nightclubs • Hotels • Restaurants • Shopping Complexes
• Flyer distribution • Cross promotion
Most Effective!
Growth and Scaling 1. Predictive Sharing Our competitive advantage increases proportionally to the size of our database of customer data. As Kai Ba expands, so will our clustering technology of location datasets. 2. Growth and Scaling As our application matures, we can • Customer loyalty/referral programs • Single application across different cities (geolocation for local version)
Rideshare from 2 sources:
• Individual Drivers (Tie permit to Car Rental Company)
• Car Rental Companies providing Chauffeurs
(Membership system: Alibaba Gold, verified,
unverified)
Passengers and drivers set routes, offering personalized trip alerts to let passengers adjust their travel time.
Competition
Kai Ba Pin Che Q R R R R
Di Di Da Che R Q Q Q Q Yao Yao
Zhao Che R Q Q Q Q Yi Dao
Yong Che R R Q Q Q Kuai Di Da Che R Q Q R Q Du Du
Jiao Che Q R R Q Q
Taxis Rental Cars
Daily Commute Rideshare Predictive
Lee Yi Hong (programmer, Android) graduated from the National University of Singapore (NUS) with a degree in Information Systems. She picked up Android programming during her spare time and has since developed multiple applications. (ShootNSell), a barcode scanning application with a marketplace for second hand textbooks, was a finalist in the annual NUS iMobility Challenge in NUS.
Kel Zhang (Business) Business Co-Founder for Kai.Ba, majored in Marketing and Finance a t S i n g a p o r e M a n a g e m e n t University (SMU). She has ove r 3 yea rs working experience in Toshiba. She helped to design an ERP system on-s i te a furn i ture m a n u f a c t u r e r i n Kunshan China. She has a lso done an internship in Pacific International Lines, Chennai India. She thinks she’s awesome.
Su Zhen (programmer, Web) graduated with a degree in computer science from Beijing Institute of Technology (BIT) and has more than 6 years of experience in server-side programming. He is proficient in Ruby and Rails, Redis, MongoDB, SQL and hopes to become a better programmer in future.
Team Wyn Zhang (CEO) Technical Co-Founder for Kai.Ba, graduated S i n g a p o r e M a n a g e m e n t University (SMU) to study Quanti tat ive F i n a n c e s a n d Economics with a perfect SAT score. She is self-taught in Ruby and Rails and dabbled in Python. She loves building new products and l a u n c h e d roadhop .com and hopcab.com before moving to China to start Kai.Ba in 2012.
Di Yang (Marketing) graduated with a Masters’ Degree in Adapted Physical Activi ty (Sports Management) from Katholieke Universiteit Leuven on a full scholarship awarded by the European Commission, the only Chinese Citizen to accomplish that during her year. She has internship experiences in Ireland and France and travelled over 20 countries.
C h e n J i a F u (programmer, iOS) graduated from the Normal University of Inner Mongolia (or Shi Fan University), in the top 10% of the cohort. He has more t h a n 1 y e a r o f experience in iOS programming and is passionate about it. H e p r e v i o u s l y c o m p l e t e d a n d launched the BMW i O S a p p l i c a t i o n during his internship there.
Advisor
Dr. Lynette Cheah (advisor, Technical) has a Ph.D. in Engineering Systems from Massachusetts Institute of Technology and an Ms.C in Management Science and Engineering from Stanford University. She has also recently won the inaugural Singapore Challenge Prize to build a dynamic, adaptive transportation network that would shift commuters’ travel modes in response to real-time feedback.
