Gone are those days when folks would have to stand and
wait for taxis or cabs to pick them up and drop them to their destinations.
Now, it’s at their fingertips. Cabs are, well and truly, only a touch away. It
has indeed become so easy. No more hollering or wasting your time. But we
should realise that there is a lot going on behind the scenes, getting you the
cab. Do they not have problems, covering so many cities, countries and
continents? Yes, they do. They have problems galore. One such set of problems can
be solved by Big Data and its analysis.
The global cab aggregator space is undergoing cut throat
competition. New players are finding it increasingly hard to differentiate
themselves from their peers. Ever since their inception, customer retention and
ensuring good ride experience has always been of maximum importance. With
abundant data at their disposal, it becomes important for the senior management
to consider solutions that uncover actionable insights and deliver customer
expectations. It is no longer just a game of marketing, as simply competing
through offers and promotional codes wouldn’t always result in revenue. The key
is simple, real time exploratory data analytics.
Ever wondered why the demand in a particular location has
gone downwards? Is it because of lack of cab supply? or poor driver performance
around that area? or is surge pricing taking its toll? Or could it be due to
political unrest or any untoward incident in the neighborhood? In today’s world
of data boom, we need to leverage real time events to track and map the
operational efficiency of your service.
Also read: Big Data in Banking Services: advantages and disadvantages
Considering the significant factors of scale and time,
your analytics platform should be capable of executing the above workflow by
handling 100s of terabytes of data in a fraction of few seconds. Managing real
time data from different sources and providing immediate visibility to critical
business metrics is always a challenge which businesses face. All of this is
made easy by Big Data. Most cab aggregators use data lakes to store large
terabytes of data generated every day, and then use Spark and Hadoop to make
sense of that data. The data comes from a range of data types and databases
like SOA database tables, schema-less data stores and the event messaging
system, Apache Kafka.
As far as their product team goes, usually the cab
aggregator’s data team does it all. All predictive models which power the ride
sharing cab service ranging from drivers’ ETA, estimation of fare prices,
calculating surge prices, to heat maps, so that drivers can position themselves
accordingly, are decided by data in the data lakes after analyzing them by the
data team. Through statistical and historical data analysis, the cab hailing
companies aim to create a positive user experience. In order to further improve
on this experience, they make use of data science driven insights and implement
new business models, in real time.
The service also relies on a detailed rating system –
users can rate drivers, and vice versa – to build up trust and allow both parties
to make informed decisions about who they want to share a car with.
Drivers, in particular, have to be very conscious of
keeping their standards high – a leaked internal document showed that those
whose score falls below a certain threshold face being “fired” and not offered
any more work. They have another metric to worry about, too – their “acceptance
rate”. This is the number of jobs they accept versus those they decline.
Drivers were told they should aim to keep this above 80%, in order to provide a
consistently available service to passengers.
Reliability can be measured by:
The number of bookings cancelled
Time for booking the service
Responsiveness can be measured by the ability to provide
services for unscheduled requests, availability of capacity and lead time
To sum it up, cab aggregators have progressed from a
simple mobile application with a clean UI to a backend and support service
which is equally potent. Their users generate several billion instances of data
which, now, they have taken the initiative to analyse in order to improve the
customer experience, reduce the manpower needed, and safer transactions.
Also Read: What is the Impact of Big Data on Mobile Marketing