simple analysis on liftago

Marek Dlugos & 최호중

Liftago is mobile app very similar to Uber operating mainly in Praha.

People can find the nearest drivers and ask them for a ride anytime. Anyone can be a driver easily.

Dataset

anonymized customers orders

374 584

Dataset

variables

7

Dataset structure

Algorithms and methods

Basic statistics

Visualizations

data preparation

— Unified the price to CZK & WON — Ignored the outliers in dataset — Split the dataset to Train & Test Data

Main Topic

Would it be suitable for people to be drivers to make petty money?

#1 interesting fact

“What percentage of users pay with credit card since their sign up?” (Their first ride)

significance

“The people who drive only from time to time would prefer electronic payment

because they don’t have to carry about change and customers are willing the

pay higher tip because it’s easier.”

#1 Process

1. Took all orders and group them by customer id 2. Found their first ride (min(datetime)) 3. Found their other rides later 4. Were they payed with credit card? 5. If yes, we found passengers we were looking for

result of our analysis

of customers use credit card since they sign up

48%

#2 interesting fact

“How long does it take to customer to convert from paying in cash to credit card payments?”

#2 Process

1. Found the users who started to use Liftago and payed in cash.

2. Found rides that were in a row and were payed with credit card (switch)

3. Subtracted min(datetime) where paymentMethod == “CASH” & min(datetime) where paymentMethod == “CC”

result of our analysis

it takes to customers in average to convert from cash payments to credit card payments

38days

result of our analysis

price of average ride has goes

UPafter converting to credit card payments

solution (recommendation)

Close partnership with any popular credit card provider and create marketing campaign to support the use of credit card.

+

#3 interesting fact

“How much passengers spent in average on Liftago rides?”

#3 Process

1. Took all orders and group them by customer id 2. Calculate avgSpent of all customer rides 3. Split them into proportions

result of our analysis

#4 interesting fact

“When do customers use Liftago most frequently?”

#4 Process

1. Took all the rides from TestData (3 years period) 2. Categorize rides based on day & hour 3. Draw a graph showing the frequency

result of our analysis

significance

“Drivers who use Liftago from time to time can take an advantage of most frequent times to make money and therefore increase the reliability of

Liftago in rush hours for customers.”

Future studies

Since there are many refusal of taxi in Gangnam Station, if Liftago would expand the service to Seoul they can take advantage of this fact and

help passengers to get a ride home.

THANKYOU