Case Study How Does a Bike Share Navigate Speedy Success

 

Introduction

Cyclistic introduced a popular bike-share programme in 2016. The initiative has expanded since then to include a fleet of 5,824 bicycles that are geotracked and locked into a system of 692 stations throughout Chicago. The bicycles can at any time be unlocked from one station and brought back to any other station in the network.

Up to this point, Cyclistic's marketing approach focused on raising public awareness and appealing to a wide range of consumer groups. The ability of its price plans to be flexible was one strategy that made these things possible. There are three Pricing Plans offered by Cyclistic Bike Share company.

1. Single-ride passes

2. Full-day passes

3. Annual memberships 

Casual riders : Customers who purchase single-ride or full-day passes.

Cyclistic members: Customers who purchase annual memberships. 

 

Cyclistic’s financial analysts have concluded that annual members are much more profitable than casual riders. As a result, the greater the number of cyclists, the greater the profit or success. In short, there is a positive correlation between cyclical success and cyclical membership.

# SUCCESS =  PROFIT =  CYCLISTIC MEMBERS  > CASUAL RIDERS #

Business Task 

How do annual members and casual riders use Cyclistic bikes differently?

Key Stakeholders

Lily Moreno: The director of marketing and  manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. 

Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. 

Cyclistic executive team: The detail-oriented executive team will decide whether to approve the recommended marketing program.

About Data source : Cyclistic is a fictional company. For the purposes of this case study, the datasets are appropriate. The data has been made available by Motivate International Inc and this is historical trip data.

Here is the dataset : https://divvy-tripdata.s3.amazonaws.com/index.html

Data Cleaning and Data Manipulation 

The Zip files were downloaded locally and copy was stored in a new folder named cyclistic project  with a csv extension.

The csv files were opened using Excel and copy of 12 datasets was stored in desktop as folder dtrip_01, dtrip_02 up to dtrip_12. Then each folder was inspected.

12 month datasets have common column names.

These are the column names  

(ride_id), (rideable_type), (started_at), (ended_at), (start_station_name), (start_station_id)

(end_station_name), (end_station_id), (start_lat), (start_lng), (end_lat), (end_lng), (member_casual)

 No duplicates were found.

start_station_name , start_station_id , end_station_name , end_station_id have some blank values.

 rideable_types are of three kinds electricbike , dockedbike , and classic bike .    

Two new column were added in all 12 datasets 

ride_length and days_of_week  column were added 

ride_length = (ended_at - started_at) format HH:MM:SS

days_of_week = weekday(C2,1) format numbers with no decimal points 1=sunday,7=saturday

mode day_of_week of each month was calulated.

average ride_length for memebers  and casual riders and average_ride_length for users by day of week was calculated using pivot tables and  functions.  

rideable_type of each month was calculated  for both casual and members  using pivot table.

Visualization 

For the Visualization purpose I have used line charts and column charts.

Key Findings

Casual riders used bicycles for longer distances, while members used them for shorter distances as compared to casual riders. During FY Apr 2020–Mar 2021, April was the month in which casuals and members had a high average ride length, and January was the month of a low average ride length for casuals. February is the low average ride length for members. Starting in January and February and ending in November and December, the ride length of cyclistic users is shorter than other months. 

                                   

                                   

The most popular bikes among casual and member cyclists are docked bikes, while classic bikes are less well-liked by both groups. Both users did not ride classic bikes from April to October. From June to September, docked bicycles were the most in demand. In July, casual users had made the most use of docked bikes; in August, it was members. In January and February, casual users have used docked bikes the least, while members haven't used any. Instead, they switch to classic and electric bikes.

                               

                               

For casual riders throughout the entire year, Sunday is the most frequent maximum rideable day, whereas for members it is Saturday. Members primarily use bicycles during the weekend to cover maximum distance , while casual users primarily use bicycles during the week to cover minimum distance. For both casual riders and members, the minimum ride length is on Mondays and Thursdays and maximum ride length day is weekend.

Top three recommendations

The most well-liked bikes among casual and member cyclists are docked bikes, although both groups find classic bikes to be less appealing. As a result company should concentrate more on docked bikes.

For longer distances- promotions , discounts, referrals and packages are advised.

During the winter (Jan -Feb & Nov -Dec) , Cyclistic can expand their range of services by offering Cars for longer distance rides.

Limitation

Since the datasets are large, SQL or R will be more efficient than Excel.

Our analysis is constrained because we don't have enough information  about users besides ride id and user type (casual_members).

Some columns were disregarded because the datasets had blank values.