About this Dataset

Nov23-Oct24-divvy-tripdata

Case study: How does a bike-share navigate speedy success?

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you work for a
fictional company, Cyclistic, along with some key team members. In order to answer the
business questions, follow the steps of the data analysis process: Ask, Prepare, Process,
Analyze, Share, and Act. Along the way, the Case Study Roadmap tables — including guiding
questions and key tasks — will help you stay on the right path.

Scenario

You are a junior data analyst working on the marketing analyst team at Cyclistic, a bike-share
company in Chicago. The director of marketing believes the company’s future success
depends on maximizing the number of annual memberships. Therefore, your team wants to
understand how casual riders and annual members use Cyclistic bikes differently. From these
insights, your team will design a new marketing strategy to convert casual riders into annual
members. But first, Cyclistic executives must approve your recommendations, so they must be
backed up with compelling data insights and professional data visualizations.
Characters and teams
● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600
docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand
tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities
and riders who can’t use a standard two-wheeled bike. The majority of riders opt for
traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more
likely to ride for leisure, but about 30% use the bikes to commute to work each day.
● Lily Moreno: The director of marketing and your 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.
You joined this team six months ago and have been busy learning about Cyclistic’s
mission and business goals—as well as how you, as a junior data analyst, can help
Cyclistic achieve them.
● Cyclistic executive team: The notoriously detail-oriented executive team will decide
whether to approve the recommended marketing program.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown
to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations
across Chicago. The bikes can be unlocked from one station and returned to any other station
in the system anytime.
Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to
broad consumer segments. One approach that helped make these things possible was the
flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships.
Customers who purchase single-ride or full-day passes are referred to as casual riders.
Customers who purchase annual memberships are Cyclistic members.
Cyclistic’s finance analysts have concluded that annual members are much more profitable
than casual riders. Although the pricing flexibility helps Cyclistic attract more customers,
Moreno believes that maximizing the number of annual members will be key to future growth.
Rather than creating a marketing campaign that targets all-new customers, Moreno believes
there is a solid opportunity to convert casual riders into members. She notes that casual riders
are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.
Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into
annual members. In order to do that, however, the team needs to beer understand how
annual members and casual riders differ, why casual riders would buy a membership, and how
digital media could affect their marketing tactics. Moreno and her team are interested in
analyzing the Cyclistic historical bike trip data to identify trends.

Data

The data has been made available by Motivate International Inc. under this license.
Dataset download link Click Here

Tables

N 202311 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202311_divvy_tripdata

17.86 MB
362518 rows
13 columns


CREATE TABLE n_202311_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202312 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202312_divvy_tripdata

11.16 MB
224073 rows
13 columns


CREATE TABLE n_202312_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202401 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202401_divvy_tripdata

7.22 MB
144873 rows
13 columns


CREATE TABLE n_202401_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202402 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202402_divvy_tripdata

10.76 MB
223164 rows
13 columns


CREATE TABLE n_202402_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202403 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202403_divvy_tripdata

14.82 MB
301687 rows
13 columns


CREATE TABLE n_202403_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202404 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202404_divvy_tripdata

20.36 MB
415025 rows
13 columns


CREATE TABLE n_202404_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202405 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202405_divvy_tripdata

29.5 MB
609493 rows
13 columns


CREATE TABLE n_202405_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202406 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202406_divvy_tripdata

33.75 MB
710721 rows
13 columns


CREATE TABLE n_202406_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202407 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202407_divvy_tripdata

33.84 MB
748962 rows
13 columns


CREATE TABLE n_202407_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202408 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202408_divvy_tripdata

33.16 MB
755639 rows
13 columns


CREATE TABLE n_202408_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202409 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202409_divvy_tripdata

35.6 MB
821276 rows
13 columns


CREATE TABLE n_202409_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);

N 202410 Divvy Tripdata

@kaggle.jacksonbecker13_nov23_oct24_divvy_tripdata.n_202410_divvy_tripdata

26.67 MB
616281 rows
13 columns


CREATE TABLE n_202410_divvy_tripdata (
  "ride_id" VARCHAR,
  "rideable_type" VARCHAR,
  "started_at" TIMESTAMP,
  "ended_at" TIMESTAMP,
  "start_station_name" VARCHAR,
  "start_station_id" VARCHAR,
  "end_station_name" VARCHAR,
  "end_station_id" VARCHAR,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "end_lat" DOUBLE,
  "end_lng" DOUBLE,
  "member_casual" VARCHAR
);