Google Data Analytics Capstone Project

Introduction

This case study is my capstone project for the Google Data Analytics Certificate. It involves analysis of historical data for a fictional company, Cyclistic, a bike sharing company located in Chicago, to make recommendations for an upcoming marketing campaign. Although the company and scenario are fictitious, the data used for this project are real data collected in January 2022 from a bike share program in Chicago. In this project I am assuming the role of the junior analyst.

Scenario

Cyclistic is a fictional bike sharing company located in Chicago. It operates a fleet of more than 5,800 bicycles which can be accessed from over 600 docking stations across the city. Bikes can be borrowed from one docking station, ridden, then returned to any docking station in the system. Data analysis has shown that riders with an annual membership are more profitable than casual riders. The marketing team are interested in creating a campaign to encourage casual riders to convert to members.

The marketing analyst team would like to know how annual members and casual riders differ, why casual riders would buy a membership. The team is interested in analyzing the Cyclistic historical bike trip data to identify trends in the usage of bikes by casual and member riders.

Stakeholders

Lily Moreno, Director of Marketing at Cyclistic, who is responsible for the marketing campaigns at Cyclistic.

The Cyclistic marketing analytics team. This team is responsible for collecting, analyzing and reporting data to be used in marketing campaigns. I am the junior analyst on this team

The Cyclistic executive team. This team makes the final decision on the recommended marketing plan.

Business Objective

Converting casual riders to annual members.

Data Preparation

The data used for this analysis was obtained from Motivate International Inc under this license https://ride.divvybikes.com/data-license-agreement Data is bike sharing data for January 2022 . It contains 13 columns containing information related to ride id, ridership type, start location and end location and geographic coordinates, start date and end date etc

Suitability of the Data to Answer Business Task

This data is suitable to solve the business task as it contains columns differentiating between the two categories of riders( the casual riders and the annual members)as well as containing other columns of key metrics relating to the different groups which we can use to determine how they use the bike sharing service differently.

Data Processing

For this project I decided to use Microsoft Excel as it could clean the data help with analysis and also visualize. The Data had some issues which were resolved in data cleaning such as containing some blank start and end station names. Blank rows were deleted from the dataset. To achieve our business task of understanding how annual members differ from casual riders new columns were created using data extracted from existing columns such as the hour of the day rides started, the days of the week of rides as well as a column to calculate the duration of the bike trips.

Data Analysis

Now that our data is cleaned and prepared we can then analyse to draw out insights to that end some calculations were run on the data to better understand the dataset . Calculations included mode of day of week, average ride length,count of trip by rider type etc

Findings

1. It was found that Thursdays are most popular day for bike riding among annual members whilst Saturday takes that prize for the casual riders

Where 0 is Sunday 1 is Monday and so on

2. 5pm(17.00) was peak bike riding time for both casual and annual members.

3 We have more annual members than casual

4 The average ride length for casual riders is higher than annual members at 27 minutes where the annual members average ride length is 10 minutes

Recommendations

Our business task is to convert casual users into annual members . Keeping that in mind I would recommend based on the analysis conducted the following actions:

1 . Targeted ads at 5pm which is the peak time for casual riders .

2 . Saturdays are the preferred day for casual riders ads should also be concentrated on this day to reach our target audience.

3. As casual riders usually peak on Saturdays the company can look into viability of offering weekend only subscriptions in order to make memberships more appealing.