Google Data Analytics Capstone Project
Cyclistic Case Study

• Cyclistic (Company): 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 them 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. 

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 very good chance 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 marketing analyst team needs to better 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. 

My team is looking to answer 3 questions: 

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

2. Why would casual riders buy Cyclistic annual memberships?

3. How can Cyclistic use digital media to influence casual riders to become members?

Moreno has tasked me with the first question:  How do annual members and casual riders use Cyclistic bikes differently?

My objective is to produce a report with the following deliverables:

1. A clear statement of the business task. 

2. A description of all data sources used. 

3. Documentation of any cleaning or manipulation of data.

4. A summary of your analysis.

5. Supporting visualizations & key findings.

6. Your top 3 recommendations based on your analysis. 

Business Task: Use historical data to discover trends in how annual members and casual riders use Cyclistic bikes differently, to increase conversion rates for casual to membership riders. Provide compelling data stories and visualizations to support my top 3 recommendations for this marketing strategy. 

I used Cyclistic's historical trip data from January 2022 - December 2022 to analyze and identify trends. Note: Cyclistic is a fictitious company, so for the purposes of the case study, the datasets were made available by Motivate International Inc., under this license. This is public data that was used to explore how different customer types are using Cyclistic bikes. We know this data is reliable, original, comprehensive, current, and cited, as it is provided directly by "Cyclistic" (Divvy).

Note: because data-privacy issues prohibited me from using riders' personally identifiable information, I was not able to connect pass purchases to credit card numbers to determine if casual riders live in the Cyclistic service area, or if they have purchased multiple single passes.

I also used some quick Google searches for general stats used to draw correlations in the data and better understand the ridership stories. Articles used are linked within the document.

Continued Data Cleaning with R

I spent far too long trying to upload my dataset to RStudio, only to have it crash every time I tried to run a read_csv() function. After realizing that my dataset was just over 1GB and the free version of RStudio does not support more than 1GB of memory per project, I decided to upgrade, vs. use any less data. Once I completed the upgrade, it was smooth sailing. To see the full RMarkdown of this project, see Index > Kaggle.

1. I first installed the necessary packages and loaded the required libraries:

install.packages("tidyverse")
install.packages("sp")
install.packages("hms")
library(tidyverse)
library(lubridate)
library(ggplot2)
library(sp)
library(hms)
library(readr)
library(deplyr)

2. Next, I created a data frame from the BigQuery results csv file.

3. Finally, prior to running a descriptive analysis and exploring the data further, I added a few columns (date, month, year, day_of_week, ride_length), and converted the ride_length column to numeric so it would be easier to work with:

cyclistic_data$date <- as.Date(cyclistic_data$started_at)

cyclistic_data$month <- format(as.Date(cyclistic_data$date), "%m")

cyclistic_data$day <- format(as.Date(cyclistic_data$date), "%d")

cyclistic_data$year <- format(as.Date(cyclistic_data$date), "%Y")

cyclistic_data$day_of_week <- format(as.Date(cyclistic_data$date), "%A")

cyclistic_data$ride_length <- 

  difftime(cyclistic_data$ended_at,cyclistic_data$started_at)

is.factor(cyclistic_data$ride_length)

cyclistic_data$ride_length <- 

  as.numeric(as.character(cyclistic_data$ride_length))

is.numeric(cyclistic_data$ride_length)

4. After using the View() command, I was ready to proceed with analyzing the data.

• Casual rides take longer on average, every day of the week

  • Why do ride times dip Tue - Thurs?

• Member rides average 12-14 minutes, regardless of the week day. 

  • Where are members going on weekends?

  • What's the actual percentage of members who use the pass for commuting purposes?

The data tells us who, what, when, and how member and casual riders use Cyclistic bikes differently, but does not detail the why of our ridership story. Combining general knowledge about our Chicago rider base with the stats we have above, we can infer some of these details, and make smart decisions around our marketing. The following takeaways have guided the suggestions I have for our marketing strategy: 

• Ridership is lowest in Jan, Feb, Mar, and Dec months, with casual riders taking the least number of rides throughout the year.

• Classic bike rides take longer than electric bike rides.

• Member rides take less time than casual rides on a daily, weekly, monthly, and quarterly average.

• Ridership is at its peak in summer months.

Marketing Strategy Suggestions

• Annual membership push for Black Friday & Cyber Monday 2023 : 13-month membership offer (Dec 2023 - Dec 2024). 

• Use targeted marketing for Cyclistic electric bike usage in winter months - "Get to warm, fast." 

  • Do we have enough electric bikes to meet the potential demand?

• "Explore the city, explore your limits" campaign: focus around building consistency as a Cyclistic rider, and exploring the Chicago community. Members average shorter ride times likely due to physical endurance from consistently riding, but may also be better versed in navigation, short cuts, etc. from the same consistency (note, further research could be conducted to validate this theory).

  • Consider partnering with local businesses for a "Rider Passport," only available with annual membership. If riders visit locations in passport, earn stamps, which turn into rewards. 

• Exclusive Summer Fun marketing series: promote this series to casual riders, with push for membership ("going to X number of events is the prices of 1 annual pass"). Promote this series to member riders to encourage community building and continue to aid peak ridership frequency.

  • Member rides happen daily, including weekends, indicating commuters may frequent the locations they work near for leisure (note: further research could be done to validate this theory), or live very close to Cyclistic docking stations.

Further Research 

My data analysis only answered the question of "how" member and causal riders use Cyclistic bikes differently. There is additional research that can be done to better understand why casual riders are not converting to annual memberships at this time, and why member riders keep coming back (or don't!). While our team can start to experiment with new marketing strategies, I would  also recommend our team take a Design Thinking approach to solve the greater questions of "why" by first running a Survey for customer empathy. We should try to discover things like: 

• • Why do member riders continue to consistently use Cyclistic in cold months? 

  • Where do casual riders vs. member riders go in winter months? 

  • What percentage of member riders commute to work, and at what frequency? (is it possible our commuter membership has changed, post-COVID?)

  • Why do repeat casual riders (not explored in this case study due to privacy regulations), choose to not upgrade to annual membership? 

Until we gain a little customer empathy, our ridership story is not fully complete, but we should have enough to move forward with some targeted marketing approaches in the interim. 

• Tableau Dashboard (visualizations)

• GitHub: Cyclistic Case Study Repository (SQL queries)

• Kaggle: Cyclistic Case Study Repository (R-Markdown & Dataset)

• Google Slides:  Internal presentation example. Speaker notes included; for best experience, View Slideshow.

• Short Report: See the consolidated pdf of this report