Introduction

This is a case study analysis for the Cyclistic Bike Share Company located in Chicago, Illinois. This study is a capstone project which is the final course included in the Google Data Analytic program which provides a certification upon completion.

As a junior analyst, I am part of a team of other analysts to gain an understanding of insights based on trends as well as insights from the analysis of the data. The tools that I am working with are spreadsheets, R programming and RStudio platform for analyzing the data.

The bike-sharing program involves equipment which consists of 5,824 bicycles and 692 docking stations. The program was initiated by the Director of Marketing. Ms. Lily Moreno who is my manager, is responsible for the development of campaigns for promoting the bike-sharing program. Currently Ms. Moreno uses email, social media sites and various other ways of reaching out to consumers.

Key Phases The key phases of this analysis are as follows: • Ask • Prepare • Process • Analyze • Share • Act

ASK The questions asked will be our guide to what is driving this analysis. 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? There are two key factors to look at to develop a strategy to move forward. Those factors are: We want to take a close look at the trends that we see regarding the two types of bike riders. We also want to convert casual riders into annual membership riders.

The marketing department can develop a program or programs to fit the needs of their riders based on quality data which will make it easier for them to offer incentives which could create more conversions of casual riders to annual member status.

PREPARE The datasets used in this analysis have a different name, however, the company name for this case study has the title Cyclistic which is a fictional company name. This data was available under a license provided by Motivated International Inc. I uploaded datasets Divvy_Trips_2019_Q1 and Divvy_Trips_2020_Q1 which are documented in the case study assignment, downloaded them to my computer for the datasets to be converted to csv files and then finally uploaded to RStudio.

The datasets provided by Motivated International Inc. for the case study are without bias. Due to the licensing, the data has integrity and is from a reliable resource. The data is in the form of spreadsheets in columns and rows. After reviewing the data, the data provided relevant information which enabled me to answer the questions to complete the analysis for how annual members and casual riders differ in their bike usage.

PROCESS The q1_2019 and q1_2020 are new dataframes from the csv file which is uploaded into RStudio.

ANALYZE

SHARE The share phase is when results of the data are presented from the numerical findings in the form of visuals. The graphs below were created with a tool called ggplot2. ggplot2 is part of the tidyverse installation and library.

## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

ACT

Summary of Findings The data used in this analysis was for quarter one of 2019 and quarter one of 2020. When you look at the bar graph for average duration, you will find that the observations in terms of duration for casual riders surpass the annual members throughout the entire week. Although the casual riders are subscriber who are in smaller numbers, compared to annual members when they ride the bikes, they keep the bike out for longer periods of time.

The average duration for annual members are consistent throughout the week for getting exercise. Annual members surpass casual members by overwhelming numbers for bike usage throughout the week. You also want to take into consideration that there are a larger group of individuals registered as annual members compared to casual riders. Casual riders frequently ride on Saturday and Sunday; however, their ridership tends to drop during the week. When the trips are aggregated to find the mean, median, maximum, and minimum figures regarding the “number of seconds” spent riding the bikes, casual riders had figures that surpassed annual members.

Recommendation Casual riders have shown that they are committed to riding on a regular basis. The marketing team are looking for ways to create a more annual members from casual members. Although the recommendations need approval first, there are three recommendations listed below based on the insights and trends from the analysis completed in the analysis for how to convert casual members to annuals members. The three recommendations for casual riders are as follows: 1. The membership for an annual membership is set at a certain dollar amount, however, an offer could be for annual membership at a reduced rate the first year for new sign-ups who are casual riders. The second year, increase the annual membership to a small percentage not to exceed what the current members are paying to casual riders. 2. The annual membership can be with one month of free rides to the same locations that the casual member normally picks up and drops off a bike. The pay rate would start at a reduced rate from the current annual member rate per year starting the second month of the contract with a two-year contract. 3. The third offer would be a three-year contract locked in at a reduced rate not to exceed the current annual member rate per year.