Data-Driven Marketing Strategies for Rider Conversion : Rider Usage Analysis with R and Tableau

Join me on my data-driven journey through the data of Cyclistic, a bike-share company in Chicago, we will explore how annual members and casual riders use their bikes differently. Utilize R for data cleaning and manipulation, and Tableau for data visualization, to seek answers to three key questions:

• How do annual members and casual riders use Cyclistic bikes differently?
• Why would casual riders buy Cyclistic annual memberships?
• And how can Cyclistic use digital media to influence casual riders to become members?

Goal

To uncover insights into rider usage patterns, identify the factors that drive casual riders to become members, and develop marketing strategies that will make it happen.

For fully narative version of this project : check out my Kaggle notebook.

Dataset

Public data of Cyclistic’s historical trip data is available on Motivate International Inc. under the license.

I have used 12 months of data , April 2020 to March 2021. To get a brief look used MS Excel and noticed each month's data is recorded in separate CSV files with fields such as ride id, rideable type, start and end time, start and end station, latitude, and longitude of the start and end stations.

Prerequisite: R ⌛ , Data analysis with R 📑, Tableau ⌛ & Statistics and probability 🗂️

Technologies used ⚙️

• R

• Tableau

R Libraries :

• Tidyverse | Skimr | dplyr | Janitor | lubridate

To answer the key business questions : 🔍

Six steps of the data analysis process

1. Ask

2. prepare

3. process

4. analyze

5. share

6. act

Also,certain roadmap is followed :

• Code, if required.
• Guiding questions and their answers
• Key tasks and deliverables as a checklist.

ASK : First step of data analysis process

Business task : Understand Annual and casual rider usage patterns to recommend marketing strategies to convert casual riders into annual members and how digital platforms could influence them.

Prepare : Second step in data analysis process:

Data collection, identify how data is organized and determine data creadibility.

Process : Third step in data analysis process

Choose a tool for data cleaning, check errors in data, and document the cleaning process.

I have used R language to import the dataset and check how the data is organized and ensure all the columns has the right data type.

Load Libraries

import CSV files for each month

know the structure data frames

* DATA CLEANING

Decided to removed missing/blank values to make the merging all CSV files into one dataframe easier.

removed duplicated ride IDs

convert date/timestamp to date/time

create separte start and end date of trip

create columns for month,day and year for all rides

* DATA MANIPULATION

new column - duration and check for trip duartion in mins less than 0

filter to include rows with duration greater than 0

Arranging data in ascending order

Save the clean data as CSV file

Analyze

1. Data Distribution : rider type

What is the percentage of Annual members and casual riders?

2. Monthly analysis : Rider type

3. Weekly Analysis

Data Distribution by days of the week

Share

For the share phase, the CSV file "Trips.csv" after cleaning is visualized in Tableau for presentation .

1. Trip Distribution

2. Trip Analysis by Months

3. Trip anlayis by Hour

4. Popular start and end Station Analysis

Summary

• The total number of rides : 2,934,265
• More Annual members than casual riders, 14.38 % more than casual riders.
• The month with the maximum number of rides is August (20.64 % ) - 605652 total rides.
• Riders type on the total number of rides each month: More annual member's rides than casual member's rides.
• Seasons influence the number of rides, the winter season recorded less number of rides.
• Weekends experienced a huge volume of rides.
• The volume of bikers increases in the afternoon.

Act : Last step in data analysis process

The top recommendations based on findings:

1. About ~ 60.87 % Casual riders took more rides during July- September which are considered months of events and festivals in Chicago city. It is advisable to build marketing campaigns focusing on the top 10 stations in the month July-September.

   Using social media to post about contests like - #daretoride will attract casual riders and create an urge to avail benefits of added membership passes by winning contests.

2. Different pricing strategies like weekly passes and monthly/seasonal passes might improve conversion rates.

3. Create an experience awarding discounts on cryptocurrencies or tokens as benefits, based on bike usage for those casual riders who avail membership.

4. Mobile application for membership holders: Building cyclistic mobile applications with fitness trackers and, also with live local traffic information and weather updates would add more value to membership passes and increase conversion rates.

Check out my other projects too : 🔍 👨‍💻 🛰️

An Analysis of Developer Communities: Insights from the 2022 Stack Overflow global Survey 📊

A Deep Dive into Indian Hardware Company's Sales Performance: Profit and Revenue Analysis with SQL and Tableau 📊