Embedded Files
Under development🔃
Problem
Usage differences between annual member riders & casual riders
Marketing team wants to know the differences between Cyclistic riders in order to be able to promote the products and tailor their strategy to target the most profitable segment and what differentiates this segment in terms of usage.
Analytical Goal
Is to understand the behavior between riders who subscribe for the annual membership and whose don’t
Preparation Phase
Dataset:
I used all the data files that store rides that took place in 2022
Data was located in our database, and our data engineering team extracted it based on the predefined criteria we discussed.
Data were organized in separate files each representing a month in 2022.
I used power Query in order to merge and clean the data.
Process Phase
I checked the data for duplicates in order to make sure that there were any.
Created a few columns in order to help analyze the data such as ride length and day of the week.
Removed all the entries that exceed 24 hours.
Found entries that don’t have destination recorded.
There are entries that don’t have recorded end times.
Analysis Phase
Member segment represent most on our users
Casual users ride longer than members
Customer Overview
The graph shows total rides among segments over the past 12 month
Our high demand season is between Apr to Jul as we see increasing volume in demand among segments
Daily Demand across Segments
The graph shows total rides among segments broken down by weekdays
Two days a week throughout the past year that we can observe that Casual users out perform Member
Hourly Distribution of Rides
The graph shows total rides among segments broken down by hours
Number of rides among Members starts to increase 6:00 Am and keeps that pace till 9:00 Am as well as between 3:00 Pm and 5:00 Pm
% of Hourly Rides
The graph shows daily % of rides among each segments broken down by hours
* Here we can see the percentages of rides that happened in each segment by hours of each day.
Recommendations
Member segment seems to be professionals that rely on our services to move between home and work.
Casual segment uses the service to move around the city as seen in the average ride time among them.
More data has to be collected in order to understand the increase in rides among different segments in specific months.
More data and details of pricing plans has to be shared with us in order to figure out is the increase is driven by unique customers or same customers but at different times, also the pricing will help us understand if there is number of daily rides assigned to each customer per day or member are free to use the service as they wish, and to know how we calculate the prices that are assigned to casual users ‘is they use the service based on time or number of miles.
Operations team has to look into the missing values in the data e.g. ride end time, end station, and rides that exceed 24 hours.
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