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Cyclistic Bike-Share Analysis: Maximizing Annual Memberships

cylistic

Introduction:

Welcome to the Cyclistic bike-share analysis case study! As a junior data analyst in the marketing team at Cyclistic, a bike-share company in Chicago, my primary objective was to maximize annual memberships by understanding how casual riders and annual members utilize Cyclistic bikes differently. This case study presents the data analysis process, insights, and actionable recommendations to boost annual memberships through targeted marketing strategies.

Business Task:

My manager, Lily Moreno, assigned me the task of analyzing the usage patterns of annual members and casual riders to identify opportunities to increase annual memberships. The goal was to leverage data insights to design a compelling marketing program and drive growth in the number of annual members.

Data Sources:

To conduct the analysis, I utilized real bike-share data from Motivate International Inc., treated as Cyclistic's company data. The dataset included 12 CSV files covering the period from February 2022 to January 2023. The essential columns included ride_id, rideable_type, started_at, ended_at, start_station_name, start_station_id, end_station_name, end_station_id, start_lat, start_lng, end_lat, end_lng, and member_casual.

Data Preparation and Cleaning:

Due to the massive size of the data (over 5 million rows), I shifted from Excel to SQL in BigQuery for efficient processing. The initial steps involved combining the CSV files using the UNION operator and performing data cleaning to ensure accuracy. I thoroughly checked for misspellings and irregularities in string columns, enabling reliable analysis. Additionally, I calculated the ride duration in minutes and introduced new columns indicating the day of the week and month for each ride.

Analysis:

I began by analyzing the bike type usage and found that electric bikes were the most popular choice for both casual and member riders, constituting approximately 22.04% and 29.09% of total rides, respectively.

Further analysis revealed that members took a significantly higher number of rides (59.31% of total rides) compared to casual riders (40.69% of total rides).

The average ride length for casual riders was longer (21.8 minutes) than for members (12.3 minutes), indicating that casual riders tend to use bikes for longer trips.

Analyzing ride length by day of the week and month revealed interesting patterns. For members, the average ride length was consistent throughout the week, while casual riders showed longer rides on weekends and during the summer months.

Data Merging:

Merging all datasets into one dataset using UNION ALL:

CREATE OR REPLACE TABLE divvy_tripdata. divvy_trip_datav1 AS 
(
 SELECT * 
 FROM `jaga-394318.divvy_tripdata.202202-divvy-tripdata` 
 UNION ALL
 SELECT * 
 FROM `jaga-394318.divvy_tripdata.202203-divvy-tripdata` 
 UNION ALL
 SELECT * 
 FROM `jaga-394318.divvy_tripdata.202204-divvy-tripdata` 
 UNION ALL
SELECT * 
 FROM `jaga-394318.divvy_tripdata.202205-divvy-tripdata_1` 
 UNION ALL
 SELECT * 
 FROM `jaga-394318.divvy_tripdata.202205-divvy-tripdata_2` 
 UNION ALL
 SELECT * 
 FROM `jaga-394318.divvy_tripdata.202206-divvy-tripdata_1` 
 UNION ALL
 SELECT * 
 FROM `jaga-394318.divvy_tripdata.202206-divvy-tripdata_2` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202207-divvy-tripdata_1` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202207-divvy-tripdata_2` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202208-divvy-tripdata_1` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202208-divvy-tripdata_2` 
 UNION ALL
   SELECT * 
  FROM `jaga-394318.divvy_tripdata.202209-divvy-tripdata_1` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202209-divvy-tripdata_2` 
 UNION ALL
   SELECT * 
  FROM `jaga-394318.divvy_tripdata.202210-divvy-tripdata_1` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202210-divvy-tripdata_2` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202211-divvy-tripdata` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202212-divvy-tripdata` 
 UNION ALL
  SELECT * 
 FROM `jaga-394318.divvy_tripdata.202301-divvy-tripdata` 
 )

Query Result:

tablev1

Data Transformation and Cleanup:

Creating a new table "divvy_trip_data_v2" with calculated columns for ride length, day of the week, and month:

CREATE TABLE divvy_tripdata.divvy_trip_datav2 AS
SELECT ride_id,
            rideable_type,
            started_at,
            ended_at,
            ROUND(TIMESTAMP_DIFF(ended_at, started_at, second)/60, 1) AS ride_length_minutes,
            EXTRACT(DAYOFWEEK FROM started_at) AS day_of_week,
            EXTRACT(MONTH FROM started_at) AS month,
            start_station_name,
            start_station_id,
            end_station_name,
            end_station_id,
            start_lat,
            start_lng,
            end_lat,
            end_lng,
            member_casual
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav1`;

Query Result:

image

Data Filtering:

Filter out erroneous data with negative ride lengths and rides lasting longer than a day:

CREATE TABLE divvy_tripdata.divvy_trip_datav3 AS
SELECT *
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav2`
WHERE ride_length_minutes > 0 AND ride_length_minutes < 1440;

Bike Type Usage and Percentages:

Calculate the count and percentages of rides for each member and casual rider by bike type:

SELECT
    member_casual,
    rideable_type,
    COUNT(1) AS total_rides,
    ROUND(100 * COUNT(1) / 5741891, 2) AS ride_percentage,
    COUNT(DISTINCT started_at) AS total_unique_dates
FROM
    `jaga-394318.divvy_tripdata.divvy_trip_datav3`
GROUP BY
    member_casual,
    rideable_type
ORDER BY
    member_casual;

Query Results:

member_casual rideable_type total_rides ride_percentage total_unique_dates
casual electric_bike 1,265,338 22.04% 1,207,357
casual classic_bike 895,053 15.59% 854,268
casual docked_bike 176,115 3.07% 172,941
member electric_bike 1,670,147 29.09% 1,592,473
member classic_bike 1,735,238 30.22% 1,639,652 
The query results reveal that electric bikes are the most popular choice for both casual and member riders, constituting approximately 22.04% and 29.09% of total rides, respectively, showcasing the significant adoption of electric bikes in the bike-sharing program.

Count of Rides and Percentages by Member/Casual:

Calculate the count and percentages of rides for each member and casual rider:

SELECT
    member_casual,
    COUNT(*) AS count_rides,
    ROUND(COUNT(*) * 100 / SUM(COUNT(*)) OVER (), 2) AS percent
FROM
    `jaga-394318.divvy_tripdata.divvy_trip_datav3`
GROUP BY 
    member_casual
ORDER BY
    count_rides DESC;

Query Results:

member_casual count_rides percent
member 3,405,385 59.31%
casual 2,336,506 40.69%

Calculate the number of rides taken by members and casual riders:

SELECT member_casual,
            COUNT(*) AS rides_taken
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
GROUP BY member_casual;

Query Results:

Row Member Casual Rides Taken
1 casual 2336506
2 member 3405385

Member Rides Count by Rideable Type:

SELECT rideable_type,
            COUNT(*) AS member_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes > 0
            AND ride_length_minutes < 1440
            AND member_casual = 'member'
GROUP BY rideable_type

Query Results:

Row Rideable Type Member Rides
1 electric_bike 1670147
2 classic_bike 1735238

Casual Rides Count by Rideable Type:

SELECT rideable_type,
            COUNT(*) AS casual_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes > 0
            AND ride_length_minutes < 1440
            AND member_casual = 'casual'
GROUP BY rideable_type

Query Results:

Row Rideable Type Casual Rides
1 electric_bike 1265338
2 docked_bike 176115
3 classic_bike 895053

Analyze Ride Length for All Riders:

Calculate the average, minimum, and maximum ride lengths for all riders:

SELECT AVG(ride_length_minutes) AS avg,
            MIN(ride_length_minutes) AS min,
            MAX(ride_length_minutes) AS max
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`;

Query Results:

Row Avg Min Max
1 16.172750910806091 0.1 1439.9

Round Trips Analysis:

Calculate the number of rides, the count of round trips, the percentage of round trips, and the count of unique dates for each member_casual category:

WITH RoundTrips AS (
  SELECT
    ride_id,
    started_at,
    ended_at,
    member_casual,
    ride_length_minutes,
    start_station_name,
    end_station_name,
    IF(start_station_name = end_station_name, 1, 0) AS is_round_trip
  FROM
    `jaga-394318.divvy_tripdata.divvy_trip_datav3`
)
SELECT
  member_casual,
  COUNT(1) AS count_rides,
  SUM(is_round_trip) AS count_round_trip,
  (ROUND(SAFE_DIVIDE(SUM(is_round_trip), COUNT(1)), 2) * 100) AS rate_round_percent,
  COUNT(DISTINCT DATE(started_at)) AS count_dates
FROM
  RoundTrips
GROUP BY
  member_casual;

Query Results:

member_casual count_rides count_round_trip rate_round_percent count_dates
casual 2,336,506 175,413 8.0% 365
member 3,405,385 117,827 3.0% 365

Average and Median Ride Lengths for Members and Casual Riders:

Calculate the average and median ride lengths for members and casual riders separately:

SELECT
  member_casual,
  ROUND(AVG(ride_length_minutes), 1) AS Avg_Ride_Length,
  ROUND(APPROX_QUANTILES(ride_length_minutes, 2)[OFFSET(1)], 1) AS median_Ride_Length
FROM
  `jaga-394318.divvy_tripdata.divvy_trip_datav3`
GROUP BY
  member_casual;

Query Results:

member_casual Avg_Ride_Length median_Ride_Length
casual 21.8 12.8
member 12.3 8.8

Monthly Analysis of Trip Data for Members:

SELECT
    CASE month
        WHEN 1 THEN 'January'
        WHEN 2 THEN 'February'
        WHEN 3 THEN 'March'
        WHEN 4 THEN 'April'
        WHEN 5 THEN 'May'
        WHEN 6 THEN 'June'
        WHEN 7 THEN 'July'
        WHEN 8 THEN 'August'
        WHEN 9 THEN 'September'
        WHEN 10 THEN 'October'
        WHEN 11 THEN 'November'
        WHEN 12 THEN 'December'
    END AS month_name,
    COUNT(*) AS rides_taken,
    Round(AVG(ride_length_minutes), 2) AS avg_ride_length_member
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE member_casual = 'member'
GROUP BY month, month_name
ORDER BY month;

Query Results:

Row month_name rides_taken avg_ride_length_member
1 January 150082 10.08
2 February 93883 11.06
3 March 193825 11.71
4 April 244212 11.36
5 May 353864 13.07
6 June 399510 13.66
7 July 416827 13.44
8 August 426427 13.09
9 September 404139 12.64
10 October 349209 11.55
11 November 236671 10.87
12 December 136736 10.35

Monthly Analysis of Trip Data for Casuals:

SELECT
    CASE month
        WHEN 1 THEN 'January'
        WHEN 2 THEN 'February'
        WHEN 3 THEN 'March'
        WHEN 4 THEN 'April'
        WHEN 5 THEN 'May'
        WHEN 6 THEN 'June'
        WHEN 7 THEN 'July'
        WHEN 8 THEN 'August'
        WHEN 9 THEN 'September'
        WHEN 10 THEN 'October'
        WHEN 11 THEN 'November'
        WHEN 12 THEN 'December'
    END AS month_name,
    COUNT(*) AS rides_taken,
    ROUND(AVG(ride_length_minutes), 2) AS avg_ride_length_casual
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE member_casual = 'casual'
GROUP BY month, month_name
ORDER BY month;

Query Results:

Row month_name rides_taken avg_ride_length_casual
1 January 39892 13.71
2 February 21289 19.64
3 March 89539 24.24
4 April 125888 23.28
5 May 279483 25.56
6 June 367814 23.43
7 July 404903 23.2
8 August 357936 21.5
9 September 295951 20.06
10 October 208522 18.48
11 November 100515 15.56
12 December 44774 13.41

Day-of-the-Week Analysis for Members:

SELECT CASE
            WHEN day_of_week = 1 THEN 'Sunday'
            WHEN day_of_week = 2 THEN 'Monday'
            WHEN day_of_week = 3 THEN 'Tuesday'
            WHEN day_of_week = 4 THEN 'Wednesday'
            WHEN day_of_week = 5 THEN 'Thursday'
            WHEN day_of_week = 6 THEN 'Friday'
            WHEN day_of_week = 7 THEN 'Saturday' END AS day_of_the_week,
            COUNT(*) AS rides_taken,
            Round(AVG(ride_length_minutes), 2) AS avg_ride_length_member
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE member_casual = 'member'
GROUP BY day_of_week
ORDER BY day_of_week;

Query Results:

Row day_of_the_week rides_taken avg_ride_length_member
1 Sunday 393537 13.63
2 Monday 481848 11.91
3 Tuesday 533481 11.71
4 Wednesday 534985 11.77
5 Thursday 540106 11.92
6 Friday 475110 12.15
7 Saturday 446318 13.75

Day-of-the-Week Analysis for Casuals:

SELECT CASE
            WHEN day_of_week = 1 THEN 'Sunday'
            WHEN day_of_week = 2 THEN 'Monday'
            WHEN day_of_week = 3 THEN 'Tuesday'
            WHEN day_of_week = 4 THEN 'Wednesday'
            WHEN day_of_week = 5 THEN 'Thursday'
            WHEN day_of_week = 6 THEN 'Friday'
            WHEN day_of_week = 7 THEN 'Saturday' END AS day_of_the_week,
            COUNT(*) AS rides_taken,
            Round(AVG(ride_length_minutes), 2) AS avg_ride_length_casual
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE member_casual = 'casual'
GROUP BY day_of_week
ORDER BY day_of_week;

Query Results:

Row day_of_the_week rides_taken avg_ride_length_casual
1 Sunday 391553 24.94
2 Monday 280141 22.21
3 Tuesday 267454 19.45
4 Wednesday 277187 18.77
5 Thursday 310961 19.41
6 Friday 336223 20.41
7 Saturday 472987 24.49

Create a table to store the count of rides taken by members from each start station.

CREATE TABLE divvy_tripdata.start_station_count_members AS
SELECT start_station_name AS station_name,
            COUNT(*) AS number_of_member_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE start_station_name IS NOT NULL
GROUP BY start_station_name
ORDER BY COUNT(*) DESC;

Create a table to store the count of rides taken by members to each end station.

CREATE TABLE divvy_tripdata.end_station_count_members AS
SELECT end_station_name AS station_name,
            COUNT(*) AS number_of_member_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE end_station_name IS NOT NULL
GROUP BY end_station_name
ORDER BY COUNT(*) DESC;

Create a table to store the count of rides taken by casual riders from each start station.

CREATE TABLE divvy_tripdata.start_station_count_casual AS
SELECT start_station_name AS station_name,
            COUNT(*) AS number_of_casual_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE start_station_name IS NOT NULL
GROUP BY start_station_name
ORDER BY COUNT(*) DESC;

Create a table to store the count of rides taken by casual riders to each end station.

CREATE TABLE divvy_tripdata.end_station_count_casual AS
SELECT end_station_name AS station_name,
            COUNT(*) AS number_of_casual_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE end_station_name IS NOT NULL
GROUP BY end_station_name
ORDER BY COUNT(*) DESC;

Calculate the total number of visits to start station for members and casual riders

SELECT start_station_name AS station_name,
       member_casual,
       COUNT(*) AS total_visits
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes > 0 AND ride_length_minutes < 1440
GROUP BY start_station_name, member_casual;

Query Results:

Row Station Name Member/Casual Total Visits
1 Monticello Ave & Chicago Ave member 92
2 Lamon Ave & Chicago Ave casual 132
3 Lamon Ave & Chicago Ave member 56
4 Kildare Ave & Division St casual 38
5 Public Rack - Kildare Ave & Division St casual 48
6 Public Rack - Kildare Ave & Division St member 29
7 Lavergne Ave & Division St casual 61
8 Lavergne Ave & Division St member 22
9 Menard Ave & Division St casual 59
10 Leamington Ave & Hirsch St casual 83

Finding the most popular start stations for member riders:

SELECT start_station_name,
            COUNT(*) AS number_of_member_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes < 1440
            AND ride_length_minutes > 0
            AND member_casual = 'member'
            AND start_station_name != 'null'
GROUP BY start_station_name
ORDER BY COUNT(*) DESC

Query Results:

Row Start Station Name Number of Member Rides
1 Kingsbury St & Kinzie St 25,208
2 Clark St & Elm St 22,475
3 Wells St & Concord Ln 21,555
4 University Ave & 57th St 21,087
5 Clinton St & Washington Blvd 20,611
6 Ellis Ave & 60th St 20,521
7 Loomis St & Lexington St 19,514
8 Wells St & Elm St 19,379
9 Clinton St & Madison St 19,232
10 Broadway & Barry Ave 18,004

Finding the most popular start stations for casual riders:

SELECT start_station_name,
            COUNT(*) AS number_of_casual_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes < 1440
            AND ride_length_minutes > 0
            AND member_casual = 'casual'
            AND start_station_name != 'null'
GROUP BY start_station_name
ORDER BY COUNT(*) DESC

Query Results:

Row Start Station Name Number of Casual Rides
1 Streeter Dr & Grand Ave 58,091
2 DuSable Lake Shore Dr & Monroe St 31,869
3 Millennium Park 25,553
4 Michigan Ave & Oak St 25,276
5 DuSable Lake Shore Dr & North Blvd 23,631
6 Shedd Aquarium 20,412
7 Theater on the Lake 18,431
8 Wells St & Concord Ln 16,304
9 Dusable Harbor 14,083
10 Clark St & Armitage Ave 13,840

Count Number of Member Rides per End Station:

SELECT end_station_name,
            COUNT(*) AS number_of_member_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes < 1440
            AND ride_length_minutes > 0
            AND member_casual = 'member'
            AND end_station_name != 'null'
GROUP BY end_station_name
ORDER BY COUNT(*) DESC

Query Results:

Row End Station Name Number of Member Rides
1 Kingsbury St & Kinzie St 25,073
2 Clark St & Elm St 22,808
3 Wells St & Concord Ln 22,201
4 University Ave & 57th St 21,609
5 Clinton St & Washington Blvd 21,389
6 Ellis Ave & 60th St 20,345
7 Clinton St & Madison St 20,071
8 Loomis St & Lexington St 19,358
9 Wells St & Elm St 19,094
10 Broadway & Barry Ave 18,353

Count Number of Casual Rides per End Station:

SELECT end_station_name,
            COUNT(*) AS number_of_member_rides
FROM `jaga-394318.divvy_tripdata.divvy_trip_datav3`
WHERE ride_length_minutes < 1440
            AND ride_length_minutes > 0
            AND member_casual = 'casual'
            AND end_station_name != 'null'
GROUP BY end_station_name
ORDER BY COUNT(*) DESC

Query Results:

Rank End Station Name Number of Member Rides
1 Streeter Dr & Grand Ave 60,047
2 DuSable Lake Shore Dr & Monroe St 29,655
3 Millennium Park 26,841
4 Michigan Ave & Oak St 26,505
5 DuSable Lake Shore Dr & North Blvd 26,174
6 Theater on the Lake 19,435
7 Shedd Aquarium 18,779
8 Wells St & Concord Ln 15,631
9 Clark St & Armitage Ave 13,887
10 Clark St & Lincoln Ave 13,626

Calculate the total number of visits to each station for members by joining the start_station_count_members and end_station_count_members tables:

-- We are using a JOIN operation on the station_name column to match the stations between the two tables.

-- Then, we are adding the number_of_member_rides from both tables to get the total_member_visits for each station.

SELECT
    start_count.station_name,
    start_count.number_of_member_rides AS total_member_visits
FROM
    jaga-394318.divvy_tripdata.start_station_count_members start_count
JOIN
    jaga-394318.divvy_tripdata.end_station_count_members end_count
ON
    start_count.station_name = end_count.station_name
ORDER BY
    total_member_visits DESC;

Query Results:

Rank Station Name Total Member Visits
1 Streeter Dr & Grand Ave 75,303
2 DuSable Lake Shore Dr & Monroe St 41,276
3 DuSable Lake Shore Dr & North Blvd 40,099
4 Michigan Ave & Oak St 39,762
5 Wells St & Concord Ln 37,859
6 Clark St & Elm St 35,554
7 Millennium Park 35,065
8 Kingsbury St & Kinzie St 34,101
9 Theater on the Lake 32,981
10 Wells St & Elm St 31,910

calculate the total number of casual visits to each station by adding the number_of_casual_rides from the start_station_count_casual table:

-- with the number_of_casual_rides from the end_station_count_casual table. The tables are joined on the station_name column.

-- Selecting the station_name column from the start_station_count_casual table as station_name.

SELECT 
    start_count.station_name AS station_name,
    -- Adding the number_of_casual_rides from both start_count and end_count tables to get total_casual_visits
    start_count.number_of_casual_rides + end_count.number_of_casual_rides AS total_casual_visits
-- Using the alias 'start_count' for the start_station_count_casual table
FROM 
    jaga-394318.divvy_tripdata.start_station_count_casual start_count
-- Using the alias 'end_count' for the end_station_count_casual table
JOIN 
    jaga-394318.divvy_tripdata.end_station_count_casual end_count
-- Joining the tables on the matching station_name column
ON 
    start_count.station_name = end_count.station_name
-- Ordering the results in descending order of total_casual_visits
ORDER BY 
    total_casual_visits DESC;

Query Results:

Rank Station Name Total Casual Visits
1 Streeter Dr & Grand Ave 150,895
2 DuSable Lake Shore Dr & North Blvd 82,326
3 DuSable Lake Shore Dr & Monroe St 81,441
4 Michigan Ave & Oak St 80,065
5 Wells St & Concord Ln 75,691
6 Clark St & Elm St 70,579
7 Millennium Park 70,520
8 Kingsbury St & Kinzie St 67,008
9 Theater on the Lake 66,051
10 Wells St & Elm St 62,712

Dashboard

Cylistic Dashboard

Tableau Dashboard Link:

https://public.tableau.com/views/CyclisticBike-ShareAnalysis_16909122669020/Dashboard1?:language=en-US&:display_count=n&:origin=viz_share_link

Key Findings:

  • Electric bikes are the most popular choice for both casual and member riders, constituting approximately 22.04% and 29.09% of total rides, respectively.
  • Members take a significantly higher number of rides (59.31% of total rides) compared to casual riders (40.69% of total rides).
  • Casual riders tend to use bikes for longer trips, with an average ride length of 21.8 minutes, compared to 12.3 minutes for members.
  • There is a significant percentage of round trips taken by casual riders (8.0%) compared to member riders (3.0%).
  • Casual riders take longer rides on weekends and during the summer months, while members show consistent ride lengths throughout the week.
  • The top start stations for member riders are Kingsbury St & Kinzie St, Clark St & Elm St, and Wells St & Concord Ln, while the top start station for casual riders is Streeter Dr & Grand Ave.
  • The top end stations for member riders are Kingsbury St & Kinzie St, Clark St & Elm St, and Wells St & Concord Ln, while the top end station for casual riders is Streeter Dr & Grand Ave.

Insights:

  • Electric bikes have gained widespread popularity among both casual and member riders, indicating that offering more electric bikes could attract even more customers.
  • Members are more engaged and frequent users of the bike-share service, making them an ideal target for membership retention and loyalty programs.
  • Casual riders might be attracted to the service due to longer trip durations. Offering special deals or promotions for longer rides could encourage casual riders to become annual members.
  • The higher percentage of round trips taken by casual riders suggests that they might be using the bikes for leisure activities or short commutes. Tailoring marketing strategies to highlight the convenience of round trips might attract more casual riders.

Recommendations:

Promote Electric Bikes:

Launch marketing campaigns focusing on the convenience and benefits of electric bikes to encourage both casual and member riders to choose electric bikes more frequently.

Targeted Marketing for Casual Riders:

Develop targeted marketing campaigns aimed at converting casual riders into annual members. Offer discounts, loyalty programs, or bundle deals to incentivize them to sign up for annual memberships.

Promote Longer Rides:

Create promotions or incentives that reward users for taking longer rides. For example, offer discounted rates for rides exceeding a certain duration.

Focus on Round Trips:

Design marketing materials highlighting the convenience and flexibility of round trips. Encourage casual riders to use the bikes for short errands or sightseeing, emphasizing the ease of returning to their starting point.

Conclusion:

The analysis revealed significant differences in usage patterns between casual and member riders. Electric bikes are popular among both groups, but members take more rides and have shorter average ride lengths. Casual riders, on the other hand, tend to take longer trips and have a higher percentage of round trips. To boost annual memberships, targeted marketing strategies should be designed to cater to the specific preferences of casual riders, promote the benefits of electric bikes, and incentivize longer rides. By implementing these recommendations, Cyclistic can drive growth in the number of annual members and increase overall customer engagement with the bike-share service.