Cyclistic Bike-Share Case Study
Rachael Taylor
2022-07-12
Business Task
Through financial analysis, Cyclistic has concluded that annual members of their bike-share offering are much more profitable than casual riders. Due to this, the company’s goal is to maximize the number of annual members by marketing specifically to their existing customer pool of casual riders.
Additionally, I am working to identify the motivation behind a casual rider becoming an annual member and how digital media marketing would influence that decision to convert.
Specifically speaking, I am analyzing the Cyclistic historical bike trip data to answer three vital questions and create a practical plan of action for an effective marketing strategy to existing customers.
Key Questions To Answer
My insights will aid the Cyclistic executive team and my manager, Lily Moreno, in understanding how to create an attractive digital marketing campaign that connects with and motivates existing casual riders of the bike-share offering to convert into annual members.
The goal of this analysis is to answer the following questions.
- How do annual members and casual riders use Cyclistic bikes differently?
- Why would casual riders buy Cyclistic annual memberships?
- How can Cyclistic use digital media to influence casual riders to become members?
Answering these questions will support Cyclistic’s need to to maximize their annual memberships and have access to a key element to their future growth as a company.
Dataset Description
This is public data that has been provided by Motivate International Inc. that will allow our analysis to explore the behaviors of different customer types that use Cyclist offerings. All data cleaning, organization, and analysis will be done in RStudio Desktop, using graphs and charts as our visualization tool.
The data files were too large for analysis on an Excel file, Google spreadsheet or BigQuery using SQL. Therefore, the data from the last 12-months to-date of when this analysis was conducted has been imported into RStudio Desktop; the dates are, respectively, from June 3, 2022 to July 15, 2021.
Each data set was saved in a specific folder and copied to preserve the original data from changes in analysis. Each month was renamed for easier use to “tripdata_yearmonth” for all 12 months to coorelate with the number of the month instead of the date when it was originally uploaded.
Overview
Our collection, cleaning and analysis can be separated into 4 main parts:
- Install required packages
- Use tidyverse for data import and wrangling
- Install lubridate for date functions
- Use ggplot for visualization
Collecting the Data
Install the required packages in RStudio: tidyverse, lubridate, and ggplot.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(ggplot2)Upload data sets
Install the last 12-months of data into RStudio Desktop; the dates are, respectively, from June 3, 2022 to July 15, 2021. Each month is renamed for easier use to “tripdata_yearmonth” for all 12 months to coorelate with the number of the month instead of the date when it was originally uploaded.
tripdata_202206 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202206.csv")
tripdata_202205 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202205.csv")
tripdata_202204 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202204.csv")
tripdata_202203 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202203.csv")
tripdata_202202 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202202.csv")
tripdata_202201 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202201.csv")
tripdata_202112 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202112.csv")
tripdata_202111 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202111.csv")
tripdata_202110 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202110.csv")
tripdata_202109 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202109.csv")
tripdata_202108 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202108.csv")
tripdata_202107 <- read.csv("~/Desktop/Case_study_1/Copy _of_data/tripdata_202107.csv")Use colnames function
Check the column names of each data set to ensure that they are all matching. None required mutation as all were the same across each dataset.
colnames(tripdata_202107)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202108)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202109)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202110)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202111)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202112)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202201)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202202)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202203)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202204)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202205)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"colnames(tripdata_202206)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"Combine all columns
Since all column names are the same, proceed with binding all data into one big data frame.
all_trips <- bind_rows(tripdata_202107,tripdata_202108,tripdata_202109,tripdata_202110,tripdata_202111,tripdata_202112,tripdata_202201,tripdata_202202,tripdata_202203,tripdata_202204,tripdata_202205,tripdata_202206)Cleaning the Data
Inspect the new table with the following functions; they perform the inspection in the order they are written in the bulleted list.
- Find the list of all column names
- Inspect how many rows are in the data frame
- Find dimensions of data frame
- Inspect the dimensions of the data frame
- See the list of columns and data types
- See the statistical summary of the data
View(all_trips)Find the list of all column names in the new table all_trips.
colnames(all_trips)## [1] "ride_id" "rideable_type" "started_at"
## [4] "ended_at" "start_station_name" "start_station_id"
## [7] "end_station_name" "end_station_id" "start_lat"
## [10] "start_lng" "end_lat" "end_lng"
## [13] "member_casual"Inspect how many rows are in the data frame and find the dimensions.
nrow(all_trips)## [1] 5860776dim(all_trips)## [1] 5860776 13Inspect the dimensions of the data frame.
head(all_trips)## ride_id rideable_type started_at ended_at
## 1 99FEC93BA843FB20 electric_bike 2021-06-13 14:31:28 2021-06-13 14:34:11
## 2 06048DCFC8520CAF electric_bike 2021-06-04 11:18:02 2021-06-04 11:24:19
## 3 9598066F68045DF2 electric_bike 2021-06-04 09:49:35 2021-06-04 09:55:34
## 4 B03C0FE48C412214 electric_bike 2021-06-03 19:56:05 2021-06-03 20:21:55
## 5 B9EEA89F8FEE73B7 electric_bike 2021-06-04 14:05:51 2021-06-04 14:09:59
## 6 62B943CEAAA420BA electric_bike 2021-06-03 19:32:01 2021-06-03 19:38:46
## start_station_name start_station_id end_station_name end_station_id start_lat
## 1 41.80
## 2 41.79
## 3 41.80
## 4 41.78
## 5 41.80
## 6 41.78
## start_lng end_lat end_lng member_casual
## 1 -87.59 41.80 -87.60 member
## 2 -87.59 41.80 -87.60 member
## 3 -87.60 41.79 -87.59 member
## 4 -87.58 41.80 -87.60 member
## 5 -87.59 41.79 -87.59 member
## 6 -87.58 41.78 -87.58 membertail(all_trips)## ride_id rideable_type started_at ended_at
## 5860771 284843EC9F8C5663 classic_bike 2022-05-30 18:34:44 2022-05-31 19:34:35
## 5860772 8891BA0053ECEC4F electric_bike 2022-05-27 22:00:02 2022-05-27 22:07:01
## 5860773 47D8B5FBCADECFC1 electric_bike 2022-05-15 16:05:39 2022-05-15 16:44:12
## 5860774 AA8D16CF38B40703 electric_bike 2022-05-21 10:10:13 2022-05-21 10:26:09
## 5860775 897EBFD44F329E0A electric_bike 2022-05-12 07:53:58 2022-05-12 08:01:18
## 5860776 AAC23AB89E8A7733 electric_bike 2022-05-11 21:14:28 2022-05-11 21:18:16
## start_station_name start_station_id end_station_name
## 5860771 Ashland Ave & Lake St 13073
## 5860772 Clark St & Newport St 632
## 5860773 Clark St & Newport St 632
## 5860774 Francisco Ave & Bloomingdale Ave 429
## 5860775 Francisco Ave & Bloomingdale Ave 429
## 5860776 Clark St & Newport St 632
## end_station_id start_lat start_lng end_lat end_lng member_casual
## 5860771 41.88592 -87.66717 NA NA casual
## 5860772 41.94456 -87.65483 41.92 -87.65 member
## 5860773 41.94448 -87.65476 41.92 -87.76 member
## 5860774 41.91000 -87.70000 41.92 -87.66 casual
## 5860775 41.91000 -87.70000 41.90 -87.69 member
## 5860776 41.94457 -87.65480 41.94 -87.65 memberSee the list of columns and data types.
str(all_trips)## 'data.frame': 5860776 obs. of 13 variables:
## $ ride_id : chr "99FEC93BA843FB20" "06048DCFC8520CAF" "9598066F68045DF2" "B03C0FE48C412214" ...
## $ rideable_type : chr "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : chr "2021-06-13 14:31:28" "2021-06-04 11:18:02" "2021-06-04 09:49:35" "2021-06-03 19:56:05" ...
## $ ended_at : chr "2021-06-13 14:34:11" "2021-06-04 11:24:19" "2021-06-04 09:55:34" "2021-06-03 20:21:55" ...
## $ start_station_name: chr "" "" "" "" ...
## $ start_station_id : chr "" "" "" "" ...
## $ end_station_name : chr "" "" "" "" ...
## $ end_station_id : chr "" "" "" "" ...
## $ start_lat : num 41.8 41.8 41.8 41.8 41.8 ...
## $ start_lng : num -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ end_lat : num 41.8 41.8 41.8 41.8 41.8 ...
## $ end_lng : num -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ member_casual : chr "member" "member" "member" "member" ...See the statistical summary of the data.
summary(all_trips)## ride_id rideable_type started_at ended_at
## Length:5860776 Length:5860776 Length:5860776 Length:5860776
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## start_station_name start_station_id end_station_name end_station_id
## Length:5860776 Length:5860776 Length:5860776 Length:5860776
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## start_lat start_lng end_lat end_lng
## Min. :41.64 Min. :-87.84 Min. :41.39 Min. :-88.97
## 1st Qu.:41.88 1st Qu.:-87.66 1st Qu.:41.88 1st Qu.:-87.66
## Median :41.90 Median :-87.64 Median :41.90 Median :-87.64
## Mean :41.90 Mean :-87.65 Mean :41.90 Mean :-87.65
## 3rd Qu.:41.93 3rd Qu.:-87.63 3rd Qu.:41.93 3rd Qu.:-87.63
## Max. :45.64 Max. :-73.80 Max. :42.17 Max. :-87.49
## NA's :5036 NA's :5036
## member_casual
## Length:5860776
## Class :character
## Mode :character
##
##
##
## Solve 3 problems in the data
Continue with cleaning the data and solving our business task by fixing 3 problems found in the data.
- Create additional columns of data - date, month, day, year, and day of week.
- Add a calculated field for ride length to the entire dataframe.
- Delete negative trip_duration rides as those do not represent ride time, but when bikes were taken out of circulaton for Quality Control reasons.
Solution to problem 1:
Create additional columns of data - date, month, day, year, and day of week. First step is to check to make sure the proper number of observations were reassigned.
table(all_trips$member_casual)##
## casual member
## 2559857 3300919Add columns that list date, month, day, year, and day of week for each ride.
all_trips$date <- as.Date(all_trips$started_at)
all_trips$month <- format(as.Date(all_trips$date),"%m")
all_trips$day <- format(as.Date(all_trips$date),"%d")
all_trips$year <- format(as.Date(all_trips$date),"%Y")
all_trips$day_of_week <- format(as.Date(all_trips$date),"%A")Solution to problem 2:
Add a calculated field for ride_length to the entire dataframe to determine difference between casual riders and members. Ride_length is calculated in seconds.
all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)Inspect the structure of the columns.
str(all_trips)## 'data.frame': 5860776 obs. of 19 variables:
## $ ride_id : chr "99FEC93BA843FB20" "06048DCFC8520CAF" "9598066F68045DF2" "B03C0FE48C412214" ...
## $ rideable_type : chr "electric_bike" "electric_bike" "electric_bike" "electric_bike" ...
## $ started_at : chr "2021-06-13 14:31:28" "2021-06-04 11:18:02" "2021-06-04 09:49:35" "2021-06-03 19:56:05" ...
## $ ended_at : chr "2021-06-13 14:34:11" "2021-06-04 11:24:19" "2021-06-04 09:55:34" "2021-06-03 20:21:55" ...
## $ start_station_name: chr "" "" "" "" ...
## $ start_station_id : chr "" "" "" "" ...
## $ end_station_name : chr "" "" "" "" ...
## $ end_station_id : chr "" "" "" "" ...
## $ start_lat : num 41.8 41.8 41.8 41.8 41.8 ...
## $ start_lng : num -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ end_lat : num 41.8 41.8 41.8 41.8 41.8 ...
## $ end_lng : num -87.6 -87.6 -87.6 -87.6 -87.6 ...
## $ member_casual : chr "member" "member" "member" "member" ...
## $ date : Date, format: "2021-06-13" "2021-06-04" ...
## $ month : chr "06" "06" "06" "06" ...
## $ day : chr "13" "04" "04" "03" ...
## $ year : chr "2021" "2021" "2021" "2021" ...
## $ day_of_week : chr "Sunday" "Friday" "Friday" "Thursday" ...
## $ ride_length : 'difftime' num 163 377 359 1550 ...
## ..- attr(*, "units")= chr "secs"Found that ride_length needs to be converted to numeric so we can run calculations on the data.
is.factor(all_trips$ride_length)## [1] FALSEall_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)## [1] TRUESolution to problem 3:
Delete negative trip_duration rides as those do not represent ride time, but when bikes were taken out of circulaton for Quality Control reasons. Created new table all_trips_v2 to look at data sets without negative values.
all_trips_v2 <- all_trips[!(is.na(all_trips$start_station_name) | is.na(all_trips$start_station_id) | is.na(all_trips$end_station_name) | is.na(all_trips$end_station_id) | all_trips$ride_length<0),]Check to ensure all_trips_v2 table is correct.
View(all_trips_v2)Analyzing the Data
Now that the data has been cleaned, we move into analyzing the ride_length of members and casual riders. Begin with finding the average, midpoint number, longest ride, and shortest ride.
mean(all_trips_v2$ride_length)## [1] 1241.466median(all_trips_v2$ride_length)## [1] 681max(all_trips_v2$ride_length)## [1] 3356649min(all_trips_v2$ride_length)## [1] 0Can also use summary() to perform all 4 calculations in one function.
summary(all_trips_v2$ride_length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 382 681 1241 1236 3356649Compare members and casual riders
Find the difference between the average, midpoint number, longest ride, and shortest ride of casual riders and members.
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 1833.0390
## 2 member 782.7016aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 916
## 2 member 547aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 3356649
## 2 member 89998aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)## all_trips_v2$member_casual all_trips_v2$ride_length
## 1 casual 0
## 2 member 0Find the average ride time of each day between members and casual riders. At first glance, the data table comes up out of order pertaining to the days of the week. Run an ordered function to fix the order and then the average ride time after.
all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)## all_trips_v2$member_casual all_trips_v2$day_of_week all_trips_v2$ride_length
## 1 casual Sunday 2121.0590
## 2 member Sunday 887.9617
## 3 casual Monday 1831.6679
## 4 member Monday 758.7213
## 5 casual Tuesday 1574.5123
## 6 member Tuesday 736.7175
## 7 casual Wednesday 1599.3681
## 8 member Wednesday 738.5552
## 9 casual Thursday 1662.4662
## 10 member Thursday 746.5689
## 11 casual Friday 1726.7447
## 12 member Friday 766.9394
## 13 casual Saturday 2010.4328
## 14 member Saturday 877.4190Analyze ridership data by type and weekday.
all_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n(), average_duration = mean(ride_length)) %>%
arrange(member_casual,weekday)## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.## # A tibble: 14 × 4
## # Groups: member_casual [2]
## member_casual weekday number_of_rides average_duration
## <chr> <ord> <int> <dbl>
## 1 casual Sun 470157 2121.
## 2 casual Mon 302073 1832.
## 3 casual Tue 287018 1575.
## 4 casual Wed 285769 1599.
## 5 casual Thu 308614 1662.
## 6 casual Fri 360007 1727.
## 7 casual Sat 546158 2010.
## 8 member Sun 394686 888.
## 9 member Mon 466109 759.
## 10 member Tue 524804 737.
## 11 member Wed 512600 739.
## 12 member Thu 501807 747.
## 13 member Fri 459798 767.
## 14 member Sat 441037 877.Visualizing Data
Use ggplot to visualize rider data and compare members vs. casual riders so we can answer the following questions:
- How do annual members and casual riders use Cyclistic bikes differently?
- Why would casual riders buy Cyclistic annual memberships?
- How can Cyclistic use digital media to influence casual riders to become members?
Visual of the number of rides by rider type
We see at first glance that the data suggests that casual riders use the service more on the weekends and less during the week, while members function in the opposite fashion. A member seems to ride more in the middle of the week and less on the weekends.
all_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n(), average_duration = mean(ride_length)) %>%
arrange(member_casual,weekday) %>%
ggplot(aes(x=weekday,y=number_of_rides,fill=member_casual))+
geom_col(position = "dodge")## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Visual of the average duration by rider type
We see at first glance that the data suggests that casual riders have just more than double the ride length, with the highest times on the weekend (Saturday and Sunday). Members seem to have a somewhat steady ride length throughout all 7 days of the week.
all_trips_v2 %>%
mutate(weekday = wday(started_at, label = TRUE)) %>%
group_by(member_casual, weekday) %>%
summarise(number_of_rides = n(), average_duration = mean(ride_length)) %>%
arrange(member_casual,weekday) %>%
ggplot(aes(x=weekday,y=average_duration,fill=member_casual))+
geom_col(position = "dodge")## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.
Conclusion
Casual riders and members differ in many different ways.
The average ride length of casual riders was 1,833.04 seconds, which translates to 31.38 minutes, while the average length of members was 782.70 seconds, or rather 13.05 minutes. This means that the average ride length of a casual rider is more than double the average length of a member.
The max or longest ride length for a casual rider was 3,356,649 seconds which translates into 38.85 days. In comparison, the longest ride of a member was 89,998 seconds or 1.04 days. Members do not use the bike-share service for long ride lengths.
The difference between the longest ride of a casual rider and a member is 37.81 days or a 3,635.58% increase. Some casual riders use the bike-share offering for 30+ days longer than members do. It could be theorized that members use the service for commuting or cyclical routes, while a casual rider uses the offer for longer, scenic adventures like a vacation or long weekend trip. Perhaps they are using the bike-share instead of a car or cab to get around the city or country for the short time period they are in town.
When we compare the days of the week for when the bike-share is used between casual riders and members, we see a trend of casual riders having a near double trip length across the board for each weekday and on the weekends.
Looking at the average time of ride length for casual riders, we find that Saturday, Sunday, and Monday are the most popular days. Sunday’s average is the highest at 2121 seconds or 35.35 minutes, Saturday is second with 2010 seconds or 33.5 minutes, and Monday is the last of the three at 1831 seconds or 30.5 minutes.
Members tend to have less varied ride trip length, their times remain between 736 seconds and 887 seconds (or 12.26 minutes to 14.78 minutes) for all 7 days. This means that the average ride length for bike-share members on all weekdays and weekends is just under 15-minutes.
Recommended Action
The analysis we’ve completed shows that casual riders use the bikes more than members on Saturdays, Sundays, and Mondays, as well as rent/ride the bikes for longer overall during each rental period.
My recommendation for converting casual riders into members would be to offer an ongoing 3-day or weekend membership where the additional length of bike rental time does not affect their rental rate. While casual riders use the bikes for longer overall, there is a considerable spike in use on Saturdays, Sundays, and Monday for rides around 30-minutes on each day.
I would start with marketing to those riders about a 2-day (weekend) or 3-day membership special that gives them access to the bikes at a reduced rate that saves them money in the long run by having a recurring membership fee.
The digital marketing campaign should be focused on weekend or 3-day membership passes to existing casual riders, outlining the potential savings in bundling their fees into an ongoing membership program. I would use the cost comparison of the current cost rate for an average 30-minute ride as a casual rider vs. the cost of a 30-minute ride as a member.
I would use language like “easy, fast, and fun” in the digital marketing campagin. The marketing should showcase how easy it is to already be a member, how having it all paid for upfront reduces the stress of having to pay as you go, and how all these features of convenience makes your bike ride and experience more enjoyable. “Less hassle, more happiness when it comes to your ride” would be my recommendation for the overall branding and messaging of the marketing campaign.