18
Suggestions for Divvy to Increase Revenue Canada Goose (Gregory Choi, Bowen Nie, Jack Taffe)

Big Data Analysis on Chicago Divvy (2015)

Embed Size (px)

Citation preview

Page 1: Big Data Analysis on Chicago Divvy (2015)

Suggestions for Divvyto Increase Revenue

Canada Goose (Gregory Choi, Bowen Nie, Jack Taffe)

Page 2: Big Data Analysis on Chicago Divvy (2015)

AgendaInsights from the Data

Travel Route analysis

Suggestions for Divvy to increase revenue

Page 3: Big Data Analysis on Chicago Divvy (2015)

Insights from the dataSome Summary Statistics:

Average duration: 17 minutes

Average age of the user: 36

Average # of trip per day: 8722

Page 4: Big Data Analysis on Chicago Divvy (2015)

Insights from the data

The heavy users are people from 24 to 40.

Page 5: Big Data Analysis on Chicago Divvy (2015)

Insights from the data

People are much more like to use Divvy in warm season than in cold season.

Page 6: Big Data Analysis on Chicago Divvy (2015)

Insights from the data

Strong correlation between temperature and usage, R-Square: 0.92, p<0.0001

Page 7: Big Data Analysis on Chicago Divvy (2015)

Insights from the dataThe two peaks are around 8 am, morning work commute, and around 5pm, when people leave work.

Page 8: Big Data Analysis on Chicago Divvy (2015)

Insights from the data

Male users are almost 3 times

of the female users.

Page 9: Big Data Analysis on Chicago Divvy (2015)

Insights from the data

2/3 of the users are subscriber and

1/3 of them are customer (=Visitors).

Page 10: Big Data Analysis on Chicago Divvy (2015)

Insights from the data

Top 40 start stations and top 40 destination stations.

Page 11: Big Data Analysis on Chicago Divvy (2015)

Insights from the dataPeople use Divvy in business hours

are elder than those use Divvy in

off hours.

Page 12: Big Data Analysis on Chicago Divvy (2015)

Top 50 Travel routes in 2015 in Chicago

Navy Pier

Museum Campus

UnionStation

McCormickPlace

* The darker, the more users ride Divvy in that route.

Page 13: Big Data Analysis on Chicago Divvy (2015)

Top 50 Female Travel routes in 2015 in Chicago

Navy Pier

MuseumCampus

South

Navy Pier

Lincoln Park

North

* The darker, the more users ride Divvy in that route.

Page 14: Big Data Analysis on Chicago Divvy (2015)

Top 50 Visitors Travel Routes in 2015 in Chicago

UnionStation

NavyPier

MuseumCampus

LincolnPark

* The darker, the more users ride Divvy in that route.

Page 15: Big Data Analysis on Chicago Divvy (2015)

Several takeaways from Travel Route AnalysisHeavy users ride Divvy in downtown area

Females in downtown don’t use Divvy as much as malesIt could be attributed to choice in clothes (skirt, heels)

Men’s reckless behavior; riding a bicycle in the middle of Chicago traffic with taxis

The female usage pattern is focused on the residential area, like Lincoln Park

Visitors ride Divvy along with Michigan lake roadTake advantage of the bike trail

Still need to attract visitors into the Chicago loop area

Page 16: Big Data Analysis on Chicago Divvy (2015)

Suggestion (1)Advertising

Breakfast , Fast food, and other goods for young and middle-age people

Business PartnerSports, health and wellness partners

Local business and service providers

Dynamic pricing based upon time, gender, age, or locationOffer a discount during non-peak hours

Offer a discount to the areas which are not frequently used

Or offer a discount along the Michigan lakeshore on Saturday or Sunday for promotion.

Page 17: Big Data Analysis on Chicago Divvy (2015)

Suggestion (2)Increase safety - Taxi and other drivers are can cause dangerous

conditionsBike Lanes

Helmets

Promote group activityDifferent scenic routes

Improve winter revenueDiscount during low volume times

Heated handlebars and seats, recharges at station

Windshield to protect

Wheels with improved handling in snow and ice

Hat, gloves, and scarves sponsored by other company

Page 18: Big Data Analysis on Chicago Divvy (2015)

Q&A