This would give you the best chance at converting non-members due to the high service utilization. Run membership sale during peak use months where casual use is at its highest, where the largest largest quantity of non members are interacting with your product.For example a summer seasonal, or weekend & Evening specific members passes might help target and convert this demographic. Evaluate alternate membership types providing more value for casual user ride trends. Perhaps monthly ‘bring a friend for free’ passes, which could also expand the overall user network. Add additional perks or incentive to membership, specifically for the recreational evenings and weekend ride behaviours.This would be an interesting month for further analysis.Ĭustomer interviews would confirm or disprove our hypothesis that Member riders use the service primarily for functional transportation and commuting, where as casual riders use leans more towards, leisure activities and pleasure, with a focus on weekends, warmer summer months, and longer rides overall. Casual ridership sees a notable drop in August, when compared to the steady increase in use leading up to July. Member ridership increases steadily all the way to August, where it begins to decline during the colder months. The casual segment have a take longer rides, with their highest ridership on on Saturday and Sunday afternoons, without the distinct peaks during commuting times. Members continue to ride into the colder months at a much higher rate than the casual counterparts. Member riders take shorter rides, peaking Monday - Friday during commuting hours. Member riders are much higher users in the colder months, approximating that the segment is using the service less for pleasure/leisure acitivies, and more as an effective form of transportation, particularly in these months where outdoor activities are less desirable.In July, casual and member rides are almost neck and neck, this would be an excellent opportunity to try to convert some of the high casual users to members.The largest differences being Oct-Dec.Īdditional data should be collected on the reason for Casual rider reduction during this summer month. In the off months, Oct - April, we see the biggest gaps between segments. Unsurprisingly, we see total rides are much higher in the warmer months. We completed our data cleaning and processing work in Python, with the pandas and numpy libraries. bike type, trip start & end time, latitude & longitude of trip start and stop, as well as info on most start and stop stations. Initially- The dataset contains a unique trip ID. that might help us make a more personalized user segment analysis. But we are limited in additional user information, such as the intention of a trip etc. With over 5 million trips tracked across 12 months, this dataset is quite robust and provides enough of a sample to represent overall user trends. Licence: Lyft has provided a royalty-free, limited, perpetual license to access, reproduce, analyze, copy, modify, distribute in your product or service and use the Data for any lawful purpose. The dataset used came in 12 individual CSV files, containing each individual month’s trip data. Data going back to 2019 is available, but for this project, we have decided on the most current 12-month range available. How can the business use digital media to influence casual riders to become members?Ī first-hand dataset, containing trip information for over 5 million trips between Sept 2021 to Aug 2022.Why would casual riders buy annual memberships?.How do annual members and casual riders use Divvy bikes differently?.Members are the more profitable segment, so exploring the difference between the two user groups will be the focus of our analysis. Tableau Dashboard linked here Code linked here Business Caseĭivvy is a Chicago-based bike share company using the Lyft platform to allow users to hop on and ride one of over 16 000 bikes at over 800 different station locations across the city. Tableau: Visualization and Dashboard creation.SQL: Validation, and exploratory, impromptu analysis.Python: Cleaning, transforming, enhancing and analyzing our dataset.This project utilized various technologies to clean, manipulate, transform, analyze and finally visualize a bike share ridership dataset. Photo by Maarten van den Heuvel on Unsplash
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