About Ninacare

NinaCare is a pioneering childcare platform committed to enhancing the lives of parents by revolutionizing the care industry through AI-driven matching algorithms. Operating internationally, NinaCare offers a comprehensive solution to the challenge of securing reliable and affordable childcare, with features such as automated screening, dynamic pricing, insurance, and seamless app-based payments. With a vision to extend its impact beyond childcare, NinaCare aspires to become the premier marketplace solution for care services, including pet and elderly care, establishing itself as a dominant force in the EU and US markets.

Problem

The Ninacare provides me data analysis tasks related to a dataset containing information about bookings, net revenue, and gross merchandise value. The specific challenges include cleaning the dataset, analyzing booking trends over time, exploring activity categories, and understanding the unique behaviour of certain devices. I did the project in Python and used the interactive visualisation library Plotly. For more details and code please click here

Methodology

1. Cleaning the Dataset:

- Imported necessary Python libraries for data analysisand visualisationn.

- Examined and cleaned the dataset by addressing issues such as data types, time formats, column names, rounding numerical values, and handling outliers.

- Created a comprehensive data cleaning function to standardize and improve the quality of the dataset.

- Processed null values in the 'activity_category' column based on net revenue criteria and removed outliers in net revenue and gross merchandise value for better data quality.

2. Analyzing Booking Trends Over Time:

- Plotted the count of bookings over time to identify trends and patterns.

- Conducted a detailed analysis of customer checkout and travel behaviour, highlighting peak days, seasonal patterns, and potential shifts in user behaviour.

- Extracted insights regarding the booking frequency and time ranges before travel, providing recommendations for targeted marketing campaigns.

3. Exploring Activity Categories:

- Analyzed net revenue and gross merchandise value differences among different activity categories.

- Investigated negative net revenue values and identified potential reasons, such as discounts.

- Explored booking frequency per activity category, emphasizing the popularity of certain packages.

- Examined customer travel interests based on seasons and activity categories.

4. Understanding Device Behavior:

- Explored unique bookingbehaviourr associated with different devices, specifically focusing on device_ids 4 and 5.

- Analyzed checkout counts and net revenue for each device, identifying popular and less-engaged devices.

- Investigated the correlation between device usage and activity categories, offering insights into user preferences.

- Examined device usage over time, highlighting trends in customer engagement and checkout frequency.

Recommendations:

- Tailor marketing strategies to target popular devices (Device 1 and Device 2) and encourage high-value purchases.

- Implement user-friendly features and optimizations for devices with lower engagement (Device 4 and Device 5) to enhance user experience and revenue.

- Focus on promoting and optimizing revenue-generating activity categories while evaluating the viability of less-engaged categories.

- Implement targeted marketing campaigns based on customer travel interests during specific seasons.

- Monitor and improve the overall customer experience, analyze customer feedback, and adapt strategies based on data-driven insights.

- Explore upselling and cross-selling opportunities for top revenue-generating devices.

- Regularly assess device performance and satisfaction to identify areas for improvement and collaboration opportunities.

Travel agency data analysis