Key Findings: The conversion rates of new visitors are high compared to those of returning customers
- Project Title
- Installation
- How to Run
- Background
- Objectives
- Dataset
- Methods
- Tasks
- Contributing
- License
Download the Anaconda distribution package from the official Anaconda website (https://www.anaconda.com/products/individual).
Follow the installation instructions provided for your operating system (Windows, macOS, or Linux).
Once the installation is complete, you can launch Anaconda Navigator, which provides a user-friendly interface for managing environments and launching Jupyter Notebook.
Open Anaconda Navigator and click on the "Launch" button under the Jupyter Notebook section.
This will open a new browser tab with the Jupyter Notebook interface.
Navigate to the directory where you want to create your notebook file.
Click on the "New" button and select "Python 3" to create a new Jupyter Notebook file with a Python kernel.
Store the dataset file (e.g., "shoppers_data.csv") in the same directory as your Jupyter Notebook file.
In a code cell, use the appropriate library (e.g., pandas) to import the necessary functions for reading data.
Use the appropriate function (e.g., pandas' read_csv) to read the data file into a DataFrame.
Assign the DataFrame to a variable for further analysis and exploration.
With the rise of online shopping, the number of consumers making purchases on the internet has steadily increased. However, despite the growing popularity of e-commerce websites like Amazon, many people tend to browse through products, add items to their wish lists or shopping carts, but ultimately refrain from making a purchase. This prevalent behavior highlights the necessity for tools and solutions that can tailor promotions and advertisements to online shoppers, thereby improving conversion rates. In this documentation, we will delve into an analysis of the various factors that impact a consumer's decision to make an online purchase.
Implement clustering and make recommendations based on the predictions. These recommendations will help you gain actionable insights and make effective decisions.
The following shows and describes the various numerical features of the dataset we are going to use:
Adminstrative - pages such as profile page
Administrative Duration - time spent on the administrative page in seconds
Informational - Pages such as contact information
Informational Duration - the amount of time in seconds spent on this page
Product Related - Pages related to products on a website
Product Related Duration - The amount of time spent of product related page
Bounce rate - percentage of visitors who access the site from a page and then leaves the page without creating any request
Exit Rate - represents the number of exits made from a particular page to leave the site
Page Value - Refers to average value for a web page that a user visited before completing an e-commerce transaction
Special Day - refers to days like mother's day
The following are categorical features
Operating Systems - I.e Windows,MacOs
Browser - such as chrome,explorer,safari
Region - Geograhical region
Traffic Type - Source of the traffic either direct or through a different website
Visitor Type - such as new,returning visitor or other visitor
Weekend - whether or not the day is weekend
Month - Month of visit
Revenue - whether the sessions accumulated in a purchase
Univariate Analysis
Bivariate Analysis
Clustering
• Revenue column
• Visitor type
• Traffic type
• Region
• Weekend-wise distribution
• Browser and operating system
• Administrative page
• Information page
• Special day
following categories:
• Visitor type
• Traffic type
• Region
• Browser type
• Operating system
• Month
• Special day
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Performing K-means Clustering for Informational Duration versus Bounce Rate
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Performing K-means Clustering for Informational Duration versus Exit Rate
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Performing K-means Clustering for Administrative Duration versus Bounce Rate
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Performing K-means Clustering for Administrative Duration versus Exit Rate
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The conversion rates of new visitors are high compared to those of returning customers.
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While the number of returning customers to the website is high, the conversion rate is low compared to that of new customers.
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Pages with a high page value have a lower bounce rate. We should be talking with our tech team to find ways to improve the page value of the web pages
The following recommendations can be made to improve the conversion rates and overall performance of the website:
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Enhance the user experience for new visitors by improving website navigation, optimizing page load times, and providing clear and compelling calls to action. This can help convert more new visitors into customers.
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Implement personalized marketing strategies for returning customers to increase their conversion rates. This can include targeted offers, loyalty programs, and personalized recommendations based on their browsing and purchase history.
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Collaborate with the tech team to identify and address any technical issues that might be impacting the page value of certain web pages. This can involve optimizing content, improving layout and design, and ensuring a seamless user experience.
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Continuously monitor and analyze website metrics, such as conversion rates, bounce rates, and page values, to identify trends and make data-driven decisions for ongoing optimization and improvement.