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https://github.com/ninadpatil09/online-store-sales-analysis
The "Online Store Sales Analysis" interactive dashboard offers comprehensive insights into the sales performance and customer behavior of the online store for the year 2022. This user-friendly dashboard provides a visually appealing and intuitive interface to explore key sales metrics and customer trends.
https://github.com/ninadpatil09/online-store-sales-analysis
microsoft-excel pivot-tables visualization
Last synced: about 2 months ago
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The "Online Store Sales Analysis" interactive dashboard offers comprehensive insights into the sales performance and customer behavior of the online store for the year 2022. This user-friendly dashboard provides a visually appealing and intuitive interface to explore key sales metrics and customer trends.
- Host: GitHub
- URL: https://github.com/ninadpatil09/online-store-sales-analysis
- Owner: ninadpatil09
- Created: 2023-08-03T15:59:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-18T11:35:51.000Z (9 months ago)
- Last Synced: 2024-05-07T17:59:19.256Z (8 months ago)
- Topics: microsoft-excel, pivot-tables, visualization
- Homepage:
- Size: 8.45 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Online-Store-Sales-Analysis
The "Online Store Sales Analysis" interactive dashboard offers comprehensive insights into the sales performance and customer behavior of the online store for the year 2022. This user-friendly dashboard provides a visually appealing and intuitive interface to explore key sales metrics and customer trends.
Insights:
Customer Engagement by Age: Customers below the age of 48 are the primary contributors to online orders, indicating that targeting a younger audience could be beneficial for future sales growth.
Peak Month for Sales: March emerges as the most favored month for making purchases, suggesting that promotional strategies or special offers during this period could boost sales further.
Key States for Sales: The top three states with the highest sales are Maharashtra, Karnataka, and Uttar Pradesh, together accounting for approximately 35% of total sales. Focusing marketing efforts and expansion initiatives in these states can yield substantial returns.
Targeting Specific Age Groups: Customers aged between 25 to 45, primarily adults, drive a significant portion (around 50%) of sales. Particularly, women within this age bracket represent a lucrative target audience.
Preferred Sales Channels: Amazon, Myntra, and Flipkart are the most preferred sales channels, capturing a substantial market share of about 75%. Leveraging these platforms for advertising and promotions could enhance customer reach and engagement.
Conclusion:
To maximize future sales and customer engagement, we recommend a targeted approach focused on specific demographics and platforms. Firstly, directing marketing efforts towards customers in the age group of 25 to 45, with a particular emphasis on female shoppers, can optimize sales conversion rates. Offering personalized offers, discounts, or loyalty programs can incentivize this group to make repeat purchases.
Secondly, prioritizing marketing campaigns and product launches during March, the peak sales month, can lead to increased visibility and customer participation.
Furthermore, concentrating expansion efforts and resources in the states of Maharashtra, Karnataka, and Uttar Pradesh would enable a more effective utilization of resources and facilitate deeper penetration into high-potential markets.
Lastly, collaborating closely with popular e-commerce platforms like Amazon, Myntra, and Flipkart will grant wider exposure and access to a substantial customer base. Utilizing targeted advertisements on these platforms will aid in reaching potential customers and driving more sales.