PHONE SALES DATA ANALYTICS
A leading mobile phone retailer is experiencing fluctuating sales and increasing competition in the rapidly evolving smartphone market. Despite offering competitive discounts and stocking a wide range of brands, they are uncertain about which factors are driving their sales and how they can optimize pricing, discounts, and product offerings to boost revenue. The retailer has amassed a large dataset of phone sales, but lacks the expertise to uncover key insights from this data that could inform strategic decision-making.
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To address this challenge, I conducted a comprehensive analysis of the phone sales data, focusing on uncovering actionable insights into brand performance, model popularity, pricing trends, and the impact of discounts on sales volumes. I cleaned and processed the dataset to handle missing values and calculated key metrics such as total sales revenue, average selling price, and market share by brand. Visualizations were created to provide a clear picture of the distribution of selling prices, popular phone colors, and the relationship between discounts and sales.
The insights generated from this analysis aimed to equip the retailer with data-driven strategies to refine their product selection, pricing structure, and marketing efforts, ultimately enhancing their competitiveness in the market and driving higher sales.
In the analysis, I sought to answer the following questions:
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Which phone models have the highest sales volume?
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How do average selling prices vary across different phone brands?
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Does a phone's color affect its pricing?
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What are the top-rated mobile models?
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Does a Higher Discount Lead to Higher Sales?
Inquiry Questions
Tools & Libraries Utilized
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Python: The main programming language used for the analysis.
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Jupyter Notebooks: For running and documenting the analysis process.
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Pandas: For data cleaning, manipulation, and analysis.
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NumPy: For numerical operations.
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Seaborn & Matplotlib: For creating detailed visualizations.
Files
Key Insights & Findings
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The analysis identified Samsung as the top-selling brand, with 696 units sold, and Apple iPhone 11 as the leading model, selling 36 units. Despite Samsung's sales success, Apple recorded the highest total sales revenue, suggesting that it is positioned as a premium phone brand.
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Significant variations in average selling prices across brands were observed, with typical price points ranging from 0 to 75,000 for most brands, except for Apple, which exceeds 100,000. This highlights the wide range of pricing strategies employed by different brands.
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Apple exhibited a broader price variation among its models, indicating a diverse product line that caters to different segments. The presence of many outliers in the Samsung brand suggests a range of high-quality models contributing to its pricing strategy.
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The analysis revealed that black is the most sold color, indicating a strong consumer preference. Space Grey, although expensive, had low sales counts, suggesting it targets a niche market. Gold, however, demonstrated both high sales and average price, reinforcing its strong market position.
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Colors like Space Grey and Silver commanded higher average selling prices, while more common colors like black, white, and blue were associated with lower prices. This indicates that unique colors may have a premium positioning, appealing to higher-end consumers.
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Apple achieved the highest customer ratings, indicating strong customer satisfaction and perceived product quality.
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The analysis also found a very weak negative correlation (-0.06) between discount percentages and selling prices, suggesting that increasing discounts do not significantly affect sales. This implies that changes in discount strategies may not have a meaningful impact on sales performance in this dataset.
1. Given the minimal correlation between discount percentages and sales volume, the Brands should consider re-assessing their discount strategies.
Instead of relying heavily on discounts to boost sales, they should explore other promotional tactics that may have a more significant impact on consumer behavior.
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2. Since colors like Gold, Rose Gold, and Space Grey commanded higher prices, the Brands should consider diversifying their color options to include more unique and premium colors that can be marketed as exclusive. This could enhance consumer perception and potentially increase pricing strategies.
Recommendations
Conclusion
The analysis provided a comprehensive understanding of key factors influencing phone sales, including model popularity, pricing strategies, the impact of color, and the effectiveness of discounts. These insights can guide strategic decisions in inventory management, pricing, and marketing, ultimately driving better sales performance and customer satisfaction.
THANK YOU!
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