Analyzing Excel Sales Data with Python Pandas and Seaborn - Part III

Visualizing Seasonal Trends, Customer Revenue, and Top Products for Optimal Sales Strategies

To explore the full details and practical examples, we highly recommend reading the entire article here . Happy coding!

Welcome back to the third part of our series on analyzing Excel sales data using Python's libraries — Pandas and Seaborn. If you missed our earlier articles, you can catch up by clicking here and here .

In today’s session, we'll cover:

• Deep Analysis of Sales Pattern for 2024: To track the sales activity of the products and observe any seasonal variations.
• Total and Average Revenue per Customer: To have an estimate of total and average revenues that a certain customer brings to the business thereby improving our understanding of customer value.
• Histogram of Average Order Value (AvgOrderValue): This contributes to understand the distribution of average order value.
• Distribution of Order Quantities: By performing the frequency distribution of order quantities, we will look at the general order sizes and identify any anomalies.
• Top 10 Customers Based on Total Expenses: Select the key clients by their contribution in sales and review their buying patterns.
• Orders from the Top 10 Customers: Analyze what our loyal consumers are buying.
• Top 10 Best-Selling Products: Determine the productivity of our prominent products.

Deep Analysis of Sales Pattern for 2024

Analyzing 2024 sales data over time is crucial for understanding trends, and seasonal patterns. By leveraging the robust visualization tools offered by Seaborn, you can gain insights into not just the peaks and troughs of your sales performance, but also the factors driving them.

Calculate total and average revenue per customer

When analyzing various types of revenue, two key metrics stand out: total revenues and Average Revenue Per User (ARPU).

Average Revenue Per User (ARPU), calculates the revenue generated from each customer over a specific period. This metric is crucial for assessing customer profitability, evaluating the success of pricing strategies, and understanding market segmentation effectiveness.

Plotting the histogram of the Average Order Value (AvgOrderValue)

Monitoring the Average Order Value (AvgOrderValue) is an important KPI in the e-commerce and retail business. It indicates generally the update of spending by consumers per order in a specific organization. This metric is very significant in analyzing customer buying behavior so that appropriate changes to a firm’s marketing and selling strategies can be made.

In creating a histogram plot of the AvgOrderValue column, what is involved is an arrangement of a graph that shows the count of the average order values. This visualization helps in:

1. Understanding Distribution: This technique of visualizing is useful in helping the business understand the extent to which customers differ in their spending patterns as indicated by AvgOrderValue.
2. Identifying Trends: Histograms are even more effective for this as it will help to see most frequent AvgOrderValue range or see that many low or high value orders are made and etc – all this can influence marketing and sales decision.
3. Strategic Insights: The visual distribution can enable managerial decisions regarding the promotional discounts, upselling and cross-sell strategies, pricing strategies, and customised marketing approach for improving the customer value.

Analyze the Distribution of Order Quantities

When comparing order quantities a method of visualization can be useful that is the box plot. In general, a boxplot or a whisker plot is one of the standardized forms of displaying the data distribution through which it is possible to comprehend the spread and skewness of a given dataset easily.

A boxplot is constructed using a five-number summary:

1. Minimum: The least value in the set or observed data that is not considered an outlier.
2. First Quartile (Q1): This is the value that separates the dataset into two equal halves; while this is the midpoint of the lower half. This one identifies the 25th percentile or in other words, 25% of the scores lie below this mark.
3. Median: The value located in the middle of the dataset when the data is arranged in increasing or decreasing order. But if the given dataset is ordered then the median is the mid-value which splits the dataset in to two equal halves. It stands for the 50th fringe.
4. Third Quartile?Q3): This is the average of all the values in the distribution, to which half of the data constitutes the greater half of the overall set of data. It is the value which describes that 75% of the given data set at least as big as this number as in this position the frequency density starts rising.
5. Maximum: Largest value of the observation in a large set of numbers; it excludes any outlying observation if there is any.

Plot of products with More Sale

By identifying which products have higher sales, you can prioritize your efforts more effectively. Whether it's marketing, inventory management, or product development, focusing on top-selling items ensures that your time and resources are spent on what's most impactful.

Conclusion

Here we highlighted the significance of utilizing histograms and boxplots to visualize data distributions. Additionally, this article delved into identifying the top customers and products.

By harnessing these insights, you should be better equipped to tackle tasks related to sales performance. Leveraging the collected data for analysis will make it easier to identify trends, gauge customer value, and evaluate product performance. This groundwork is essential for refining marketing strategies, boosting sales tactics, and enhancing organizational productivity.

Effectively using these tools and techniques not only enables you to analyze historical data but also primes you and your business for future analysis, ensuring continued relevance. Continuously analyze and process your datasets, and feel free to explore and implement various functions from Python's data analysis libraries.

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To explore the full details and practical examples, we highly recommend reading the entire article here . Happy coding!