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This is an analysis made on a superstore to understand why they had been making losses over a four year period, yet it had accounts on a substantial amount of sales over the same year period.

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dataanalysis dataanalysisusingpython dataanalyst datacleaning dataproject datascience datavisualization superstore-data-analysis

superstore_analysis's Introduction

Step into the world of a prominent superstore, where everything seemed to be running smoothly - sales were soaring and customers were flooding through the doors. But behind the scenes, a mystery was brewing. Despite these impressive numbers, the store was grappling with a huge financial loss. An investigation as a data analyst was launched to uncover the truth, and what I uncovered will shock you. Join me as I delve deep into years of data, unraveling the hidden story behind the store's struggles and uncovering the clues that will lead to a dramatic turnaround. Get ready to be enthralled in an epic tale of discovery, where the truth was stranger than fiction

Following my previous discovery in the superstore's data, I took the next step in understanding the issue by performing data cleaning. My steps were as follows:

  • Checked the summary statistics to identify any inconsistencies or outliers in the data.
  • Removed any duplicate records to ensure a high level of data accuracy.
  • Adding new columns to the dataset to capture additional information and insights.
  • Performed advanced techniques such as data imputation, outlier detection and handling, and data normalization to further improve the quality of the data.

By going through this thorough data cleaning process, we were able to uncover more information that would have been obscured by the inconsistencies and inaccuracies in the raw data. And with this newfound clarity, we were able to take the next step in our investigation and bring the superstore's financial struggles to a close.

Analysis Insights from EDA

  • Count of subcategory

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  • Most Preferred shipmode

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  • Segment

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  • City

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  • State

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  • Region

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  • Category

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-Subcategory

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FINDINGS, RECOMMENDATIONS AND CONCLUSIONS

  • On analyzing the Super Store's sales and profitability data, it was found that the Technology sub-category and specific products such as Phones and Chairs are our highest selling and most profitable items. To offset losses from less profitable products such as Bookcases and Tables, I recommend bundling them with the high-performing items. image

  • For customers looking for a home office setup, I suggest creating a package deal that includes a variety of office essentials such as a table, chairs, phone, copiers, storage, labels, fasteners, and bookcases. This is a great option for customers who are busy and may not have the time to select individual products.

  • To address the issue of losses from selling Supplies, Bookcases, and Tables, I recommend considering dropping these products from our catalog or renegotiating with suppliers for a cheaper price.

  • Our customer base is primarily made up of Consumer and Corporate segments, which make up over 70% of our customers. To target these segments, I suggest offering special promotions and bundles for mass Consumer and Home Offices in the top 10 cities with the highest sales, and sending promotional emails or flyers to customers in the East and West regions. image image

  • The main reason for losses is discounts. To improve this, I recommend offering more discounts during festival seasons, as this will result in more sales. During these seasons, the discount rate is recommended to be not more 20% as more than a 20% discount brings in great losses. image

  • Additionally, the home office segment needs improvement. In some cities, we have fewer sales, which may be due to a lack of awareness. Advertising in these cities may help increase sales. For advertising strategy, in the event that the company wants to run product package promos, It is advised that the most common items that are usually bought together to inform the company's decision.

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