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How to Generate Automated Sales Report in Python

9 September, 2020

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Contributors

What's the idea?

Utilize python to create a pipeline that can import sales data given any date range as input into the relational database and instantly output an automated sales report on an assigned date range. Once we get the updated sales data from the sales team, we can have a quick analysis to understand the sales performance on a daily basis, increasing efficiency by 90%.

Methodology

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Input

Input specific date range to the pipeline to extract sales data for the assigned date range.

Output: Sales Report

An automated sales report for the date between 2017-10-01 and 2017-10-03 was generated in only 10.7s!
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This automated sales report efficiently completes data management, data integration, data manipulation, data cleansing, and exploratory data analysis in only 10 seconds. It helps analysts identify the profitable items and potential customers, and provide actionable recommendations to grow more sales.

Recommendations

The Laptop has better sales than the Desktop. The company can store more laptops in its inventory and promote laptops to potential customers through various marketing channels.

A customer was more often to spend 1k to 2k in a purchase, showing that most customers prefer middle-end products. The company is highly recommended to store and promote more middle-end products to increase sales.

There're some potential customers whose average daily purchase is over $4000. These customers are more likely to purchase more products in the company. We highly recommended the company leverage the membership program to increase these customers' loyalty. In the meanwhile, the company can invest more money in marketing similar or complementary products to this segment based on their purchase behaviors through various approaches like email marketing.
Languages
Python

Python

Databases
SQLite

SQLite

python

sql

sqlite

etl

pipeline

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2

0

python

sql

sqlite

etl

pipeline

Lili Guo

Los Angeles, CA, USA

Research Data Analyst @ Seattle University | 2 years of data analysis experience extracting essential business insights and offering actionable strategic recommendations | Open for opportunities

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