Data Analyst — Sales Analysis Project (Building of pipeline and the dashboard)

 Data Analyst — Sales Analysis Project

Introduction:

"As a Data Analyst, I was responsible for helping the business team make data-driven decisions by analyzing sales performance and identifying growth opportunities. One of my key projects involved conducting a detailed Sales Data Analysis to address revenue gaps and guide strategic planning."

Problem Statement:

"The company noticed irregularities and a dip in sales performance, particularly in the wealth product segment. My task was to analyze the sales data, identify underlying patterns, and deliver actionable insights that could help the business recover lost revenue."

My Approach:

Data Extraction & Preparation:

"I worked with large volumes of sales data, stored across AWS cloud infrastructure. Using SAS and SQL, I performed data extraction from different tables and applied ETL (Extract, Transform, Load) operations to clean, transform, and prepare the dataset for analysis."

My Data Analysis:

"Once the data was structured, I used advanced Excel and Power-Bi techniques for exploratory data analysis (EDA) — including pivot tables, dynamic charts, and conditional logic — to uncover sales trends by product, region, and time period."

Insight Generation:

"The analysis revealed that the wealth products were underperforming in specific regions due to inconsistent promotional strategies and lower customer engagement compared to competing financial products."

My Recommendation & Solution:

"Based on these insights, I recommended targeted marketing campaigns and refined discount strategies specifically for wealth products in underperforming areas."

Business Impact:

"The actions driven by my analysis helped the company recover a significant portion of lost sales and strengthened the alignment between the sales and marketing teams for future planning."

✅ Closing Line Suggestion:

"This project not only improved my technical skills in SQL, SAS, and AWS-based data handling but also emphasized the importance of translating data insights into strategic business decisions."

💡 Data Analyst — Sales Analysis 

Introduction:
"In my role as a Data Analyst, one of my key responsibilities was to help the sales team make data-driven decisions to maximize revenue and identify growth opportunities. One particular project involved analyzing a sudden drop in sales performance across specific regions and product categories."

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Business Problem:
"The sales team had observed a 10-15% decline in sales over two consecutive quarters in some regions, but the root cause wasn’t clear. My goal was to investigate the reasons behind this decline and identify actionable insights to recover and boost sales."

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Approach:

1. Data Collection:
   "I collaborated with the sales and IT teams to extract historical sales data from the CRM and ERP systems, including product details, customer segments, regions, time periods, and sales representatives’ performance."

2. Data Cleaning & Preparation:
   "I cleaned the dataset to handle missing values, removed duplicates, standardized date formats, and created calculated columns such as YoY Growth, Sales per Customer, and Average Order Value."

3. Exploratory Data Analysis (EDA):
   "Through trend analysis, correlation studies, and segmentation, I identified that the sales decline was mainly concentrated in two regions and linked to a combination of two factors: (a) delayed product shipments due to supply chain issues and (b) a specific product line underperforming due to increased competitor discounts."

4. Data Visualization:
   "I used Power BI to create an interactive sales dashboard that highlighted:

• Sales trends by region and month.

• Top declining products.

• Customer churn rate increase during the same period.

• Sales rep performance side-by-side with regional targets."

5. Recommendation & Business Impact:
   "Based on the analysis, I recommended optimizing the inventory allocation for affected regions and suggested the marketing team revise discount strategies for specific products. These changes helped recover about 8% of the lost revenue in the following quarter."

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Conclusion:
"This project showed the impact of combining analytical thinking with business knowledge, helping the company turn raw data into clear, actionable steps that directly influenced sales recovery and future planning."