Wholesale Data Analytics Case Study - Insightogram
Data Intelligence

Transforming Wholesale Data into Actionable Insights

Automated Web Scraping, Data Analysis, and Tableau Visualization. Creating a seamless data system for a leading wholesale distributor.

Client
Wholesale Distributor
Industry
Logistics & Sales
Solution
Scraping & Visualization
Background

Client Overview

A leading wholesale distributor managing multiple product categories across regions approached Insightogram to streamline how they collect, analyze, and visualize their business data. The client relied heavily on manual data collection from multiple supplier portals and e-commerce sources, which made it difficult to track pricing trends, inventory levels, and competitor insights in real time.
The Problem

The Challenge

The client's manual processes created a lag in information, making it impossible to react to market changes swiftly.

01

Manual Data Entry

Staff had to manually download and compile pricing and stock data from multiple wholesale websites, a process prone to human error.

02

Time Consuming

Analysts spent hours each week cleaning, validating, and organizing data instead of focusing on strategic analysis.

03

Limited Visibility

There was no clear view into fast-moving vs. slow-moving products or regional demand patterns.

04

Pricing Fluctuations

Without real-time data, it was difficult to track competitor pricing trends and adjust margins effectively.

They needed a scalable, automated, and visual solution to help decision-makers access fresh, reliable insights instantly.
Our Approach

The Solution

Insightogram designed a comprehensive data intelligence solution combining web scraping, processing, and visualization.

1

Data Collection (Web Scraping)

  • Built automated Python-based scraping scripts to extract real-time data from multiple wholesale and competitor websites.
  • Captured essential fields such as product name, SKU, price, availability, and discount trends.
  • Scheduled daily and weekly data refreshes for consistent accuracy.
2

Data Cleaning & Analysis

  • Cleaned and standardized raw datasets using SQL and Python (Pandas).
  • Applied statistical analysis to identify pricing anomalies, demand gaps, and supplier trends.
  • Combined scraped data with internal sales data for comprehensive performance comparison.
3

Visualization (Tableau Dashboards)

  • Developed interactive Tableau dashboards to provide clear insights at a glance.
  • Created views for price comparison across suppliers and product performance by category.
  • Visualized stock movement, replenishment cycles, margin analysis, and profitability insights.
Results

The Outcome

The automated system transformed unorganized data into a strategic decision-making engine.

80%
Reduction in manual effort for data collection
Real-time
Visibility into pricing and inventory trends
Faster
Decision-making speed across sales teams
Optimized
Profitability through smarter pricing strategies
Technology

Tools & Technologies

Py

Python

Automated Web Scraping

SQL

SQL

Structured Data Storage

Tab

Tableau

Dynamic Visualization

PBI

Power BI

Integrated for Scaling

Impact

Conclusion

Through a blend of automation, analytics, and visualization, Insightogram helped the client transform unorganized wholesale data into a strategic decision-making engine. The project not only improved operational efficiency but also demonstrated how data-driven intelligence can directly impact business growth.