Shubham Naithani

Data Analyst

Elcom Digital

Noida

10.2023 - Current

Regional Sales Analysis of Tally Licensing
Situation: The company lacked visibility into regional sales performance for Tally licensing, with data scattered across various sources like Google Sheets and the Tally track portal.
Task: Developed a Python script to collect and upsert data from the Tally track portal and Google Sheets into an SQL database for analysis.
Action: Conducted exploratory data analysis (EDA), RFM analysis, and statistical analysis to identify top-performing regions and districts, as well as customer behavior insights.
Result: Created a real-time Power BI dashboard showcasing daily sales trends, region-wise sales, and agent-wise sales, enabling the organization to make data-driven decisions.
TallyPrime on AWS
Situation: Needed to analyze data to achieve targets of 10,000 active VMs and 30,000 active users for TallyPrime on AWS.
Task: Analyzed data to understand customer provisioning and deprovisioning patterns, as well as the performance of different plans in achieving active VM and user targets.
Action: Used Python for data analysis to identify insights such as customer retention rates, top partners with active VMs, and leading zones.
Result: Provided actionable insights to the business, helping achieve the target of 10,000 active VMs and 30,000 active users.
Stock Inventory ProjectSituation: Faced challenges in tracking Tally license purchases and sales, leading to operational inefficiencies.
Task: Developed Python scripts to maintain a stock inventory, retrieving and merging data from the Tally and Zoho portals using REST APIs.
Action: Utilized pandas for data manipulation and upserted the data into the database for better operational workflow.
Result: Improved tracking of Tally license purchases and sales, leading to better operational efficiency and decision-making.
Project: Shield Eye
Situation:

Problem Statement: The company was facing challenges with overdue payments from customers who had been given credit days to pay for products. Overdue amounts and days were increasing, leading to the need for frequent follow-ups via calls and emails.
Task:

Objective: Develop a solution using machine learning to predict more accurate credit days for customers based on their payment behavior, thereby reducing the instances of overdue payments.
Action:

Methodology: Employed Random Forest algorithm to analyze all customer invoices and their corresponding payment histories.
Steps Taken:
Collected and cleaned the invoice and payment data.
Analyzed the average payment days versus the given credit days for each customer.
Used Random Forest to predict new, more accurate credit days based on past payment behavior.
Result:

Outcome: Successfully predicted new credit terms for customers that were more aligned with their actual payment behavior.
Impact: This prediction reduced overdue amounts and the number of overdue days, leading to fewer follow-up calls and emails. Improved cash flow and customer satisfaction by setting realistic payment expectations.

Summary

Overview

Work History

Data Analyst

Data Analyst

Education

Masters in Information and Technology - Information Technology

Certification in Data Analytics & Data Science -

Accounting And Business Management

Bcom - Accounting

Skills

Timeline

Data Analyst

Data Analyst

Masters in Information and Technology - Information Technology

Certification in Data Analytics & Data Science -

Accounting And Business Management

Bcom - Accounting

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