Sales Data

MAYANK GUPTA

469-***-**** ************@*****.*** LinkedIn Profile Plano, TX

EDUCTATION

The University of Texas at Dallas May 2017

M.S., Business Analytics, GPA: 3.5/4

Visvesvaraya Technical University, Belgaum, India May 2013

B.Tech, Electronics and Communication Engineering GPA: 3.7/4

TECHNICAL SKILLS

Languages: R, SAS, Python, STATA, C, SQL, HTML

Analytics Tools: Rattle, SPSS, Alteryx, SAS EMiner, Hadoop, Tableau,Shiny, Qlikview, Dataiku, MS Excel, Adobe Analytics

Certification: Google Analytics IQ, Google AdWords

Database: Teradata, Oracle, MySQL, SQL Server

Project Utilities: Microsoft Excel(advanced), Word, PowerPoint, Visio, Access

BI & Analytics skills: Data Mining, Visualization, Statistical Analysis, Predictive Modeling, Business Intelligence

Data Mining: Variable reduction (FA, PCA), Segmentation/clustering techniques, Time series analysis

Machine Learning: Decision tree, Random forest, Bagging, Boosting, SVM, Neural networks

PROFESSIONAL EXPERIENCE

Southwest Airlines, Dallas Jan 2017 – Present

Data Analyst Intern

Redesigned and automated a workflow application in Alteryx 10, combining 5 data sources for extracting data to load into Tableau, Optimized passenger and baggage demand using workflow

Designed and generated 20+ baggage related Tableau interactive dashboards to reduce analysis time by 70%

Executed SQL queries to perform data analysis and implement complex logic representing baggage handling system

Analyzed 10 years worth historical data on excel and R Studio to find trends and seasonality for all the stations, generated reports and created live visualizations on Tableau; presented findings to management executives

Ingram Micro, California June 2016 – Aug 2016

Business intelligence analyst Intern

Analyzed intent data on Excel and R to find trending technology topics for Ingram’s upcoming web IT community.

Performed social media data analytics and infused data to analyze likelihood of demographic, marketing and revenue index features

Created Tableau dashboards for customer data, visualized potential target locations and customer segments

Using Google Analytics, defined KPIs to measure website performance, improve content and hence, Increase website traffic.

RS Components, Noida, India July 2014 – Feb 2015

Marketing Analyst

Achieved 150% YTD sales targets and forecasted monthly sales for set of 80 active accounts in North Indian region

Analyzed customer data using SAS to support the sales team to identify valuable leads on monthly basis

Designed and developed dashboards in Tableau from multiple sources.

Established KPI’s for sales team which helped in optimizing and making the territory allocation process efficient

Spur Semicon Pvt. Ltd., Bangalore, India July 2013- July 2014

Business Analyst

Designed business development plan for company by market research and competitor analysis for 5 sales regions

Proposed and implemented market strategies to improve customer base equaling 30% of the revenue for 2013

Created monthly demand forecast reports from CRM insights for 25 active accounts using Excel spreadsheets

ACADEMIC PROJECTS

Market Structure Analysis using SAS, IRI Data Jan 2016- Mar 2016

Processing more than a million scanner data points spread over 10 years for butter’s retail market of the U.S

Assessing brand loyalty patterns and price elasticity metrics to find insights for top 3 brands of butter

Recommending target marketing plan for 2nd best brand, based on competitor and customer profiling

Web Traffic Analysis using Adobe Analytics (Omniture Site Catalyst), UT Dallas Jan 2016- Mar 2016

Examined and reported website traffic trends using ad-hoc analysis to identify patterns in incoming traffic

Segmented traffic based on campaign tracking codes, using geo-segmentation to find marketing insights

Exported reports from Omniture to excel using report builder tool create 5 dashboards and visualizations

Predicting Customer Churn- Data Analysis using R programming, AT&T Jan 2016 – Mar 2016

Performed predictive analysis on 100,000 data points to identify customers that are most likely to churn

Identified 10 most important factors (out of 173) influencing churn using principal component analysis

Predicted a logistic regression customer churn model (60% accuracy) and recommended solutions to churn

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