Insights Report


Case Study on Drug Rehab

This was one of the PPC experiences where I had to constantly monitor the PPC account both on Google AdWords and Microsoft (Bing) adCenter. The campaigns were experiencing lower ad positions, higher competition for keyword search phrases and not relevant traffic…

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Wilderness Therapy PPC Account – Part I

I was given an AdWords Campaign Report(dataset) on which I had to perform analysis and provide what attributes made the account successful, keyword planning, negative keyword management, a set of recommendations to strengthen the accounts performance and opportunities (positive/negative).

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Wilderness Therapy PPC Account – Part II

I was given an AdWords Campaign Report(dataset) on which I had to perform analysis and provide what attributes made the account successful, keyword planning, negative keyword management, a set of recommendations to strengthen the accounts performance and opportunities (positive/negative).

I also mentioned what attributes could be carried over from Wilderness Therapy PPC Account – Part I to Wilderness Therapy PPC Account – Part II.

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Work Experience

Data Analyst                                                                                                                                                                                                                   06/2017 – 02/2018
Wexler Consulting Group – Alexandria, Virginia
Responsibilities: Pay-Per-Click (PPC) Advertising & Management, SEO Optimization, Keyword Research, Web Analytics & Reporting

  • Managed 22+ PPC Campaigns with an approximate spend of $72,000 in Google AdWords and Bing Ads for clientele ranging from Drug Detox & Rehab Industry, Immigration Law Firm, Financial Partners, Sales Recruiting Firm, Catholic University.
  • Google Ad Grants for Non-profits (Faith Community): Determined goals of increasing donations for Giving Tuesday as well as getting more web traffic suffering from mental illness. Ongoing Optimization of keywords, bids & budget allocation, there has been an increase in clicks & impressions and maintained a 5% click-through-rate.
  • Executed SEM documents such as Tier 1, Tier 2 & Tier 3 Keyword Generation & Segmentation, Forecasting & Competitor’s Analysis, Insertion Orders, Ad Copywriting Data Analysis & Optimization, Negative Keyword Management & Performance Reporting.
  • Monitored Pay-Per-Click (PPC) campaigns progress, identified opportunities & presented performance recommendations to improve campaigns effectiveness. Collaborated with the design team to implement optimization strategies for landing pages that increased conversion rates.
  • Targeted ads to a more-relevant audience, which lowered the impressions by 11%, resulting in 20% more clicks & 49% more site visits, at a 25% lower CPC value and the CTR went up from 2.4% to 3.59%.
  • Worked with the Digital Media team to establish KPIs and developed data analysis for click data, ads effectiveness, and ad-cost effectiveness.
  • Compared & analyzed month-to-month PPC data using Pivot Tables & VLOOKUP formula to figure out “what’s changed” in the account to identify opportunities/troubleshoot issues within a campaign; also maintained a strong account structure.
  • Utilized Remarketing list strategies to re-establish contacts & drive existing/potential customers back to the website with a purpose & increase website conversion rates.
  • Proactively generated custom monthly reporting deliverables for each client by analyzing website & paid search traffic through Google Analytics & SEMRush.


Graduate Teaching Assistant                                                                                                                                                                                                    09/2016 –12/2017
George Mason University – Fairfax, Virginia
Responsibilities: Assisted professors in Lesson Planning & Blackboard Administrative Support, Learner Engagement & Assessment, and Monitored Student Progress. Conducted lab sessions on MySQL, Secure Shell (SSH), MS Visio. Graded assignments, discussion-forums & problem-solved queries related to Entity-Relationship Models, Dependency Diagrams, Normalization, Complex SQL queries & Advanced Data Modeling Techniques.


Data Analyst                                                                                                                                                                                                               05/2014 – 12/2015
Tech Mahindra Limited – Hyderabad, India
Project 1: Stress Analysis on Twitter                           

Responsibilities: Streamed & mined the stress-related tweets using tweepyAPI. Performed Sentiment Analysis by using the NLTK Sentiment Analyzer to understand the polarity of the tweets being negative/positive to distinguish between types of stress. Built the training & testing sets and ran them through the Classification Algorithms. Logistic Regression provided the best prediction probability with 78% of accuracy. Visualized the stress level trends on Tableau throughout the United States and discovered that acute stress was very high.

Project 2: Microsoft Retail Technology Group

Responsibilities: Configured & validated all the requirements for Microsoft Stores based in the US, Australia, and Puerto Rico using Microsoft AX 2012 R3. Performed pre-production, production support, and monitored system interfaces. Worked on scenarios that encompass incident management on System Center – Service Manager and Operations Manager (SCSM and SCOM), handled user groups, tested & assigned access-level permissions.

Technical Skills

Programming Languages: Python, R, C
Databases: MySQL, Oracle – SQL*Plus, Microsoft SQL Server Management Studio 17
Pay-per-Click: Google AdWords, Microsoft (Bing) adCenter, DoubleClick
SEO Tools: Google Keyword Planner, SEMRush, Moz Open Site Explorer & Keyword Explorer, Google Search Console, Google Trends
Analytical Suite
: Google Analytics, Google Tag Manager, Hotjar, Google Sheets, R Studio
BI Tools: Tableau, MicroStrategy Desktop 10.10, DOMO, Power BI
Microsoft Office Suite: Advanced MS Excel, MS Access, MS Word, MS PowerPoint, MS Visio
Web Technologies:
HTML, CSS, JavaScript, WordPress, JSON
Applications: Jupyter Notebook 5.4.0, Eclipse IDE, MS Visual Studio, MS Dynamics AX 2012 R3, WEKA, Git

Digital Marketing Certifications

Google Analytics Individual Qualification                                                                                                                                 01/2018 – 07/2019
Google AdWords Fundamentals and AdWords Search Certification                                                                            01/2018 – 01/2019

Academic Project

Analysis of Airline Passengers Dataset                                                                                                                                                                   10/2017 –12/2017

Key Tasks: Data Acquisition, Preprocessing, Metadata Extraction, Classification & Cluster Analysis, Prediction Modeling & Visualization.

Technology used: Python, R, WEKA, Tableau, Tagxedo (Word Cloud), NAME-PRISM API [External API]

  • Preprocessed the data using JSONLint, Python and R scripts, extracted meaningful information, & discovered knowledge using Simple K-means cluster analysis method. Used Text Mining to create a corpus to remove all the stop words, and to know the term frequency of each term.
  • Extracted the descriptive metadata features like ethnicity, nationality, and gender for a classification model using call requests from the external NAME-PRISM API. Imputation of data values was performed to fill-out the missing/NA values.
  • Performed cluster analysis and generated word clouds to identify the terms that stand out in each cluster. Interpretations of the observations were based on the DIKW framework. Visualizations were performed based on combinations of attributes being compared with the “success” count of the potential passengers.
  • Used WEKA Data Mining tool to identify & report top-2 attributes (gender & fare) with 10-fold cross-validation & InfoGainAttributeEval by running the dataset through Attribute Selection test and utilized J-48 Decision Tree algorithm to visualize the classification performance using the Precision-Recall curve to help understand why a potential customer will fly/not fly.
  • Utilized stratified cross-validation to understand that the correctly classified test instances were 79.12%, and incorrectly classified test instances were 20.87% (the error rate) with a kappa statistic value of 0.55 and ROC Area of 0.741 (approaching 1) which resulted in an optimal classifier making the model strong.
  • A list of recommendations was provided to the Advertising team to target audience according to the success rate of the potential passengers.

George Mason University, Fairfax, Virginia                                                                                                                                                                                01/2016 – 12/2017
Master’s Degree in Applied Information Technology                                                                                                                             GPA: 3.97

Bhoj Reddy Engineering College for Women, Hyderabad, India                                                                                                                             
06/2010 – 05/2014
Bachelor’s Degree in Information Technology                                                                                                                                         GPA: 3.5

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