Divya Joshi

Domain: Customer Rewards Analytics(Google)

• Target setting process: Identifying and assigning goals to each Google Partner to receive a reward on completion
• Defining priority growth areas (PGAs): Identifying PGAs for partners in order the boost the performance
• Incrementality testing: Analysis of Impact of each PGAs(goals) towards the overall revenue in each quarter
• Winner/Non-Winner Analysis: Analysis of winner/non-winner Google partners in Rewards Program
• Forecasting analysis and simulating win rate for effective Budget Utilization
• Promotion (Kicker Reward) Impact Analysis: Difference-in-Difference Methodology
• Customer Benchmarking: To display performance of similar companies on partners portal.
• Challenge setup pipelines: Setting up scripts in a workflow using Google Pipelines to pull out daily performance data which feeds directly to the dashboards.

Client Handling Experience: End to end client handling experience from requirement gathering till project delivery.

• Providing regular updates on projects
• Sharing QA results every week on backend scripts/workflows’ health
• Collaborated with multiple teams for end-to-end delivery of quarterly process
• Created a good client satisfaction with quick turnaround on urgent queries and ad hoc analysis.

Domain: Retail Analytics

• Target audience selection Analysis: Created PySpark script which calculates the target audiences for a campaign
• Feasibility Analysis: Created SAS/Python scripts and have used modelling
• Test and Control data calculation to target customers for a campaign
• Evaluation Analysis: Evaluating campaign data and Calculating Uplift & other KPIs for retail client
• Segmentation: Identifying Customer segmentation during Christmas/Easter/Summer
• Churn Analysis: Analyzing customer buying behavior to identify lapsed customers
• Covid19 Impact Analysis: Analyzed impact of COVID19 in various categories across the market
• Derived insights at category and other segment (Life stage, Lifestyle) level
• Campaign long term Impact analysis: Analyzed impact of a campaign over a longer period after the campaign period has end using t-test
• Recommender system: Developed a machine learning model which provides recommendations for relevant products when primary product is out of stock or not available

• MS Product license Categorization: Classified Microsoft license data into their respective hierarchy
• Involved in designing, developing and deploying ETL (SSIS) in MS SQL Server environment using in
• Business Intelligence Development Studio (BIDS)
• Created ETL packages to fuzzy match licensing data from Sql server
• Techniques: SSIS, PL/SQL, Text Mining, Regex, Fuzzy Matching (Soundex, FuzzyWuzzy)