SHILPA TR

Machine learning pilot projects, proof-of-concept projects, using Python and Primavera P6:

• Actively involved in the development and testing of experimental machine learning models in small-scale pilot projects specific to marketing, to evaluate their feasibility and potential impact on key business processes, focusing on data collection, model training, and performance assessment before full-scale implementation.
• Experimenting with classification models for predicting churn, regression models for predicting purchase amounts, and deep learning neural networks to analyze complex patterns in customer interactions, purchase history, and browsing behavior to personalize recommendations and optimize the shopping experience.
• Assessing the feasibility and impact of the ML models before full-scale deployment.

Marketing mix modeling for retail clients using AC Nielsen sales data:

• Data collection, manipulation/treatment, exploratory data analysis, and modeling the influence of advertising, promotions, and other marketing activities on AC Nielsen CPG and retail data using SAS and Excel.
• Data gathering, data cleansing, analysis, interpretation, presentation, and providing recommendations.
• Leveraged data types and structures to design the target state data design.
• Developed data frames and preprocessed data.
• Conducted regression analysis and cluster analysis.
• Calculate effectiveness, elasticities, contributions, ROIs, and optimal marketing spend to maximize sales and/or profit.
• Presentation and discussion with the sales and marketing team at the client’s on data outputs, business insights, and recommendations.
• Team Management: Trained and developed junior team members to deliver quality results, and promote professional development.
• Bangalore, India.

Data analysis, text mining, and presentation:

• Analysis of structured and unstructured data captured during customer interactions in the marketing support and customer assistance center at automobile dealer centers.
• Extracting the drivers of satisfaction and dissatisfaction from the customer verbatim, as well as the positive and negative sentiment around the brand, product, and dealer.
• Building word clouds for highlighting the essential issues and identifying opportunities for enhancing service to the customer.

Data Analysis, Data Mining, Data Modeling, and Presentation:

• Data collection, manipulation/treatment, exploratory data analysis, and modeling the influence of advertising, promotions, and other marketing activities on sales using SAS and Excel.
• Calculating effectiveness, elasticities, contributions, ROIs, and optimal marketing spend to maximize sales and/or profit.

Data collection and data analysis:

• Collect recruitment assessment data.
• Analyzing candidate and employee data for tests like Cognitive Abilities, Communication Skills, Domain Assessments, and Workplace Behavior Tests across industry verticals, and identifying trends using Excel.
• Helping to improve tests, difficulty levels, instruction, hiring decisions, and targets.