John Molenda

Crm Partnership Analyst @Brooklyn Sports & Entertainment

Developed SQL queries to extract BSE partnership deal information, track Salesforce pipeline development/CRM data, Brooklyn Nets/New York Liberty/Barclays Center ticket buyer information and demographics Designed Python machine learning sentiment script analyzing call transcript information from ticket sales reps to find which questions asked were being received by customers Additional insights found involved which reps were consistently receiving the most positive sentiment from customers and which lead sources were most efficient at generating positive interactions between rep and client Libraries used: TensorFlow, Pandas, NumPy, Matplotlib, Seaborn, Nltk, and Sentence-Transformers Leveraged data analysis and ETL expertise to provide organizational wide recommendations on best target market, most effective email messaging formats and partnership pipeline best-practices from extracted information Delivered automated reports and analyses to executives via Tableau and Salesforce dashboards powered by SQL scripts to provide up-to-date pipeline snapshots and inform on current/future partner analysis sliced by category and partnership rep so leadership could stay informed on pipeline size, partners up for renewal, and forecasted revenue based on size of contract being pitched and current stage in Salesforce Tableau dashboard refreshing from our data warehouse for sales leadership to get a live look at what their reps are saying on the phones and providing recommendations on what to train their reps to say that will be more likely to lead to a sale