I have worked as a Sr. Data Scientist in Decision Minds for the last 5 years. I have worked on many data science projects like predictive modeling, recommendation models, etc. Before this, I worked as Sr. Data Scientist in Decision Minds for the last 5 years.
Any deal pitched to customers is difficult to negotiate without giving good discounts. For any company, discounts mean compromising on revenue/profit. So, to provide optimal discounts considering revenue, built this model based on product segments.
Based on product and deal size, the list price model recommends minimum discount values for each geo, which can provide to customers. Providing higher discounts in deals affected revenue and optimizing discounts provided to customers as necessary.
The tools used to solve this issue were Machine learning (clustering technique), built-in Python, and R. After implementation, it was observed that last year there was a 10-12% growth in quarterly revenue (4 quarters) based on several deals that happened in those quarters.
We created clusters based on geo-based product and deal size. Using discount as our target variable, we extracted rules from the decision tree for each segment. Then we calculated for which discount value there was max revenue observed in historical data. Using cumulative revenue data, we calculated optimum discount values. After implementation, it was observed that last year there was a 10-12% growth in quarterly revenue (4 quarters) based on several deals in those quarters. A detailed understanding of the clustering technique also helped me understand how to optimize discounts in any product company.
