Dunn Solutions' Data Science team has developed dynamic statistical pricing models that are capable of modeling a buyer's decision process, including what they are willing to pay for a product.
Are you pricing your products for maximum profitability? Is your data helping you understand how much your customers will pay?
Dunn Solutions' Data Science team has developed dynamic statistical pricing models using Azure Databricks that are capable of modeling a buyer's decision process, including what they are willing to pay for a product.
Every customer is not the same however, and creating personalized pricing strategies for each combination of product and customer ensures both short- and long-term profitability. Azure Databricks provides the scale and flexibility needed to support the model customization, data processing and storage for your situation.
Imagine if you had the ability to increase profit margins by 10% and achieve an increase in units sold merely by adjusting your pricing optimally. Using Microsoft Power BI we deliver an interactive story that describes the insights and foundational evidence delivered by the model.
With Azure Databricks and Power BI, plus our services, you can develop dynamic statistical pricing models that can maximize profits by leveraging personalized pricing strategies and know exactly when and how much to discount products.
Deliverables Our team will analyze past Point Of Sale (POS) and e-commerce sales data to develop a predictive model which can determine the optimal price for each item. These predictive analytics models consider any or all of these factors to maximize gross sales and profit margin:
• Current vs. optimal price • Cross-product cannibalization • Seasonality • Competitive pricing • Discounting
Once the model is executed, this creates multiple opportunities for the number of buyers to increase. Ongoing consultation with your data scientist will ensure that your model is continuously optimized to maximize both short and long profitability.