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Pricing Prediction & Optimization

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Pricing Prediction & Optimization

Problem/Challenge – The retail and P&C industry is always growing which is therein resulting in low cost risk, decreasing store footprints, low profits etc. Markdowns for many retailers are driven by tried and rudimentary techniques with x% at 6 weeks, y% at 8 weeks etc. The lowest cost may win the business, but may be underpriced relative to the risk. This results in costing a company potentially exorbitant amounts of money in the end.

Resolution – Machine learning algorithms can accurately discover patterns that explore cost versus risk, you can determine whether any risk you consider taking on is priced appropriately. With machine learning models one can identify best price for each item using data on seasonality and price elasticity along with real-time inputs on inventory levels and competitive products and prices. An AI based pricing tool can help push adverse selection on to competitors, which, over time will increase your growth and profitability.

Why CafeEDA – CafeEDA’s aim is to leverage AI in accurately predicting pricing/sales to maximize profits for various businesses, it will automatically engineer and identify the individual reasons for why the predicted price/sales value should be taken into consideration. It empowers data science teams to scale by dramatically increasing the speed to develop highly accurate predictive models with lesser number of resources. CafeEDA includes innovative features including data ingestion from different data sources, data blending, data visualization, automatic machine learning and deep learning, model deployment and predictions, and interpreting the machine learning model built.