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Price Optimization Models

Price optimization is a big data analytics offering that helps you determine how shoppers will respond to different product pricing levels, which can maximize sales and profitability.
Price Optimization Models

Data Science Usecase (Part-6)

Price Optimization Models are mathematical programs that calculate how demand varies at different price levels and then combine that data with information on costs and inventory levels to recommend prices that will improve profits. The modeling allows companies to use pricing as a powerful profit lever, which often is underdeveloped. Price Optimization Models can be used to tailor pricing for customer segments by simulating how targeted customers will respond to price changes with data-driven scenarios. Given the complexity of pricing thousands of items in highly dynamic market conditions, modeling results and insights helps to forecast demand, develop pricing and promotion strategies, control inventory levels and improve customer satisfaction.

How Price Optimization Models work:

Price Optimization Models should factor in three critical pricing elements: pricing strategy, the value of the product to both buyer and seller, and tactics that manage all elements affecting profitability. Practitioners should:

  • Select the preferred optimization model, and determine desired outputs and required inputs
  • Collect historical data, including product volumes, the company’s prices and promotions, competitors’ prices, economic conditions, product availability, seasonal conditions, and fixed- and variable-cost details
  • Clarify the business’s value proposition and set strategic rules to guide the modeling process
  • Load, run and revise the model
  • Establish decision-making processes that incorporate modeling results without alienating key decision-makers
  • Monitor results and upgrade data input to continuously improve modeling accuracy

Source: https://www.bain.com/insights/management-tools-price-optimization-models/

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