Solution architecture: Demand forecasting and price optimisation for marketing

Pricing is pivotal for many industries, but it can be one of the most challenging tasks. Companies often struggle to accurately forecast the fiscal impact of potential tactics, fully consider core business constraints, and fairly validate pricing decisions once they have been made. As product offerings expand and complicate the calculations behind real-time pricing decisions, the process grows even more difficult.

This solution addresses those challenges by using historical transaction data to train a demand-forecasting model in a retail context. It also incorporates the pricing of products in a competing group to predict cannibalisation and other cross-product impacts. A price-optimisation algorithm then uses that model to forecast demand at various price points and factors in business constraints to maximise potential profit.

By using this solution to ingest historical transaction data, predict future demand, and regularly optimise pricing, you will have the opportunity to save time and effort around the process and improve your company’s profitability.

Deploy to Azure

Use the following pre-built template to deploy this architecture to Azure

Deploy to Azure

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Data Factory: Move data, orchestrate, schedule and monitor Power BI Data Simulator Web Job Azure Data Lake Store Spark on HDInsight

Implementation guidance

Products/Description Documentation

Data Lake Store

Data Lake Store stores the weekly raw sales data, which is read by Spark on HDInsight.

Apache Spark for Azure HDInsight

Spark on HDInsight ingests the data and executes data preprocessing, forecasting modeling, and price-optimisation algorithms.

Data Factory

Data Factory handles orchestration and scheduling of the model retraining.

Power BI

Power BI visualises sales results, the predicted future demand, and the recommended optimal prices for a variety of products sold in different stores.

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