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Empowering Retail and E-Commerce Enterprises to optimize their Pricing & Promotions strategies

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Why do Traditional ML approaches fail with Pricing & Promotion strategies?

They are unable to discover the real drivers of demand
They are unable to adapt to new situations never before seen in data
They lack flexibility to customise algorithms for business differentiation

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In this demo we will:

Show you how to explore 'what-if' scenarios from pricing and promotions planning at the SKU level
See how a team of analysts can analyse the cause & effect relationship of different variables that impact price & demand
Explore the executive dashboard that allows retail and e-commerce to analyse the impact of pricing & promotions on potential revenue increase

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First, let's explore the Executive Summary page.

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Executive Overview, within decisionOS, allows Merchandisers to get insights into the core metrics impacting their revenue and compare the potential revenue with debiased optimal pricing.

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decisionOS allows Merchandisers to understand the impact of debiased pricing on the overall revenue for a given period of time.

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Merchandisers are now able to review the difference between biased and debiased pricing so better decisions can be made.

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Let's explore the Causal Graph to better understand the cause-and-effect relationship between variables that are impacting price elasticity.

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decisionOS allows us to explore cause and effect relationships from both the Price (on the left) and Demand (on the right) angle as an initial step.

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decisionOS allows analysts to combine both of the graphs and look at the challenge holistically

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Now that we have the fully explainable model, merchandising teams are able to apply our decision intelligence engines to explore and interactive with the price elasticity.

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In the first step, we are able to select particular SKUs of interest for exploring their price elasticity.

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We can then select the date range for which we would like to run the counterfactual analysis.

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Select the desired price increase or decrease for this scenario.

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Run this scenario using Structural Causal Models, as they are the only models that allow us to perform such counterfactual analysis.

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