March 29, 2024 by Cyril Noirot

Optimizing prices and promotions

Problem defintion

Your department is tasked with maximizing profits while hitting ambitious sales targets. You have a treasure trove of data on product prices, costs, and historical demand. How can you use this data to craft the perfect pricing and promotion strategy?

This is where the magic of data science comes in! Let’s build a simple tool using Python to optimize prices and promotions for maximum return on investment (ROI).

Here’s the scenario:

  • We have four products (A, B, C, D) with varying prices, costs, demand forecasts, and shelf-life.

  • We have specific sell-through targets (percentage of stock sold) for each product.

  • We have a limited budget for promotions.

Objective

Leverage data science to find the optimal pricing and promotion combination for each product to maximize profit while achieving sell-through targets and staying within budget.

Solution

We’ll use python libraries Pandas, NumPy, and SciPy to create our optimization engine. Here’s a simplified breakdown:

  • Data generation: We’ll first create a synthethic sample of product data

  • Objective function : We’ll define a function to calculate profit, factoring in the cost of promotions. This function considers how price reductions due to promotions might affect sales.

  • Constraint check:

    • Sell-Through target: We’ll create a function to ensure our pricing strategy meets the desired sell-through targets. This function compares the forecasted sales based on optimized prices and promotions to the target percentages.

    • Budget : Another function will keep our promotion spending in check by ensuring it stays within the allocated budget.

  • The Optimization part: We use SciPy’s minimize function to find the sweet spot. This function will adjust prices and promotions simultaneously, aiming to maximize profit while adhering to our sell-through and budget constraints.

  • Profitability & Shelf-Life insights: After optimization, we’ll calculate the resulting profit for each product and estimate the remaining shelf-life based on the optimized sales forecast.

Results

By running this optimization tool, we can identify the optimal pricing and promotion strategy for each product. This might involve lowering prices for specific products with high promotion budgets to achieve sell-through targets. Conversely, products with lower demand might benefit from maintaining higher prices with minimal promotions.

This is just a starting point! In the real world, we can incorporate more complex factors like competitor pricing, seasonality, and even customer demographics to create a more robust model.

Data science empowers retailers to make data-driven decisions, go beyond intuition, and achieve peak profitability while hitting sales targets and staying within budget.

So, the next time you see a perfectly priced product with an enticing promotion, remember, there might be a data scientist working behind the scenes, wielding the power of optimization!

LET’S WORK TOGETHER