E-com Price Optimization

E-com Price Optimization

In retail, price optimisation is an art form that aims to strike a harmonious balance between the cost of products and the willingness of customers to purchase them. The ultimate objective is to establish a price that generates substantial revenue and attracts a significant customer base. Through the intelligent analysis of data and the implementation of strategic pricing approaches, businesses can determine the optimal price point. This meticulous process enables them to maximize sales and profits while ensuring customer satisfaction and loyalty. In the following case study, we will analyse different price optimization strategies for a company.

Business Problem

TrendElite is a clothing retailer specializing in a diverse selection of apparel and accessories, operating both physical stores and an online e-commerce platform. To enhance its revenue and market competitiveness, the company aims to optimize its pricing strategy.

TrendElite faces several challenges in effectively pricing its products.

  • Competitive Landscape: With numerous rivals offering similar products, the retail industry is highly competitive. TrendElite aims to stand out by offering attractive prices while ensuring profitability.

  • Inventory Management: Ensuring effective inventory management is vital for TrendElite, as pricing strategies must balance supply and demand. Optimizing prices based on inventory levels can help prevent overstocking or understocking of products.

  • Seasonal and Trend Variations: The fashion industry's rapid trend changes and fluctuating demand throughout the year present challenges for TrendElite. Adjusting prices to reflect seasonal and trend shifts is crucial to capitalize on sales opportunities.


Price optimization can address the challenges mentioned above by implementing the following strategies:

  • Demand-based pricing: This strategy involves analyzing the demand for each product over time to determine the optimal price for each product. The approach includes setting a base price for each product and then adjusting the price based on the demand for the product.

  • Competitor-based pricing: This strategy entails analyzing the prices of competitors for the same product and adjusting the price based on the competitor’s pricing.

  • Price elasticity-based pricing: This strategy involves analyzing the price elasticity of each product to determine the optimal price. It includes setting a base price for each product and then adjusting the price based on the price elasticity of the product.


We will use the revenue earned as a metric.

Data Description

data description

The dataset consists of monthly sales data of products, freight charges, product score, competitor price and their product score data

product idA unique identifier for each product in the dataset.
product category nameThe name of the product category to which the product belongs.
month yearThe month and year of the retail transaction or data recording.
qtyThe quantity of the product sold or purchased in a given transaction.
total priceThe total price of the product, including any applicable taxes or discounts. Calculated using qty*unit price
freight priceThe average freight price associated with the product.
unit priceThe average unit price of a single unit of the product.
product name lengthThe length of the product name in terms of the number of characters.
product description lengthThe length of the product description in terms of the number of characters.
product photos qtyThe number of photos available for the product in the dataset.
product weight gThe weight of the product in grams.
product scoreAverage product rating associated with the product’s quality, popularity, or other relevant factors.
customersThe number of customers who purchased the product in a given category.
weekdayNumber of weekdays in that month.
weekendNumber of weekends in that month.
holidayNumber of holidays in that month.
monthThe month in which the transaction occurred.
yearThe year in which the transaction occurred.
sThe effect of seasonality.
VolumeProduct Volume
Comp 1Competitor1 price
Ps1Competitor1 product rating
Fp1Competitor1 freight price
Comp 2Competitor2 price
Ps2Competitor2 product rating
Fp2Competitor2 freight price
Comp 3Competitor3 price
Ps3Competitor3 product rating
Fp3Competitor3 freight price
Lag pricePrevious month price of the product.


My Recommendations For TrendElite

Pricing Strategy

  • The Price Elasticity to Demand strategy appears to be the most effective pricing strategy. This strategy bases pricing decisions on customer demand and their reaction to changes in price.
  • With this strategy, we've only adjusted the prices of products with inelastic demand, meaning that changes in price do not significantly impact customer demand.
  • As a result, we've observed increased sales consistently.
  • Holidays play a crucial role, as months with more holidays tend to have higher sales.
  • With the higher influx of customers during holidays, it's an opportune time to introduce new products and raise awareness.
  • Aim for product descriptions to be around 1000 characters in length.

Jupyter Notebook

Here are the links to the Jupyter Notebook containing detailed code, insights, and recommendations:

Following is the embedded version of above notebook hosted on Kaggle

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