CPG Analytics

Regression models improve sales.

Company Overview

A CPG company that produces and distributes a range of skincare products. With a diverse product portfolio and a competitive market, accurate sales forecasting is paramount for optimizing production, inventory management, and meeting customer demands.


The CPG company encountered several challenges in forecasting sales at the product level. The company relied on traditional forecasting methods that were largely manual and lacked the ability to capture complex demand patterns and drivers. The company faced challenges in dealing with seasonality, promotions, competitive pressures, and other external factors that impacted sales. Additionally, the company struggled with managing a large number of SKUs, each with its own unique demand characteristics, making it difficult to accurately forecast sales for each product.


The CPG company embarked on a data-driven approach that incorporated advanced web analytics and regression modeling to improve sales forecasting accuracy. The company formed a cross-functional team to implement the following solution.

Data Collection and Cleaning
The team collected historical sales data at the product level, and other relevant data such as pricing, promotions, seasonality, competitor data and those interested in skin care. The data was cleaned and transformed to ensure accuracy and consistency.

Exploratory Data Analysis (EDA)
The team conducted EDA to gain insights into the data, identify trends, patterns, and outliers. They used visualizations, statistical analysis, and correlation analysis to uncover underlying patterns in sales data.

Regression Modeling
The team applied regression modeling techniques to build predictive models that could capture the relationship between various demand drivers and sales. They employed techniques such as multiple regression, time-series regression, and machine learning algorithms to create robust and accurate models.

Model Validation and Selection
The team validated the models using historical data and selected the best-performing models based on the company stated performance metrics. They also conducted sensitivity analysis to evaluate the robustness of the models and their ability to handle different scenarios.

Forecasting and Insights Generation
The selected models were used to generate product-level sales forecasts for different time horizons, including short-term, medium-term, and long-term forecasts. The forecasts were analyzed to generate insights and recommendations for optimizing production, inventory management, and pricing strategies.

Successful Results

The implementation of analytics and regression model insights for product-level sales forecasting yielded significant improvements for the CPG company. With this busines intelligence (BI), the company achieved the following outcomes.

Improved Forecast Accuracy
The accuracy of product-level sales forecasts improved significantly, leading to better demand planning and inventory management. The company was able to reduce out of stock items and overstocks, resulting in increased sales and reduced costs.

Enhanced Visibility into Demand Drivers
The regression models provided insights into the underlying factors driving sales fluctuations, such as seasonality, pricing, promotions, and competitor data. This enabled the company to make data-driven decisions and optimize pricing, promotions, and other marketing strategies to maximize sales and profitability.

Better Production Planning
The accurate sales forecasts enabled the CPG company to optimize production planning and reduce production costs. The company was able to align production with demand resulting in improved operational efficiency and reduced costs.

Enhanced Promotional Planning
The insights generated from the regression models helped the company optimize promotional planning. The CPG company was able to identify the most effective promotional marketing approach for product.