Table of Contents
Contrasting Regression vs. Machine Learning based approaches
Understanding the impact of price changes and promotional investments on business outcomes is crucial for all commercial teams (revenue management, finance, analytics, sales, and marketing). In today’s data-driven world, it is essential to have a deep understanding of price and promotional elasticities at the customer-product level to optimize sales, profitability, and market share.
Many businesses face the challenges of excessive promotions and discounts, ill-advised indexing strategies to competitor prices, and deploying pricing tactics that lead to declining operating profits and lower margins. Estimating price elasticities through demand models can help address these challenges by identifying the optimal price and promotional investment ranges for a given product and customer segment.
In this regard, we recommend the following five rules to ensure that the demand model is robust and the estimated price elasticities are accurate:
Collaborate with internal experts to build and vet the model with cross-functional leaders in sales, marketing, supply chain, and finance.
Choose simplicity over sophistication when selecting the best approach to estimate price elasticity. There are times and places for using advanced machine learning techniques over traditional regression methods, and this decision should be made based on the specific business problem at hand.
Pay attention to outliers in the data and perform exploratory analysis and summary statistics to understand the variance in the data. Normalize the data or exclude gross outliers if necessary to ensure the accuracy and stability of your demand model.
Consider multi-collinearity, as price and other variables are often highly correlated in most demand models. Use regularization techniques or tree-based ensemble models such as Random Forest or Gradient Boosting to handle multi-collinearity.
Test the effectiveness of the demand model by comparing new in-market results with the model’s predictions to ensure that the demand model and price elasticities remain accurate against previously unseen sales results.
The below guide expands on our previous article on the “Science (and Art) of Estimating Price Elasticities” and gives an overview of the typical modeling approaches for price elasticities and contrasts regression vs. machine learning methods for industry best practices.
If you have trouble viewing the presentation player below, you can also access the guide here.
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Frequently asked questions about price elasticity modeling
What is price elasticity modeling?
Price elasticity modeling uses demand models to estimate how price changes and promotional investments affect sales. With price and promotional elasticities at the customer-product level, commercial teams can identify the optimal price and promotional investment ranges for each product and customer segment, and optimize sales, profitability, and market share.
What problems can price elasticity modeling help solve?
Many businesses struggle with excessive promotions and discounts, ill-advised indexing of their prices to competitor prices, and pricing tactics that lead to declining operating profits and lower margins. Estimating price elasticities through demand models helps address all three, which is why every commercial team, from revenue management and finance to analytics, sales, and marketing, needs this understanding.
Should you use regression or machine learning for elasticity modeling?
Choose simplicity over sophistication. Advanced machine learning techniques have their time and place, but the choice between them and traditional regression methods should depend on the specific business problem at hand. The Revology guide on this page gives an overview of the typical modeling approaches and contrasts regression and machine learning methods against industry best practices.
How do you handle outliers and multicollinearity in a demand model?
Run exploratory analysis and summary statistics to understand the variance in the data, then normalize the data or exclude gross outliers where needed to keep the model accurate and stable. Price is often highly correlated with other variables, so use regularization techniques or tree-based ensemble models such as Random Forest or Gradient Boosting to handle multicollinearity.
Who should be involved in building a price elasticity model?
Collaborate with internal experts. Build the demand model together with cross-functional leaders in sales, marketing, supply chain, and finance, and have the same leaders vet it. Revology lists this collaboration first among its five rules for a robust demand model with accurate price elasticity estimates.
How do you test whether price elasticity estimates are accurate?
Compare new in-market results with the model’s predictions. This test checks the demand model and its price elasticities against sales results the model has not seen before, which is how you confirm that the estimates stay accurate after the model is built and put to use.
Related Reading
For broader industry perspective on pricing analytics and revenue growth management, see McKinsey’s Growth, Marketing & Sales insights.
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