Knowing the best price for a product or service can be hard to judge. It takes a thorough examination of many factors, such as historical demand and competition. To find the optimal price, you must really understand how your customers will react to a price change.
What is pricing optimization?
Pricing optimization is the process by which the ideal price point for an item or service is chosen. By using market and consumer data, a balance can be achieved between value and profit.
How does price elasticity of demand affect price optimization?
Price elasticity is a measurement for changes in product or service demand versus changes in price.
Price elasticity of demand = % change in quantity demanded / % change in price
If demand stays the same when the price changes, the product or service is considered inelastic. However, if the demand decreases after a price change, it’s then considered elastic.
Inelastic products or services are staple items, such as bread and gas. Elastic products or services are generally luxuries or desired items, such as clothes or jewelry. Shifting prices can affect demand for luxury items and prices should be carefully considered.
What are some use cases for price optimization?
What is the best approach to optimize prices?
Machine learning can help companies accurately pinpoint the right price based on many data points. The price recommendations are very precise. Such as they can tell you what to price each SKU (Stock Keeping Unit), not just a general category.
“Setting optimal prices is one of the most difficult tasks even for retail veterans. With the continuous digitalization of our world, data is becoming more available and the number of relevant pricing factors is increasing. People, contrary to machines, are slower, prone to making errors, and simply lack capabilities to consider all factors at once. This calls for the next logical step — automated, ML-driven price optimization tools.”
ElectrifAi’s pre-built machine learning model, Pricing Optimization, has proven to be very effective at recommending the most accurate prices. The model uses a three-pronged approach to optimize prices and monetize unused pricing power. It does so by identifying inelastic SKUs and transactional data to grow margins with price increases.
What are some technical highlights of the Pricing Optimization model?
What data sources and features are used in the Pricing Optimization model?
What data is used to run the Pricing Optimization model?
How does all this data tie together?
The Pricing Optimization model outputs recommendations on which SKUs to increase price versus reduce price at each store. This makes it easier to precisely target the right price without having to manually dig through thousands of data points.
Has the Pricing Optimization model been used in the real world?
ElectrifAi’s pre-built Pricing Optimization machine learning model has been extremely effective at solving real business problems. Previous usage of the model achieved:
What’s the next step to take?
Decide how you would like to proceed to gain the benefits of machine learning for your business. You could start your own data science team to build a machine learning model from scratch. But that takes a lot of money and time that could otherwise be strategically used elsewhere in the business.
And why put money and time towards something that may not work? After months or even years, the model may fail to produce actionable insights.
ElectrifAi’s pre-built machine learning models, however, have been used in the real world and can quickly produce results. Start optimizing your prices now rather than waiting for a solution to appear.
If you are ready to begin your machine learning journey, contact us for a custom demo today!
 Koptelov, A. (2020, July 13). Pricing Optimization With Machine Learning: Is it Worth it? https://www.mytotalretail.com/article/pricing-optimization-with-machine-learning-is-it-worth-it/.