Price Optimization with Machine Learning
Machine Learning has proven to be a game changer in the effective price optimization problem. First, ML algorithms can analyze huge diverse datasets within seconds and consider much more variables than is possible with traditional pricing such as Historical sales and transaction data, seasonal changes, weather conditions/ special events, inventory levels, operating costs, competitors’ prices.
Previously, pricing managers had to manually define and adjust pricing rules. Machine learning models, on the contrary, use algorithms that continually learn from previous results in an automatic way. Thus, retailers can use machine learning models to set prices in line with sales targets. They can do this completely automatically, much more accurately and with minimal effort. Moreover, Machine Learning pricing tools are able to improve the finding of the best price tier for retailers over time.
Four Steps to Define and Optimize Prices using Machine Learning
Data collection
First of all we need to collect data that will fuel our ML algorithms. There are different types of data that can be used, for example, transactional data, product description, operational costs, competitors’ data, inventory data, and price history.
Modeling and training of a Machine Learning Algorithm
The next step is to build and train the machine learning model. First, the model analyzes all variables and determines the possible impact of price changes on sales. Once created, the original model can also be manually optimized on a regular basis. With each correction the algorithm learns and independently improves its results. Over time, training costs decrease, while effectiveness of the software is constantly increasing.
Using the power of data in practice
Once developed, a machine learning model can determine optimal prices that meet specific business goals and determine price elasticities for thousands of products, all within minutes. Internal marketing and product teams can use these calculations to experiment more boldly with starting prices and discounts as they can better assess the potential impact on sales and demand instead of relying on gut feeling and experience.