Machine learning applied to price optimisation

Machine learning forms part of our daily lives. It’s present around us almost without our noticing. One example of this is Netflix. This platform learns about what we like to offer personalised recommendations and achieve increased user engagement. Beyond personalisation, machine learning can also help companies optimise their prices to increase sales. Want to know how?

Machine learning is based on the idea that systems can learn from data, identify patterns, and make decisions. Its implementation in pricing software allows it to predict variations in supply and demand based on historical data and anticipate actions that will be taken by the competition. This data makes it easier for eCommerce businesses to adapt their prices to the needs of the market at all times, in order to boost their sales growth.

Continuous and automatic learning is the key that makes it easier for companies to launch dynamic pricing campaigns. For these to be effective, our artificial intelligence must learn, not only from the sector and the competition but also from the decisions that the company itself has made in the past. These decisions will be taken into account in the present when considering new price changes, thereby reducing the margin of error while optimising sales.

What do I need to start optimising my prices?

To successfully implement a pricing system, you should follow this roadmap:

1. Define the information that you need to analyse. Tell the program which KPIs it should base its analysis on to recommend the best ways to optimise your prices. This includes information about the sales history, the complete catalogue of products, promotions, marketing campaigns launched in the past and their results, customer reviews, stock data, and your main competitors, among others.

2. Identify your objectives. In addition to increasing your sales, what do you intend to achieve by optimising your prices? Machine learning can help you to define future personalised campaigns to earn the loyalty of your best customers, for example, or to improve your conversion rate. This is another way to work on your sales in the medium to long term.

3. Start with your star products. To allow the system to begin learning about your company and catalogue, we recommend you start by asking questions about the products or services that have functioned very well in the past. This incorporates quality data which the system can base new predictions on and begin adjusting prices to tendencies in the market.

The main advantage of implementing a pricing system based on machine learning, in addition to its speed, is its capacity to analyse a large amount of data. These systems can simultaneously and automatically compare the prices of all of your competitors by tracking their websites, online stores, and social networks to obtain quality information about their products and services.

What you should remember is that the ultimate goal of machine learning should be to enhance overall customer satisfaction. These are the users of our eCommerce business, on whom the success of our company ultimately depends.