Machine learning algorithms will learn patterns from the past data and predict trends and best price. These algorithms can predict the best price, discount price and promotional price based on competition, macroeconomic variables, seasonality etc.

To find out the correct pricing in real-time retailers follow the following steps:

Gather input data

In order to build a machine learning algorithm, retailers collect various data points from the customers. These are:

Transactional data

This includes the sales history of each customer and the products, which they have bought in the past.

Product description

The brands, product category, style, photos and the selling price of the previously sold products are collected. Past promotions and campaigns are also analysed to find the effect of price changes on each category.

Customer details

Demographic details and customer feedback are gathered.

Competition and inventory

Retailers also try to find the data regarding the price of products sold by their competitors and supply chain and inventory data.

Depending on the set of key performance indicators defined by the retailers, the relevant data is filtered.

For every industry, pricing would involve different goals and constraints. In terms of the dynamic nature, the retail industry can be compared to the casino industry where machine learning is involved inonline live dealer casino games too.

Like casinos, retail also has the target of profit maximisation and retention of customer loyalty. Each of these goals and constraints can be fed to a machine learning algorithm to generate dynamic prices of products.

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news and analysis for omnichannel retailers - Retail Technology Innovation Hub