These data points often include product ID, master-variant assignment, current price, RRP, lower and upper price limit, seasonal identification, brand, color, size, stock level, expiration date or target sales date and much more. Product attributes (or product master data) is the digital representation of a retailer’s assortment and an important tool for dynamic price optimization. Product Attributes: This includes essential information such as cost, margin ceiling, base price, and MAP price. Learn more about applications of machine learning in retail here. competitor data, time-based and weather factors). stock levels, target sales dates) as well as external (e.g. A machine learning based approach can calculate demand for individual items in a business’ assortment – which when dealing with thousands of SKUs, is a monumental task – and it can automatically consider a range of influencing factors, both internal (e.g. With this advanced technology, it’s possible to run processes on quantities of data that an individual pricing manager would not be able to manage. In contrast, by utilizing big data in conjunction with a machine learning based approach, retailers are now more equipped to define the most appropriate pricing strategy for their business. Managing and monitoring this rule-based approach also takes a fair amount of time and effort. The algorithm continuously learns from new data.Ī limitation of traditional algorithms is that they can only consider a limited amount of influencing factors, often less than three. This algorithm and its logic of prediction is not explicitly programmed. Based on that forecast, one can run optimizations to reach business targets. Machine learning based algorithms: these algorithms learn on a set of training data to make predictions on the price effect on sales, revenue and profit.They often consist of a range of “if/then” rules that determine prices based on a range of influencing factors. Traditional, rule based algorithms: the logic of these algorithms has been explicitly programmed.We can differentiate between two types of algorithms that are being used in pricing: The wealth and sheer quantity of data online consumers generate is enabling new, better-informed strategies to drive customer happiness and company profitability.īy analyzing their massive quantities of available data, in combination with current market events and other external data sources, retailers can optimize their prices for the customers with the help of algorithms. Today, thanks to artificial intelligence and machine learning, retailers can more readily get a robust view of what both competitors and customers are doing at any given moment, as well as a better sense of the influences and reasons behind their buying behavior. Inputs to this new approach to pricing come both from the data generated by the buyers’ behavior, as well as from the larger competitive landscape. By considering consumer behavior when approaching pricing, companies continue to price at the value a customer ascribes to a given product, they also work to manipulate that perception of value, measure, and increase it. Machine learning technology takes dynamic pricing to the next level, as it can process much larger data sets and can consider various influencing factors to predict the effect of price changes.įor companies retailing online, consumer behavior and that data generated by it should be a major focus. The explosive growth of big data and the potential it contains for developing AI and machine learning approaches to pricing strategies has unlocked new opportunities for intelligent pricing solutions. In today’s hyperfast, highly competitive retail landscape, data-based dynamic pricing strategies are harnessing the power of this consumer data and using it to drive pricing decisions. Data which could inform smarter, more agile pricing decisions! cost base, conversion rates, etc.) With this approach, massive amounts of important data – both transaction data and non-purchase data – went under utilized. Traditionally, pricing in retail was set based on static price rules that utilized a limited amount of data inputs (e.g. The ability to take quick, informed action around pricing has a massive impact on overall profit margins. Prices need to make sense within an increasingly competitive landscape, and your business’ pricing model needs to be ready to adapt to fluctuations in customer demand and purchasing behaviors. With the ongoing boom in online shopping, implementing dynamic pricing has never been more important than it is right now.

Dynamic pricing algorithm