Ability to use a pricing optimization strategy
First of all, the company must determine whether the demand for its goods and services is elastic. The most important factor is whether price optimization is appropriate for the company, that is, whether its customers are willing to pay dynamic prices for goods or services. A price is considered inelastic when its increase leads, as a percentage, to a slight drawdown in demand, less than the percentage of the price increase.
Large volume and high quality data
Data is the internal and main component for building any system using a machine learning model. Structured and cleansed historical data (past activity data) is a prerequisite for training a high-performance model, as the accuracy of its performance depends on the quality of the data. Training a model entails feeding the algorithm with training data needed for analysis, after which a final model is formed that can find the target value in the new data. The dataset should contain information representing as many options as possible: price history for each service or product, accompanying information about customer needs, internal and external factors affecting prices.
Price boundaries in the model
It is important to set a minimum price in order to provide the required profit, and a maximum one so that the brand image matches the price. Experts recommend that retailers include price caps on competitors to avoid prices significantly higher. For basic products, a price difference of more than 30-50% may demotivate the buyer to return to this store again.
The need to monitor the results and change prices if necessary
The solution allows companies to specify the intervals at which prices need to be changed. This tool can automatically update prices and do so often – every few minutes, weeks, or months, depending on the system. Monitoring model performance and adapting characteristics (in this case, price factors) are also needed. Data scientists consider data deterioration rates to plan model performance testing.
The ability to predict the reaction of buyers to prices and demand
Machine learning does more than help set the right prices. By using machine learning to optimize pricing, the algorithm can accurately predict how buyers will react to prices and demand for a particular product. Thus, machine learning-based price optimization offers the right prices for thousands of products, taking into account the main purpose of the retail chain (increase in revenue, increase in profits, etc.).
Machine learning pricing minimizes the risk that is typically associated with unpredictable customer demand following a price change. Retailer experts can use machine learning to test hypotheses about the effect of promotions or pricing strategies. Machine learning pricing does not yield a single product price – it offers prices based on millions of different conditions, offering the best price to increase revenue, the best price to increase profit, the best price for a promotional product, and so on.
ML-based pricing capabilities are not comparable to the expert-only approach. The pricing models of technology pricing platforms are not only capable of handling 60 price and non-price factors simultaneously, but can save up to 4 hours of expert time during each re-valuation cycle. The predictive power of machine learning leaves specialists with time to experiment with their data. By understanding how buyers will react to a new strategy, they can use the strategy they prefer, for example, increase revenue while maintaining profit, or increase profit while maintaining sales in pieces. Regardless of which path the specialist chooses, he will know in advance the result and the right price to achieve this result.
The main pricing problem for all retail stores is the answer to the question: what is the right price based on market conditions, the current season, product quality and other factors that affect demand? This question is difficult to answer correctly, as the factors are constantly changing. The traditional approach to pricing relies entirely on the word of the expert. However, when revaluing, the manager is only able to take into account three price and non-price factors.
With the advent of machine learning, the art of retail pricing has become a science. More and more retailers are using machine learning to help their experts make the right decisions.
Machine learning uses complex algorithms to take into account many factors and set the right prices for thousands of products in almost seconds. Innovative technologies can take into account up to 60 factors. Machine learning-based pricing models define the patterns of the resulting data, which makes it possible to determine prices based on factors that the retailer might not even know about.
Using machine learning algorithms to optimize the pricing process is a basic thing for the pricing teams of all mature retailers with thousands of products. As this technology gains popularity among retailers, the ability to manage software solutions with machine learning will soon become an integral part of every pricing manager's job responsibilities.
The value of an effective pricing strategy for any business is hard to deny. Companies operating on the Internet operate in a highly competitive environment where a consumer can easily compare prices for goods or services (even before going to a grocery store) and choose the offer that suits his needs and capabilities.
At the same time, entrepreneurs can take advantage of the technologies that have come along with increased computing power, reduced data storage, and greater availability of data for in-depth analysis to meet market conditions with the right prices.
Dynamic pricing can be applied to both revenue management (when inventory may go bad or a limited number) and to optimize pricing. In this regard, machine learning allows companies to implement dynamic pricing on a large scale, taking into account hundreds if not thousands of price factors, including price elasticity, and show specific prices to customer segments that are willing to pay them.
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