Price Optimisation

What is price optimization?

One of the most critical factors that drive the sales of a product is price. Pricing of a product requires assessing various factors, like the market conditions, competition, branding, cost, and value of the product. Strategies like price penetration and price skimming are used, considering initial costs incurred, like that of research and development. Where there is little or no competition, companies may price the product depending on what the market may bear. Price optimisation refers to the process of determining the best price for the products offered. It helps businesses maximise profit by finding the most effective price using data from customers and the market. 

Price optimisation includes qualitative and quantitative assessments of the product and its market. Due to increased computational capabilities and inefficiencies in traditional price optimisation models, machine learning-based price optimisation is of growing importance. Traditionally, employees may have to repeatedly assess the market and optimise price, which can be time-consuming and costly. Machine learning algorithms can be used by businesses to tackle this problem by building frameworks that can integrate new information and detect market trends. It is a powerful tool in the retail sector but is also used in other sectors. Where businesses use markdown strategies to permanently reduce the price of products, machine learning can be used to fix the price. In addition, algorithms can help businesses to forecast the impacts of different pricing for a single product. When compared to classical general linear models, machine learning models are observed to have higher accuracy and discrimination power [1].

Machine learning-based price optimization [2]


Why is it used?

Price optimisation models can be fed a variety of data, like transactional data, competitor data, and market information. Records of inventory, product information, seasonality are also used. Depending on the algorithm, price optimisation models can also use customer reviews, marketing campaigns, and operating costs. Sentiment analysis is often used in the process, using data from sources like social media. Data scraping tools can be used to categorise a piece of text to a positive, neutral, or negative attitude, thereby impacting the pricing decision. Access to internal business data and external sources of data, like weather, stock market, and surveys, can largely help the process. 

There are various factors affecting the customer’s perception of a product’s price. The company’s branding and reputation play an important role, along with the historical pricing of the product. Due to advancements in machine learning algorithms, there is a scope to feed various data points and narrow down the most important factors. Algorithms are now able to predict the response to prices that are not yet executed. This reduces the loss incurred by a business in the process of trial and error. Data experts can provide enterprises with a mechanism that can automatically evaluate the market and optimise price in real-time, with the option to tweak and adjust when necessary. Retail businesses like fashion stores and the hospitality industry implement these mechanisms on a large scale to take data-backed decisions. Due to the increase in competition, the pressure to leave traditional pricing methods and accommodate algorithms to optimise pricing is growing. The ability to predict entry prices, discount and promotional prices allow businesses to reduce unwanted costs incurred by manual pricing methods. Machine learning algorithms eliminate bias caused by human evaluation and also speeds up the response to market changes. Businesses can use optimisation along with other tools like market basket analysis to determine group pricing. Depending on the goal, algorithms can be tweaked. For example, pricing can be changed if the product is supposed to be sold within a short time. 

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