Pricing is recognized as a pivotal determinant of success in many industries and can be one of the most challenging tasks.
Companies often struggle with several aspects of the pricing process, including accurately forecasting the financial impact of potential tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions.
The history of AI price management can be traced back to 1985 when American Airlines introduced the first yield management system. The airline company had come under heavy pressure from low-cost airlines that were emerging at that time. American Airlines shook the entire airline market. This process caused the bankruptcy of American’s main low-cost competitor, People Express, a year after the yield management system was launched.
What areas of pricing have we worked on?
1. Price optimization
Price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for their products and services through different channels.
It is also used to determine the prices that will best meet the objectives such as maximizing operating profit.
Artificial intelligence has enabled pricing solutions to track buying trends and determine more competitive product prices.
The AI automatically selects a product’s data attributes, and it computes for loss in sales volume, as well as the revenue or market share of a product when the same company launches a new product. It also computes for the potential change in the product’s demand when the price for other products changes, as well as competition, leading price, and total sales.
2. Price prediction
Accurately predicting the winning price at the next auction is the key to beat the competitors and increase the profitability of each sale.
For example, AI algorithms are used for price prediction at medicine-auctions in the Pharma industry. At these auctions, Hospitals or Health Departments procure the medicines they need through public auctions. Pharmaceutical companies to offer the best price and win the lots at stake. The closest the winning price is to the second-best competitor, the bigger the margin won by the bidding company.
3. Demand forecast
A demand forecasting AI solution will enable companies to ingest historical transaction data, predict future demand, and obtain optimal pricing recommendations. As a result, the solution drives opportunities for improved profitability and reductions in time and effort allocated to pricing tasks.
In retail, for example, demand forecasting does not only feed with data from the past to predict the future but also evaluates external variables such as the calendar of events, seasonality and even the weather to deliver accurate forecasts.
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