Do you want to receive maximum customer traffic, improve the price and assortment perception of your store, and increase gross profit?
We understand you, AI pricing optimization will help you solve these issues. This is what this article will discuss.
Machine Learning For Everyone
Having heard about this technology, many retailers think that it is only available to such giants as Walmart or Amazon.
And while it is true that these networks use ML for pricing, this technology can be used by any company.
However, developing your pricing optimization platform will require more than one year, a couple of dozen man-hours.
This will require not only financial costs but also time to find people who can manage the creation of such a solution within the company.
Nevertheless. There are software solution providers on the market that use machine learning for their pricing systems, thereby making them available to almost every retailer.
Over the past decade, pricing platforms have been actively improving. Thanks to artificial intelligence and machine learning, they are now at the peak of their productivity.
Even though ML technology is new, ML-based pricing optimization has already proven its effectiveness.
Study after study shows how it increases sales and revenue, even in a short time.
What Problem Does An ML Model Solve For Pricing Optimization?
Optimizing pricing to maximize profits and entice people to buy has always been difficult for merchants. Retailers typically utilize traditional approaches to optimize their prices.
First, professionals manually evaluate customer and market data, and pricing managers use simple mathematical models (linear regression, for example) to assess how price changes affect profit margins and consumer willingness to pay.
This procedure needs a significant amount of time and effort from businesses.
The utilization of classic pricing optimization approaches has gotten even more challenging in recent years as e-commerce has grown explosively and the market has become more digitally integrated.
These two causes have resulted in a huge rise in the volume of customer and sales data that merchants must cope with.
The vast volume of accessible data makes it difficult to effectively and consistently evaluate it.
The market circumstances in which firms compete are growing more complicated.
As a result, standard pricing optimization approaches are no longer effective in assisting merchants with price setting.
However, new advances in price optimization technology are allowing retailers to harness the full potential of data and easily and effectively set prices that maximize their profits.
A key technology for pricing optimization is machine learning.
Pricing And Forecasting Models
Machine learning doesn’t just help you set the right prices. When using ML in pricing optimization, the algorithm can accurately predict customer reactions to prices and demand for a particular product.
Thus, ML-based price optimization offers the correct prices for thousands of products, taking into account the main goal of the retail chain (increasing revenue, increasing profits, etc.).
Additionally, ML-based pricing minimizes the risk that is typically associated with unpredictable customer demand following a price change.
Retail experts can use ML to test hypotheses about the effects of promotions or pricing strategies.
That is, ML pricing does not give one single price for a product – it offers prices taking into account millions of different conditions, offering the best price to increase revenue, the best price to increase profits, the best price for a promotional product, etc.
Using machine learning algorithms to optimize the pricing process is a staple for the pricing teams of all mature retail chains with thousands of products.
As this technology gains popularity among retailers, the ability to manage software solutions with ML will soon become an integral part of every pricing manager’s job responsibilities.
Extensive Forecasting Capabilities
One of the most valuable capabilities of machine learning is prediction. Previously, business decisions were made based on past results.
Today, machine learning uses sophisticated analytical tools to make predictions. Instead of relying on outdated data, companies can make proactive decisions.
Conclusion
These technologies may help indicate which items are in relatively consistent demand (and so can be safely optimized), and which play a significant influence in overall sales, necessitating cautious pricing adjustments.
Machine learning-based pricing is becoming mainstream in retail. To remain competitive, retailers need to move to machine learning-based pricing methods.
