On the popular game show The Price is Right, players must attempt to guess the price of products in order to win. What is not evident on the show, however, is that the retailers setting the prices of these products are often guessing as much as the players are. For both parties, their predictions don’t always lead to victory.
The contestants and retailers run into the same issue when trying to figure out the right prices: human error. It’s human nature to be imperfect. We are biased, we are afraid to take risks, and we can’t solve complex algorithms in our head fast enough to come up with a truly accurate number in time, so we have to make an educated guess and cross our fingers.
Machine learning has none of these issues, so it’s no surprise that data scientists have proved it can easily win The Price is Right. This article will explain how machine learning can win the retail pricing game as well, and why every retailer should invest in ML-based pricing optimization to be a strong player in the modern market.
Machine Learning in Pricing Models
The main dilemma for retailers when trying to accurately price their products is when they attempt to tackle this question: What is a fair price for this item considering the market, the current time of year, demand, and the product’s attributes? This question is insanely hard to answer correctly, because these factors are constantly changing.
Depending on the product, they can change in a matter of minutes, especially in the eCommerce market. This is why retailers like Amazon change the prices of their products millions of times per day. For retailers that are not as large as Amazon, however, doing so is painstakingly difficult. They are left with no choice but to compromise; either don’t consider many factors in order to change the prices in a timely manner, or consider as many factors as possible and hope that the market hasn’t changed too much by the time prices are set.
Traditional approaches to pricing rely on human-centric decisions which are not only vulnerable to mistake but are also limited in the scope of factors managers can consider at once. The illustration below gives a glance at the fundamental difference between ML-driven pricing and human-centric one.
Human-centric and ML-driven decision-making in pricing
Machine learning can utilize complex algorithms in order to consider a myriad of factors and come up with the right prices for thousands of products near-instantly. ML-based pricing models can detect patterns within the data it is given, which allows it to price items based on factors that the retailer may not have even been aware of.
New information can always be added to further refine the estimates ML pricing models give as time goes by. Essentially, this means that not only are ML models more accurate than traditional pricing methods to begin with, but they also continue to become even more accurate as the retailer uses them over time.
Price Optimization and Prediction Models
Machine learning can go a step beyond accurate pricing models for retailers as well. When it is applied to price optimization, ML-based algorithms can also be used to accurately predict how customers will react to certain prices and forecast demand for a given product. Price optimization using machine learning considers all of this information, and comes up with the right prices for thousands of products considering the retailer’s main goal (increasing sales, increasing margins, etc.).
Pricing optimization with machine learning also minimizes the risk usually involved in changing prices thanks to its prediction capabilities. A retailer can essentially use machine learning to test out various promotions or pricing strategies to understand what its impact may be, turning their educated guesses into a data-backed science.
In other words, price optimization using machine learning doesn’t just give one potential price for a product — it can give the retailer the best price considering a myriad of conditions, meaning it gives the best price for sales, best price for revenue increase, best price for promotion, etc.
As you've seen on the picture above, the capabilities of ML-driven pricing are incomparable with the human-centric approach. Competera's predictive models are not only capable of processing 60 pricing and non-pricing factors at once, but also save 4 hours per category manager in each repricing cycle. In addition to that, our price optimization software enables retailers to shift from SKU-based to portfolio-level pricing with no limits for the number of categories or products being managed.
The predictive capability of ML-based pricing optimization gives retailers a lot of room for experimentation with the knowledge of how customers will react to their strategy, so they can utilize whichever strategy they prefer, whether that be competitive pricing, dynamic pricing, keystone pricing, etc. No matter what they choose, they know what the outcome will likely be, and they know what the best price for that strategy is.
Machine Learning is for Everyone
With such an advanced technology, many retailers think that it is a hefty investment reserved for retail giants like Walmart or Amazon. Though it is true that these companies are definitely utilizing ML-based algorithms to price their products, this technology isn’t exclusive to them. Setting up an in-house ML-based pricing optimization platform is definitely a huge investment that for smaller retailers may not be worth its cost.
Luckily, external software providers are already incorporating machine learning within their pricing solutions, making the technology available to nearly every retailer willing to invest in new software. Purchasing the services of an external software provider costs a fraction of the amount it would take to set up an in-house system, and because this software is their main focus, the usability of the product is likely to be much better than anything created from scratch by a retailer.
Price optimization software has come a long way in the past decade, and thanks to AI and machine learning, it’s about as close to perfect pricing as a retailer can get. And despite its recent developments, ML-based pricing optimization is very established; study after study exists proving its ability to increase sales and revenue, even within relatively short timeframes.
The Future Belongs to Machine Learning
It is a general future trend that AI and machine learning will be incorporated into most software in one way or another, and the story is no different for pricing optimization. As pricing software providers continue to use ML-based algorithms and enhance its capabilities, more and more retailers will have access to the solution for their pricing dilemmas.
At Competera, we are looking towards the future where retailers will no longer be guessing the right prices for their products, and where every offer we see from retailers will be based on indisputable data, complex pricing models, and accurate forecasting. We beilive that the combination of ML-fueled demand-based pricing and rule-based dynamic approach empowers retailers to find the most effective, balanced, and sustainable way to provide every customer with a unique shopping experience.