Every retailer knows how tough and cut-throat the retail industry can be. E-commerce has revolutionized the retail market, giving businesses access to a larger consumer base than ever before. As a result, however, customers also are more knowledgeable about their purchasing options, creating an intensely competitive market for retailers trying to provide the best prices at all times. Being the most attractive offer on the market is easier said than done; product prices change multiple times per day, and with profit margins sometimes as low as one percent, retailers risk losing substantial profits if they fail to change their prices appropriately.

Price management and price optimization is no easy feat, even for large companies. Today, more product data exists than ever before, and again, prices need to be changed at the snap of a finger if companies wish to remain ahead of their competition. Traditional pricing methods leave pricing teams swarmed with an array of excel sheets to fill and little time to do so. Additionally, traditional pricing methods are often short-sighted as they fail to consider various patterns in product prices and sales. If traditional pricing methods simply cannot keep up with the modern market, how are companies both large and small expected to remain competitive?

The best way to adapt to a high-tech market is by using high-tech solutions. Many successful retailers like Amazon are using artificial intelligence to conduct price optimization, and have made substantial progress implementing AI in their pricing process. To better understand why and how AI provides superior price optimization to traditional methods, this article will give you a more detailed understanding of AI and why AI is the future solution for all retailers to stay competitive in such a dynamic market.

The Shortcomings of Pricing Processes in Retail

A great way to take a look into how outdated pricing processes are in the retail industry is by looking into how companies conduct promotions, deals and discounts. Typically, promotional deals have a rather rigid format in the retail industry; they often revolve around holidays, and are repeated annually without much consideration into their overall effectiveness. This method may have worked decently well in the past. However, in the age of the Internet, the market changes far too rapidly for promotional methods to be effective unless they’re equally flexible. Additionally, the retailer has a very limited outlook to determine whether or not any discounts they provide are effective. They may be able to record and analyze individual product pricing information in decent time, but remain unaware of how the promotion of one product may be affecting the sales of other products. Considering such detailed patterns in pricing data remains extremely time-consuming using traditional pricing methods.

We can dive deeper into this idea by looking at an example scenario. Let’s say a certain retail company offers flower seeds and soil on discount every spring. From individual product information, they know customers buy these products more frequently and in higher volume at the start of the season, so it therefore makes sense to promote these items at lower prices to stay competitive. Traditional pricing methods are perfectly capable of providing retailers with the information needed to determine these items are good candidates for promotional campaigns at this time of year.

Despite how simple and effective this may sound on paper, such a narrow view of individual products blinds retailers to several potentials for profits. A customer’s cart rarely consists of one item, and the multiple items a customer buys often relate to each other in some form. Traditional price optimization methods might have provided the retailer with the information to determine the importance of their flower seeds and soil at the beginning of each spring. These pricing methods lacked the complexity to determine that soil was a KVI, or key value item in this case. Simply put, customers often bought flower seeds and soil together regardless of promotional deals. The retailer could have offered a discount on only one of these items knowing the likelihood that the customer will buy the other in tandem. By not doing so, a considerable chance to increase profit margins was missed.

Inversely, the promotion of an individual product can lead to a decrease in sales of other products. If the retailer were to promote a certain brand of soil, a so-called “halo effect” could occur, making their other brands less popular in comparison to the brand of soil on sale. The halo-effect, in addition to countless other relationships between products and their demand, is difficult to observe or predict when analyzing individual product sales manually. Doing so manually using traditional pricing methods is not only costly, but also time consuming, short-sighted, and prone to human error, making it an unfavorable method for detecting and analyzing such complex relationships. This makes promotional deals high-risk for retailers unless they manage to incorporate these complex attributes into their price optimization methods using a simpler, more cost-effective pricing strategy.

AI’s Retail Market Potential: How it Works

How exactly can AI be used in price optimization? Let’s take a look at the steps involved when implementing machine-learning into the pricing process.

  • Data input. AI’s advanced algorithms can process massive amounts of historical data. Based on a company’s previous one to ten years (or more) of sales information, AI can determine a myriad of pricing factors including but not limited to elasticity, seasonality and customer behavior.
  • Determination of “rules.” Just as we discovered before, observing several factors such as KVIs can impact the profit and sales of several products. These relationships can be given to AI as “rules” in the form of mathematical models. Learning these rule sets will allow the AI to run simulations in a detailed, accurate manner.
  • Testing Simulations. Once sufficient amounts of data have been given and rule sets have been determined, AI can run billions of simulations on the data to determine key products and their respective prices that provide retailers with the best profits. AI can then deliver this complex pricing information to the retailer in a comprehensive pricing forecast.

Considering the precision and detail required to conduct price optimization in retail and the current shortcomings that retailers often face with traditional methods, it is easy to see how AI can prove to be a promising solution for retailers. In other instances ranging from chess to DNA, AI has proven more than capable of processing massive amounts of data more accurately than humans could ever dream of doing, so it is no surprise that machine-learning can revolutionize pricing processes as well.

Not only can AI answer important questions retailers are asking regarding their product sales and pricing information, AI can answer questions retailers could not have thought to ask about their pricing methods in the first place. AI can not only analyze the effects of current and previous pricing decisions, but also the effects of potential decisions that were never made. This allows AI to provide incredibly accurate predictions for retailers, which in turn allows retailers to price their products in a way that both maximizes profits and avoids major risk.

Additionally, AI can do so in such a timely manner that retailers can change their prices every hour if they need to. This allows even smaller retailers to remain competitive with big companies like Amazon, who is also known for changing their prices several times per day.

AI’s advanced algorithms can be advantageous for retails in terms of how it delivers this complex data as well. Considering a variety of variables often overlooked by current price optimization methods, the retailer can be given all pricing information analyzed by AI in an informative data “tree,” with each potential decision and its outcomes organized into groups and subgroups. This allows the retailer to view the extensive amounts of data that analyzed in a simple and easy to understand format. Managers can then decide for themselves the best pricing options for their company’s unique objectives and requirements.

Machine learnings allows companies to better understand why certain pricing decisions could lead to potentially positive or negative outcomes. This in-depth context is vital to retailers as it helps them avoid high-risk decisions. Such vital context is a vital asset other price optimization software and methods currently lack.

Machine Learning is the Solution

Many companies have already acknowledged the shortcomings of standard price optimization methods. These pioneers in the utilization of AI continue to experiment with machine-learning in hopes of creating a more efficient pricing strategy. Luckily for them, AI’s advanced algorithms proved vital in increasing profit margins and providing the best prices on the market faster than their competitors.

In the modern and fast-paced retail market, retailers cannot afford to neglect their pricing strategy if they want maximum profit. With the increasing amount of data a retail company must handle, Excel spreadsheets and simple software simply cannot provide the accuracy and context retailers need to analyze complex product pricing information. Considering this, utilizing AI for price optimization is without a doubt the best option for companies to stay competitive.

Advanced simulations that consider several variables related products and their sales provide retailers with the opportunity to make pricing decisions that provide consistently higher profit margins with lower risk. For any retail company seeking to modify their pricing strategy to better accommodate the increasing complexity and pace of the market, machine learning retail is the solution that puts the “AI” back in retail.