Every business owner on Earth spends sleepless nights worrying and brainstorming about one question – “What can I do to make my business grow?” Autonomous pricing with effective price optimization using artificial intelligence might just be one of the ways to grow your business too. Efficient marketing, orchestration of intelligent inventory management, and personalized in-store experiences with AR and VR are all okay – but at the end of the day, the price of a product is what bags the sale from a customer. The importance of price optimization with autonomous pricing cannot be easily dismissed or underscored. This blog goes into detail about optimal pricing, AI-led optimal pricing, various ways retailers can better price products and also explores the stages to build a complete autonomous pricing algorithm-operated system.  

AI-suggested Optimal Pricing  

Delving deep into the definition of optimal prices would be the foundation to build upon your understanding of price optimization with autonomous pricing algorithms. Not only an optimal price for a product helps retain your customers but also does not increase your marginality. 

Effectiveness of AI-led price optimization 

Deep and machine learning algorithms in conjunction with reinforcement learning can scan through vast swamps of data, go through countless pricing scenarios, and then suggest the most optimal price for a product. They can also unearth countless hidden relationships between several factors previously unknown to the particular retailers.  

Ways Retailers can Price Better Today 

The pricing of products today is highly competitive, responsive, and personalized to the consumer. It is also highly dynamic – often in near real-time. Let’s understand it at a high level. First, they start by identifying Key Value Categories and Key Value Items and taking in input factors such as transaction, basket data, shopper price perception data, and merchant judgment. 

Following are the weighted factors to arrive at an optimal price for a particular product: 

  • Consumer Demand 
  • Competitive Positioning  
  • Internal Economics 
  • Category Dynamics  

The following illustrates the weighting scenarios 

Developing AI-led Pricing Optimization 

There are five steps in developing AI-led autonomous pricing algorithms: 

  • Preparation of necessary data  
  • Hiring a technology provider or building an in-house team  
  • Selecting products that have to be priced by AI 
  • Equipping managers with the necessary know-how 
  • Keeping the systems running (maintenance) 

Preparation of necessary data  

To begin with, a retailer must start acquiring historical and competitive data as well as data regarding business goals and restrictions so that they ensure compliance with regulations right from the start. Often, data is stored in several different storage systems such as data warehouses and databases in different formats. Some of them will most certainly be outdated and error-prone. In order to develop an algorithmic model, data has to be high quality, complete, error-free, in the same format and must be no less than three-year long in span. If the retailer faces a problem in restoring the data or cannot restore the missing data, then below would be the most appropriate solution they might want to consider: 

  • Wait for a span of at least one year and collect the data in the same format 
  • Buy data from external sources and third-party data providers 

Build or Hire  

Negotiating prices with vendors and setting optimal prices for products are two totally different skills. Companies like Amazon invest in building an expensive in-house pricing optimization team. Other retailers such as Target and Metro launch start-up labs by funding their solutions. Other small retailers who do not have the budget to do the above generally outsource the entire development of software solutions to third-party technology providers. 

Select the products to be priced by AI 

It is not necessary for every product in your portfolio to be priced by AI. Here are two suggestions to identify such products. Products offered exclusively by your label can be higher priced by AI-led algorithms. Also, it is not needed to lower the price of the products that are similar to your competitors using AI. This way, AI-led pricing will allow you to attract more customers and augment your revenue.  

Equipping Managers with know-how  

Before the deployment, it is important to equip your pricing managers to interpret the pricing recommendations of the algorithm and remind them that they have complete control over the system. It is also important to test the prices with small experiments before they are deployed. There should also be a back door to put constraints over the system, in case the algorithm misbehaves and millions of dollars are lost.  

Maintenance of the AI system  

To be able to come up with the most optimal prices in the future, your system has to be updated with data in real-time pricing strategy, and business goals. The system has to be incorporated with the latest tools and advancements to stay productive. Although this goes against the word ‘autonomous’, pricing managers should be able to make periodic corrections on the go and monitor how it works to prevent malfunctioning.  

Benefits of AI-led Price Optimization  

In order to outperform their competitors and boost their revenue and sales, companies with above-average earnings quickly spot market conditions and leverage opportunities during fluctuating demand. They also quickly sense the consumer sentiment and mood to do better than their peers. This section explores how AI-led autonomous pricing algorithms boost revenue and sales of retailers.  

Raising prices while not negatively impacting sales 

Contrary to popular belief, higher prices need not necessarily always translate into lower sales. In fact, autonomous pricing algorithms can preserve and also in some cases boost sales while allowing you to maintain your margin. In fact, autonomous pricing algorithms can factor in the demand elasticity of a product and can then recommend a price point at which you will be able to sell more units at a higher margin.  

Incorporate past consumer behavior  

There are more than hundreds of factors that influence consumer behavior that must be taken into account for the perfect optimal price and flash sales just to name two. In fact, artificial intelligence considers them all when setting the perfect optimal price. This allows the retailer to offer products to the consumer at a price point that resonates with both of them.  

Get maximum effects  

The idea behind AI machines learning from data and experiments without being explicitly programmed. Combining data from brick-and-mortar establishments and different digital platforms is a winning formula for retailers that allow the algorithm to learn better and factor in a large number of parameters to better attract consumers to make purchases for a thriving revenue. Retailers such as Amazon and Walmart have used this to emerge as the largest retailers worldwide.  


The AI-led world is slowly becoming a reality today. Our whole digital ecosystem across the globe is being run with AI. It is time for retailers to embrace this growing trend to outperform their peers and stay ahead of the competition. Imagine a world for your business where every product you sell maximizes your revenue and sales. That world will become a reality by adopting autonomous pricing algorithms for your retail business.