5 Cool Things You Can Do with Pricing AI

In a competitive marketplace where price points are becoming increasingly complex, you recognize by now that manual pricing methods lack the precision and efficiency your company needs to scale its pricing strategies for success. You are considering bringing artificial intelligence into your pricing and are curious to learn more about the real value data-driven technologies can bring to your business processes.  

Here at Pricefx, with over 10 years’ experience as a leading cloud-native pricing software solution with AI-powered price optimization among our core offerings, we’ve gotten a lot of questions from our customers about what artificial intelligence in pricing can do for their business. 

At this point, you may already know some of the benefits of adopting pricing AI; some use cases which may come to mind when thinking about AI-driven pricing software range from optimizing price lists to choosing the ideal product mix and setting rebates effectively, among others.  

In this article, we examine 5 cool things pricing AI can do to bring your company added value, some of which you may have never considered before. 

What is Pricing AI?  

First, let’s quickly revisit what artificial intelligence means in the context of pricing. 

Pricing AI is a data-driven pricing strategy which is trained on massive amounts of current and historical data and uses the learnings from this data to predict optimal future pricing. In very simple terms, pricing AI gives us the gift of automation and complex analysis of our pricing history, without which it would be difficult to meet many of our more challenging pricing goals at the same level of efficiency.  

5 Cool Things You Can Do with Pricing Artificial Intelligence 

1. Get Creative with Your Data Sources to Drive Optimal Pricing 

Your business can use pricing AI to analyze an increasingly diverse pool of data sources to uncover more accurate, and ultimately more gratifying, insights to inform future pricing decisions. 

The 3 most important data sets for pricing which are front of mind for most businesses include its customer data, product data, and sales and transaction data. But what other pricing data sources are out there for you to use?  

Your business can use pricing AI to go even farther with the kinds of data to consider when calculating optimal price points for your customers, and depending on your product, this could include other data variables such as: 

• Weather and seasonality 
• Days of the week and time of day  
• Events and holidays 
• User search engine activity 

Weather and Seasonality  

Take an ice cream or cold beverages company which supplies weather-sensitive products that reach their highest demand in warmer temperatures. Seasonality and even more granular day-to-day weather forecasting data can play an important role in accurately matching your pricing to the perceived product value. 

Days of the Week and Time of Day 

If you have ever shopped around for cheap flights with varying degrees of success, you are well aware of the highly customized prices you can expect from airline companies. Beyond seasonality, companies can also instruct their machine learning algorithm to optimize its pricing calculations according to days of the week and even times of day with the highest consumer traffic. 

Events and Holidays 

Companies within the hospitality industry have finetuned their yearlong pricing strategy to consider a number of events impacting consumer demand for accommodation which run the gamut from concerts to business conferences to sporting events. Unsurprisingly, major holidays also play a critical role in these rate fluctuations. Pricing AI is a great way to easily streamline your calendar year in one place and automate the price fluctuations across different events.  

User Search Engine Activity 

Businesses can also pull in data from analytical search engine tools like Google Trends, which provide insight into user activity on search engines, to determine the level of attention consumers pay to a particular product over time. These insights can be incorporated into a willingness to pay algorithm to set prices at the right threshold at the right points in time when customer perception of their product value is at its highest. 

2. Continuously Learn from and Improve Your Pricing Decisions 

Pricing AI is a powerful tool in part because it is designed to learn intelligently about your pricing decisions with less human brainpower involved.  

Provided the right data and pricing parameters are in place, pricing AI enables companies to ensure repeatable pricing successes — and leave behind less optimal approaches – by analyzing your company’s past pricing decisions using historical data and allowing for more experimentation with alternative price scenarios through simulations. This approach allows pricing professionals to skip the costly trial and error and intuitive guesswork inherent in more manual systems.  

What’s more, pricing AI is as robust as the information it’s fed – so depending on how dynamic your pricing needs to be, you always have the option to improve upon or retrain your AI pricing models using online or up-to-date data to learn on the fly in case of unprecedented activity in the market like high inflation or supply chain disruptions.   

3. Reach Customers on a Hyper-Personalized Level

Artificial Intelligence is rapidly scaling its analytical capabilities to consider increasingly larger amounts of data in its algorithms, allowing businesses to understand their customers in ways never before possible.  

By bringing in pricing AI strategies like machine learning trained to pick up on meaningful patterns in customer spending habits, businesses are no longer limited to a one-size-fits-all pricing logic that ignores variability in customer willingness to pay.  

If a less price-conscious customer does not make it a habit to shop around and compare prices within a certain range before making a purchasing decision, a machine learning algorithm will take note of this in its calculation of their willingness to pay in the future to ensure that this customer subsegment is paying for products or services at the most optimal price.  

Armed with a wealth of historical sales and online data, businesses can use AI to get incredibly targeted in their price offer to customers to ensure no money is left on the table. They can leverage intelligent software to create specialized offers based on historical buying decisions and tailor their communication of these offers, maximizing customer engagement with their services.  

4. Use Machine Learning-Powered Dynamic Pricing for Efficient Capacity Management

Depending on your business needs, pricing AI’s capabilities don’t need to end with pricing management and optimization.  

Businesses relying on a dynamic pricing system can leverage machine learning algorithms to manage the capacity of their workforce more efficiently in times when they would otherwise struggle to meet high consumer demand. 

Companies based on a sharing economy model like Uber are a great example of using ML-backed dynamic pricing for quick and precise capacity utilization in the B2C space. Surge pricing, a kind of dynamic pricing method which increases prices in response to demand, among other variables, uses AI to re-calculate prices in real time — limiting the number of customers willing to use the platform at a given point and in effect unloading the burden from Uber drivers struggling to keep up.  

While capacity utilization can also be carried out with more manual approaches, ramping up this practice with AI methodologies helps management to quickly strike the right balance between supply and demand in real time to maximize productivity levels. 

5. Get Granular with Your Data and Uncover Hidden Pricing Opportunities

One of the greatest advantages that a pricing AI-backed solution gives you is the power of granularity.  

Take a car dealership enterprise which is struggling to gauge whether they are pricing too aggressively or too conservatively. With a portfolio of seemingly infinite product, geography, and customer variability to consider, a more birds eye view of their pricing will at best offer average prices across their business – and no singular points to focus their attention.   

When you introduce AI to your pricing scheme, you can view your pricing metrics at a micro level at a fraction of the effort. That car dealership can use price optimization software to segment its historical sales data into similar customer and product groupings and set optimal prices for each of those subsegments. Once you are allowed to go deeper into the data with analytical tools, you’ll be able to uncover those nuances that were otherwise out of sight and mind in more traditional pricing setups.  

In short, the more granular insights afforded by pricing AI gives companies the ability to pinpoint exactly what is going on in their business and take more strategic approaches to those challenges.  

Pricing AI Is Here to Help Your Business Make Smarter, More Profitable Pricing Decisions  

No matter how varied your pricing goals are, there are always parts of your business which can benefit from a finer level of analysis. Ultimately, which tasks you decide to carry out using AI-informed pricing tools should depend on the complexity and business impact of the pricing questions those tasks address.  

If your business is working with highly complex data, it may be time to consider a data-driven approach with pricing AI.  

Interested in finding out more about pricing software with AI-powered price optimization? Check out our article below to discover whether price optimization software is the right choice for your business.

Meanwhile, Happy Pricing!