AI-Powered Pricing: the New Future of Pricing?

AI for pricing? Sounds like an odd pairing, doesn’t it? However, leaders need a new approach to handle the current turbulent market. As they search for innovative ways to price for growth and embrace new pricing models, there’s no doubt that AI will be the key to their future success. An overwhelming 73% of leaders firmly believe that AI-powered pricing will be the future of pricing models. You can check here to learn more about metering and billing software (

Our article explains how things will unfold with AI at the helm in the pricing scenario. But let’s first explore what AI means in the context of pricing.

Pricing Optimization With Artificial Intelligence: The Changing Face of Pricing

AI-powered pricing is all about harnessing massive volumes of existing and historical data to predict optimal pricing for the future. With AI, pricing is completely automated to maximize revenue and profitability. AI-generated real-time analytics are beneficial for evaluating and predicting competition, customer behavior, and market trends.

Togai, (, a leading metering and billing platform, explains 5 ways to leverage Artificial Intelligence to create pricing that works in your favor.

5 Ways to Optimize Pricing With Artificial Intelligence

1. Use your data sources diligently.

Your business can leverage AI-powered pricing analysis to draw more accurate insights from a diverse range of data sources, such as:

  • Product and customer data
  • Sales and transaction data

It would help if you also looked into other specifics that can tell you more about your customer’s purchasing patterns, like:

  • Special events and holidays

AI-powered pricing can help you manage your calendar year effortlessly in one location and automate price changes across various events.

  • Days of the week and specific times of the day

Beyond seasonal trends, companies can optimize pricing algorithms based on daily and even hourly consumer traffic.

  • Weather and season

Accurately matching pricing to perceived product value can be achieved using granular day-to-day weather and seasonal forecasting data.

  • User search engine activity

Data taken from analytical search engine tools allow you to evaluate the amount of attention customers pay to a particular product. Using these insights, you can create a willingness-to-pay algorithm to set prices when your customer’s perception of the product is the highest.

2. Have a consistent learning process and refine your pricing decisions.

Pricing optimization with AI can be a potent tool because it can learn about your pricing decisions with less human involvement. AI-powered pricing helps you achieve success by:

  • Moving away from less optimal approaches
  • Analyzing past pricing decisions with historical data
  • Experimenting with alternative price scenarios

AI pricing models can be improved and retrained based on the latest data. This allows for dynamic adjustments to pricing needs, mainly when unpredictable market activity occurs, such as high inflation or supply chain disruptions. The accuracy of these models depends on the quality of the information fed into them.

3. Reach out to customers on a personalized level.

By leveraging AI, you can access vast amounts of data and gain new insights into previously unconsidered factors. These insights give you the advantage of serving your customers in unique ways.

Furthermore, by harnessing the collective potential of AI and ML, you can train algorithms to detect meaningful patterns in customers’ spending habits. How is this possible?

It’s important to understand that no standard pricing logic completely ignores the diversity in customers’ spending and purchasing habits. For instance, when a customer is more willing to spend without comparing prices within a specific range before making a purchase, the machine learning algorithm notes this. This will help the algorithm calculate their willingness to pay in the future and ensure they spend the most optimal price for products or services.

This specific data helps you target specific customer segments to ensure your sales generate the desired ROI.

4. Simplify your capacity management with ML-powered pricing.

AI’s pricing management and optimization capabilities can also extend beyond just pricing. Businesses that use a dynamic pricing system can use machine learning algorithms to manage their workforce’s capacity more efficiently during high consumer demand. This can help your staff work with more productivity.

Surge pricing is a dynamic method that utilizes AI to increase prices based on demand and other variables. This allows prices to be re-calculated in real-time, which helps to limit the number of customers willing to use the platform at a given point.

Using AI to optimize capacity utilization helps management quickly balance supply and demand in real time, maximizing productivity.

5. Dig deeper into your data to uncover hidden opportunities.

An AI-powered pricing solution’s most significant advantage is granularity, otherwise known as the measure of detail in a data structure. Granularity strips down every detail of your pricing metrics at a fraction of the effort when you do it with AI.

Businesses can leverage pricing optimization software to set optimal prices for product segments by getting more profound insights from historical sales data. Analytical tools can also help uncover unreachable nuances with traditional pricing methods.

Make Smarter Pricing Decisions With AI-Powered Pricing

Your pricing goals may vary, but that’s very much a part and parcel of your business. If your business deals with highly complex data, you may want to consider adopting an AI-powered data-driven approach. You should also consider the complexity and impact of pricing questions before deciding how to wield your AI-based pricing tools.

AI and ML are just the tip of the iceberg. A lot of groundwork goes into ensuring desired results with AI-optimized pricing. Hence, fortify your pricing strategy, implementation plan, and data to integrate AI seamlessly into your pricing system.

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