Over the past year, I’ve learned about many use cases for AI across sales and marketing. But, one use case I haven’t seen talked about as much is AI pricing models.
AI is already a useful solution for many sales processes, from automating manual tasks to uncovering personal data. So why not apply AI’s data-driven approach to pricing models and optimization, too?
I wanted to learn more about AI pricing models and how AI can help optimize pricing for all industries, so I talked to the experts. I’ll share everything I learned with you here!
Table of Contents
- What Is an AI Pricing Model?
- How Can AI Pricing Optimization Help Sellers?
- Using AI Pricing Models
- Why AI Pricing Models Are the Key to Sales Growth
What Is an AI Pricing Model?
An AI pricing model is a way to describe a pricing model or strategy that has been created with the help of AI. Using AI to create pricing models or optimize existing pricing involves feeding data into an AI tool and using machine learning to generate data sets, analyze historical data, forecast purchasing trends, and make predictions based on buying behaviors.
How Can AI Pricing Optimization Help Sellers?
I was curious about how companies use AI to optimize or create pricing strategies, so I asked a few people who know the industry best. Here’s how AI pricing can help sellers optimize their pricing, according to the experts.
AI can make real-time adjustments.
One of the best qualities of AI is its ability to analyze data and generate predictive outcomes. As a seller, you can use AI tools to assess both historical and real-time customer data to leverage dynamic pricing.
“AI can dynamically adjust pricing based on various factors such as customer segmentation, willingness to pay, and competitive landscape,” suggests Valeria Gutowski, chief accounting officer at Enfusion. “By continuously analyzing customer data, AI helps companies implement adaptive pricing strategies, tailoring offers to different customer tiers, which maximizes conversion rates and retention.”
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AI can help identify trends or anomalies.
Trends or market values often inform pricing strategies. While sellers can manually look through customer data or analyze market trends, it can be challenging and time-consuming to pinpoint patterns, outliers, or even errors with your current pricing model.
AI can reveal customer trends or identify anomalies that may have otherwise gone unnoticed and do so in a more precise — and error-free — way. That’s why Gutowski recommends using AI to create price charts.
“These charts allow teams to visualize how pricing correlates with customer behavior and revenue and to identify any outliers, such as customers who may be overpaying or underpaying compared to their usage or market value,” she says.
According to Gutowski, AI can automatically plot these charts. The system uses data from sales, product usage, and support interactions to generate a visual map that highlights customers outside the normal range.
“This identification process, driven by machine learning, helps companies quickly review pricing anomalies that may need adjusting, whether due to unusual customer behavior or potential errors in the pricing model,” Gutowski says.
AI can audit your current pricing strategy.
I learned that another notable benefit of using AI for pricing optimization is to audit your current pricing strategy.
Coming up with the best price for your product or service is an evolving practice. The goal is to maximize profits while also considering consumer and market demand, making it something to constantly monitor. AI can help you analyze the overall effectiveness of the current pricing strategy.
By comparing historical performance metrics like revenue per user, churn rates, and customer acquisition costs, AI models can assess whether price changes positively impact key business metrics.
Gutowski suggests AI can also simulate potential changes to the pricing structure, such as introducing new tiers or adjusting pricing for certain customer segments, predicting how those changes might affect profitability and customer retention.
“These simulations offer insights into the long-term sustainability of the current pricing approach and suggest proactive adjustments before revenue losses or customer churn become significant,” she says.
AI can make price recommendations.
AI price optimization can help sellers not just set prices but also make price recommendations. Meghan Anzelc, founder and CEO at Three Arc Advisory, shared an interesting use case with me: using AI to forecast different pricing scenarios.
“AI can show sellers different scenarios tied to price, including the trade-offs of choosing one price over another,” she suggests. “For example, if a seller wants to use a new discount to attract new customers, AI can show how many items can be sold at a discount before profitability goes below the business plan.”
Not only does this give sellers multiple options to choose from, but AI can also provide data-backed reasoning for each scenario, which provides more context as they make price decisions.
AI pricing saves time.
Sales teams already use AI to save time on manual processes like writing cold call scripts and repurposing sales content. So, it makes sense to use AI to save time and resources on pricing optimization.
AI pricing optimization can be particularly useful for project-based pricing or industries that do competitive bidding, suggests Patrick Murphy, the founder and CEO of Togal.AI, an AI solution for the construction industry.
“In construction, AI eliminates the tedious, time-consuming processes that often delay projects, helping us work more efficiently and accurately,” he says. “This translates into faster decision-making, fewer mistakes, and more competitive bids.”
If your sales team works in an industry that requires detailed bidding, consider using AI tools to shorten the process and reduce the risk of manual errors.
Using AI Pricing Models
To better understand how AI can be applied to different pricing models, I thought it would be helpful to share a few use cases, so I asked the experts. Here are some common pricing models and how to optimize them using AI pricing.
Dynamic Pricing Models
After talking to the experts, I noticed that the most common AI-driven pricing model people mentioned is dynamic pricing.
Dynamic pricing, also called demand pricing, is a strategy where companies adjust their price based on demand. It’s usually time-based and sometimes called surge pricing for this reason. Think of Uber ride prices increasing during rush hour or plane ticket prices rising for the holidays.
How to Use AI for Dynamic Pricing
While dynamic pricing is often associated with time or location-based scenarios, it can also be optimized for customer behaviors using AI. For instance, an ecommerce company may see customers adjust their spending habits based on the season. Or, a SaaS company could use AI to uncover when the customer churn rate goes up.
“In the SaaS industry where pricing must be flexible and data-driven, AI offers a cutting-edge solution to manage, optimize, and continually adapt pricing strategies,” suggests Gutowski. “By visualizing customer behavior with price plot charts, companies can easily review outliers and ensure that their pricing aligns with customer value.”
She adds, “AI can assess the effectiveness of pricing strategies in real-time, suggesting refinements that enhance both profitability and customer satisfaction, making it a vital tool for SaaS pricing optimization.”
Free Sales Pricing Strategy Calculator
Determine the best pricing strategy for your business with this free calculator and template.
- Cost-Plus Pricing
- Skimming Strategy
- Value-Based Pricing
- And More!
Download Free
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Project-Based Pricing Models
Project-based pricing is a model that charges a flat fee for a project. The price is based on the value of the deliverables, the costs of goods, and the estimated project time. This pricing model is common for contractors, freelancers, consultants, or service businesses. As a freelance content writer, this is the pricing model I use for my clients, but I haven’t tested it out with AI… yet.
How to Use AI for Project-Based Pricing
A great use case for AI project-based pricing is contract work. Murphy suggests contractors, specifically in the trades, can use AI to accurately price their projects before bidding on a job.
“For example, a painting contractor can upload blueprints, and AI will instantly calculate the exact amount of paint required, down to the last drop,” he says. “This level of precision allows contractors to estimate costs quickly and accurately, enabling them to bid on more jobs with confidence.”
Value-Based Pricing Models
Value-based pricing looks at the value of the deliverables to determine the price of their service or product. This pricing model requires a deep understanding of the customers’ challenges, desires, and goals to understand how to position your product as the best solution.
How to Use AI for Value-Based Pricing
One way AI can be used in value-based pricing is to understand the specific needs of your buyers and users and create specific bundles of products or features, suggests Anzelc.
“AI can be used on your customer data, including customer service data, to better understand specific customer needs and pain points and correlate this against products and features,” she says. “With AI-derived insights on customer values and needs, you can tailor messaging and sales approaches that address what your customers most want.”
Tiered Pricing Models
Tiered pricing models offer customers different tiers or packages to choose from. There are typically three tiers, which vary in price and features, from “basic” to “premium.” Tiered pricing models work well for SaaS companies or anyone who offers subscriptions or memberships.
How to Use AI for Tiered Pricing
As a seller who uses tiered pricing, it’s important to make sure you’re pricing each tier correctly. While you want to maintain a level of trust and loyalty with your customers by offering consistent pricing, you also want to make sure your tiered pricing remains competitive. That’s where AI comes in.
Gutowski recommends using AI to assess historical data to forecast customer lifetime value (CLV), usage patterns, and churn risks.
“All of which contribute to fine-tuning subscription tiers and pricing plans,” she says. “This level of precision can lead to more granular pricing strategies, such as usage-based pricing or feature-tiered plans that align closely with customer value perception.”
Why AI Pricing Models Are the Key to Sales Growth
There’s a reason pricing optimization is so important. How you price your product or service can directly impact customer acquisition, sales, and retention.
As someone who’s written about and researched plenty of AI use cases across sales and marketing, I know this is one area of business where AI truly shines. In my opinion, AI is best used as an analytics tool. It generates the most valuable outputs when it’s given data to analyze and asked to predict behaviors and trends. Sellers can get the most out of it when using it to create dynamic, customer-driven pricing models or for pricing optimization.
Simply put, if you want to grow your sales, I recommend using AI to optimize your pricing.
Free Sales Pricing Strategy Calculator
Determine the best pricing strategy for your business with this free calculator and template.
- Cost-Plus Pricing
- Skimming Strategy
- Value-Based Pricing
- And More!
Download Free
All fields are required.
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