As a former salesperson in hyper-competitive industries like tech, telecommunications, and media, I’ve seen firsthand the importance of getting your product and service pricing approach right.
A well-crafted AI-powered pricing strategy helps companies to be competitive in their target market. It can help them to be resilient to changing market conditions, and achieve their profitability goals. Striking the right balance between profitable margins and winning competitive deals is challenging.
If your company is faced with this daunting task, adopting an AI pricing strategy could be your best way forward.
Table of Contents
What Is an AI-Powered Pricing Strategy?
AI-powered pricing strategies are the use of artificial intelligence functions like machine learning and prescriptive analytics to set and adjust prices dynamically based on a variety of factors, including:
- Marketing conditions
- Customer behavior
- Competitive forces
- Volume or usage-based variables
- Reseller or contract-based pricing
AI pricing uses predictive analytics, real-time data, and advanced algorithms to determine the best pricing approach for each product or service offer. AI pricing helps to maximize revenue and profitability while ensuring that prices remain competitive and aligned with market trends.
Several years ago, I worked for a SaaS company that offered a vast number of applications, and we used a bloated Excel calculator to list legacy product licensing and subscription options. The calculator produced dynamic pricing based on factors like seat volume, host hardware configuration, and digital storage capacity. It worked at the time, but the times are changing. Now, I’m seeing companies use AI platforms to help them make better data-driven decisions around their pricing strategies.
Unlike static approaches of the past, AI pricing strategies:
- Can be programmed to dynamically adjust prices based on factors like
- Marketing conditions
- Competitive forces
- Volume or usage-based variables
- Reseller or contract-based pricing
- Use real-time data and advanced algorithms
Pricing Strategies that Work Well With AI
Value-Based Pricing
Value-based pricing is one strategy that works well with AI. If your company is deciding on which features have the most impact on value, your product team can use AI to determine how features align with your product tiers.
Cost-Plus Pricing or Cost-Based Pricing
Identifying the ideal profit margin for your products and services across the board relative to your fixed and variable costs can be puzzling. There are many ChatGPT-based pricing and margin calculators available that can prove helpful here.
Competition-Based Pricing
This strategy focuses on the going market rate for a company’s products and services without accounting for product costs or consumer demand.
Benefits of Applying AI to Pricing
According to Accenture, AI pricing strategies such as personalized and dynamic pricing can generate outcomes such as:
- Up to a 15% increase in revenue.
- Up to 5% margin growth.
- Up to 40% decrease in promotional spend.
Here are some of the many advantages I see around implementing AI-powered pricing strategies:
Accuracy
Competitive pricing for business and consumer products has never been more accessible. AI-powered pricing strategies enable accurate decision-making based on current market conditions such as supply, demand, and competitive forces.
Increased Conversions
AI-powered pricing strategies can help increase online conversions from browsers to buyers by predicting what a customer will pay based on their previous purchasing behaviors. It is pivotal for modern businesses implementing AI pricing strategies to adapt their pricing in real time to maintain customer loyalty.
Better Data Analysis
AI-powered pricing strategies reduce the reliance on guesswork or manual market analysis and pricing updates. Dynamic, optimized pricing maximizes margins and profitability without pricing products or services beyond what the market will bear.
Adaptability
AI quickly adapts to market changes at a pace humans simply can’t keep up with. It helps ensure that prices are always up-to-date and relevant. This agility benefits companies in fast-paced industries (think ride-sharing, retail, utilities, and automotive), where market conditions can shift rapidly.
Integrating AI into pricing strategies can foster better customer satisfaction, increase sales, and improve your competitive edge – and it’s already showing success while being leveraged by major companies. Some examples of AI pricing strategies you will likely recognize include:
- Amazon. Algorithms dynamically adjust product prices based on factors like demand, competitor pricing, and inventory levels.
- Uber. Surge pricing increases fares during peak demand periods to ensure there are enough drivers to get riders where they need to be.
- Walmart. Tracks competitor pricing and customer behaviors across multiple channels to dynamically adjust prices to attract price-conscious customers while maintaining profitable margins.
- Airbnb. Uses machine learning algorithms to recommend the best rates to hosts to optimize revenue and occupancy. The algorithms recommend variable pricing based on factors like location, seasonality and local events.
The Challenges of Implementing AI Pricing
AI-based pricing is generally accurate, but humans should monitor pricing analytics to detect anomalies. For instance, last week I was sent an offer for a $125 gaming PC ($1,200 MSRP) that seemed too good to be true. Naturally, it was an error and my order was undoubtedly canceled by a human auditor.
Just as with all AI use cases, businesses need to consider the challenges and risks inherent in adopting AI pricing strategies. Many configure, price, and quote (CPQ) tools in the marketplace provide some level of AI pricing functionality.
Here are some challenges businesses may face when implementing AI pricing strategies.
Buyer Perception
Companies that adopt AI dynamic pricing need to ensure that they manage customer expectations as to how dynamic pricing will impact the prices they will pay on subsequent visits.
According to the Boston Consulting Group, value perception is vital, and sellers can foster goodwill with buyers by holding or lowering prices on key value items (KVIs) and categories. It is important to be transparent with your customers about the factors that influence your pricing elasticity.
Regulatory Compliance
Businesses must ensure they adopt an AI pricing strategy that complies with local laws. The American Bar Association says that in some circumstances, when AI-powered algorithms are used to price goods and services, it may lead to higher prices or invoke privacy, equity and ethical concerns.
AI-powered pricing algorithms sometimes learn to make their own decisions and adjust to new information, possibly ending up in a price-fixing arrangement. Companies should closely monitor their pricing to ensure it doesn’t violate antitrust laws, as the US Department of Justice, the Federal Trade Commission and other regulators are developing and implementing tools to detect anti-competitive behaviors.
Scarcity of Skilled AI Pricing Expertise
Many companies lack skills in disciplines like data science, machine learning, and AI that are necessary for the successful implementation and maintenance of AI strategies. Often, integrators like Accenture with the required expertise are too costly for many businesses to afford or to realize the ROI to justify the expense.
Luckily, SaaS-based ecommerce platforms that handle the AI and offer no-code rule configurations can help address these skill gaps. Harnessing the power of machine learning and analytics to automate dynamic functions requires seamless integration.
Pro tip: A customer platform like HubSpot Sales Hub is the perfect example of a tool that integrates AI into daily sales operations and does not require intense AI expertise.
How to Implement AI-Powered Pricing
Depending on what kind of AI skills your employees or consulting partners have — and the complexity of your products, services, and customer buying journeys — your implementation will be unique. That being said, here are the key steps involved.
Step 1
Define what data sources need to be integrated with the AI pricing tool and ensure they support integrated workflows. Examples of these workflows could include data related to historical sales, the customer buying journey, your configure-price-quote system, and your online store.
Step 2
Choose an AI tool or plugin like AI Studio for HubSpot or DealHub CPQ.
Step 3
Configure the AI tool you choose, set up workflows and pricing rules, and then integrate them with your existing pricing processes.
Be sure to train the algorithm on real customer buying behaviors and competitive intelligence.
Thoroughly test the pricing algorithm before putting the dynamic pricing process into production.
Step 4
When your AI pricing operations are live, monitor your customer transactions using reporting tools, like HubSpot’s. Adjust your tool’s settings if you spot any anomalies or inconsistencies. Ensure your workflows are set up to adapt to changing market circumstances as described above.
Step 5
Ensure your team has the training and support resources they need to use the AI tools properly and efficiently.
Gain a Competitive Edge with an AI Pricing Strategy
AI-driven dynamic pricing is a great way to maximize your profitability in industries like retail, SaaS, and manufacturing. Yet succeeding requires consideration of how you can track your customer buying behaviors and competitive price fluctuations — and demonstrate the value of your products and services relative to cost. Training your AI tool’s algorithm on your product and services is crucial at the start, as is monitoring pricing accuracy and customer sentiment after you implement the solution.
I also recommend that before you implement your pricing strategy, you create a communications plan for your prospects, clients, and partners to explain how your new pricing model will impact their future purchases from your company. Make sure your sales team and other internal stakeholders are up to speed on it, too.