Three Potent Ways Artificial Intelligence Can Assist With Pricing

Three Potent Ways Artificial Intelligence Can Assist With Pricing

Companies employ AI-powered algorithms to price things based on data such as competitor pricing, inventory levels, and customer response to sales campaigns. Using Artificial Intelligence (AI) may assist boost profit margins, generate loyalty from price-conscious customers, and remove the complexity of manual price determination.

Customer engagement is a challenge for many firms and industries, especially when it comes to marketing and price methods. Choosing prices for items or services is a difficult issue. To arrive at the proper pricing, a number of studies and hard work are required. This is where AI enters into the picture.

According to a recent Bain global survey of over 1,700 company executives, 85% of B2B management teams feel their pricing choices might be better, and just 15% have efficient tools and dashboards to establish and monitor prices. Many businesses that rely on price as a competitive advantage should begin assessing AI and Machine Learning (ML) today as part of their IT platform roadmaps.

AI and Pricing

Brands are unable to keep up with rapidly shifting trends and must undertake surveys to identify opportunities. Providing clients with a regular rotation of offers is more difficult than it appears. Retailers are harnessing the power of AI and machine learning to roll out deals and discounts for this reason. When it comes to determining the best pricing, AI is quite effective. To get at the best pricing, it examines competition, and historical data, customer behavior, and seasonal trends.

Dynamic pricing based on consumer engagement reports can be aided by intelligent AI. The manager must be a demigod to determine ideal pricing for thousands of goods on a weekly basis. It is practically impossible for a person to handle so many things. This is where AI comes into play, since it may assist with quick decision-making.

Here is how B2B companies are already employing AI and machine learning to enhance pricing and revenue management.

Concentrating on price optimization

Due to the complexity of the internal and external data at hand, most B2B organizations find it difficult to set proper prices. Machine learning allows for the rapid and accurate analysis of massive volumes of data, as well as the extraction of insights that may help organizations move forward. Companies may utilize value-based pricing and optimum price tiering to optimize profits using this technique since decision-makers have a better understanding of a customer’s Willingness-To-Pay (WTP), as well as their behavior depending on various pricing strategies.

Create Better Customer Relationships

AI may assist staff in learning what their consumers want, making it simpler to maintain positive client connections. Customer Experience (CX) is a primary issue for companies, and applying AI to improve it can increase sales and save money.

Also Read: How to Make Enterprise Comfortable With Artificial Intelligence

AI can be used to better track leads and move consumers through the sales funnel. When businesses use AI to their advantage, they may get more leads without spending a fortune.

While these cost-cutting methods are efficient, AI also has a number of additional advantages that firms should consider when enhancing their internal processes. Since AI’s popularity and the number of applications are rising, it’ll be intriguing to see what new ways firms may save money by integrating AI. 

Gathering buyers through fine-grain consumer segmentation

Companies can better segment consumers, uncover value drivers for customers, and allow dynamic pricing. Companies may use propensity models based on customer personas to anticipate which consumer groups are more likely to respond to a pricing offer or pricing package. Predictive analytics, which depend on machine learning to forecast the likelihood of a specific customer’s activity based on pricing offers or other incentives, are used in these models.

Propensity models boost client retention, minimize churn, and give more usable data regarding customer preferences and prior behavior by using fine granularity customer segmentation. It’s feasible to forecast a customer’s willingness to pay using this information, and then segment and price accordingly.

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Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain