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AI-Driven Pricing and Predictive Analytics

AI-driven pricing is a type of dynamic pricing that adjusts prices based on external factors and the buying behaviour of consumers. It’s used by online retailers to optimize their revenue and margins.

For example, if customers prefer certain brands of clothing, an AI-powered system may recommend products they’ll like. It can also monitor how each consumer responds to a price change.

Predictive Pricing

Predictive pricing, also known as predictive analytics, is a pricing strategy that uses artificial intelligence and machine learning to analyse historical data. This information helps businesses better understand how their prices have changed over time under different market conditions.

Predictive AI can be used for many purposes, including product and stock optimization, promotion optimization, point of sale traffic forecasting and more. It can help companies to improve their performance, especially in sectors where customers are more price sensitive and demand is often changing quickly.

In the pharmaceutical industry, for example, artificial intelligence-based software can predict a drug’s acquisition cost more accurately. This can help to achieve a more profitable margin.

To get the best results from predictive pricing, companies need to invest in a data infrastructure that allows them to harness and integrate internal data as well as external data sources. This includes automation platforms, CRMs, content management systems and financial data.

Dynamic Pricing

When used correctly, dynamic pricing can help businesses to maximize profit and sales. It also reduces costs and improves customer satisfaction.

A key factor in dynamic pricing is the elasticity of demand. This refers to how much a price change affects the demand for the product.

Unlike static pricing, dynamic pricing changes the price automatically to reflect market trends, competition, and cost. It can also adjust prices based on seasonality or supply constraints.

Dynamic pricing also allows retailers to increase or decrease prices when necessary, thereby maximizing profitability and optimizing inventory management.

In addition to the core elasticity estimation, dynamic pricing algorithms consider a wide range of other factors, including competitor prices, promo activities, and more.

While dynamic pricing tools can be helpful, they can also cause confusion to customers and hurt brand loyalty. As a result, it is important to choose the right solution for your business.

Life Cycle Pricing

A life cycle pricing strategy is one that determines the price of a product based on each presented stage. If a product successfully navigates through the market introduction, it is then ready to enter the growth phase where increasing demand promotes production and it becomes more widely available.

As the product moves into the mature stage, there is a growing market share and competition, so brand, price and differentiation are more important to maintain profitability. Eventually, the interest in the product will plateau and decline.

The CDAC AI life cycle consists of three phases (design, develop, and deploy) and 19 constituent stages across these three phases from conception to production. This life cycle addresses several critical gaps in the literature related to method and approach limitations, exclusive focus on AI, depth of technical detail in each phase, responsibilities of an AI team, and the contribution of pre-trained models, code repositories, and ethics and governance frameworks toward expediting AI project outcomes and enhancing inclusivity.


Artificial intelligence (AI) and machine learning (ML) are reshaping the ways brands interact with customers. They’re enabling brands to increase customer engagement, improve loyalty and increase sales — all in real time.

Personalization is the act of tailoring experiences or communications to an individual based on information a company has learned about them. This includes modifying website content, ads, emails and call center interactions.

For example, a B2B tech site that modifies its homepage experience to speak differently to different companies is using personalization. A B2C shoe retailer that features nursing shoes on its homepage only to visitors that have shown an interest in nursing shoes is also utilizing personalization.

Personalized pricing can match shoppers’ prices to their needs, preferences and behaviors. It can do this by culling through vast amounts of data and focusing on who the shoppers are, what they want and what they’ll pay for it.

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