Sales and Fullfilment
How AI and Machine Learning Will Change Your Pricing Strategy
In a fast-paced and dynamic market, businesses require several strategies to stay relevant and ahead in the game. These include strategies for business growth, products, services and pricing. The pricing strategy impacts the operating margin of the business. It also influences the brand value, attracts customers and determines the profitability of the business. However, pricing is often a tricky tightrope walk between brand value and the profit margin.
Pricing analytics is a critical aspect of a successful business. When it comes to open marketplaces, pricing is a significant differentiating factor. Traditional pricing models have relied heavily on the deep experience of pricing managers, market research, competitor strategies and statistical models such as regression analysis and cluster analysis. They are, however, limited by the expertise of pricing managers and manual analysis of humongous amounts of data. The significant penetration of online commerce requires dynamic and data-driven pricing models.
Ecommerce businesses that have adopted a dynamic pricing model have seen significant success. Dynamic pricing involves changing the product pricing based on market conditions, internal and external factors like market trends, seasonal demand, inventory on-hand, demand and supply and competition, and consumer expectations/perception. Dynamic pricing keeps prices flexible and optimises them, taking into account the required operating margins as well as market conditions.
Data-driven pricing models use analytical tools and techniques to process and evaluate large volumes of data, based on several factors such as competitor pricing, demand, market conditions, customer preferences and so on. Data-driven pricing can easily pick up on trends and patterns and help optimise pricing, resulting in improved profitability. Algorithms based on artificial intelligence (AI) and machine learning (ML) add high value to pricing strategies. In fact, they can completely transform pricing methods. Generative AI (Gen AI) can be used to increase dataset diversity and produce synthetic data points to improve demand forecasting precision.
AI-driven pricing transformations can be highly successful. In fact, according to a survey conducted by Massachusetts Institute of Technology (MIT) and BCG Henderson Institute (BHI), technology companies that used AI for pricing transformation succeeded twice as often compared to those which utilised the technology in other functional areas.
Using AI to strengthen pricing processes has a direct impact on revenues. Statistics back this. Large companies with more than $10 billion in revenue which deployed AI-powered pricing strategies witnessed a $100 million augmentation in revenue, 70% more often than companies that adopted AI for other types of transformation.
AI-driven pricing analytics can assess industry trends, customer patterns and cost structures to determine optimal pricing that maximises profit margins while maintaining market share. Real-time factors such as inventory levels, competitor pricing and demand fluctuations serve as inputs for AI-based pricing algorithms that facilitate dynamic pricing. For example, online ecommerce giants use AI to determine high-demand products to automatically maximise profit margins while staying competitive. Similarly, AI can recommend product bundling or discounts for slow-moving products without eroding profitability.
Businesses across industries can employ AI-based analytics to transform their pricing models. For instance, fast-moving consumer goods (FMCG) companies can use AI to rethink their price-pack architectures and focus on products that have higher margins. In the B2B sector, AI-based analytics can mine transaction data to determine incremental price differentiation and improve discounting. AI-based tools can be utilised to set price levels and determine price metrics.
Additionally, AI-based analytics can analyse several data points such as past purchase history, current transactions and browsing history to determine consumer behaviour. This information can be used by predictive analytics to determine prices that a consumer might be willing to pay. AI-based personalisation can also deliver tailored offers to consumers at optimal times that increase the chances of a purchase. For example, ride-hailing platforms use AI to analyse factors such as customer demand, ride history, location, traffic conditions and competitor pricing to offer personalised fares to customers. If a customer frequently uses rides during peak times, the app may offer a higher fare based on the consumer’s past history of accepting a ride at that hour.
Utilising AI for pricing boosts operating margins and revenues, while enabling accurate data-driven decision-making. Companies can create more effective pricing strategies and offer customers personalised pricing and tailored offers. But before integrating AI into the pricing strategy, companies must ensure the availability of clean and normalised data, identify priorities and set a robust governance framework in place. Adopting an incremental and ethical approach towards AI-driven pricing with human collaboration will create a competitive and transparent pricing model.
How can Infosys BPM help?
Infosys BPM’s Sales & Fulfillment AI-first solutions combine deep domain expertise and advanced AI capabilities to deliver exact and accurate pricing solutions. With AI-powered forecasting, predictive analytics and proactive operations, Infosys BPM helps clients develop pricing strategies to drive business growth and stay ahead of the competition.
