As commodity prices continue to fluctuate, retailers face mounting pressure to protect their profitability. At the same time, rising price sensitivity among consumers is reshaping purchasing behaviour. In this volatile landscape, businesses must adapt their pricing strategies and embrace technology to stay competitive and sustain growth. This is where AI and Machine Learning (ML) are revolutionising the field of price optimisation by enabling retailers to make data-driven decisions that maximise revenue and customer satisfaction.
This shift, known as dynamic pricing, is already driving success for retailers, resulting in a significant increase in revenue and improvement in margins.
The power of machine learning for price optimisation
Traditional pricing methods often fall short, as they rely heavily on manual analysis and static models that fail to capture the nuances of real-time market fluctuations. For instance, factors such as weather, economic conditions, and social media trends can significantly impact demand, yet these are difficult to incorporate into traditional pricing models.
ML offers a robust solution to these challenges by leveraging vast datasets to identify patterns and predict future trends. ML algorithms can analyse historical sales data, customer demographics, competitor prices, and market trends to generate insights that inform pricing decisions. Its key benefits include:
Scalability: ML algorithms can handle large datasets and intricate models, making them ideal for managing vast product catalogues.
Flexibility: ML models are adaptable and can be customised to meet specific business objectives, such as increasing sales or margins.
Accuracy: ML identifies subtle patterns in data that may elude human analysts, resulting in improved demand estimation and pricing forecasts.
Real-time insights: ML algorithms process data instantly, enabling businesses to swiftly adapt to shifting market trends and customer preferences in real time.
AI in price optimisation
Artificial intelligence further enhances price optimisation by incorporating real-time data on market conditions and consumer behaviour. AI ensures retailers remain competitive and responsive to customer needs by enabling:
Real-time adjustments: AI enables retailers to adjust prices in real time, responding to immediate changes in demand or competitor pricing.
Predictive analytics: ML can predict how customers will react to different pricing strategies, allowing retailers to test and refine their approaches.
Personalisation: AI can help personalise pricing at an individual customer level, maximising revenue opportunities while maintaining customer satisfaction.
Unlock profit potential with dynamic pricing strategies
Businesses can meet fluctuating market demands by implementing the right dynamic pricing strategy to improve profitability. These strategies include:
Competitive pricing: It adjusts prices based on competitors to stay relevant in the market, preventing customer loss to rivals.
Penetration pricing: This starts with lower prices to attract customers and gain market share helping new businesses to quickly establish a strong customer base.
Skimming pricing: Setting high initial prices for new or in-demand products before gradually lowering them to maximise early profits and appeal to early adopters.
Demand-based pricing: This allows prices to fluctuate based on real-time demand trends, which means increasing sales during peak demand and attracting buyers with lower prices during slow periods.
Time-based pricing: Prices change based on dynamic factors such as seasonality or time of the day, encouraging purchases at optimal times and maximising revenue.
Segmented pricing: It offers different prices for different customer segments based on location, purchase history, or preferences, thereby improving customer loyalty.
Overcoming the obstacles in ML- and AI-driven dynamic pricing
Retail businesses must navigate several challenges to fully realise the benefits of AI- and ML-driven dynamic pricing. Addressing these obstacles early is key to effective integration.
Data quality issues: Clean, integrated data is essential for AI to deliver accurate pricing decisions.
Security and compliance risks: Processing vast amounts of sensitive data raises security and privacy concerns. Strong encryption, access controls, and regulatory compliance are crucial.
Legacy system limitations: Cloud migration and scalable solutions are necessary, along with investments in electronic shelf labels for instant updates.
Change resistance: Employees and stakeholders may be hesitant to adopt AI-driven pricing models. Effective communication, pilot programs, and upskilling can help ease the transition.
Outsourcing AI-powered dynamic pricing to specialised providers offers a cost-effective way for businesses to adopt advanced pricing strategies without heavy in-house investment. These experts bring industry knowledge, seamless integration, and advanced algorithms, helping companies improve pricing accuracy and stay competitive.
Who can benefit from dynamic pricing models?
Companies across industries can benefit from dynamic pricing models, including transportation, ride-sharing, hospitality, tourism, airlines, automotive, and manufacturing. For example, a large e-commerce company adjusts prices millions of times per day to stay competitive. Smaller retailers can also automate pricing decisions, saving time and boosting profitability.
How can Infosys BPM help you leverage AI for price optimisation?
Whether through dynamic pricing, predictive analytics, or personalised customer experiences, the future of retail pricing is tied to the power of ML and AI. Infosys BPM offers a suite of services, including pricing analytics and supplier management, and leverages advanced AI and ML technologies to optimise pricing strategies, ensuring real-time adjustments based on market conditions.
Frequently asked questions
Pricing algorithms create antitrust exposure when competitors use the same vendor model trained on shared non-public data, which regulators may treat as coordination even without an explicit agreement. The US Department of Justice and several state attorneys general have pursued this theory in rental housing and hospitality cases, and more than 40 pricing bills are pending across US states. Enterprises typically require vendors to confirm that models are trained only on the retailer's own and public data, and keep a pricing audit trail for each change.
Machine-learning pricing should run inside explicit business rules: margin floors, maximum price-change frequency and magnitude, category and key-value-item exclusions, price-ending rules, and human approval above defined thresholds. In the EU, Directive 2019/2161 requires any announced price reduction to reference the lowest price of the prior 30 days, which the model must respect automatically. Enterprises typically run the model in recommendation mode for one or two quarters before allowing autonomous execution on low-risk categories.
Dynamic pricing return should be measured on gross margin dollars and sell-through against a control group, not on revenue alone, because price cuts can lift sales while destroying margin. Published retail programmes typically report margin improvements of 2% to 5% and revenue uplifts of 1% to 3% when models are applied across large assortments. Enterprises typically hold out a matched set of stores or SKUs for at least one full seasonal cycle and track price perception surveys alongside financial results.
Machine-learning pricing requires at least two years of transaction history at SKU-store level, cost data, competitor price feeds, promotional and markdown history, and inventory positions, plus an execution path such as electronic shelf labels or automated e-commerce price publishing. Without execution automation, recommended prices age before they reach the shelf. Enterprises typically find that data cleansing and integration, not model building, consumes 60% to 70% of the programme effort.
