What are the 5 Areas that AI pricing and analytics look into?

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Modern AI pricing expands its capabilities more than traditional pricing systems. It can set prices based on accurate price elasticity and sensitivity analyses. In the case of elasticity, this process involves carefully understanding your product’s real-time demand change. It also assesses the various drivers that determine a product's price sensitivity.

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It’s also crucial to know your ‘real’ competition. A new large retail beer brand can see a nationwide beer chain like Budweiser as their competition. But, using the analytics, the AI pricing tool would accurately show other local and equally large beer brands that have a direct impact on the region. Likewise, you will set prices that are more impactful and effective rather than assuming and missing margins.

Below, we explore 5 key techniques businesses can use to enhance their pricing strategies.

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1. Elasticity Analysis

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There's sometimes fluctuating demand for a product after its price changes. Hence, pricing systems should be on top of those trends. This process is the price elasticity analysis. AI pricing and analytics are capable of analysing your product's elasticity score. From there, it can study the market and other real-time factors and suggest an optimal price. This is an ongoing process and alerts you with a notification and a new price suggestion.

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Also Read: A Complete Guide to Implementing Price Elasticity Using a Pricing Tool

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One key aspect is knowing your customer's willingness to pay after analysis. Once you find the range, you can set prices that serve both the customer's budget and your profit goals. Pricing analytics made it easier to analyse historical sales data. It can also predict customer responses to price adjustments.

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For instance, AI can segment products or categories based on elasticity. This helps you identify where a price increase is acceptable and where reductions can drive higher volume and revenue. By using this data, companies can balance profitability with customer satisfaction.

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2. Price Sensitivity Analysis

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Price sensitivity is different from price elasticity. Various drivers determine a product’s price sensitivity. It can be the brand, competition, product lifecycle stage and many others. Each product/ product category can have a unique set of factors.

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Your pricing team must collaborate with stakeholders to determine those price drivers. Then, you must assess the price sensitivity step-by-step using Machine Learning. You can also refer to our 5-step approach to assessing sensitivity.

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Then, the pricing system can act upon the deductions made in the steps you take. It will suggest the optimal price recommendation. In this approach, human expertise identifies key drivers and ranks them by importance. But, computers, excel at finding patterns, building algorithms, and processing large amounts of data.

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Business managers play a crucial role in reviewing Machine Learning outcomes. Their input helps refine the model, ensuring more precise price sensitivity scores. This is an ongoing process where experts learn from data, and the model adapts.

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We call this Hyperlearning™—a close collaboration between business and AI. Here, both contribute their strengths for better decision-making.

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Download Whitepaper: The Ultimate Price Sensitivity Guide

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3. Assessing Competitor Impact

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Traditional competitor analysis assumes that major industry players are the main rivals. But that’s not always the case.

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AI offers a clearer picture after a thorough competitive pricing analysis. It monitors buying patterns, market trends, and customer behaviour. It helps identify the 'real' competitors that influence pricing decisions.

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Also Read: Competitor Price Monitoring: A Guide to Set Profitable Competitive Prices

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For example, a retailer like Walmart may see Target as its biggest competitor. Yet, AI might reveal that local businesses have a stronger impact in certain regions. These insights help businesses adjust prices where it matters most. Likewise, you can stay competitive in the right places.

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4. Data Aggregation and Accuracy

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Good pricing decisions rely on accurate data. They are only as good as the data behind them.

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AI gathers data from many sources, cleans it, and keeps it up to date. By automating this process, businesses spot trends, detect anomalies, and set smarter prices in real time.

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With precise data, companies boost efficiency and adjust prices to stay competitive.

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5. Accurate Future Demand predictions

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AI pricing and analytics help businesses stay ahead by predicting demand before it shifts. By analysing past data, market trends, and external factors, AI forecasts future demand with high accuracy. This allows businesses to adjust prices in advance, optimise inventory, and seize revenue opportunities.

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Instead of reacting to market changes, they can plan and stay competitive. With AI-driven forecasting, companies make smarter decisions, reduce risks, and improve profitability.

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