This approach involves adjusting prices based on seasons, day(s) of the week, or specific hours. For instance, hotel rates may be higher during peak travel seasons or weekends.

Sites like Amazon test different prices throughout the day and year based on customer behaviour and the volumes of data. They may also change prices seasonally, for example, during the holiday season.

Some products can be offered to different customer segments at various price points based on their purchasing power and habits. SaaS companies, for example, have different tiers of pricing based on the kind of user they sell to, whether individuals or businesses.

This step uses the regression output to calculate the price sensitivity scores of each product. The equation yields a price sensitivity prediction, which represents how a 1% change in price impacts the % change in quantity sold or demanded. For instance, price sensitivity of -4% means that a 1% rise in price results in a 4% drop in the quantity sold.

Continuous analysis of market prices is necessary to promptly reprice your products either to stay competitive or to gain margins with a powerful value proposition. The dynamic pricing model considers this approach to maintain competence in the market.

Based on the coefficient computation from the regression model, we assign weightage differently to different coefficients to calculate the sensitivity score. A higher sensitivity indicates that customers are more responsive to price changes, while a lower score indicates a less significant response.

After the price sensitivity test for a set time period, you can implement strategies for the product group in order to balance profit margin expansion and sales growth.

Pricing based on demand entails setting prices higher during periods of high demand and lower during periods of low demand. Airlines often use this strategy, with prices fluctuating based on factors like seat availability and time of booking.

You can calculate the quantity, average price, and other numerical or categorical factors for each product to construct a data frame for the customer. Using this dataset, we at SYMSON built a regression model. Studying these datasets allows businesses to quantify the influence of each driver on price sensitivity and sales volume.

During times of inflation, companies are likely to see a decrease in consumer spending and an increase in costs. As suppliers' costs increase, most companies increase their prices accordingly to keep profit margins intact. Others may choose to slash prices to encourage spending.

This is usually seen in luxury brands where the initial price for a product may be much higher at first, compared to later in its lifecycle when the hype (and demand) die down.

As you know by now, the first step would be to find new drivers that influence price sensitivity. To do this, you can combine customer point-of-view along with datasets. This process ensures more precision in calculating the sensitivity level.

You may have noticed that prices fluctuate for these industries greatly depending on seasonality. These industries are primarily looking to maximise revenue since they have a limited supply (i.e., they have a fixed number of rooms or seats).

Cab services usually increase their prices in areas or timings where they detect high demand, like events, pubs, and commute times.

This approach includes dividing customers into different segments and offering customised pricing based on their characteristics or behaviours. For instance, a software company may offen differ.

This occurs when a price change has a relatively small effect on the quantity demanded. In other words, consumers are not very sensitive to changes in price and will likely continue to buy a product even if the price increases.

This occurs when a change in price results in a proportional change in quantity demanded. In other words, consumers are exactly as sensitive to changes in price as they are to quantity demanded.

This occurs when a slight change in price results in an infinite change in quantity demanded. In other words, consumers are susceptible to changes in price and will only buy a product at a specific price.

This occurs when a small change in price results in a fairly significant change in quantity demanded. In other words, consumers are sensitive to changes in price and will likely buy more of a product if the price decreases. 

This occurs when a price change does not impact the quantity demanded of the product. In other words, consumers are willing to buy a product regardless of its price.

The algorithm gathers historical and real-time data from various sources. This includes internal data like sales history, production costs, and inventory levels, as well as external data such as market trends, competitor prices, and customer behavior.

The collected data is cleaned and prepared for analysis. AI algorithms require high-quality, relevant data to make accurate predictions. This stage often involves handling missing values, removing outliers, and ensuring the data is in a format suitable for analysis.

The algorithm identifies which factors (features) are most relevant to pricing. This could include product types, time of day, seasonality, customer segments, and more. Selecting the right features is crucial for the effectiveness of the pricing model.

The AI uses the prepared data to train a machine-learning model. This involves feeding the data into the model and adjusting the model's parameters until it can accurately predict prices. Common techniques include regression analysis, decision trees, and neural networks.

The model is tested on a separate set of data to validate its accuracy. This helps ensure that the model will perform well on real-world data and not just the data it was trained on.

Once validated, the AI model analyzes current market data and predicts the most effective pricing strategy. It considers how different pricing options might affect demand, competitor responses, and overall profitability.

The AI algorithm continuously updates its predictions based on new data. As market conditions change, the algorithm adjusts its pricing recommendations to remain optimal. This is often referred to as dynamic or real-time pricing.orm well on real-world data and not just the data it was trained on.

The performance of the AI pricing model is continuously monitored. If the market changes or the model's predictions become less accurate, it may be retrained or adjusted to improve performance