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The Price Sensitivity Guide

Why, benefits, use cases and more...


Understanding Price Sensitivity Pricing and its Impact on Business Outcomes


What is Price Sensitivity?

Price sensitivity refers to how responsive consumers are to changes in the price of a product or service. If a small price change leads to a significant change in demand, consumers are considered price-sensitive.

The ability to navigate the delicate correlation between pricing and customer behaviour can dictate a company's success in an increasingly competitive market landscape.

In this discussion, let’s dive deep into the essence of what is price sensitivity, the role of the price sensitivity model, the methodology of price sensitivity analysis, and the transformative effects on business strategies.


What is the Role of a Price Sensitivity Model?

The role of a price sensitivity model is to provide businesses with a structured framework. This helps to understand and predict how changes in pricing will impact consumer behaviour and demand for their products or services.

Older sensitivity models like the Van Westendorp pricing model are inaccurate as they rely only on the customer perspective. On the other hand, modern pricing counts a plethora of relevant drivers that affect price sensitivity.

Ultimately, modern pricing models assists businesses in optimising pricing for revenue and profitability while catering to customer preferences.


The Price Sensitivity Formula: How to Measure Price Sensitivity?

The price sensitivity formula, as mentioned above, requires considering specific drivers and building a regression model to compute the price sensitivity scores of individual products. The equation includes price, quantity and the identified drivers.

Following the process of identifying relevant drivers and computing the sensitivity scores helps ensure accurate prediction. Therefore, the pricing strategy assigned to each tends to be optimal and effective in maintaining profitability. We will discuss this further in our blog: Optimising Profits through Price Sensitivity.

What is Price Sensitivity Analysis?

  • Price sensitivity analysis is a market research technique that helps businesses understand how changes in pricing impact consumer behaviour and purchasing decisions. It involves studying how variations in price levels affect the demand for a product or service.
  • The goal of price sensitivity analysis is to determine the optimal price point that maximizes revenue and profitability while considering customer preferences and market dynamics.

However, the process of price sensitivity analysis starts by identifying new drivers using both customer and data inputs.

Let’s understand a few significant drivers that are common to all businesses, keeping in mind that specific parameters are necessary to make price recommendations precise.


How to Identify New Drivers for Price Sensitivity?

Engaging in market research and customer surveys helps companies gain accurate insights into customer preferences, perceptions, and purchasing decisions.

At SYMSON, we discussed with a few of our clients as well as analysed data to gauge a combined perspective on the parameters that regulate their sensitivity towards purchasing a product.

Read the details about the new drivers affecting price sensitivity here: »

Of all the aspects we uncovered, here are a few drivers that could be of help to most businesses:

Combining price stakeholders' opinions about how pricing is influenced by which drivers with Data

Brand value

Consumers often associate higher prices with better quality, leading to decreased sensitivity for well-established brands and vice versa for lesser-known brands.


Product lifecycle

Depending on the stage of the product’s lifecycle, the price sensitivity will change. For eg, introductory phase may be less price sensitive than the maturity phase.


Price Level

This refers to whether a price is categorised as cheap or expensive. This grouping is based on customer input and sales data. A higher price may increase sensitivity. Additionally, assessing the product's price sensitivity elasticity can provide insights into the price level's impact on purchasing decisions.


Price Change Frequency Driver

Price Change Frequency determines the number of times a client changes the product’s price in the selected time period. High frequency suggests the price strategy follows the market dynamics closely and thus can be correlated with higher sensitivity.


Competitive intensity

In a market with numerous alternatives, consumers are likelier to exhibit heightened sensitivity and seek lower-priced options. Conducting competitor analyses and staying abreast of market dynamics can help businesses gauge competitive intensity and its effect on price sensitivity.


Additionally, businesses offering highly differentiated or unique products and services may be able to justify higher prices, even if they are not directly competitive with other offerings in the market. Ultimately, the choice of a pricing strategy should be informed by a thorough understanding of the business's unique circumstances, market dynamics, and customer preferences.

In summary, AI in pricing equips organisations with the tools and insights to make smarter, faster, and more customer-centric pricing decisions. This leads to enhanced profitability, competitive advantage, and a better understanding of the market and customers,all of which are crucial for success in today's fast-paced and ever-evolving business landscape.

How to use software to optimise Prices based on Price Sensitivity

By using pricing software, companies can identify their product’s elasticity, their demand’s behaviour and consumer reaction to price changes. You can also analyse historical data to find out the demand at a certain price sold then. Likewise, you can lower your prices or set high prices for maximum revenue and profits respectively.

Watch and learn with these videos on how to get started on using SYMSON’s platform to optimise your prices. »


5-Step Approach to Assess the Price Sensitivity of Your Products

Starting with identifying new drivers, the next step is building a regression model using the data to gauge the sensitivity of individual products. Testing is crucial to understand the performance of the computation and also to measure the accuracy and consistency.

Finally, the pricing strategies can be assigned differently to the sensitive and insensitive groups. To understand in details about the 5-step approach to the price sensitivity approach, you can read more here:

Price Sensitivity Drivers with Significant Influence

Perfectly Elastic Demand

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.


Perfectly Inelastic Demand

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.


Relatively Elastic Demand

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. 


Relatively Inelastic Demand

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.


Unitary Elastic Demand

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.


Costs of Goods Sold (COGS)

Understanding the cost structure of a product or service is essential when implementing a competitive pricing strategy. This includes considering direct costs, such as materials and labour, and indirect costs, such as overheads and marketing expenses. By comprehensively analysing costs, businesses can determine the minimum price at which they can sell their offerings without incurring losses while ensuring they remain competitive.


Competitor prices

A thorough analysis of competitors is crucial when adopting a competitive pricing strategy. Businesses must identify their primary competitors, monitor their pricing strategies, and assess the price range within which their offerings fall. This information allows businesses to set their prices in a manner that is competitive while still delivering value and maintaining profitability.


Market conditions

Market conditions, such as supply and demand, also play a significant role in competitive pricing. When demand is high, businesses may have more flexibility in setting higher prices without losing customers. Conversely, during periods of low demand, businesses may need to lower prices to remain competitive and attract customers. By staying up-to-date with market conditions, businesses can make informed pricing decisions that optimise their competitiveness and profitability.


Product differentiation

The level of differentiation a product or service offers compared to competitors influences the effectiveness of a competitive pricing strategy. If a business can demonstrate unique features, superior quality, or additional value, it may be able to justify higher prices than its competitors. In contrast, for products or services with little differentiation, the price may become the primary factor in attracting customers, necessitating a more aggressive pricing approach.


Target customer segment

Identifying the target customer segment is another essential factor when determining competitive pricing. The price sensitivity of the target market will influence the business's pricing decisions. For example, if a business targets price-conscious customers, it may need a lower pricing strategy to remain competitive. Conversely, if targeting a more affluent customer segment, the business may have greater flexibility to set higher prices.


Identifying New Price Sensitivity Drivers

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.


Create a Regression Model

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.


Aggregate Coefficients from the Regression Model

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.


Interpret Price Sensitivity Scores

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.


Act upon Price Sensitivity to increase Margin or Sales

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.


Time-based Pricing

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.


Demand-based Pricing

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.


Segment-based Pricing

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.


Competitor-based Pricing

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.


Seasonal Pricing

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).


Surge Pricing

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


E-Commerce Sites

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.


Market-Based Pricing

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.


Customer-Segment-Based Pricing

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.


Product Lifecycle-Based (price skimming)

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.


Collecting your historical data

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.


Processing the accumulated data

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.


Selecting relevant factors

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.


Training the 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.


Testing and Validation

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.


Predictive Analysis:

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.


Dynamic Pricing:

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.


Pricing Monitoring and Adjustments:

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

In summary, competitive pricing requires a comprehensive understanding of costs, competitor prices, market conditions, product differentiation, and target customer segments. By considering these factors, businesses can develop an effective pricing strategy that balances competitiveness, value, and profitability.

The procedure of synthesising expert human judgement and the power of Machine Learning to identify new sensitivity parameters is the HyperlearningTM approach. Now, let’s look at how analysing customer price sensitivity helps companies enhance business outcomes.

In summary, competitor pricing analysis is a critical component of an effective pricing strategy, helping businesses to understand the market landscape, identify opportunities, and develop strategies that maximise profitability and competitiveness. By continuously monitoring and adapting their pricing strategies, businesses can stay ahead of the competition and deliver value to their customers.


How to Get Competitor Data?

Obtaining competitor pricing data is essential for businesses looking to establish an effective competitive pricing strategy. By gathering and analysing this data, businesses can better understand the pricing landscape, identify trends, and make informed decisions that help them remain competitive. Here are several methods businesses can employ to gather competitor pricing data. Here’s a thorough guide to data scraping.


Manual research

One straightforward approach to collecting competitor pricing data is through manual research. This can involve visiting competitor websites, physical stores, or online marketplaces to gather information on product prices, discounts, and promotions. Although this method can be time-consuming and may not provide real-time data, it can offer valuable insights into competitor pricing strategies.


Price tracking tools

Several price tracking tools are available in the market that can help businesses monitor competitor prices. These tools enable businesses to track prices for specific products or categories across multiple websites and platforms. Some price tracking tools also offer additional features, such as historical price data, competitor analysis, and market trend analysis, which can provide valuable insights for developing a competitive pricing strategy.


Web scraping

Web scraping is the automated process of extracting data from websites using specialized software. Businesses can use web scraping tools like Beautiful Soup, Scrapy, Selenium, Cheerio, etc., to gather competitor pricing data from e-commerce platforms, competitor websites, or other online sources. This method can provide more accurate and up-to-date data compared to manual research. Still, it may require technical expertise and ongoing maintenance to ensure the accuracy and reliability of the data.


Industry reports and data providers

Businesses can subscribe to industry reports or purchase data from third-party data providers to access competitor pricing information. These sources typically offer aggregated data on pricing trends, market dynamics, and competitor activities, providing businesses with a comprehensive view of the pricing landscape in their industry.


Social media and online formula

Monitoring social media platforms and online forums can also provide businesses with insights into competitor pricing strategies. Customers often share their experiences, opinions, and even product prices on these platforms, allowing businesses to gather valuable information on competitor prices and customer preferences.


Competitor collaborations

In some industries, businesses may benefit from collaborating with their competitors to share pricing data. This approach can promote transparency and help create a more competitive market environment. However, businesses must carefully consider the potential risks and legal implications associated with sharing pricing information with competitors.


How are ERP systems and Smart Pricing Software Different?

Enterprise Resource Planning or ERP systems integrate various business processes to improve overall efficiency. On the other hand, smart pricing software uses artificial intelligence to set optimal and intelligent prices by analyzing market and customer data.

The former streamlines internal operations and the latter enhances profitability through data-driven pricing strategies. Read more about how ERP systems and price intelligence software are different. Let’s have a look at the basis of their differences:


Functionality and Specialisation

ERP Systems offer broad business management capabilities, including basic pricing functions. They're designed to integrate various business processes but aren't specialized in pricing.

AI-Powered Price Management provides specialized, advanced pricing capabilities. They focus exclusively on optimizing pricing strategies using sophisticated algorithms and are tailored for this specific function.


Pricing Strategy and Adaptability

ERP Systems generally support basic pricing strategies with manual updates and rules-based adjustments. They're less adaptable to rapid market changes and typically require manual update intervention.

AI-Powered Price Management
employs dynamic and real-time pricing strategies, automatically adjusting prices based on market conditions, demand, and competitor actions. They offer a more adaptable and responsive approach to pricing.


Analytics and Insights:

ERP Systems include general analytics and reporting features which cover a broad spectrum of business data, including some pricing insights. However, these are usually not as in-depth or specific to pricing optimization.

AI-Powered Price Management
provides advanced analytics and predictive modelling specifically for pricing. They can forecast market behaviour, predict the impact of price changes, and suggest optimal pricing strategies based on deep learning and data analysis.


Automation and Efficiency:

ERP Systems handle various business processes with a certain level of automation, but pricing updates often require manual intervention and are subject to human error.

AI-Powered Price Management offers a high degree of automation, particularly for pricing adjustments. They reduce the need for manual updates, thereby minimizing errors and increasing operational efficiency.


Scalability and Customization:

ERP Systems are scalable across various business functions but may have limitations when it comes to handling complex, evolving pricing strategies. Customization is possible but can be broad and not as deep for pricing needs.

AI-Powered Price Management
is highly scalable and customizable when it comes to pricing. They can accommodate an expanding array of products and complex pricing structures, adapting as the business and market evolve.

In essence, while ERP systems provide an integrated solution for managing various business operations with basic pricing capabilities, AI-powered price management systems offer a focused, sophisticated approach to pricing. They bring advanced analytics, real-time adaptability, and a high degree of automation to the table, specifically catering to the dynamic needs of modern pricing strategies.

What is Price Scraping?

Price scraping is when automated web scraping tools or software extract pricing data from various e-commerce websites, online marketplaces, or competitors' websites. This data can then be analysed to inform pricing strategies and make better business decisions.

What is a Price Engine?

A price engine is a specialised software tool that determines the optimal prices for products or services by analysing various factors such as market demand, competitor pricing, cost of goods, and customer behaviour.It uses algorithms and data analytics to dynamically adjust prices in real-time, helping businesses maximise profits, improve sales, and remain competitive in the market. Essentially, it automates and optimises the decision-making process for pricing.

How to Scrape Prices from Websites?
  • Identify competitors
  • Determine the kind of data you want to collect
  • Pick a data scraping tool
  • Analyse the scraped data
  • Create a powerful competitive pricing strategy
How to Scrape Prices from Websites?
  • Identify competitors
  • Determine the kind of data you want to collect
  • Pick a data scraping tool
  • Analyse the scraped data
  • Create a powerful competitive pricing strategy

Price Sensitivity Examples

In a market where choices abound, understanding which products are highly responsive to price changes can be the key to strategic pricing decisions and staying ahead of the competition. Although there are multiple product categories that are price sensitive, let’s take a look at the top three. Here is an in-depth discussion of price sensitivity examples and the new drivers you can consider: ».

Fast-food chains
Customise your pricing
Use competitive pricing
Unlock seasonality insights

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Optimising Profit Margin with Precision

Intelligent businesses automating prices using mature datasets will experience healthy margin growth over a certain period. According to a study by McKinsey & Company, a 1% rise in price, with stable sales volume, led to an 8% rise in operating profits. In contrast to the profit growth from other variables- like reducing costs or increasing sales by 1%, the price rise brought a significant difference. Similarly, in the context of competitive, price-sensitive products or pricing during crises, correct optimal prices can increase sales, thereby keeping the inflow of business revenue.

Optimising Profit Margin with Precision

Intelligent businesses automating prices using mature datasets will experience healthy margin growth over a certain period. According to a study by McKinsey & Company, a 1% rise in price, with stable sales volume, led to an 8% rise in operating profits. In contrast to the profit growth from other variables- like reducing costs or increasing sales by 1%, the price rise brought a significant difference. Similarly, in the context of competitive, price-sensitive products or pricing during crises, correct optimal prices can increase sales, thereby keeping the inflow of business revenue.

Staying Agile and Competitive with Dynamic Pricing

Technological advancements have led to a hyper-competitive market landscape, where staying ahead is imperative for survival. Price optimization empowers businesses to respond swiftly to market changes, demand fluctuations, and competitive moves. Companies can proactively capitalize on opportunities and maintain their market leadership by continuously monitoring their competitor's prices and offers and adjusting prices accordingly.

Using Data Analytics to Beat Competition

Businesses are flooded with vast amounts of data, however, data alone won't make any difference unless it is organized and translated into insights. A study by Forrester Consulting says that enterprises using data management systems are 162% more likely to exceed their revenue goals.

By choosing the right competitive pricing tool, businesses can gather valuable insights into competitor pricing strategies and develop data-driven pricing strategies that enhance their competitiveness, profitability, and market positioning.

Price Sensitivity Examples

In a market where choices abound, understanding which products are highly responsive to price changes can be the key to strategic pricing decisions and staying ahead of the competition. Although there are multiple product categories that are price sensitive, let’s take a look at the top three. Here is an in-depth discussion of price sensitivity examples and the new drivers you can consider: ».

Air Travel
Clothing and Fashion
FMCG Goods
How a Price Engine Sets Accurate Prices in a whiff

With an advanced price engine, you can automate product prices across platforms without exceeding margins, while also having complete control of your pricing process.

The Top 10 Pricing Softwares

Selecting the proper software solution for pricing can be tricky with so many different options available. we’ve compiled a list of well-known and lesser-known pricing platforms into a single, easy-to-read document to help expedite your search for the best solution.


Disadvantages of the Traditional Van Westernport Price Sensitivity Model

The Van Westendorp pricing model is incompatible with today’s modern pricing challenges. Let’s have a look at the areas where this sensitivity meter falls short. Read more »

It is biased towards customer perspective with lower price points ignoring primary crucial factors making you leave money on the table.

It doesn’t consider the competitive landscape thereby making the price recommendations highly inaccurate.

It’s limited predictive power hinders you to translate the price points into actionable pricing strategies.

It sees the market as a homogenous group rather than considering their unique preferences and sensitivities. Thereby, it ignores the need to tailor prices for optimal sales volume.


How is Price Sensitivity different from Price Elasticity

Amount of Drivers Considered

Price Sensitivity includes more practical and specific drivers to recommend prices. Price Elasticity focuses on the relation between price and quantity.

Price Recommendations Quality

Price Sensitivity ensures higher accuracy in recommending prices. Price Elasticity compared to Sensitivity is less accurate.

Risk Management Approach

Price Sensitivity model categorises products that have limited and controlled discounts and markups.

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Price Sensitivity in SYMSON’s AI Software

Currently in the development stage, SYMSON’s price sensitivity model aims to enable you to identify products’ sensitivity level and set smart pricing strategies accordingly. It will calculate the sensitivity scores based on the drivers you consider using a regression model equation. Using sensitivity pricing, our algorithm will be able to calculate optimal price points (small discounts and mark-ups) to improve the margin and revenue growth.

Quantify driver impact on price sensitivity

SYMSON automatically tracks the prices of your competitors, giving you a better overview of your price position in the market and how it relates to your expected growth.

Interpret price sensitivity scores

Automatically respond to price changes based on your current strategy and apply your own business rules. Also, you can set parameters to always act within boundaries, thus avoiding mistakes during manual input.

Act upon price sensitivity to grow sales & margins

Setting a powerful AI-driven competitive pricing model allows your business to stay in control of your position relative to the competition, preventing the loss of potential revenue.


6 Areas Where Price Sensitivity is Used for Optimal Prices

Using price sensitivity for unrelated product categories doesn't fetch the desired results. Hence, products must be strategically assessed to gauge profitability overall. Here are some areas where price sensitivity is used to optimise prices.

While looking for a pricing software, one should take into account the following data system integrations and features.

In SYMSON, besides the following top attributes, you can apply psychological pricing tactics, import and export seamlessly in your preferred file type and much more. Read more about what you can do with the Genius Dynamic Pricing in SYMSON.

Retail Pricing: Retailers analyze price sensitivity to set competitive and profitable prices for their products.

E-commerce: Online stores adjust prices dynamically to respond to customer sensitivity and market trends.

Product Launch: Companies assess price sensitivity before launching new products to determine pricing strategies.

Discounts/Markups: Businesses use price sensitivity to design effective promotions and discounts.

Market Entry: Companies entering new markets analyze local price sensitivity for successful market penetration.

Subscription Services: Subscription-based businesses tailor pricing tiers based on customer sensitivity.

How SYMSON’s Pricing Algorithm works

We created the algorithm with the help of Scientific Researchers from Erasmus University: bringing in scientific models, data science, and software engineering together in an AI platform where users can optimise product prices.

Gathers and Analyses Historical Product Data

SYMSON collects historical product data to calculate the price-elasticity, Key Value Items products, and other aspects to spot patterns and learn from the past.

Identifies the nature of your product assortment

Our system analyses your product assortment and identifies their price sensitivity for better categorisation. It then suggests the correct prices accordingly.

Allows to Customise the Algorithm to your Business

You can customise primary drivers like price elasticity, Key Value Items, margins, and more to your industry with customised drivers. Connect your data source to SYMSON to build your pricing strategy.

How SYMSON’s Elasticity 
Algorithm Helps Companies

How SYMSON’s Price
Engine Helps Companies

How SYMSON Helped WitwayGain a Competitive Edge in its Market

How SYMSON’s AI Pricing Tool Works

How SYMSON’s Competitive 
Pricing Helps Companies

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How SYMSON’s elasticity feature helped La Place enhance their pricing strategy

What you can do with Genius Dynamic Pricing?

What you can do with Genius Dynamic Pricing?

How SYMSON’s competitive pricing feature helped 
INDI enhance their pricing strategy

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Frequently Asked Questions on Price Sensitivity

Got a question? We're here to answer! If you don't see your question here, drop us a line on our Contact Page.

What is price sensitivity
What is price sensitive and price insensitive?
How do we measure price sensitivity in modern pricing?
What is another name for price sensitivity?
What is the difference between price sensitivity and price elasticity?


The Next Steps on your Pricing Journey

We’d love to share some incredible resources that will help you further understand pricing strategy and give you the best head start on your pricing journey.