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Pricing
Discover all ins and outs about Artificial Intelligence Pricing

AI Pricing Guide

Why, benefits, use cases and more...

ABOUT COMPETITIVE PRICING

Artificial Intelligence in Pricing: Using Price Intelligence to Enhance Profitability and Process Sustainability

1

What is Price Intelligence or AI Pricing?

The pricing process is another area in the business ecosystem where Artificial Intelligence is revolutionising. Here, it's not just changing the game but redefining it entirely.

Today, companies are constantly looking for innovative ways to increase profitability and improve their internal processes. AI pricing collates large datasets and dynamically optimises your prices while taking care of your margin goals.

Thanks to the integration of Artificial Intelligence (AI) and Price Intelligence, brands are now able to take full control of their pricing ecosystem and their profitability.

2

What is Explainable AI in Pricing?

Artificial Intelligence in pricing, often referred to as 'AI pricing' or 'pricing machine', is a sophisticated approach that involves using machine learning models and data analytics to set, adjust, and price intelligently and optimally.

  • Pricing optimisation tools with explainable AI, can share insights and explain the logic behind reaching those predictions and outcomes. Thereby, it makes the tool accessible to other stakeholders of the company instead of limiting it to the Data Scientists.
  • To break it down with an example, if you sell a highly competitive product, the new price suggestion may be lower. Explainable AI would share insights that show, the price of the product is currently down among your competitors and the new price would continue to attract more customers.

Price Intelligence software uses large datasets, AI pricing algorithms can predict market trends, understand customer behaviour, and dynamically adjust prices in real time.

3

Avoid Profit Margin Leakage with an Advanced AI Pricing System

The principal purpose of AI-powered price intelligence software is to enhance company profit margins sustainably. The process focuses on efficient growth according to the market scenarios, competition, elasticity and product price sensitivity. Here’s how to work out profit margins for your business. Let’s break this down further in this guide.

This can be achieved by analysing historical sales data, conducting customer surveys, and monitoring competitor price changes. Here are a few factors that you need to consider while developing a competitive pricing strategy.

How is Machine Learning Pricing and Business Profitability

Machine Learning (ML) in pricing empowers businesses to dynamically adjust prices and optimize profitability. It does so by analyzing real-time market data, customer behavior, and competitor strategies. Here’s an in-depth blog about how Machine Learning is revolutionizing business profitability.

By predicting optimal pricing points and customer demand, Machine Learning enables businesses to maximize revenue during peak times and maintain a competitive edge through strategic pricing adjustments.

Using AI in pricing likewise leads to enhanced decision-making, reduced human error, and improved efficiency in resource allocation. You can also consider a product's price sensitivity level to set accurate and intelligent prices and not leave money on the table.
The automation of the pricing process not only saves time but also allows businesses to focus on strategic growth initiatives. Overall, ML in pricing is a transformative tool that significantly boosts profitability and adapts swiftly to ever-changing market conditions.

AI pricing benefits

Benefits of AI Pricing Systems: Make your Organisation’s Processes More Efficient

Artificial Intelligence in pricing is a transformative tool that helps organizations succeed in several compelling ways. By integrating AI into pricing strategies, companies can make more informed, efficient, and dynamic decisions, leading to increased profitability, competitiveness, and market adaptability. Here's how AI in pricing contributes to organizational success:

Ability to use Dynamic Pricing

AI enables dynamic pricing, allowing businesses to adjust prices in real time based on various factors like demand, inventory levels, competitor prices, and market trends. This responsiveness ensures prices are always aligned with the market and customer expectations, leading to increased sales and revenue.

1

Optimize prices automatically

AI algorithms analyze vast amounts of data to determine the optimal price points for all your products or services. This price optimization considers customer behavior, production costs, and market conditions to maximize profitability while also maintaining competitiveness.

2

Set competing prices without hurting profit margin

Competitive pricing intelligence involves understanding competitors' pricing strategies and positioning your prices accordingly. AI enhances this process by providing real-time data and predictive insights, enabling businesses to stay ahead of the competition and price intelligently.

This competitive pricing intelligence allows businesses to proactively adjust their prices, ensuring they remain attractive to customers while safeguarding profit margins.

3

Better Customer Segmentation for tailored pricing

AI helps in identifying and understanding different customer segments based on their purchasing behavior and sensitivity to price changes. By tailoring prices to each segment, organizations can increase conversion rates and customer satisfaction while optimizing revenue from each group.

4

An efficient pricing process frees up more time

Automating the pricing process with AI reduces the manual effort requiredand minimises human error and other inconsistencies. This efficiency freesup resources, allowing teams to focus on strategic initiatives ratherthan the time-consuming task of pricing monitoring and adjustments.

5

Helps mitigate price wars by changing focus to value

Instead of engaging in detrimental price wars, AI helps organizations adopt more sophisticated pricing strategies, focusing on value-based pricing rather than just competing on prices. This approach preserves brand integrity and profit margins!

6

Predicts future market shifts to prepare with smart pricing

AI uses historical data and predictive models to forecast future market trends, demand patterns, and customer responses to pricing changes. This foresight allows businesses to anticipate market shifts and adjust their pricing strategies accordingly, staying one step ahead of the competition.

7

Manages growing complexities as your business scales:

AI systems can scale with the business, handling more data and complexity as the company grows. They can also adapt to new markets and products quickly, making it easier for businesses to expand and diversify.

8

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 Does an AI Pricing Algorithm Work?

AI pricing algorithm funtionalities

How Does an AI Pricing Algorithm Work?

An AI pricing algorithm works by using machine learning techniques to analyze large datasets and make informed decisions about optimal pricing strategies. Learn more about how a pricing algorithm works. Here's a quick breakdown of how it typically operates:

1

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.

2

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.

3

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. 

4

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.

5

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.

1

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.

2

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.

3

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.

4

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.

5

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.

1

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.

2

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.

3

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.

4

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.

5

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.

1

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.

2

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.

3

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.

4

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.

1

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).

2

Surge Pricing

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

3

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.

4

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.

5

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.

6

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.

1

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.

2

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.

3

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.

4

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.

5

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.

6

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.

7

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.

8

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, AI pricing algorithms work by gathering and analyzing large datasets to make informed pricing decisions. They use machine learning to understand complex market dynamics and continuously adjust pricing strategies to optimize profitability. This process involves a cycle of training, prediction, and adjustment to ensure that the pricing recommendations remain accurate and effective over time.

To name a few old-school pricing methods other than Excel, are the Van Westendorp pricing model and the Gabor-Grange methods. However, their biases and inaccuracies were identified quickly.

Similarly, even today, many businesses still use Excel sheets to manage prices. Find out how to combat the challenges of using Excel sheets.

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.

GET COMPETITOR PRICING DATA

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.

1

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.

2

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.

3

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.

4

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.

5

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.

6

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.

ERP SYSTEM VS PRICING MANAGEMENT TOOL

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:

1

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.

2

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.

3

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.

4

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.

5

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

Top Features to Look for in a Pricing Software

Advanced pricing software has multiple features that free up the time and resources of pricing experts. Through automation and in-depth data analysis, a pricing machine makes the process of pricing futuristic. This efficient approach to setting optimal prices helps businesses sustainably grow and price intelligently. Take a look at the exhaustive features that SYMSON enables you with.

E-commerce
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Airlines
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Fast-food chains
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Customise your pricing
strategy
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Use competitive pricing
intelligence
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Unlock seasonality insights
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In these examples, businesses use competitive pricing strategies to identify market trends, react to competitor price changes, and optimise their own prices. This approach enables them to balance attracting customers, offering value, and maintaining profitability.

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Prysinc

Prysinc is a powerful pricing intelligence tool designed to help businesses track and analyze competitor pricing data. With its real-time price monitoring capabilities, Prysinc provides businesses with accurate and up-to-date information, allowing them to make informed pricing decisions and stay ahead of the competition.

Prysinc

Prysinc is a powerful pricing intelligence tool designed to help businesses track and analyze competitor pricing data. With its real-time price monitoring capabilities, Prysinc provides businesses with accurate and up-to-date information, allowing them to make informed pricing decisions and stay ahead of the competition.

Blackcurve

Blackcurve is a comprehensive pricing optimization platform that helps businesses to develop data-driven pricing strategies. By combining competitor pricing data, market trends, and internal cost data, Blackcurve provides businesses with actionable insights and recommendations, enabling them to optimize their pricing and boost profitability.

Omnia Retail

Omnia Retail is a comprehensive pricing and marketing automation platform that combines competitor pricing data with advanced analytics and machine learning algorithms. This powerful combination enables businesses to develop and implement intelligent pricing strategies that adapt to changing market conditions and maximise profitability.

Competerra

Competerra is a competitive intelligence platform that provides businesses with in-depth insights into competitor pricing, promotions, and assortment strategies. With its advanced analytics and visualisation capabilities, Competerra helps businesses to identify pricing opportunities and develop effective pricing strategies based on data-driven insights.

PROS

PROS is an AI-powered pricing and sales optimisation platform that helps businesses to maximise their revenue and profit margins. By combining competitor pricing data with advanced machine learning algorithms, PROS provides businesses with actionable recommendations for optimising their pricing strategies and staying competitive.

Vendavo

Vendavo is a leading enterprise pricing solution that offers a range of tools and features designed to help businesses maximise their profit margins. With its advanced analytics, segmentation, and price optimisation capabilities, Vendavo enables businesses to develop dynamic and responsive pricing strategies based on competitor and market data.

Price2Spy

Price2Spy is a versatile competitor price monitoring tool that provides businesses with real-time pricing data from a wide range of sources. With its customisable alerts, reports, and integrations, Price2Spy helps businesses to stay informed about competitor pricing activities and make data-driven pricing decisions.

PriceFX

PriceFX is a flexible and scalable pricing platform that provides businesses with various pricing tools and features, including competitor price monitoring, analytics, and optimisation. By leveraging the power of the cloud and advanced analytics, PriceFX helps businesses to develop and implement effective pricing strategies that maximise their profitability.

Price Edge

Price Edge is a cloud-based pricing solution that helps businesses to develop, manage, and optimise their pricing strategies. With its robust competitor price tracking, analytics, and price optimisation capabilities, Price Edge enables businesses to make data-driven pricing decisions and maximise their profit margins.

SYMSON

SYMSON is an AI-driven pricing optimisation platform that combines competitor pricing data with advanced machine learning algorithms to provide businesses with actionable pricing insights. By automating the pricing process and providing real-time recommendations, SYMSON helps businesses to optimise their pricing strategies and stay competitive in the market.

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.

Top Features to Look for in a Pricing Software

Advanced pricing software has multiple features that free up the time and resources of pricing experts. Through automation and in-depth data analysis, a pricing machine makes the process of pricing futuristic. This efficient approach to setting optimal prices helps businesses sustainably grow and price intelligently. Take a look at the exhaustive features that SYMSON enables you with.

Customise your pricing strategy
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Use competitive pricing intelligence
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Unlock seasonality insights
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In these examples, businesses use competitive pricing strategies to identify market trends, react to competitor price changes, and optimise their own prices. This approach enables them to balance attracting customers, offering value, and maintaining profitability.

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A Peek into SYMSON’s Infrastructure

When it comes to the infrastructure of a pricing tool, security and scalability must be a priority. SYMSON can handle huge datasets and leverage them for use. All this while maintaining top security and accommodating more products. Likewise, our product scales with your business. Take a closer look at SYMSON’s infrastructure.

Proven Our infrastructure is based upon Microsoft components to deliver smooth and reliable performance.

Scalable We can handle large and complex data sets and real-time calculations thanks to our microservices architecture.

Cutting Edge Our Smart AI and ML Infrastructure delivers cutting-edge learning to the pricing algorithm.

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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.

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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

More accurate price positioning

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.

Automate your pricing process

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.

Increase sales volume and revenue

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.

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

MegaGroup

Implementing SYMSON enabled MegaGroup to significantly enhance their pricing strategies. It bolstered human expertise with data-driven insights, leading to more informed decisions on promotions and pricing. The company achieved structural optimization of margins and profitability. Moreover, SYMSON facilitated the adoption of new pricing strategies tailored to regional dynamics, elasticity, and key value items, marking a substantial leap in their pricing sophistication.

Fotocadeau

SYMSON's introduction came at a critical time, transforming its pricing approach from manageable Excel sheets to a sophisticated, automated system. This shift effectively secured crucial pricing knowledge, and accommodated an expanding product range, diversified sales channels, and a growing workforce, ensuring swift and automatic implementation of pricing strategies to sustain their rapid growth trajectory.

INDI

SYMSON significantly improved the client's pricing strategy, automating and optimizing prices to save time and enhance margins. The client praised the system's efficiency and the reduction in manual errors, leading to a more profitable, data-driven approach. This powerful combination of quality data, advanced technology, and a dedicated team enabled SYMSON to drive the client's success in the competitive wholesale market.

HAVE A QUESTION?

Frequently Asked Questions on Price Optimisation

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

How to optimize prices for maximum profit?
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Resources

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.