Price Optimization

Price optimization

What is Price Optimization?

Price optimization is a software-enabled method of intelligent pricing that allows businesses to understand how customers will respond to different prices for their products and services in each channel. It uses mathematical algorithms to determine the product price that hits the “sweet spot” between profitability, value proposition, and customer satisfaction.

Briefly, the price optimization process works as follows:

  1. Collect data. Gather customer, market, competitive, inventory, sales, and financial data from different sources.
  2. Analyze it. Use software to understand customer behavior as it pertains to pricing.
  3. Test prices. Adjust product prices that yield suboptimal results (e.g., customers not purchasing the product despite a solid product-market fit).
  4. Compare tested prices to company financials. Determine whether or not prices favorable to customers are sustainable for the business long-term.
  5. Optimize pricing. Over time, figure out which prices elicit the desired reactions from customers and ensure that the company’s financial objectives are met.

Determining the optimal price for a product or service isn’t an exact science. It’s an ongoing iterative process that relies on observation of market dynamics and the help of artificial intelligence and machine learning models.

In some cases — such as in the airline and hotel industries — price optimization occurs in real-time. Called dynamic pricing, this form of price optimization continuously accounts for fluctuations in demand and automatically reflects prices accordingly.


  • Pricing engine
  • Price optimization software
  • Dynamic pricing strategy

Why Companies Use Price Optimization

Companies use price optimization for different reasons, but they all boil down to one thing: making more money sustainably while reflecting customer preferences as closely as possible.

Price optimization allows companies to:

  • Increase profits while maintaining customer satisfaction. Considering internal variables like inventory and company financials in addition to market trends improves the chances of keeping customers happy without sacrificing a healthy profit margin.
  • Increase sales volumes. When pricing reflects customers’ perceived value, more of them are likely to convert (with higher sales velocity).
  • Hedge against competitor pricing strategies. In highly competitive markets, effective price optimization can give a company a crucial edge over competitor prices. Businesses can differentiate themselves and expand their market share by pricing their products or services more favorably.
  • Make data-driven decisions. By comparing how different pricing strategies play out in reality to their long-term sustainability for the company, organizational leaders can make pricing decisions that grow the company and retain customers long-term.

Companies that Use Price Optimization

Since it’s in every business’s best interest to have “optimized prices,” practically every business uses price optimization in some form.

Companies that use advanced forms of price optimization include:

  • Airlines
  • Hotels
  • Real estate and rental businesses
  • Rideshare services
  • Food delivery platforms
  • B2B manufacturers
  • Major online retailers
  • SaaS companies

Many others use price optimization, but the AI/ML backend of the software is less important for smaller companies.

Benefits of Price Optimization

Automated price optimization tools take the guesswork out of pricing. For organizations large and small, optimized pricing is among the most critical elements of sustained competitive advantage.

Let’s take a look at a few hypotheticals to understand how pricing optimization works and what real-world benefits businesses can gain from it.

Pricing Based on Data

Company decision-makers need to have confidence in each pricing decision before setting it live.

Smaller companies might be able to get away with manually pricing their products for a while. But even then, this makes understanding their impact more difficult.

Businesses take a loss (often without realizing it) when the pricing process doesn’t reflect accurate data.

Let’s consider a hypothetical company, Acme Electronics, that sells high-end audio equipment. This includes products such as headphones, speakers, turntables, and sound systems. Acme operates both an ecommerce site and several physical retail locations.

Pricing Based on Different Channels

Acme’s customer behavior significantly varies between the online and offline channels. Online customers tend to be more price-sensitive, often comparison shopping between multiple websites.

In-store customers value the personal interaction, product demonstrations, and immediate gratification. As such, the online store could potentially have lower prices to compete on the digital market, while the physical store could afford slightly higher prices due to the added value provided.

Price optimization tools can help Acme analyze and understand these different customer behaviors and their willingness to pay. They can then dynamically adjust the prices based on the channel and the customers’ purchasing habits.

Time-Based Pricing

Acme notices that sales tend to spike around specific events, like holidays or music festivals. Using a price optimization tool, Acme anticipates these demand surges and adjusts prices to maximize revenue during high-demand periods.

Competitor-Based Pricing

Acme’s market has several direct competitors, and its products aren’t much different from theirs. Price optimization software can track competitors’ pricing and dynamically adjust Acme’s structure to ensure competitive prices.

Based on competitors ‘ actions, the tool can determine when Acme can charge a premium and when it should reduce prices to remain consistent with customer expectations.

Bundling and Cross-Selling

Through transaction data analysis, Acme realizes customers who buy turntables often purchase speakers within the same month. Price optimization tools could suggest bundling these products in CPQ for a special price, stimulating more immediate cross-selling opportunities and increasing overall order value.

Profitability Analysis

Not all products contribute equally to Acme’s bottom line. Price optimization software can help analyze each SKU’s profitability, considering operating costs, sales velocity, and market demand. This information can guide pricing adjustments to improve margins, phase out low-profit items, or push high-profit products more aggressively.

Accurately Measures Price Elasticity

Connecting the dots between Price Input X and Sales Output Y allows Acme to accurately measure customers’ price sensitivities on both a macro level and a per-market-segment basis. This gives them confidence to adjust their prices without affecting sales volume, ensuring they remain competitive in the market while maximizing profitability.

In all the above scenarios, Acme can use the optimization tool’s predictive capabilities to test out different pricing strategies and see their potential impact before implementing them. This ensures they’re making data-backed decisions, maximizing profitability, and minimizing potential risks.

Pricing Specific to Location and Customer Segment

Now, consider a SaaS company, CloudSoft, that has recently expanded and opened new offices across the country. It offers different services like cloud storage, cloud-based software, and enterprise-level cloud security services.

The SaaS vendor recognizes that different regions and different customer segments have varied needs and pricing sensitivities. So its sales team uses price optimization software to tailor its pricing strategies to specific circumstances to drive customer loyalty.

Regional Pricing

CloudSoft knows the cost of doing business and level of competition vary wildly across regions.

In newly-targeted regions, CloudSoft may want to establish a foothold before charging full price for its product. Price optimization software could help them offer a short-term discount to new businesses in these areas to establish a foothold.

Customer Segmentation

CloudSoft’s customer base is diverse, ranging from small startups to large corporations. Each segment has different needs and pricing sensitivities.

A startup may be more price-sensitive but require less extensive services, while a large corporation may be willing to pay a premium for comprehensive, top-tier services.

Using its price optimization software, CloudSoft can create pricing models suited to each customer segment.

For example, they can offer basic tiers geared toward their small business customers and offer an enterprise tier to larger companies with unpredictable usage needs.

Competitive Analysis

Like most B2B companies, CloudSoft faces intense competition from other SaaS providers. The pricing tool can analyze competitors’ pricing strategies in these regions and adjust CloudSoft’s prices to ensure they remain competitive.

Maintain Competitive Prices

Competitive prices are central to the success of any organization, and some companies rely on real-time, automatic pricing updates to maximize revenue and capitalize on demand.

Demand fluctuations, varying operational costs, and competitive pressures all make for a wildly volatile pricing process. AeroFly — a made-up airline — uses price optimization software to maintain competitive prices and optimize its revenue.

Real-Time Pricing

Flight prices often fluctuate based on the time of booking, day of the week, season, and how full the flight is. They also increase as the flight date nears and the plane fills up.

AeroFly’s price optimization software monitors these factors in real-time and reflects changes across the web multiple times per day.

Competitor Analysis

When one airline raises or lowers its prices, others soon follow suit. Even “budget” airlines leverage the last-minute surge pricing technique (to make themselves seem more attractive).

AeroFly’s price optimization software closely monitors competitors, assessing how their prices compare — and whether they should be adjusted in response. Unlike most businesses, the airline’s software will execute these changes automatically.

Segmented Pricing

Different customers have different travel needs and pricing sensitivities. For example, business travelers value schedule convenience and are less price-sensitive, while leisure travelers prioritize low costs.

The machine learning backend would segment AeroFly’s customer base and create tailored pricing strategies for each segment (e.g., suggesting higher prices for last-minute bookings and lower early bird prices to attract price-sensitive leisure travelers).

Revenue Management

AeroFly’s price optimization software can also help with revenue management. For instance, it can analyze the profitability of each route considering factors like passenger demand, flight frequency, and operational costs.

Event-Based Pricing

The tool can also track major events, like sports matches, music festivals, or trade fairs, that will lead to spikes in demand on certain routes. AeroFly can either raise (capitalize on demand) or lower (offer a special deal for these specific travelers) prices, depending on the situation.

Simulation and Forecasting

With its predictive capabilities, the price optimization model simulates different pricing strategies and forecasts their potential impact.

AeroFly can test out multiple pricing scenarios, such as a price war with a competitor, a surge in fuel prices, or a change in demand due to a major event.

Data Required for Price Optimization

The more frequently a business updates its pricing, the more data it needs. Amazon, for instance, adjusts its prices 2.5 million times per day. Other online retailers update their prices frequently as well.

Most importantly, a price optimization strategy requires data about the product or service, customer segmentation, market conditions, and competitors.

Customer Data

Customer data is the Holy Grail of price optimization. Pricing is one of the many ways a company delivers value to its end users, and information from current customers can help companies tailor their pricing strategies to the needs of their target audience.

Customer data includes:

  • Financial metrics (average revenue per user, customer lifetime value, customer acquisition cost)
  • Churn rate per customer segment
  • Conversion rates
  • Customer responsiveness
  • Purchase and browsing history
  • Website engagement
  • Product usage

Comparing these key indicators before and after implementing a new product price helps measure the impact of pricing changes on customer behavior.

Demographic Data

Demographic data is a specific type of customer data that helps companies understand target audiences and create pricing strategies accordingly.

Demographic data includes:

  • Age
  • Gender
  • Location
  • Income level
  • Education level

Demographics significantly impact how well-received a pricing model is. Younger consumers, for example, are generally more price-conscious than older ones.

Firmographic Data

B2B companies rely on firmographic data, which is essentially demographic information, but for other companies.

Firmographic data includes:

  • Company size
  • Industry

The goal is to understand how different business types may respond differently to pricing and which price tiers may be most attractive.

Geographical Data

Location is an important factor when it comes to pricing. Consider the airline example: they often set different prices for different regions, as economic conditions and demand vary greatly.

Geographical data includes:

  • Time zone
  • Local competitors
  • Weather
  • Language/culture
  • Currency exchange rate
  • Taxes and duties regulations

Market Data

Although each company’s product will be somewhat differentiated, market dynamics shape how pricing is set.

Market data includes:

  • Demand/supply gap
  • Competitors’ prices and offers
  • New market entrants
  • Regulations and restrictions
  • Price elasticity of demand

The Role of Price Elasticity in Price Optimization

Demand is elastic when a small price increase results in a substantial drop in sales. If demand remains relatively steady despite a price change, it’s inelastic.

Price elasticity plays a pivotal role in price optimization because it:

  • helps set prices to balance volume and revenue based on elasticity.
  • facilitates tailored pricing strategies for different customer segments according to their price sensitivity.
  • acts as a pricing engine in industries like travel and hospitality, where pricing and demand change frequently.
  • informs effective promotional campaigns or discounts for products with elastic demand.
  • allows strategic pricing in relation to competitors by comparing product price elasticities.
  • helps predict and manage the impact on sales of necessary price changes.

Price optimization is only possible thanks to software. Here are a few of the major technology trends in price optimization solutions:

Price Optimization Software

Machine Learning

To deliver pricing predictions and analyses with the accuracy modern software does, machine learning does most of the legwork.

Rather than looking at data retrospectively, machine learning learns from the continuous flow of sales data to predict future trends and behavior patterns.

Pricing Engine

The pricing engine is the backend of a price optimization solution. It takes customer data, market conditions, and competitor prices into account to create up-to-the-minute prices for products and services.

Dynamic Pricing

Dynamic pricing supports continuous fluctuations in demand and complex, personalized pricing models. It allows companies to adjust prices on the fly, giving them the flexibility to compete with price-sensitive customers or capitalize on revenue opportunities as they arise.

Configure, Price, Quote (CPQ)

Modern CPQ supports dynamic pricing and price optimization as a standard. Since it is a critical source of sales and product data, CPQ automation has dramatically improved the accuracy of price optimization models.


What is the difference between price optimization and dynamic pricing?

The main difference between price optimization and dynamic pricing is that the former is a broader term. Price optimization takes into account a wider range of data, such as customer segmentation, market dynamics, and competitors’ prices, to arrive at the optimal price for any given situation. Dynamic pricing is a tactic used in price optimization that responds to changes in demand by adjusting prices accordingly.

What are examples of price optimization?

Examples of price optimization include airfare, real estate (and rentals), event ticketing, food delivery, rideshare, online retail, and SaaS subscriptions.

CPQ Integrations