Predictive Pricing

What is Predictive Pricing?

Predictive pricing is a data-driven pricing strategy in which companies establish prices based on collected and analyzed data. Predictive analytics — the backend of predictive pricing — uses modern technology to gather this data and make forecasts that help business leaders set prices at a level that is optimal for profitability, revenue optimization, and current market dynamics.

Although some organizations use predictions to set prices that aren’t subject to change, “predictive pricing” typically refers to dynamic pricing and pricing automation. Since it entails the use of AI, machine learning, and data mining tools, the entire process is sometimes carried out without human intervention.

Predictive pricing sits at the intersection of business strategy and data science — using historical data and predictive analytics techniques, businesses can effectively forecast demand and find the optimal price for their products.


  • Dynamic pricing
  • Predictive pricing analytics
  • Predictive price modeling
  • Predictive price optimization

How Predictive Price Optimization Works

Predictive price optimization works by leveraging predictive analytics to determine customer behavior, market trends, competitive forces, and other factors that influence pricing. The data collected is then used to create a pricing model that helps businesses adjust their price points in real time.

Briefly, predictive pricing works like this:

1. Data Collection

The process begins with collecting data points from various sources, such as historical sales records, customer behavior, market trends, competitive intelligence, and other pricing factors. This data serves as the foundation for building the predictive pricing model.

To collect data, businesses use a range of tools, such as web scraping, machine learning algorithms, and data mining techniques (e.g., clustering, predictive analysis, and natural language processing).

2. Data Preprocessing

Once the data is collected, it undergoes preprocessing to clean, structure, and organize it in a way that makes it suitable for analysis. This step may involve removing duplicates, filling in missing values, and normalizing the data to ensure consistency.

For example, global pricing data accounting for multiple locations or currencies won’t help companies set local prices if it’s all lumped together in one dataset.

3. Feature Engineering

Feature engineering refers to the process of selecting, transforming, and creating features from the dataset. Feature engineering helps ensure that only relevant information is included in the predictive pricing model.

In this stage, machine learning modules identify similarities and patterns within the data. These modules extract relevant features from the data, which help companies understand the relationships between different variables and how they impact pricing.

This step also involves feature selection — choosing which variables are most important for building successful predictive models.

4. Model Training

After feature engineering, a predictive pricing algorithm is trained using the preprocessed data. The algorithm learns from the data by identifying patterns and relationships between the input variables (features) and the target variable (price). This training process helps the algorithm make accurate predictions about future prices based on the input data.

5. Model Validation

Once the model is trained, it’s validated using a separate dataset to evaluate its performance and accuracy. Model validation verifies that the model generalizes well to new data without overfitting or underfitting.

For instance, if the model is trained on a small set of sales data, it might predict prices accurately for one customer segment but struggle to predict responses to pricing in larger pools. In some cases, it may not even represent the true behavior of customers.

In any case, model validation identifies and fixes these issues before they can affect the accuracy and effectiveness of the statistical analysis.

6. Price Forecasting

With a well-trained and validated model, the system can generate price forecasts based on current market conditions and input data. Accurate forecasts can determine optimal price points for products or services.

If an organization is using a BI tool like Power BI or Tableau, this price forecast could be visualized as a graph or dashboard to help business leaders make better decisions. If it’s part of a pricing engine, the prices can be adjusted in real-time based on market trends and customer behavior.

7. Price Adjustment

Finally, the model-generated price forecasts are used to adjust prices in real-time. Depending on the organization’s chosen pricing strategy, the adjusted prices can be updated manually with human intervention or automatically using automated pricing software (such as in real-time pricing and dynamic models).

How Predictive Analytics is Useful in Pricing Goods and Services

Most modern companies use predictive analytics for at least some of their pricing strategies. Given the continuous improvement and increased accessibility of artificial intelligence and machine learning models, it’s quickly become one of the best ways to remain agile while maximizing profits.

Here are some specific ways predictive analytics is useful in setting prices for goods and services:

Best Practices for Using Predictive Pricing to Increase Sales

Predictive pricing can be a powerful tool for businesses, but it’s important to follow best practices to ensure success. Here are some tips for using predictive analytics in pricing:

Ensure continuous data access.

To leverage the full power of predictive pricing strategies, companies need to have complete access to the information they need, including pricing data, customer behavior insights, and market trends. Since predictive analytics “teach themselves” with the data they get, it’s important to have real-time access to fresh and reliable input.

Some of the data required for predictive price strategies is hard to come by. For example, a company may need to access competitor pricing data, which is often hidden or manipulated. Without expensive tools or the budget to build out a data science function internally, comapnies might not be able to access this data.

Use the right variables and inputs.

Pricing analytics tools correlate other variables to the data they analyze. While this is precisely what makes them so powerful and informative, it steers businesses in the wrong direction entirely when they present the wrong variables.

For example, a new local hotel might try to predict its services’ prices by analyzing nearby hotels’ prices. But if the data presented only includes hotel room size and number of beds, it won’t accurately reflect the quality or amenities that customers are looking for, nor will it account for seasonality and annual events taking place throughout the year.

In that instance, the predictive software would offer inaccurate results and suggest a pricing strategy that wasn’t as useful as they’d hoped for.

Account for timing when evaluating model credibility.

Timing is a critical factor in whether or not a predictive model plays out as it should. Some factors — such as exact consumer behavior, new regulations, or changes in external conditions — can’t be accurately predicted ahead of time.

For example, if a business runs predictive analytics and then adjusts its prices for the coming holiday season, it can’t expect to achieve the same results from month to month over that period. The predictive model needs a larger historical data set to understand seasonality trends.

Don’t rely solely on predictive models.

Unless an organization uses a real-time pricing algorithm (like Amazon’s, which makes over 250 million changes per day), it won’t be able to react to every variable as it comes up.

Relying too heavily on a predictive model and neglecting to consider external factors and manual optimizations leads to missed opportunities and pricing mistakes.

It’s important for organizations to maintain a balance between automatic adjustments and manual fine-tuning when it comes to pricing strategies. Talking to real customers and conducting market research, for example, can help businesses capture real customer sentiment most predictive models would miss.

Don’t focus too heavily on competitors.

Almost every company is differentiated in some way, be it one or more of its customer segments, product add-ons, business model, or niche offerings. Unless an organization operates in an industry with extremely well-defined prices (such as soft drinks or fast food), over-focusing on competitive prices fails to account for a brand’s unique qualities and its customers’ perceived value and specific expectations .

Instead, companies should focus on both their competitive position and the factors that make them stand out from the competition. To set the right product price, they need to understand customer demographics, preferences, and needs and cross-reference those with other external data points (such as market trends and industry shifts) for the most informed pricing decisions.

Industry Examples of Predictive Pricing

Although predictive pricing is used in plenty of verticals, the best examples of its effectiveness are in retail, travel, and real estate companies, which rely on current market intelligence the most to maximize their sales and profit margins.


Setting a retail price is harder than it looks, especially for new companies without much data (or the budget to access it).

As previously mentioned, enterprise companies often use predictive pricing to adjust their prices in response to market fluctuations. Since they have access to vast amounts of data, they can use advanced machine learning algorithms to analyze customer behavior, competitor pricing, and other relevant factors to do so.

Smaller retailers may not have the same resources or scale, but can still benefit from predictive analytics by using it to identify when to offer discounts, raise prices, or launch promotions.

For instance, a boutique clothing store could use predictive pricing to determine the optimal time to markdown seasonal items or offer discounts on slow-moving inventory.

The use case is the same in both cases: Make informed decisions about when to modify prices to maximize profit margins and minimize the risk of overstocking.


Profit margin isn’t always the key motivator for companies in the travel industry. Airlines, for example, receive government subsidies to solve complicated logistics problems, such as connecting flights and keeping routes available to smaller areas that would otherwise not be profitable.

Uber is another example of predictive pricing without a focus on profits. The company adjusts fares based on current demand and supply while making sure drivers get fair compensation for their time (which is part of the reason why the rideshare dominator has never had a profitable year).

Hotels do care about profitability — they use predictive pricing to keep their rooms occupied. By studying customer behavior, they can adjust prices according to the number of bookings and other external factors such as weather, events in town, or competitive occupancy rates.

Whether it’s a rideshare service, airline, or hotel, most travel industry companies use a real-time approach to dynamic pricing. It’s not uncommon to see price fluctuations daily based on algorithmically-predicted demand.

Real Estate

Companies like Zillow employ data scientists who analyze market trends and use machine learning algorithms to produce property values.

The same technique is used for rental prices to help landlords maximize their revenues and minimize vacancy periods.

By analyzing external factors such as nearby amenities, school ratings, crime rates, and public transportation availability (as well as internal criteria like tenant/owner history and remodels), a predictive model can generate accurate rental estimates that landlords can use to adjust their prices.

Technology Trends in Predictive Pricing

Predictive pricing as we currently know it is relatively new. It’s seen in a lot of industries, but for the most part, organizations still struggle to keep up with the ever-changing market conditions and customer demands.

The key trend now is automating price adjustments based on external events or internal policies (such as loyalty programs). That way, businesses don’t have to manually input pricing data every time something changes.

Machine Learning

Advancements in machine learning have driven process automation initiatives across the board, including predictive pricing.

Organizations can now combine external data sources like customer preferences and market trends with internal criteria (e.g., product availability, brand loyalty, price elasticity) to build accurate price models that adapt in real-time.

The main advantage of machine learning is it trains itself in the background, so businesses don’t have to update it with new data constantly.

Integration with CPQ and Billing

CPQ software and billing platforms are both critical data sources for predictive pricing models and elements that string together the whole process.

CPQ (configure, price, quote) is a critical link between existing products and real-time sales data. Since it’s in charge of creating product configurations and setting prices for them, it provides further insight into which customers are more likely to buy certain items at a given price.

Billing generates invoices and collects customer payments, but it also underscores customer retention efforts and customer lifetime value (CLV) metrics.

By collecting customer payment histories, organizations can keep track of their customers’ behavior and tweak pricing strategies based on their actual buyers.

Further integrating billing and CPQ with predictive pricing models means they’ll have better and more abundant data to use in future predictions, which gives businesses more accurate predictions and helps them develop a sustained competitive advantage based on their data infrastructure.

People Also Ask

What is the basic principle behind predictive modeling?

The basic principle behind predictive modeling is to use historical data and external variables to predict future outcomes. Behind the scenes, a predictive model uses complex algorithms to analyze data and identify patterns that can be used to make predictions.

What are the benefits of using machine learning in price prediction?

Using machine learning in price prediction is primarily beneficial because it eliminates the need to manually update pricing models, allowing businesses to act in real-time and adjust their prices accordingly. Machine learning also improves the accuracy of predictions by analyzing large amounts of data that would be too intricate for humans to process.