What is predictive pricing?

Predictive pricing is a way for companies to set prices based on data that they have collected and studied. The back end of predictive pricing is called predictive analytics. It uses modern technology to collect this data and make predictions that help business leaders set prices at the best level for making money, optimizing income, and considering how the market is changing right now.

“Predictive pricing” usually means dynamic pricing and pricing automation, but some businesses use estimates to set prices that won’t change. The whole process is sometimes done without any help from a person because AI, machine learning, and data mining tools are used.

Predictive pricing is a mix of business strategy and data science. Using historical data and predictive analytics techniques, businesses can accurately predict demand and find the best price for their goods.

Synonyms

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

How to Use Predictive Price Optimization

Predictive price optimization uses predictive analytics to determine how customers will act, how the market is changing, how much competition there is, and other things that affect prices. The collected information is then used to make a pricing model that lets companies change their prices immediately.

In short, this is how predictive pricing works:

Getting the Data

The first step is to gather information from various sources, such as past sales records, customer behavior, market trends, competitive intelligence, and other factors that affect prices. This information will be used to build the predictive pricing model.

Web scraping, machine learning algorithms, and data mining methods (like clustering, predictive analysis, and natural language processing) are some tools businesses use to gather data.

Preprocessing of data

Data is cleaned, structured, and organized to be analyzed after receiving it. This is called preprocessing. In this step, you might eliminate duplicates, fill in missing numbers, and normalize the data to ensure it is all the same.

For instance, global pricing data considering more than one place or currency won’t help businesses set prices in their own countries if it’s all put together in one dataset.

Engineering of Features

Feature engineering is choosing features from a dataset, changing them, and making new ones. Feature engineering helps ensure that the predictive pricing model only has valuable knowledge.

At this point, machine learning modules look through the data for trends and similarities. These tools take essential parts of the data and show them in a way that helps businesses figure out how different factors affect prices and how they are connected.

This step also includes feature selection, which means picking the most essential factors for making good prediction models.

Train the model

Once the features are chosen, the preprocessed data is used to train a predictive pricing algorithm. The algorithm finds patterns and links between the input variables (features) and the goal variable (price) as it learns from the data. Based on the data given, this training process helps the algorithm make correct predictions about prices in the future.

Checking the model

After the model is taught, it is tested on a different data set to see how well it works and how accurate it is. Model validation checks that the model works well with new data and doesn’t fit the data too well or too poorly.

For example, if the model is trained on a small set of sales data, it might be able to predict prices for a small group of customers correctly but not be able to do so for larger groups. In some cases, it might not even show how people act.

In any case, model validation finds these problems and fixes them before they can change how accurate and valuable the statistical analysis is.

Making price predictions

The system can make price predictions based on current market factors and data that has been trained and tested. You can find the best prices for goods and services with accurate predictions.

If a company has a business intelligence (BI) tool like Power BI or Tableau, this price prediction could be shown as a graph or display to help executives make better choices. If it’s part of a pricing tool, prices can be changed immediately based on how customers act and the market moves.

Changes to the price

Lastly, prices are changed immediately based on the model’s price predictions. Depending on the company’s chosen pricing strategy, the new prices can be changed either by hand or instantly with software that does this (for example, in real-time pricing and dynamic models).

How predictive analytics can help you set prices for goods and services

Most businesses today use prediction analytics to set at least some of their prices. AI and machine learning models are improving and becoming more accessible for more people to access. This has quickly made them one of the best ways to stay flexible while making the most money.

When setting prices for things and services, predictive analytics can help in the following ways:

• Predicting what products will sell
• Managing inventory and the supply chain
• Figuring out why prices change and how to deal with price-conscious customers
• Dynamic and real-time pricing models
• Price optimization for online shops
• Personalized pricing and dynamic discounting
• Making the product stand out
• Boosting sales and customer satisfaction
• Getting closer to the ideal customer profile (ICP)

The best ways to use predictive pricing to make more sales

Businesses can use predictive pricing to significant effect, but it’s essential to stick to best practices to ensure it works. Here are some tips on how to use prediction analytics to set prices:

Guarantee constant access to information.

Companies need everything they need, like price data, insights into how customers act, and market trends, to get the most out of predictive pricing strategies. The data that predictive analytics use to “teach themselves” is why it’s essential to have access to new, accurate data all the time.

You might have difficulty getting some of the information you need for price prediction strategies. For instance, a business might need to see rival pricing information, often hidden or changed. Companies might not be able to get to this data if they don’t have the money or expensive tools to build out their own data science departments.

Put in and use the correct values.

Tools for pricing analytics connect other factors to the data they look at. This makes them so helpful and powerful, but showing the wrong variables leads businesses in the wrong direction.

For instance, a new hotel in the area might look at the prices of other nearby hotels to try to guess how much its services will cost. But if the data only shows the number of beds and size of the hotel room, it won’t show the quality or services that customers want, nor will it consider seasonality or events that happen annually.

In that case, the predictive software would give them the wrong information and suggest a pricing plan that didn’t work as well as they had planned.

Take timing into account when judging the trustworthiness of a model.

When something happens, knowing how well a forecast model works is essential. It’s not always possible to correctly predict what will happen, like how customers act, new rules, or changes in the outside world.

Say a company uses prediction analytics to determine what prices to charge during the holiday season. You can’t expect to get the same results every month during that time. For the predictive model to understand seasonality trends, it needs a more extensive set of past data.

Don’t depend on forecast models alone.

It won’t be possible for a business to respond to every change as it happens unless it has a real-time price algorithm like Amazon’s, which makes over 250 million changes every day.

When you rely too much on a predictive model and don’t think about outside factors or manual optimizations, you miss out on chances and set prices incorrectly.

Regarding pricing strategies, businesses must balance making changes automatically and doing them by hand. For instance, businesses can understand how real customers feel by talking to them and doing market research. This is something that most prediction models would miss.

Don’t pay too much attention to your rivals.

Almost every business is different, whether through a niche service, a different type of customer, or an extra feature on a product. If a business isn’t in the soft drink or fast food industries, where prices are apparent, focusing too much on competitive prices doesn’t consider what makes a company unique, how much customers value it, or what they expect.

Instead, businesses should consider how they compare to others in the same field and what makes them unique. To set the right price for a product, businesses need to know the types of people who buy it, what they like, and what they need. They should then compare this information with other data, like market trends and changes in the industry, to make the best pricing choices.

Examples of Predictive Pricing in the Business World

Predictive pricing is used in many industries, but retail, travel, and real estate companies that depend on up-to-date market information to boost sales and profits are the best examples of how well it works.

Shop around

Setting a selling price is more complicated than it looks, especially for new businesses that don’t have a lot of data (or the money to get it).

As we’ve already said, big businesses often use predicted pricing to change their prices when the market changes. They can use advanced machine learning algorithms to look at customer behavior, competitor prices, and other essential factors because they can access a lot of data.

Even though smaller stores may not have as many resources or customers, they can still use predictive analytics to determine when to start sales, discounts, or price increases.

A boutique clothing shop, for example, could use predictive pricing to determine when to discount seasonal items or items that aren’t selling quickly.

Both times, the use case is the same: know when to change prices to get the best profit margins and the least risk of overstocking.

Travel

In the travel business, companies aren’t always driven by making money. For instance, the government gives money to airlines to help them deal with logistics issues like connecting flights and keeping routes open to smaller places that wouldn’t be profitable.

Uber is another company that uses predicted pricing without focusing on making money. This is one reason the rideshare leader has never had a profitable year: the company changes fares based on supply and demand and ensures drivers get paid fairly for their time.

Hotels want to make money, so they use forecast pricing to ensure that all of their rooms are booked. By looking at how customers act, they can change the prices based on the number of reservations and outside things like the weather, events in the town, or other hotels’ occupancy rates.

Many companies in the travel business use real-time pricing, whether they’re an airline, a hotel, or a ride-sharing service. Prices can change daily based on what a program thinks people will want.

Data scientists work for real estate companies like Zillow. They look at market trends and use machine-learning techniques to determine how much a house is worth.

The same method sets property prices so landlords can make the most money and have the fewest vacancies possible.

A predictive model can accurately guess rent by looking at both internal and external factors, such as the history of tenants and owners and any renovations. External factors include nearby amenities, school ratings, crime rates, and the ease of access to public transportation.

Current trends in technology for predicting prices

The form of predictive pricing we know now is pretty new. Even though it happens in many fields, most businesses still struggle to keep up with how the market and customers’ needs constantly change.

The current trend is automating price changes based on outside events or internal rules (like loyalty programs). That way, companies don’t have to enter price information whenever something changes manually.

Learning by Machine

Process automation projects all over the place, like predictive pricing, have been driven by progress in machine learning.

Companies can now use outside data, like what customers want and market trends, and internal data, like product availability, brand loyalty, and price elasticity, to make correct price models and change constantly.

The best thing about machine learning is that it learns independently, so companies don’t have to keep adding new data.

CPQ and billing are linked together.

CPQ software and billing tools are essential data sources for predictive pricing models and connect the whole process.

Configuring, pricing, and quoting (CPQ) are crucial ways to connect existing goods to real-time sales data. It gives more information about which people are more likely to buy certain things at a specific price because it sets prices and makes product configurations.

Billing not only sends out invoices and gets payments from customers but also keeps an eye on efforts to keep customers and measures the customer lifetime value (CLV).

Companies can keep track of their customers’ behavior and change their prices based on the people who buy from them by keeping track of their payment histories.

By combining billing and CPQ with predictive pricing models even more, companies will have better and more data to use in making predictions. This gives them more accurate predictions and helps them build a long-term competitive advantage based on their data infrastructure.