Table of contents
What is a dynamic pricing algorithm?
Dynamic pricing optimization is the pricing strategy directed at setting prices for products and services based on the existing state of the given market demands. Clients generate a massive amount of data. When appealing to analytics and social media platforms, companies employ the data to adjust prices on products and services. Price optimization entails considering the input of data to adjust prices properly.
Dynamic pricing algorithm includes a special pricing algorithm illustrating how and what data is used to design a proper pricing strategy. Maximization of different opportunities through pricing algorithms can be achieved through different types of actions:
- Demand-based. This type of data entails information about the change on the foundation of demand and supply. Dynamic pricing can be properly adjusted following shifting supply and demand trends with demand-based details.
- Competitor-based. This type of action entails making decisions based on the pricing strategies used by competitors. When looking at competitors, companies can make accurate decisions on approaching pricing. At this point, the price optimization algorithm relies on the input received from investigating competitors.
- Cost-based. This type of data includes altering prices, including the cost of doing business. Dynamic pricing relies on cost-based information because it shows how to keep revenues in place.
Dynamic pricing algorithms operate with massive quantities of data. While there are different types of data included, several kinds of algorithms are also included.
- First and foremost, there are traditional algorithms. These are rule-based, meaning that the algorithm is entirely programmed. Following a conventional algorithm entails using if/then rules that determine key prices based on the available variables. Such an algorithm helps curtail the pricing strategy and boost dynamic pricing.
- Second, there are machine learning algorithms. Machine learning is the technology allowing making predictions based on the input data. These algorithms train themselves and offer major predictions on sales, revenue, and profit. Machine learning is vital for price optimization as well as it helps design a good pricing strategy. Machine learning is the future of dynamic pricing because of its predictive analytics capabilities.
There are distinct limitations to traditional algorithms. They include only a limited number of factors incorporated into the algorithm’s preprogrammed core. In turn, machine learning is based on advanced technology. It helps process massive amounts of data and make accurate predictions. At this point, dynamic pricing algorithms use three types of data, namely cost-based, demand-based, and competitor-based, while also utilizing two types of algorithms - traditional rule-based algorithms and machine learning ones.
How can dynamic pricing be improved?
Dynamic pricing can be improved through better tools of data analysis and better data itself. At this point, price optimization relies on the quantity and quality of data.
The primary aspect to consider from the quantity of data is that not all data points are available for every given business. Besides, the evidence indicates that it might not be applicable even if the information is accessible. Each given pricing strategy directly depends on the quantity of data received.
As an example, one can appeal to the example from the retail industry. For instance, if a company is new to the market, it might not have all the access data because it does not have good customer testimonials. Besides, given the current personal data protection laws and regulations, even if data is accessible, a company might not be legally allowed to use it. In such a case, machine learning algorithms can be a massive advantage. They can be designed to adapt to particular cases and deliver results without more excessive data points. Machine learning can engage in price optimization at the product level without input to personal data required. Machine learning methods are tailored to learn from past events and determine how they affect the current prices.
Furthermore, there is the quality of data involved. The evidence dictates that the quality of data directly impacts the machine learning model designed for dynamic pricing. The higher the data quality, the easier it is to ease it in dynamic price optimization. Yet, what does it mean for data to be high quality? There are three key elements to consider:
- Completeness. Data is complete when all the required variables are included. Also, it means that existing data points match the designed data tables.
- Cleanliness. Data clean means it has no error or only a small amount of them—unclean data results from missing sales tracking, poorly numerical values, and incorrect product data.
- Consistency. Data consistency depends on the degree of correlation between data points used. For instance, when companies can calculate profit from revenue, data is consistent.
Quantity and quality of data have a direct impact on dynamic price optimization. To improve dynamic pricing, it is necessary to improve the quality and quantity of data. Essentially, it means making data more clean, consistent, and complete. Besides, getting access to data is important. Use social media and analytics to ensure there is enough data to process. Yet, remember about data protection regulations.
How to implement dynamic pricing?
To implement price optimization, there is a range of techniques to follow. When engaging in dynamic pricing optimization, a company needs to have sufficient data to work with. One should rely on both structured and unstructured data obtained from macro and micro levels. It helps fuel machine learning price optimization algorithms.
The first phase of dynamic pricing implementation relies on four key elements:
- Collection of historical data. The element includes a data collection on different price points used in the past. Besides, it is crucial to observe other demands for these price points.
- Estimation of the demand function. The demand function estimation depends on knowledge of price points.
- Solving optimization problems. The key issue is to find the optimal price allowing a company to maximize profit or revenue. This issue exists because of the existing pricing policies or inventory constraints.
- Optimal price application. The final element is applying the optimal price for a particular period to observe demand.
The second phase focuses on exploration-exploitation issues. In a highly dynamic environment, the primary premise is that it is vital to minimize the time dedicated to pricing testing and demand-based data collection following the current demand curve. In such a context, dynamic pricing is implemented through several phases:
- The exploration-exploitation optimization. The action includes the premise that the demand function is unknown in advance. There is a tradeoff entailing the optimization process. The process consists of calculating the difference between the actual and hypothetical revenue.
- Limiting the number of price changes. The action focuses on decreasing the number of price changes during the product’s life cycle. For instance, while there are almost unlimited price change frequencies in the digital realms, companies can impose particular limitations to avoid inconsistencies in customer experience.
- Ability to specify value price levels. Most sellers choose particular price points as a restriction. Specification of such price points ensures smooth dynamic price optimization.
- Enabling dynamic price optimization. Given all the above elements, it is tied to dynamic pricing. One of the key aspects within the stage is to consider inventory constraints and dependencies between products.
Dynamic pricing is implemented within the two steps mentioned above. The first phase includes working with available data. The second step correlates to optimizing price changes and enabling dynamic price optimization as a result.
How does dynamic pricing work?
Dynamic pricing works through internal and external data. The pricing strategy heavily relies on the available information.
Starting with internal data, there are several aspects to consider:
- Product attributes. This internal data type includes information about cost, base price, and margin ceiling. It is an important tool in dynamic price optimization. The product attributes worth considering correlate to the product’s size, color, brand, stock level, and expiration date. Grouping the attributes into categories allows a better data implementation.
- Transactional data. This data includes all transactions, price history, conversions, and units sold. Also, the data entails buyer information and sourcing costs. Any given machine learning algorithm relies on calculations made from different sorts of transactional data.
- Inventory levels. This is considered an essential part of internal data to ensure proper dynamic pricing operation. Knowing the inventory levels and overall supply is a key determinant in how dynamic pricing software calculates dynamic pricing.
Going further, there are several points of external data required for dynamic pricing to work:
- Competitor data. A range of competitor data is accessible, from list prices and ship prices to product reviews and ratings. This data is gathered and analyzed by the dynamic optimization software. The majority of competitor data is publicly available.
- Weekdays. Consumer demand depends on the days of the week. Depending on the given business model, the companies can witness sales increasing or decreasing. Considering such a correlation provides major insights into how dynamic pricing works.
- Holidays. Holidays are a major insight into how prices will shift. The historical data plotted against particular holidays allows companies to pinpoint the shifting demand.
- Regional trends. There are particular regional trends available. If a company can use data to measure different regional variances, the approach can help create pricing strategies down to the regional level.
Taking into account internal and external data shows how dynamic pricing algorithms work. Companies fuel the software with sufficient data to engage in predictive analytics. As a result, based on the current and historical data, one can make accurate predictions on how the market and consumers will behave.
The best indication of how dynamic price optimization functions can be achieved through various scenarios.
Scenario 1: Limited price experimentation
The first scenario includes the situation when the demand is constant during the entire life cycle. At this point, the company engages in a limited degree of price experimentation. Usually, the number of price changes is limited following a particular pricing policy included. Such cases are often applied in time-limited deals and flash sales.
Within the limited price experimentation approach, it is important to specify how the prices are generated in correlation to each time interval. When achieving such a correlation, the companies set parameter functions in advance while determining whether such hypotheses correlate properly to what happens with the prices. The scenario with limited price experimentation shows how dynamic pricing optimization is used to ensure the pre-set hypotheses properly illustrate market pricing trends.
Scenario 2: Continuous experimentation under pricing algorithms
The next scenario includes continuous price experimentation using particular pricing rules and policies. One of the key aspects is when the demand function is not stationary. In a dynamic setting with constant price experimentation, one should use generic tools to explore the given environment.
The main idea of the scenario is to control the continuous price experimentation by sampling different model parameters. Within such algorithms, probabilistic distributions can be used. If the distribution variance is high, there is a broader range of demand functions to explore. The demand distribution relies on the dependency between the price and demand. In short, with continuous experimentation under pricing algorithms, it is possible to change parameters and witness how the price and demand link shifts.
Scenario 3: Multiple products or time intervals
For multiple products in the inventory, there are particular constraints and features to explore. The case includes the possibility of adding inventory constraints to the overall routine to find the optimal price. The approach is considered when one needs to exclude the options in the case of demand exceeding the inventory. To optimize the prices for multiple products, the optimization relies on dependencies between products and time intervals included.
Price optimization for multiple products should consider inventory dependencies. In such a case, one can face an optimization problem with the total required inventory exceeding the inventory available. Price optimization for multiple time intervals works similarly. Yet, there is greater importance in forecasting demand. Considering all the aspects, price optimization for numerous products and numerous time intervals presents specific constraints linked with a correlation between the required inventory and the available one.
Scenario 4: Complex demand models
The final scenario considered complex demand models. Those are often probabilistic models with complex sampling procedures included. However, even for the most complex models, there is still a degree of manual work that should be done, for instance, when implementing updated rules.
Working with complex demand models relies on building and testing. While the models are useful for multiple related products, there is always a correlated demand function to consider. The correlated parameters of different demands can be obtained from a multivariate distribution. Compex models help work with multiple products. Yet, managing complex models requires more input and machine learning capabilities.
The bottom line
Dynamic price optimization has a major impact on revenue and sales volume. The utilization of the model depends on the product category and the business model at hand. Major players in the market utilize dynamic pricing models. The key focus is gathering internal and external data while using traditional and machine learning algorithms. The scenarios mentioned above illustrate how dynamic pricing operates in different environments. Essentially, it all depends on the degree of price experimentation and available inventory.
