One of the critical decisions a company makes about a product is its price. Nowadays, merchants require sophisticated pricing strategies to succeed in a highly competitive marketplace and satisfy all consumers’ needs. Businesses face two key challenges: optimizing prices and managing income in a constantly changing environment. So, companies use a strategy called dynamic pricing to solve them simultaneously.
The method’s efficiency can be noticeably increased with artificial intelligence (AI) and machine learning (ML) capabilities. AI and ML technologies open up opportunities to find new revenue sources, flexibly adapt to market requirements, and strengthen customer focus.
In this short article, we’ll analyze the advantages of machine learning for dynamic pricing and examine the use of the technology in e-commerce, drawing on examples from CHI Software’s case studies.
What is dynamic pricing?
Dynamic pricing is a flexible strategy in which the product or service price is determined by the current (and continually changing) market conditions. For instance, the merchant can increase rates when it knows that the competitor’s stocks are running low or demand is getting higher.
Dynamic pricing uses big data to understand and act upon changing terms. Following this strategy, companies set the optimal product prices, considering their costs, sales volumes, competitors’ rates, market trends, etc. Dynamic pricing calculations are based on different approaches:
- Prices are dynamically adjusted following business expenses to maintain the desired return on investment (ROI);
- Pricing is based on competitors’ decisions; or
- Prices rise with increasing demand or decreasing supply (and vice versa).
Analyzing a massive amount of information in real-time allows businesses to instantly adjust prices, adapt to consumer activity, and thus extract the maximum benefit. Dynamic pricing is commonly used in e-commerce, entertainment, travel and hospitality, transport, energy, and other sectors. This approach works great for ground and air transport rates, the cost of vouchers and hotel rooms, and tickets to the zoo, the cinema, or concerts.
Big business has been using dynamic pricing for a long time. Hilton and InterContinental Hotels Group embraced the strategy back in the early 2000s. Flexible prices are now offered by Amazon, Airbnb, eBay, Zara, Uber, and many other market players.
The advantages of the strategy are most evident in e-commerce and online/offline retail. The field looks to have strong prospects: according to eMarketer, the e-commerce sales sector has been exploding over the past several years and will hit US$4.479 trillion worldwide by 2021.
This niche is highly competitive, and participants have to fight for leadership positions. But with machine learning, even small e-commerce companies and retailers can benefit from dynamic pricing 100 percent and win the race.
Dynamic pricing benefits for e-commerce
The main advantage of a dynamic model is fast adaptive pricing. E-commerce companies can fully or partially automate price adjustments based on their needs. Pricing tools evaluate many internal and external factors to generate metrics that are consistent with the strategy.
Another benefit for business is increased competitiveness. Using dynamic pricing, a company can respond to current demand, allocate inventory wisely, develop brand perception through specific pricing decisions, and stay afloat regardless of current market conditions. For instance, a bus carrier can hedge against poor sales during low season or increased demand for tickets just before the day of travel.
Dynamic pricing also improves the business’s customer focus. The model monitors and considers data about product demand and buyers’ digital footprints, such as the most viewed products/services web pages, abandoned carts, or clicks on content times. Then the algorithm generates the most reasonable price to be shown to the customer or specifies price limits.
Each of these strategies requires processing vast amounts of data. And machine learning helps with that.
Why ML is necessary for e-commerce dynamic pricing
E-commerce generates large amounts of data —so large that humans simply can’t handle it on their own. To build effective pricing solutions, merchants must take advantage of machine learning. ML-powered software solutions process data faster and never stop getting information to produce dynamic strategies.
Most of the big e-commerce and retail players analyze data using machine learning. ML helps Zara to minimize promotional costs and adapt to fashion trends. Ralph Lauren and Michael Kors manage inventory and calculate discounts to increase margins. Airbnb sets prices based on data on the apartment location, the owner’s characteristics, and many other factors (even the number of photos in the property card).
Machine learning algorithms continuously progress, make suggestions in real-time, and automate tasks that are almost impossible to do manually. The larger the data amount, the better the learning.ML-based software continuously increases its performance and sets product prices based on supply and demand in real-time.
Machine learning optimizes the results of dynamic price changes given the target function, where price is a function of time.AI/ML-based solutions help retailers to:
- Repeatedly search for and collect important information about competitors’ prices for similar products, consumers’ opinions, and the pricing history over the last period;
- Get quick access to broader data analysis, which results in better functionality;
- Quickly associate a new product/service with similar items to clarify a price segment;
- Predict demand for items that don’t have transaction data; and
- Anticipate early trends, aid product bundling, and create favorable discounts.
Why ML is better than a rule-based approach to dynamic pricing
In terms of software architecture, there are two types of dynamic pricing solutions: rule-based and ML.
Systems of the first type use a base containing the rules. They are patterns and facts based on domain expert knowledge and represented in “if-then” statements. An inference engine (part of the software) defines a relationship between rules and known facts. Therefore, such systems rely solely on the “built-in” knowledge to respond to the environment’s current state.
Rule-based solutions implement rules written to meet the particular organization’s business needs. This approach is not flexible enough. It is hard to adjust, add, or delete options in response to a changing environment, or unusual or unpredictable situations, so that any solution will be cumbersome. As objects in the system grow larger, more manual maintenance and effort is required to add, change, and adopt rules. The more points we want to control, the more difficult it is to achieve, and the more expensive the solution’s implementation will be.
Compared to rule-based solutions, software powered by machine learning demonstrates much better performance. ML gains knowledge from data and finds ways to solve problems itself. The more data is in the system, the more it learns and improves its performance without detailed instructions.
AI/ML-based pricing software has many exciting capabilities:
- Cluster analysis for granular customer segmentation. ML makes it possible to uncover hidden relationships between buyer characteristics and behavior patterns. Therefore, the customer persona groups are determined with high accuracy.
- Consideration of a vast number of variables for different elements. Merchants that use competitor and attribute-based pricing have to assess many influencing factors to set the price. ML-based software works with massive amounts of data, both internal and external. Depending on the use-scenario, a company might incorporate information on the device, booking history, competition, demographic features, weather, traffic, etc.
- KPI-driven pricing. ML can be of great help and have an enormous impact on key performance indicators (KPIs).The developed algorithms learn patterns from data, continuously integrate new information, and detect emerging trends or demands. So, businesses can align pricing recommendations with performance metrics of interest (e.g., margin, turnover or profit maximization, inventory optimizations).
- Real-time market data analysis. It’s possible to automatically calculate the price elasticity and optimize prices to changing demand and market conditions without specifying complex rules. Merchants can predict whether customers will accept new conditions before making a pricing decision.
- Legacy data analysis. Once the data has been collected, it can be used for ML purposes.
How to use ML in dynamic pricing: CHI Software’s experience
Even if you use the most advanced ML algorithms, you need to know how to collect and prepare data for analysis. Otherwise, your efforts will not bring the desired result.
We at CHI Software use a methodology based on CRISP-DM. It breaks the ML solution development process into six major phases:
- Business Understanding,
- Data Understanding,
- Data Preparation,
- Evaluation, and
In most cases, besides deployment, these phases are applied several times in a cyclical way.
To provide a clearer understanding, we will demonstrate the procedure for processing data and illustrate it with CHI Software’s case of developing and implementing a dynamic pricing system. Our client, an online store, was looking to reduce time on market and competitor research, and increase the pricing strategy’s efficiency. We crafted a research and development plan to address their concerns and created a custom-tailored neural network that analyzes sales volume, competitors, and market trends.
At the beginning of an ML dynamic pricing project, it is necessary to understand input variables and gather different types of structured or unstructured datasets to train the ML models. It can be:
- Transactional data: for instance, a sales history with a list of the products and customers who purchased them;
- Product descriptions: information about each item (e.g., price, category, size, brand, style, color, photos, manufacturing);
- Data on promotions and marketing campaigns in the past;
- Competition, inventory, and supply data;
- Customer reviews and feedback about the products, information about physical stores’ geolocation, etc.
You have to define the strategic goals and constraints to set unique, clear objectives of profit maximization, increase customer loyalty, or attract a new segment. Here we formulate restrictions considering legal nature, the brand’s reputation, or physical aspects (e.g., store capacity and the average time of supply). Each scenario impacts the way the problem is modeled. It is recommended to test different approaches for the same client and use different models to find the best one.
The next step is modeling. Previously gathered data is used to train ML models. Historically, engineers used Generalized Linear Models (GLMs), particularly logistic regression, but we have more complex and robust methods today. For instance, we apply deep learning methods or reinforcement learning techniques. ML models know how to find similar products and be effective. That’s why machine learning is useful in the case of new, rare, or exotic products.
When the model is trained, you can estimate and test the prices. The estimation may be an exact price or a range. The prices obtained can be subsequently adjusted and optimized regularly.
We successfully completed the project and reduced the time required to track the competitors’ pricing and warehouse prices, and determine the best price point they should use. CHI Software’s combination of AI/ML and algorithms helped to automate this process and focus on the essentials: customer satisfaction and revenue growth.
In conclusion, ML capabilities can be used for tasks related to pricing in e-commerce and retail effectively. Machine learning offers an excellent approach for understanding the relationship between sales of related products, forecasting demand, and determining customer sensitivity to ad campaigns.
We at CHI Software are expanding the scope of ML by offering solutions for automation of price–tag tracking and developing modules integrated into the clients’ CCTV systems to show the factors that lead to revenue losses. Using Keras, Optical Character Recognition, Python, TensorFlow, OpenCV, and many other technologies, we create smart solutions that help eliminate time-consuming manual processes and dramatically improve the quality of analytics and pricing.
We prove in practice that AI and ML are some of the most influential and promising tools for business, and the future belongs to them. Best of all, we can help you take advantage of all their benefits — today.