According to Paddle, optimizing prices by just 1% can lead to an average profit increase of 11.1%. However, top executives still need help determining the most effective pricing strategies for their products or services. Traditional pricing methods often lack the flexibility and data-driven insights needed to adapt to market changes rapidly. Here's where price optimization with Machine Learning (ML) comes into play, offering an advanced approach to price optimization. Among the top players that already leverage Artificial Intelligence and Machine Learning development to set prices are Amazon, Uber, and Walmart.
Let's look closer at how ML-based price optimization can be implemented, best practices, and real-world success stories that showcase its effectiveness.
What is price optimization with Machine Learning?
Using Machine Learning for price optimization means applying ML algorithms to choose the most effective pricing strategy. In other words, Machine Learning models analyze the factors that influence pricing, such as customer demand, seasonality, and competitor prices, and automatically set optimal prices for products or services. That way, enterprises can adjust their pricing to meet specific business objectives like maximizing revenue while keeping reasonable prices for their customers. Let's look at the critical components of an ML system for price optimization:
- Source data is the foundational layer for any Machine Learning price optimization system and includes information on past sales, customer behavior, and other market variables.
- The algorithm or their combination is selected according to your specific business case. The common options are clustering, reinforcement learning, linear regression, and neural networks.
- The system's outputs, including optimal price points, pricing tiers, or dynamic pricing strategies.
With a Machine Learning price optimization model in place, pricing managers will only have to oversee the system's performance, as it takes care of the data analysis and real-time price adjustments. Instead of manually analyzing large sets of market data, you can delegate this task to the system that considers more variables than a human does. In addition, your model continuously learns from its results, enhancing its predictive accuracy over time. The algorithms are trained to measure the following factors:
- Historical sales. Understanding past sales performance helps enterprises predict future demand and pricing elasticity.
- Competitors' prices. Leveraging competitive pricing intelligence can give enterprises a knowledge of where they stand in the market landscape.
- Weather conditions. Unexpected weather patterns can influence consumer behavior. For example, hot weather could boost sales of cooling products.
- Seasonal aspects. Businesses can adjust prices for specific times of the year when the demand may rise or fall. For instance, prices for winter clothing usually go up in the colder months but drop as the season ends.
- Inventory levels. The prices might vary depending on the quantity of products the business holds in stock. Low stock levels can trigger price increases, while excess stock may require discounts to accelerate sales.
- Customer behavior. You can use the information on customer preferences and behaviors to set optimal prices.
- Product features. Unique or premium features can justify higher prices compared to competitors.
The trained ML system can forecast how different pricing strategies can influence KPIs. For instance, if the goal is to boost revenue, the model can estimate the effect of price alterations on the sales volume and, subsequently, the total revenue. To get recommendations applicable to specific industries or business goals, you can later fine-tune the models by incorporating sector-specific data or setting custom KPIs.
Let's take price optimization with Machine Learning for the retail industry as an example: the model can predict the best price points for high-traffic periods like Black Friday or Christmas. And it works–Statista's research has shown that 62% of US responders find better pricing the key reason for trying a new store or online retailer during the holiday season. What other price-related business issues can you solve using Machine Learning algorithms?
Key benefits of Machine Learning for price optimization
Let’s discover how implementing an ML-based price optimization system can be a game-changer for your enterprise as we delve into its key benefits. Here's what enterprises gain with ML price optimization system adoption:
Sales boost during the low-peak periods
According to SaleCycle, November, December, and January are the biggest three months in terms of online sales. In 2022, online Black Friday sales in the UK only reached $10.57B. In contrast, average daily online sales in the UK could range from a few hundred million to perhaps over a billion USD depending on various factors like season, weekday vs. weekend, etc.
So, what about the rest of the nine months? Just as Machine Learning algorithms can help enterprises build their pricing strategy during high-traffic times, they can increase the sales frequency in low seasons. For instance, your ML model can recommend bundling several products at a discounted rate to increase their appeal or identify specific customer segments that are more likely to buy during off-peak times.
Price elasticity used to your advantage
Price elasticity measures how sensitive the demand for a product or service is to a change in price. Your price optimization with machine learning algorithms can identify which customers prioritize price over time and vice versa. With that knowledge, enterprises can maintain regular or premium pricing for time-sensitive customers who want to buy products or services immediately.
In contrast, you can offer discounts or lower-cost options to customers willing to wait. Let's look at the example from the transportation domain–during holiday seasons, the demand is inelastic as people need to travel. Airlines might increase prices but also ensure that more flights are available. However, they may offer discounted fares during off-seasons to attract price-sensitive customers.
Streamlined inventory management
ML algorithms for price optimization can help enterprises clear excess stock and stimulate sales. After identifying surplus inventories, the algorithm can recommend specific markdown strategies. For example, items that have been in inventory the longest may be discounted first, or items with the highest storage costs may be prioritized for sale. In addition, dynamic pricing algorithms can automatically identify overstocked items and reduce their prices to speed up sales.
Targeting of new customer segments
Your goal is to tap into new markets, isn't it? Price optimization with Machine Learning can help you enhance the effectiveness of pricing optimization strategies by leveraging sophisticated customer segmentation models, location intelligence, and logistical insights.
Machine learning models and techniques for pricing optimization
Machine Learning offers various valuable tools for pricing optimization, so it might be challenging to select the ML model and algorithms that align best with your business objectives. However, a trusted Machine Learning services company like N-iX can assist you in choosing and implementing the most effective models and techniques, such as:
Demand prediction modeling
Demand prediction models provide the quantitative basis for setting optimal prices and implementing dynamic pricing. Knowing when and where demand will rise or fall will allow you to adjust prices proactively and set prices that are more aligned with customer expectations. Here are key techniques used in Machine Learning for price optimization:
- Regression-based models are applied to forecast customer demand, which can then inform pricing strategies based on current market conditions and historical sales.
- Sequence models such as LSTM can capture long-term trends and are often used for time-series prediction problems.
- Similarly to sequence models, time-series models like ARIMA are designed to analyze time-ordered data. They are a good fit for capturing seasonality, trends, and other recurring patterns in the data, allowing you to make more accurate future demand forecasts.
- Enterprises also use tree-based models (Random Forest and XGBoost) when the relationship between variables is complex and non-linear. These models can consider multiple external features like seasonal holidays, promotions, and competitor prices.
- The price elasticity of demand models helps enterprises evaluate how demand for a particular product or service changes in response to a change in price.
- The linear programming optimization technique is a mathematical approach used to find the best possible outcomes for a specific goal, such as maximized revenue, under a set of constraints. When integrated into a dynamic pricing strategy, this technique can help you continuously adjust prices based on predicted demand.
Multiple regression modeling and optimization
If you want to maximize your revenue while minimizing discount levels, you can utilize linear regression models. But first, you should train a multiple regression model to get the pricing coefficients. Before fully implementing the new pricing strategy, test it on a smaller scale to validate the model's predictions and make adjustments if needed.
Price optimization with Machine Learning: Step-by-step implementation
Now that you're familiar with the most popular ML techniques for price optimization, let's dive into the process. Implementing a Machine Learning-based price optimization system spans eight key stages, each critical to ensuring the effectiveness of the final product:
- Data collection. In the first step, your team of data engineers and data analysts will be responsible for gathering historical sales data from your database. They also collect customer segmentation data from your CRM and gather external factors like economic indicators, market trends, and competitor prices.
- After the data is collected, you can move to data cleaning. Here's when your data experts will standardize and clean the data to remove inconsistencies. The key tasks are to remove duplicates and handle missing values.
- Next comes feature engineering. At this step, your team will analyze variables affecting pricing, like season, location, and competitor prices.
- One of the most important stages is model selection. The effectiveness of your price optimization system will greatly depend on the model's match to your requirements. Your experts will choose the best ML algorithm and technique.
- The model training stage presupposes splitting your dataset into training and testing sets and feeding the training set to the chosen model.
- At the validation and testing step, data engineers will apply the model to your testing set, calculate error metrics, and adjust model parameters if necessary.
- The testing stage is followed by deployment. Once the model is validated, it can be integrated into your pricing strategy system.
- Finally, the model requires continuous monitoring and updating. Once your ML system is operating, your team's task will be to check on its performance and update the model with new data as needed.
How does N-iX help enterprises optimize their prices with ML?
Let's explore how N-iX implements custom Machine Learning price optimization systems by looking at our clients' success stories:
Automation of tariff recalculation for a leading UK energy supplier
The project was delivered to the energy supply company from the UK, which buys and sells electricity and gas to supply domestic properties throughout the country. Due to the considerable rise in energy and gas prices, the client had to frequently revise the variable tariffs on their website to adhere to UK government regulations. The prices were updated manually, and the process took an hour.
N-iX implemented the system that serves as the website backend and can forecast gas and oil tariffs for the next months based on the competitors' data. Our engineers collaborated with the client's team to automate the process of updating the tariffs. We utilized AWS S3 and two Lambda functions: the first Lambda reads a CSV file containing price changes, updates the database, and then activates the second Lambda for API-based verification. Moreover, we built a lookup page that displays the current tariffs and new consumer proposals by postal code.
As a result of cooperation, N-iX:
- Cut down the tariffs' update from one hour to 5-10 minutes through automation of the price upload process on the client's website;
- Helped the client comply with the UK's government requirements regarding the actual tariff price depending on the consumer.
Implementation of a product pricing platform for a top US retail enterprise
Our client is a company owning a chain of luxury departments that spans over 40 stores across North America. The client's third-party pricing platform was not flexible, had a low accuracy level, and lacked live reporting functionality.
After our team had conducted the Discovery Phase, we decided to build a microservice-based solution. Our data engineers developed a product pricing platform that retrieves pricing data from competitors' websites by web scraping. Moreover, we integrated real-time reporting and designed an intuitive UI for the product pricing solution.
Our engineering team helped the client to:
- Streamline their product offerings with a custom-built pricing platform;
- Boost data analytics capabilities;
- Enhance the accuracy of operational processes by implementing real-time reporting.
Machine Learning for price optimization: Implementation risks and best practices to mitigate them
Utilizing price optimization with Machine Learning comes with various challenges that can significantly impact the system's effectiveness and reliability. What are the common risks, and how should you deal with them at the level of the solution's development?
Historical data scarcity
When there's limited historical pricing data available, generating reliable price predictions becomes challenging. This issue is particularly relevant for pricing new products and is known as a cold start problem.
N-iX tip: Consider using data from similar products or categories to bootstrap your newly implemented model. In addition, we recommend employing techniques like reinforcement learning that allow the model to learn and adapt as more data becomes available.
Lowering the discount on one product might boost sales for competitors' products. Suppose you manage an online electronics store. You decide to reduce discounts on Samsung phones. A multivariate model could help you predict the sales of Samsung devices and how this pricing strategy will impact sales of iPhones and other devices.
N-iX tip: Incorporate cross-pricing effects into your model. In other words, use implement ML models that can consider multiple variables and their interrelationships simultaneously. Another thing is that you should also regularly update the model to capture dynamic market behaviors.
Supply chain volatility
Unexpected disruptions in the supply chain can significantly affect your pricing strategy. Factors such as shipping delays, increased manufacturing costs, or raw material scarcity can create a discrepancy between your pricing model's predictions and real-world outcomes.
N-iX tip: Integrating real-time supply chain data into your ML model can make it more adaptive to external factors. We recommend utilizing additional features like current stock levels, lead times, and supplier conditions. By doing this, you can dynamically adjust pricing in response to supply chain changes, thereby maintaining profitability and customer satisfaction.
By leveraging machine learning algorithms, businesses can automate data analysis, improve pricing strategies, and ultimately drive revenue growth. However, to maximize the technology's potential, enterprises should be well aware of its limitations, including data quality and quantity requirements, the need for expert oversight, and related risks. The solution is to opt for a reliable ML partner like N-iX with experience in ML model development and implementation of intelligence pricing solutions. For decades, we have helped businesses navigate these complexities and successfully deploy machine learning strategies for price optimization.