Pricing Optimization & Machine Learning Techniques

pricing optimization and machine learning use cases

Pricing is a critical component of price optimization. In this blog post, we will dive into pricing optimization techniques and machine learning use cases. Price optimization techniques are used to optimize pricing for products or services based on customer response. AI / Machine learning can be leveraged in pricing optimization by using predictive analytics to predict consumer demand patterns and identify optimal prices for a products or services at a given time in the future.

What is pricing optimization?

Pricing optimization is the process of pricing goods and services to maximize profits by taking into account various pricing factors. These pricing factors can include but are not limited to, competitor pricing, customer demand, market conditions, and more. Before learning about different pricing optimization techniques, it is important to understand different pricing strategies.

What are different pricing strategies for setting pricing of goods & services?

There are many different pricing strategies that have been used for years. Some of them are following:

  • Price skimming: Price skimming is a pricing strategy where pricing is set high at the product’s introduction and then lowered over time. This is done to recover the initial development costs of a good or service, plus generate profit through sales volume before competition enters the market. Examples where price skimming can be used, include luxury items, new pharmaceuticals, and vehicles.
  • Penetration pricing: Penetration pricing is a pricing strategy in which pricing is set low to increase product or service adoption. It is about setting prices low in order to penetrate a market or capture a large share of the customer base before competitors are able to enter the market with their own offerings. The pricing strategy can be used when a new entrant into the market seeks to attract customers by offering products at prices below those of competitors. Examples where penetration pricing can be used, include new market entrants.
  • Value-based pricing: Value-based pricing is the pricing strategy in which pricing is based on customer perceptions of value. This pricing strategy takes into account the utility, durability, quality, and brand image when pricing a product or service. It is the process of pricing goods and services based on their expected contribution to customer satisfaction. Value-based pricing can be used when a business has an intangible product that cannot be easily compared with other products or services, or if there are different means by which customers may derive value from using it. An example where this pricing strategy would work well is selling consulting services, such as pricing a project based on the value it could provide.
  • Price lining: Price lining is the pricing strategy in which pricing is set at a certain price point for each product variant. The price lining approach uses price discrimination, where identical goods are priced at different levels according to the quantity purchased.
  • Promotional pricing: Promotional pricing is when prices are set low to introduce new products or stimulate demand in-between seasons for established goods and services. This pricing strategy can be implemented by businesses that want to increase product awareness without increasing profit margins too much or those who want to take advantage of pricing power during periods when demand is low.

What types of pricing optimization problems exist?

There are three different types of pricing optimization problems that exist. Each type has its own challenges to consider while pricing a product or service. Some of them are the following:

  • Single objective pricing optimization: Single objective pricing optimization is a pricing problem that has to meet one specific goal such as maximizing revenue or profit margin by adopting optimal pricing strategy. The single-objective pricing problems can be solved using linear programming techniques, which provide an optimal solution within a reasonable time and computational complexity. In single objective pricing optimization problem, there is single objective function to be minimized or maximized, and there are decision variables involved in pricing strategy for every product/service that could impact the objective function.
  • Multi-objective pricing optimization: Multi-objective pricing optimization is the process of setting prices for products in the mooost optimal manner to optimize two or more conflicting objectives such as maximizing total revenue while achieving maximum profit margin. The multi-objective pricing optimization problems has two or more objective functions, and pricing strategies for every product/service that could affect the objectives. These kind of problems can be solved by using heuristic algorithms such as evolutionary computation techniques which will provide an optimal solution within a reasonable time frame but not guaranteed to find global optimum. Multi-objective pricing problem can be solved using Linear Programming techniques based on Pareto optimality.
  • Dynamic pricing optimization: Dynamic pricing optimization is a pricing problem that can have changing product costs, discounts, and cross-elasticities. The dynamic pricing problems are usually intractable due to the expensive computational nature of solving them using traditional linear or integer programming techniques. In such cases, approximation algorithms provide promising results for dealing with nonlinear time-dependent pricing problems.
  • Nonlinear pricing optimization: Nonlinear pricing involves setting prices for products that are not identical, where the price of each product is determined by its features. The non-linear pricing problem can be solved using machine learning techniques such as neural networks and support vector machines to learn the relationships between different features in data; then predict an optimal set of feature weights or parameters that will produce the best pricing plan for a given set of products.
  • In-store pricing optimization: In-store pricing optimization is a pricing problem faced by retailers where prices are optimized in real-time, with changing costs and demands, while satisfying other constraints such as profit targets or availability. The in-store pricing problems can be solved using machine learning techniques such as data mining and artificial intelligence to make pricing decisions in real-time.

What are some examples of pricing optimization problem?

The following represents few examples of pricing optimization problem:

  • Products pricing recommendation on eCommerce: This can be a single objective or multi-objective pricing optimization problem. The objective functions can be related with revenue and profit or both. The goal is to find the optimal pricing of different products based on base price and discounts in order to create maximum sales/revenue on a particular day. Demand prediction on a particular day plays key role. In addition, the price elasticity of demand plays a key role in solving the pricing optimization problem. Lower the price, higher the demand and vice versa.
  • AirBnB price recommendation model: AirBnB pricing optimization is a pricing problem in the hospitality industry where prices are optimized based on customer demand. AirBnBs use machine learning techniques to predict optimal pricing for different properties/services given certain constraints such as availability, capacity and so forth. A binary classification model can be trained to predict booking probability of each listing in the platform; Then, a regression model can be used to predict the optimal price for each listing for one or more nights. Finally, a personalization layer canbe applied to generate the final price suggestion to the hosts for their property.
  • Pricing recommendation for AWS spot instances: To predict the price for next hour, a linear model was trained by taking the last three months and previous 24 hours of historical price points to capture the temporal effect. The weights of the linear model is learned by applying a gradient descent based approach. This price prediction model works well in this particular case since the number of instances in AWS is very limited.
  • Price recommendation methodology based on customer segmentation: In this method, firstly, customer segments are created using techniques such as K-means clustering; then, a price range can be assigned to each cluster using regression modeling. Another classification model, then, can be used to find out the customer’s likelihood of buying a product using the previous outcome of user cluster & price range. The customer’s likelihood is calculated by using the ensemble techniques or logistic regression model. One can get the price range at a user cluster level using this method.
  • Airline pricing optimization: Airline pricing is a multi-objective pricing problem, where airlines use machine learning techniques to predict demand for flights on different routes based on historical data. The objective functions are related with revenue and profit margins.

What are some challenges in pricing optimization problem?

The following represents a few challenges for the pricing optimization problem:

  • Cold start problem: When there isn’t much historical data on pricing, we can’t make accurate predictions about future prices. This is called as cold start problem. For new products pricing recommendation, cold start problem is the biggest challenge.
  • Cannibalization (cross-pricing effects): If you decrease the discount of a product, it can lead to an increase in sales for other products competing with it. For example, if the discount is decreased for Nike shoes, it can lead to a rise in sales for Adidas or Puma shoes since they are competing brands & the price affinity is also very similar. Similarly, if the discount on a particular airline ticket is decreased, it can lead to an increase in sales for other competing airlines.
  • Price elasticity of demand: If you decrease the price, the demand increases and vice-versa.

What are some machine learning techniques that can be used in pricing optimization problems?

The following is a list of machine learning techniques that can be used for pricing optimization problems.

  • Demand forecasting or prediction: Pricing optimization problems usually involve pricing goods and services in real-time, considering the current state of demand for products. This is where it is important to know about the demand, later same day, next day or near term. This is where demand forecasting model comes into picture. From the demand forecasting model, we would get the demand for a particular product for the next hour, next day (etc) based on its base discount. T In such cases, Machine learning techniques such as regression based models, sequence model LSTM, and time series model such as ARIMA can be used to predict future demand for all the products based on historical sales data or market trends; this will help retailers come up with pricing strategies that could maximize revenue while minimizing demand risk. Tree-based models can also be used. In the tree or forest based models, there is no need to establish a functional relationship between input features and output labels. Thus, they can be useful when it comes to demand prediction. Also, trees are much better to interpret and can be used to explain the outcome to business users adequately. Various types of tree-based models like Random Forest and XGBoost can be used.  The demand prediction model would generate demand estimates for all the products for tomorrow at the base discount value. To get demand at different discount values, the economics concept of “Price Elasticity of Demand (PED) is used. Price elasticity represents how the demand for a product varies as the price changes. After applying this, one can get multiple price-demand pairs for each product. One then have to select a single price point for every product such that the chosen configuration maximizes the overall revenue. To solve it, the Linear Programming optimization technique is used.
  • Multiple regression modeling and optimization: In some of the pricing related optimization problem such as sales vs pricing, one can first train a multiple regression model to get the pricing coefficients. Then using these pricing coefficients, one can use linear regression models to solve pricing optimization problems such as revenue maximization while minimizing discount levels.

Pricing optimization can be a challenge for any business, but it’s an especially difficult process when pricing new products. In this post, we have learned how machine learning techniques such as regression based models and tree-based models can help retailers come up with pricing strategies that maximize revenue while minimizing demand risk. If you want to use these pricing optimization techniques or are looking for someone who has experience using them in the past, give us a shout!

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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