Maximize revenue and optimize pricing strategies with AI-powered dynamic pricing. Learn how it works and how it can benefit your business.

Ebru Şevik, Selin Nur Barlak
BMS - Dynamic Pricing
9 Minute Read
Machine Learning
Automated Machine Learning
Unbalanced Data Analysis
Price Optimization
Ecommerce Dynamic Pricing
Automative Spare Parts Pricing


Table of Content

  1. What is Dynamic Pricing?
  2. Actual Price Optimization
  3. How to Implement Dynamic Pricing Strategy
  4. Importance of Price Optimization
  5. Automotive Spare Parts Wholesale Price Optimization


Price optimization is the process of capturing the right pricing point. In other words, it is to maximize profit according to the purchase and payment demands of customers and competitors. Companies in all segments need to increase their income through price optimization at every stage of their activities. The pricing issue is mainly a management decision. So, the question is, how is the price determined to gain maximum profit? The determination of the price is pretty simple. Income and expenses are examined depending on the demand curve and cost functions. Points can be determined as prices, where revenues are equal to or greater than expenses. Many small companies still set their prices manually in this way. Even if this approach is economically meaningful, it is only in theory. Many factors affecting the price, such as long-term customer behavior, anomalies, and the influence of competitors, are overlooked.

What is Dynamic Pricing?

Dynamic pricing is adjusting products’ prices throughout the day. In a way, it’s making sure to have optimum prices while variables are changing. The main reason to use this method is to increase product profit and the chances of a sale simultaneously. In addition, dynamic pricing is a fast alternative to fixed prices. COVID-19 showed us that the world can change so much in some time. So instead of setting prices for a while, companies can adjust to the ever-changing market by updating their prices daily.

Sometimes dynamic pricing needs to be clarified with personalized pricing. This is because dynamic pricing looks at the bigger picture, while personalized pricing focuses on an individual. So dynamic pricing analyzes your products and their relative value concerning the rest of the market. Customized pricing, on the other hand, looks at individual consumer behaviors and changes the product’s value based on the previous shopping experience of that particular consumer.


Actual Price Optimization

Price optimization studies are carried out in many sectors, such as retail, insurance, airlines, telecom, automotive spare parts by OEMs, and finance. The factors that affect the price in real-world conditions and to consider in the price optimization process are:

  • Retail or wholesale
  • Customer consumption behavior in the long run
  • Historical unit of sales prices of each of the product
  • Promotion and campaign strategies
  • Production, procurement, service, costs, etc.
  • Prices and campaigns of competitors
  • Customer segments
  • Economic variables
  • Stock Status
  • Special days & events and season


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In addition to all these conditions, there are also variations of the optimized price such as;

  • Starting price
  • Optimal (the best) price
  • Discounted price
  • Promotional price
  • Campaigns

Price optimization allows companies to determine the prices as mentioned above. But these variations and the variability of factors affecting the price make it challenging to optimize prices manually. Thus, various price optimization tools and artificial intelligence & machine learning-based price optimization studies have emerged.


How to Implement Dynamic Pricing Strategy

Implementing dynamic pricing can be very profitable when it's done right. That's why you should consider implementing dynamic pricing to improve your price optimization strategy and overall margin. Implementing dynamic pricing can be tricky, to begin with. So here is a five-step process to help you through this journey:

  • Decide on your commercial objective:

The commercial objective is what keeps your company in the right way. It'll help you handle any institutional changes. The commercial objective concerns more than just pricing and marketing; it's the main element of having a successful dynamic pricing strategy.

  • Have a pricing strategy:

Your pricing strategy uses your commercial objective to create an outline your team can use to sell products. For example, your commercial purpose is to be known as the cheapest on the market. Therefore, your pricing strategy would ensure your products are always more affordable than the competition's alternatives.

  • Choose your pricing method(s):

Your pricing strategy shows you your future pricing goals. Your methods are how you'll achieve those goals. Your pricing methods should be more sharp and specific than your pricing strategy.

  • Set down pricing rules:

Pricing rules tell your dynamic pricing software what to do. You should set a direction for every product that the software needs to track and change.

  • Test and monitor:

Once everything's all set up, your job still needs to be completed. First, you should keep track of the product sales, technical errors, and effectiveness of the software you use. Then, if it's necessary, you should make changes to it.


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Importance of Price Optimization

Firstly, the number of decisions affecting pricing is increasing daily in all industries. For this reason, more dynamic choices are needed. For example, hundreds of new e-commerce sites are being established every day—the increase in competitors and consumer consumption behavior changes. In addition, expenses will increase with the growth of the market. These are all sudden changes that affect pricing. With machine learning-based price optimization, you can be affected by these changes least possibly and protect your profitability.

Additionally, the best price may differ from customer to customer. This requires customization of each offer and price. For example, in holiday reservations, every service included in the package will change the price. For this reason, each customer should be given a different price according to their demand. Price optimization allows you to manage customized costs most profitably. Finally, it is vital to make quick decisions, as the pricing process will be affected by more than one sector and affect many industries. You can make the most effective decisions as soon as possible with artificial intelligence-supported price optimization tools.

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Price Optimization with Automated Machine Learning (AutoML)

The price optimization process can be complicated, but it is easy to create a strong pricing strategy with the help of machine learning models. Machine learning-based pricing processes are essential nowadays, where customers can easily compare prices offered with special search tools. AutoML, which significantly impacts KPIs, provides a fast and effective solution by learning the patterns given. Machine learning enables related units such as sales and marketing to develop complex strategies and optimize prices. A typical example of ML price optimization is dynamic pricing. However, changes made with dynamic pricing may need some fixing.

For this reason, dynamic pricing should be used with price optimization techniques. As we mentioned before, considering the diversity of parameters that affect pricing and decision-making features by reviewing many factors provided by ML is a significant factor in using ML in price optimization. Machine learning algorithms allow you to reach the best decision by examining many parameters and situations. It is almost impossible to look at many such parameters individually and accurately manually. Besides, with machine learning, you can analyze customer behavior at current prices and predict customer buying behavior at possible prices.

The main steps of price optimization with ML are as follows:

  • Data collected from various sources of product & historic price
  • Defining goals
  • Train the model & automated model selection
  • Automated feature engineering to find the best features
  • Getting the optimized cost for each product

Thanks to the AutoML technology offered by B2Metric, you can do all these steps the easiest and fastest way, with accuracy. B2Metric automates your data preparation, feature engineering, and model selection process with supervised, unsupervised, and semi-supervised algorithms.

Data Gathering & Cleaning with B2Metric

B2Metric automatically brings the data preparation feature. This technology has improved the process of data collection & cleaning to a higher level and offered users a new perspective and high-level experience.

Defining Pricing Target with B2Metric Price Optimizer

While creating a model with the B2Metric Machine Learning Studio product, you can easily select the target value and input from your ready data and run the model with these parameters.

Automated Model Selection

B2Metric works with supervised, unsupervised, and semi-supervised algorithms and automates the processes of champion model selection for data teams with AutoML.

Modeling & Deployment

All models that run with B2Metric ML Studio are prepared and interpreted for you. B2Metric interpretable ML models explain all causes, consequences, feature relationships, micro-segments, and outputs. Thus, accessible and understandable models are created for relevant non-technical teams.

B2Metric blogpost


Automotive Spare Parts Wholesale Price Optimization

Research indicates that an average industrial or automotive company generates 10% of its revenues from spare parts sales and more than 40% of its profit. Given the profitability of the spare parts market, manufacturers have realized that this element is critical for company operations. Pricing is the key to harvesting the untapped potential of the spare parts market and is the best lever for improving profitability. Industry estimates show that a 1% increase in price can lead to an 11% increase in operating profit. Manufacturers can significantly increase sales volumes, operating profit, and customer satisfaction by using the right pricing tactics for spare parts. However, pricing spare parts are challenging since each spare part has different competitors, consumer behavior, and market potential. The most common pitfall in pricing is applying standard markup (cost-plus) pricing or competition-based pricing, both of which are attempts to elevate earnings without understanding the implications (i.e., failing to tailor the service delivery model due to lack of authority and resources to spare part managers). Instead, using dynamic pricing software can maximize profit.

Dynamic pricing software can identify critical price points, analyze competitors’ pricing strategies, and determine what customers would pay for the piece. Then it can combine these with much more data and give you the optimum price. Also, it may seem like a long, extensive process, but it can give you new numbers a few times a day -which is the optimum for changing prices. Everything written above (and more) can be achieved with B2Metric. To get more info, you can visit our products or contact us.

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