Perform better, smarter experiments by creating user segments.

BMS - Dynamic Pricing
8 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. It is of great importance for companies in all segments to increase their income by price optimization at every stage of their activities. Pricing issue is mainly a management decision. So, the question here is how is the price determined to gain maximum profit? The determination of the price seems pretty simple in theory. Income and expenses are examined depending on the demand curve and cost functions. Points, where revenues are equal to or greater than expenses, can be determined as prices. Many small companies still set their prices manually in this way. Even if this approach is economically meaningful, it is only this 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 increasing product profit and chances of sale at the same time. Dynamic pricing is the fast alternative for fixed prices. COVID-19 showed us the world can change so much in a little time. So instead of having set prices for a period of time, companies can adjust the ever-changing market with updating their prices multiple times per day.

Sometimes dynamic pricing gets confused with personalized pricing. The difference is that dynamic pricing looks at the bigger picture while personalized pricing focuses on an individual. So dynamic pricing analyzes your products and their relative value in relation to the rest of the market. Personalized pricing, on the other hand, looks at individual consumer behaviors and changes the product’s value based on 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 also 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, etc. costs
  • Prices and campaigns of competitors
  • Customer segments
  • Economic variables
  • Stock status
  • Special days & events and season

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 above-mentioned prices. But these variations and the variability of factors affecting the price make it difficult to manually optimize prices. 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 as an opportunity to improve your price optimization strategy and your overall margin. Implementing dynamic pricing can be tricky to begin with. So here is a five step process to help you through this journey:

1) Decide on your commercial objective:

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, but it’s the main element of having a successful dynamic pricing strategy.

2) Have a pricing strategy:

Your pricing strategy uses your commercial objective to create a strategy that your team can use to sell products. For example, let’s say your commercial objective is to be known as the cheapest one on the market. Your pricing strategy would then be to make sure your products are always cheaper than the competition’s alternative.

3) 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.

4) Set down pricing rules:

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

5) Test and monitor:

Once everything’s all set up it doesn’t mean your job is done. You should keep track of the product sales, technical errors and effectiveness of the software you use. If it’s necessary you should make changes about it.



Importance of Price Optimization

Firstly, the number of decisions affecting pricing is increasing day by day in all industries. For this reason, more dynamic decisions are needed. For example; hundreds of new e-commerce sites are being established every day. The increase in competitors, consumer consumption behavior changes. 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 in the least possible way 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 his demand. Price optimization allows you to manage customized prices most profitably. Finally, it is important to make quick decisions, as the pricing process will be affected by more than one sector and will affect many industries. You can make the most effective decisions as soon as possible with artificial intelligence-supported price optimization tools.


Price Optimization with Automated Machine Learning (AutoML)

The price optimization process can be difficult, but it is not at all difficult to create a strong pricing strategy with the help of machine learning models. Machine learning-based pricing processes are of great importance nowadays, where customers can easily compare many prices offers with special search tools. AutoML, which has a great impact on 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 common example of ML price optimization is dynamic pricing. However, changes made with dynamic pricing may pose some problems. For this reason, dynamic pricing should be used together with price optimization techniques. As we mentioned before, considering the diversity of parameters that affect pricing, decision making features by reviewing many factors provided by ML is a big factor to use 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 manually examine such a large number of parameters individually and accurately. Besides, with machine learning, you can analyze customer behavior in current prices, as well as predict customer buying behavior at possible prices.

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

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

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

Data Gathering & Cleaning with B2Metric

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

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 easily run the model with these parameters.

Automated Model Selection

B2Metric works with supervised, unsupervised, 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 to you all causes and consequences, feature relationships, micro-segments, and outputs. Thus, easy and understandable models are created for  relevant non-technical teams.


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 their profit. Given the profitability of the spare parts market, manufacturers have realized that this element is critical for company operations. Pricing is the key for 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. By using the right pricing tactics for spare parts, manufacturers can realize significant increases in sales volumes, operating profit and customer satisfaction. Pricing of spare parts is challenging since each spare part has different competitors, consumption 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 a dynamic pricing software can maximize the profit.

A dynamic pricing software can identify the critical price points, analyze competitor’s pricing strategy and find out what customers would really 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, big process but actually it can give you new numbers a few times a day -which is the optimum for changing prices. Everything that’s written above (and more) can be achieved with B2Metrics. To get more info you can visit products or simply contact us.

Register and try B2Metric ML Studio B2Metric Machine Learning Studio

Subscribe to
our newsletter

Get a weekly round-up of articles about building better products.

By submitting this form, you agree to our Terms of Use and acknowledge our Privacy Statement

Sign up Today

Sign up to receive the latest best practices, news, and product updates.