The Do’s & Don’ts Guide of Machine Learning for Dynamic Pricing

September 22, 2021

Models, Benefits and Examples of Machine Learning for Dynamic pricing 

What is the definition of Machine Learning pricing?

Machine Learning Pricing (ML Pricing) is the constant automatic adjustment of product prices to optimise margins or to maximise sales opportunities. With this pricing model you get variable prices instead of fixed prices. Variable prices are not that new. For example, some fruits in the supermarket cost more in winter because they need to be imported. This has been done for many decades. However, variable prices that change multiple times a day is rather new. In this relatively new age of digitization and big data, companies now have the resources and opportunities to change prices based on multiple factors more frequently. Although this practice is most common in online web shops, there is also the opportunity to do this in brick-and-mortar stores and in B2B sales.  

Machine learning pricing has thus become a significantly more valuable tool to a lot more companies. Most of the time, a machine learning pricing model takes multiple variables into account, not just 1 or 2. Examples of what could be considered are: competitor’s prices, items left in stock, season, customer value and more. Machine learning for pricing could be a very good tool to each business as it is an excellent way for boosting sales, maximizing profits or to generate more traffic to your website. Next to this it could also provide more information about your target audience than other pricing models.  

In this ultimate guide you will read about what companies use machine learning for pricing, what the advantages and disadvantages are and what steps you must take to successfully implement ML pricing in your own organization.  

What companies use Machine Learning Pricing? 

Uber, Amazon, AirBnB, not only are they some of the most successful innovative companies of the last decade. However, they are also relying heavily on machine learning to optimize their margins, revenue and business opportunities. But how do they do this exactly?  

  • Amazon – who hasn’t heard of them? The former bookstore is now one of the biggest (online)   retailers in the world and it sells everything – from books, electronics, wine and home  improvements.  One of their strategies for maximizing profits is changing products prices  millions of times a day. Meaning that the price of a product changes every 10 minutes, which  means  that if you do not like the price of a product on Amazon, you might just have to wait a  few minutes to get a new price! Amazon has gathered so much data on consumers’ buying  behaviour, competitors, profit margins, inventory and much more that it can (almost) always  predict the best price for the product at that moment of time. Amazon uses its own  predictive AI & machine learning models to always get the right pricing. And the result of all  this effort? Amazon boosted its profit by 25%. That’s how effective a dynamic pricing model based on machine learning techniques could be. 
  • AirBnB – started small in 2008 during the economic crisis, but now the website has more than 3 million locations in 192 countries. Basically, any country in the world has a property that is being up for hire on AirBnB. Although AirBnB acts as a platform and it doesn’t sell goods directly, it offers “Smart Pricing” (aka ML pricing) to renters. When activated, this tool sets the prices of your property to automatically go up or down based on changes in demand for listings like yours. Other factors that they consider are season, demand, property features and location. Of course, rules could be applied such as the minimum or maximum prize for a night. AirBnB states that people that follow their smart pricing method are four times as likely to be booked than non-users and they are able to increase their revenue by 12% on average.  
  • Uber – uses ML pricing too, just like the other two giants. However, they have a two-fold reason for this. On the one hand to maximize earnings and on the other hand to make sure that there are enough taxi drivers at the right location. Uber makes use of surge pricing, which is when prices go exponentially up when demand increases. This increases margins of course, but next to that, it makes Uber drivers  go to places where the fares are high. After a certain amount of time, the number of drivers matches the number of drives needed, which lowers the price again.  

These are examples from big tech giants. With SYMSON we make it possible to apply machine learning on a lower scale.

How SYMSON uses Machine Learning Pricing for its clients

An irrigation system wholesaler used to sell their products based on discounts. The discount was mainly volume-driven and sales managers were still free to make their own adjustments. The goal of the collaboration was to identify new pricing strategies to optimise margins and to limit the “gut-feeling”. The SYMSON team kicked the project off with a brainstorm in which the current and future pricing strategies were discussed. And the following solutions were defined:

  • 1) a key-value item strategy where several key items were marked as traffic generators for their business;
  • 2) a geographical pricing strategy where exceptions were possible among Europe and;
  • 3) a rule-based pricing strategy where prices were given certain limitations.

In the end, SYMSON included various data sources such as customer groups, discount groups, seasonality effects, price elasticity and pricing business rules in its algorithms. This all led to accurate results that the client needed to succeed. Curious to what SYMSON could do for you? Schedule a call! Or keep on reading about the (dis)advantages of Machine learning pricing. 

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What are the advantages of Machine Learning Pricing? 

Now that we discussed what machine learning pricing is and by what companies it is used, we can look at the advantages and disadvantages of it. Let’s keep it on a positive note and start with the advantages. Machine learning for pricing is most often a set of pricing rules for groups that changes dynamically. This process is automatic and can most often be replicated on a large scale for a large variety of products. The main advantages of it are: 

  • Responsiveness – One of the main benefits of ML pricing is that this makes you able to automatically change prices according to the changes of certain variables such as demand, season or competitors’ prices. Prices can be updated quickly, without the need for a pricing manager to interfere. You do not constantly have to look at what your competitors are doing and if they are doing something – the model will have an almost immediate response to it. 
  • Ability to maximize profits – Another pro of using machine learning for your pricing models is that this maximizes margins and thus also the profits of your company. The pricing model can be instructed to try to achieve the maximum available profit margin on each product. Since, the model can update prices more frequent and time-effective than people, it will give you the best possible option to maximize your profits. 
  • Time and cost saving – Lastly, and it is already touched upon in the previous advantages, a ML pricing model is time and cost saving. Since the process of updating prices is handled automatically by the model, pricing managers, business owners or decision makers have more time to spent on other challenges. The machine learning model takes over most of the mundane and routine tasks and lets your organization spend time on other challenges. This decreases the time of doing mundane tasks and saves costs since more work is getting done in the same amount of time. 

What are the disadvantages of Machine Learning Pricing? 

  • Negative sentiment – One of the main pitfalls of ML pricing is that it can cause negative sentiment under consumers. To come back to the Uber example, their surge pricing model was based on charging more if more people wanted to book an Uber driver. However, one time there was a shooting in America and consequently, people wanted to get out of that area sooner than later. These people caused a spike in the demand for uber drivers in the area and this led to Ubers being very expensive. People were, softly said, not amused. This caused a huge negative sentiment surrounding Uber. Although this is a unique scenario, you should watch out that your automated pricing model doesn’t profit from disastrous moments. So that consumers won’t get a negative feeling for you. 
  • Price wars – Another downfall of a ML pricing model is that if you and your competitors all use it, it could cause a price war. If organization A and organization B, both want to be the cheapest, both pricing models will keep lowering their price in reaction to the others price change. This could lead to a never ending downwards trajectory. However, this is also easily avoided with a few simple pricing rules, such as a minimum selling price.  
  • Loss of sale – The last con of the machine learning pricing model is that people could figure out that the prices are dynamic. This could make people leave if the price for a product is too high according to their perception. For example, some aircraft organizations increase the price for you if you look at a certain flight too often. However, if you then visit the same page in a new anonymous web browser, the same flight is sometimes offered for way cheaper. According to this principle, people could postpone their purchase until they feel the time is “right”.

How to successfully implement Machine Learning Pricing? 

Now that we discussed the dynamic pricing model and its (dis)advantages you may wonder how you could set up your own dynamic pricing model in your organization. According to research done by McKinsey, dynamic pricing should not be treated as an IT tool, but as a new business idea. In the past organizations that did not treat it like an IT tool were not able to capture all the value that it has to offer. As an organization, you need to get multiple departments on board – sales, marketing and senior management. Here are 5 steps to incorporate a machine learning pricing model successfully into your organization.  

  • 1: Define your commercial objective – First, you need to identify your goal. Do you want to increase your profit margin, maximize your revenue or get a larger market-share? You need to decide this first so that you can then make strides towards your goal. A good way of doing this is to make a strong business case, which aligns everybody in the organization. Having a strong organizational alignment is import for completing the next steps successfully. 
  • Step 2: Build the right team – As mentioned before, dynamic pricing is not an IT tool, it is a business idea that affects many work processes of different teams. For this to work, you need the right people or team to implement this across the organization. Two qualities of the team need to be met:  1: the team will need to have advanced analytical knowledge and 2: the team will need to be able to work closely together will marketing and sales to include their input as well.  
  • Step 3: Focus on capability building – Focus on building the capabilities of people within the organization, make sure to incorporate everyone in the process. Not only the people that are advocates but also people that may not see the value (yet). Bring them up to speed and show everyone what is possible. To include them in the process but also to show how this will bring more value to the organization. 
  • Step 4: Fail fast, learn fast It is better to implement a minimum viable product soon than to wait for a finished product for months. Gather data from your first minimum viable product and incorporate that as soon as possible. This way, the system will get you results in the first weeks and months, and it will be optimally deployed a lot sooner than if you wait for a longer period. 
  • Step 5: Hire the right team For this to work, you need to have the right talented people. It may be that you do not have the right analytical capabilities in-house yet. If you do not, start hiring the right analytical people and combine them with people that know the business, industry and historical prices well. By doing this, dynamic pricing will have a lasting impact on the organization.  

What we do at SYMSON

Machine Learning goes hand in hand with AI (artificial intelligence) & dynamic pricing. AI and machine learning models can analyse large amounts of data very quickly and come up with the best possible prices. For humans, these kind of analyses take significantly longer. Luckily, AI & machine learning have become more widespread, and it is now easier than ever to use dynamic pricing based on ML & AI generated advice.  

With SYMSON, you can get this all, in one machine learning platform. SYMSON makes it easier for pricing and data managers to implement dynamic pricing strategies and to do demand forecasting. The platform helps you to automate mundane tasks and it gives advice on how to increase your profit margin. Interested in how we could help you? Schedule a call now!

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