What exactly is pricing optimization? Price optimization is the method of determining the best price or set of prices for your business offerings.
Talking about the “best price” is easy, but price optimization is all about strategy. As odd as it seems, we humans are pretty bad at determining how much something should cost and our biases often hold us back in this area.
Unfortunately, these bad pricing decisions cost businesses big money. In fact, most don’t even know how much they are losing due to bad pricing. Lucky for us, machine learning has helped to completely reshape the way we think about and tackle pricing optimization.
Before we jump into any more details of price optimization, you need to have a solid understanding of the factors that go into it from the customers side of things.
How Do Customers React to Pricing?
Developing a price is all about the numbers, but the way customers react to it is deeply human and therefore quite complex. Let’s break down some of the most important elements at play.
Obviously, if a product was previously $10 and is now $7, that $7 price will not be perceived the same by humans compared to a product that was formerly $5. This pricing history is as important a factor for machine learning as for humans addressing price optimization.
Your company’s reputation may come into play. Are you known for competitive prices? Much like the previous example, this can account for customer reactions and price sensitivities that wouldn’t otherwise make sense.
If competitors’ prices are adjusted, customer perception of your prices will be affected. This should be kept in mind when making any adjustments to your own pricing strategy.
Look at the time that Walmart faced customer backlash by offering different prices for items online and in stores. The context in which someone sees a price matters, primarily when it comes to online retailers vs brick and mortar stores. The ease with which products and services can be price-compared online means customers have different expectations and reactions to pricing.
As seasons change, so do humans and buying patterns. Price optimization needs to take seasons into account as well (or even hour-to-hour weather data). This affects product price and customers’ willingness to pay as demand varies.
Common Pricing Optimization Strategies
There are a few common pricing optimizations strategies worth highlighting. These include:
- Initial price: Above, we mentioned how the initial price can affect how customers perceive a later price. Price optimization strategies can actually determine optimal initial prices based on the season, time of day, and other factors.
- Best price: This is the general price, which best meets your unique KPIs.
- Discounted price: This answers the question: If you’re starting with an initial price, what is the ideal discount price based on that starting point?
- Promotional price: When considering pricing and promotions, taking human psychology into account is key. What price is optimal in the context of a one-time promotion? How does that change based on the promotion (e.g. How Black Friday expectations compare to Christmas expectations)?
Key Considerations of Pricing Optimization
- Operating costs: Price optimization has to take input costs into account if it’s going to optimize a final sale price and increase profits, so using operating costs is essential.
- Demand: There’s hardly a more central concept to price than demand, so this must be built into the core of any price optimization strategy. While you want to make sure you’re not leaving money on the table, you also need to make sure there is adequate demand for the price you’re charging.
- KPIs: Ask yourself what matters most to your company. Is it customer loyalty? Quantity of products sold? Average price per product sold? Your organization’s KPIs are crucial in determining optimal pricing.
- Rivalry: Is it more important for your firm to beat the price of a said competitor? For example, do you need to make sure your prices are always equal to or lower than Amazon’s?
Machine Learning Is Reshaping Price Optimization
We’ve established the complexity of price optimization, but the advanced capabilities of machine learning can help build a stronger price optimization strategy.
How can a model successfully take all of these factors into account when determining optimal price? Remember, a machine learning model is only as good as the data its fed.
The process begins with data scientists carefully evaluating your data sources, then ensuring that they’re accurate and fed into the model correctly.
With quality data, these price optimization models determine whole price distributions. Say, comparing money earned upfront to customer lifetime value along with numerous variables that can help you determine the best price for your goals.
Machine learning can also predict how targeted customers will respond to prices they haven’t yet encountered. By plotting responses and predicting patterns, you can evaluate pricing strategies at a basic level without necessarily having to execute each one.
These techniques can even be applied on the level of individual customers, determining the optimal price for a specific person based on what your company knows about them.
How to Achieve Optimized Pricing with Machine Learning
Here’s the process for how a machine learning model can be used to improve your pricing strategy:
Machine learning models can either work entirely off of a historical data set, live data, or – as is most often the case – a combination of the two. In any case, the model must first be trained using an initial data set before it can begin price optimization.
Define goals & limits
Here, you input the parameters to shape the model. More specifically, these parameters will tell the model which KPIs are most important to you.
Choose an algorithm
While machine learning is a catch-all term, there are many variations. Such algorithms can be supervised or unsupervised, explainable or unexplainable, generative or discriminative, etc. Figure out if it’s possible to use deep learning methods? You’ll need to work with a data scientist to determine the optimal algorithm for your needs.
Modeling & training
The individual model is then built and prepped with the training data. At this point, you can begin to determine whether you’ve made the right choices in steps 1-3.
Tweak the Prediction Mechanism
At this stage, the model goes through thousands of iterations, testing assumptions, and adjustments of its prediction mechanism. This is, essentially, the machine learning model “learning”.
Execute & Adjust
Once you have a price, it’s time to test it, gather data, and repeat the process. This step is always necessary and should be ongoing, as adjustments will likely need to be made in the future.
Biggest Advantages of Using Machine Learning for Price Optimization
Just in case it’s not already apparent to you, there are quite a few benefits to taking a machine learning based approach to pricing! A few of our favorites include:
- Not thinking like a human: As briefly noted above, humans are plagued by biases and tend to think in relatively similar ways when approaching problems. Machine learning models, by contrast, are left to approach problems in ways a human may have never considered.
- Number and nature of parameters (large number of products): There are only so many things the human mind can consider at once. With each additional variable potentially affecting all the others, the complexity of these pricing structures increases exponentially. That makes them ideal for machine learning.
- Multiple sources and channels (optimizing prices globally): As the number of data sources increases, so does the difficulty for humans to take them all into consideration.
- High level of accuracy: It’s not simply that machine learning models are highly accurate, but that you can determine the level of accuracy that’s appropriate for your needs. Do you need to be 99% certain of a conclusion or just 90%? Adjustable confidence gives you more options.
- Anticipating trends at earlier stages: With enough data, machine learning models can spot and anticipate trends that a human may never have noticed.
3 Examples of Successful Price Optimization
Due to the many clear benefits that come from using ML for pricing optimization, there are many companies that are successfully doing so across industries. Here are a few examples:
Brands from fast fashion Zara to higher-end fashion companies like Michael Kors don’t just use machine learning for pricing: They rely on it. Machine learning in fashion and retail can impact everything from deciding when to stock products to retail prices.
Artificial intelligence touches nearly every stage of the fashion industry’s products and is viewed as an essential tool in this highly competitive space. Real time price optimization in retail is necessary as consumer behaviors and trends are constantly fluctuating.
You’ve likely heard that your location, previous buying habits, and a number of website visits can all impact airfare prices. Airlines have been on the cutting edge of price optimization since the 1970s, but today, that extends far beyond simple tickets. Airlines need to consider how options like baggage policies, loyalty programs, aircraft type, and departure times impact the prices customers are willing to pay. Since all of this impacts profit margins, machine learning is the only option for the task.
2004 was a big year for major hotel chains like Hilton and Intercontinental, when they ended fixed pricing and introduced their first variable pricing model. Today, customized pricing is an industry standard, allowing hotel resorts to factor in innumerable variables, strategies, and tactics to calculate the optimal price for each individual customer. These techniques have been so successful for hotels that major chains have increased the adoption rates of dynamic pricing. Hotel price optimization for room rates is now an industry standard.
It’s no wonder that companies across numerous industries are building their price optimization with machine learning. What do you need in place before using this technology to address your own business needs?
Prerequisites for Price Optimization with Machine Learning
As much as we’d like to imagine that machine learning algorithms will solve our pricing problems on their own, success wholly depends on cooperation with data scientists and business professionals.
For this (and other big data analytics solutions) to work, there are certain requirements:
Be open to using external sources of data
This may include information from your competitors, stock market, and even the National Weather Service. What matters is the complexity of customer psychology, meaning that it’s nearly impossible to conduct reliable price optimization without some external data sources for context.
Access to all kinds of internal data
Larger organizations often face the challenge of various departments siloing themselves off and not sharing data or learnings. This poses enormous business challenges, but for machine learning, it can be disastrous.
As mentioned above, a model is only as good as the data you give it, so having free access to quality data from all parts of an organization is paramount for the pricing process.
Quality professionals on both sides
While you obviously need quality data analytics professionals with experience in using machine learning for price optimization, you also need other business professionals ready to work with them.
These data professionals will need help translating business KPIs into algorithm parameter, and the business professionals will need help analyzing the data which comes out. It’s a symbiotic relationship which is just as important as the quality of the technology you’re using.
Tackling Price Optimization with RapidMiner
Are you ready to take on machine learning and start impacting your business? Data science platforms like RapidMiner can help jumpstart your project.
Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. 40,000+ organizations use RapidMiner to drive revenue, reduce costs, and avoid risks.
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