This is generally caused by the lack of high-quality data that B2B companies experience. AI and ML algorithms require very high-quality data in order to produce the most effective results that can augment pricing strategies.
The importance of high-quality data
Today, price optimization is being reshaped and remodeled by machine learning, enabling businesses to build a stronger pricing strategy.
But any machine learning model is only as good as the data it receives. Therefore, it is essential to ensure that the data your machine feeds upon is of the highest quality possible. To accomplish this, you need the support of data scientists who will thoroughly evaluate your data sources, verify data accuracy, and correctly feed that data into your machine learning model.
If all steps are performed correctly, your price optimization model will determine entire price distributions that can enable you to align your prices with your business objectives. Additionally, machine learning capabilities can also predict the responses customer segments may have to price changes. These insights allow you to review your pricing strategy before executing your plan and ensure that you adjust it where necessary in order to get the reactions you want from your customers.
Likewise, you can use data insights to personalize your price for a particular client and determine the ideal price for them based on information about their preferences, behavior, habits, agreement level, type of contracts, industry, etc.
Leveraging data for pricing decisions
The advent of big data and innovative pricing systems has changed the way companies are approaching their pricing practices. Traditionally, they would’ve looked at competitor prices to establish their own and use manual methods to set and manage their prices. In the B2B world, this is no longer sustainable.
Now, having data that informs you about order history, customer behavior, inventory, marketplaces, and other dynamic factors can help you put things into perspective and determine the right prices for specific circumstances and customers. Additionally, a variety of data sources will help you paint a bigger picture. Your business transactions, social media, and even machine-to-machine data systems (i.e. sensors) can tell you a lot about a particular customer and the market as a whole.
However, simply having access to data is useless without a way to properly derive insights from it. You need best practices and solutions to analyze data at a granular level and build data-driven pricing strategies. Through data analysis, you can identify patterns, preferences, and spending habits that your customers display and you can use these insights to create machine learning algorithms that respond to customer actions.
For example, you can implement an algorithm that can predict with almost 100% accuracy when a customer is about to leave your site without making a purchase. The same algorithm can have the capability to offer only to that customer (or customer segment) a discount to facilitate an instant purchase.
Measuring price elasticity
For most B2B companies, measuring price elasticity is not a priority. Instead, they prefer relying on statistical distributions of prices. But with price optimization, this can change.
The sole purpose of price optimization is to help you determine the optimal prices for maximized profits. But to use price in order to hit a specific margin target, you need to be able to envision how your customers will react to price changes and how a variety of circumstances will influence their reactions. This is where price elasticity comes in to help you understand each price segment and minimize revenue risks and profit losses.
Using price elasticity, you can calculate the profit-maximizing prices for each given segment, optimize those prices to serve different goals, and detect prices that are either too low or too high compared with industry benchmarks.
Key benefits of data-driven price optimization
Being able to factor in price elasticity is a great perk created by data-driven price optimization. But there are also a number of other key benefits that you will experience as a result of using machine learning to sift through data and determine the ideal prices for your products and services.
- An objective pricing perspective
The biases that influence the way human beings approach problems and conceive solutions can stand in the way of progress. However, machine learning models that are fed quality data correctly exhibit unique problem-solving capabilities that are not plagued by human biases.
- Complex computing
The average human mind can’t compute in the same way AI can. Therefore, the increasing complexity of pricing structures can become impossibly overwhelming to even consider. Luckily, machines can take over this aspect, taking into account each additional variable and computing the ways in which it affects the already existing variables. This is important, especially because the number of data sources is also increasing every day and they need to be considered in almost real time for positive results.
- Free of human error
It goes without saying, but machine learning models are inherently mathematical and accurate constructs, so they are reliable and they eliminate human error. Additionally, depending on a variety of circumstances and scenarios, maybe you don’t need 100% accuracy to determine the ideal price. Maybe you want some room to move and adjust. In that case, you’d be glad to know that you can adjust the level of accuracy according to your specific needs.
- Predictive capabilities
Perhaps the most enticing benefit of using machine learning to optimize your price is the fact that, with enough data, your pricing models can identify patterns that would otherwise remain unnoticed and anticipate pricing trends that may otherwise take your business by surprise. Leveraging these predictive insights, you can prepare and adjust your prices in the most optimal way.
To be continued
Now that you have a fundamental understanding of why data is essential for price optimization and know what benefits to expect, it’s time to look at how you can incorporate it into your data-driven pricing strategy. We’ll explore best practices in part II.