There is so much to consider when determining the right prices for your products and services. Like most B2B companies in the industrial sector, you also have multiple contract agreements to manage and increasingly complex product portfolios to consider — all while the competition influences the market, the prices for raw materials rise and fall, and regulatory constraints interfere with your scheduled pricing activities.
It goes without saying, but that’s a lot for anyone to handle, especially in an increasingly digital environment with data sources that continue to multiply and manual, traditional solutions that fail to cope with the intricacy of the situation.
In part I of this article series, we explored how high-quality data can be leveraged to measure price elasticity and make pricing decisions that result in maximized profits. We’ve also looked at some key benefits it creates for those who use it for data-driven price optimization, including complex computing that is free of human biases and errors, and predictive capabilities that will enable you to anticipate trends and customer buying behavior as well as price fluctuations.
In this article, we’ll explore why data-driven price optimization is the most sustainable pricing approach in today’s climate.
A sustainable pricing approach
The business world is becoming increasingly digital and dynamic. Additionally, events such as the global COVID-19 pandemic are fundamentally changing the way people do business, how consumers buy, and how much they are willing to pay for something.
As the world of business is reinventing itself, pricing should ideally become one of the main drivers for growth for a larger number of B2B companies. Although it is widely known that optimal pricing leads to maximized profits, only 12% of businesses have considered pricing as the main driver for growth. As of 2019, most businesses continued to place sales and cost reductions as top growth drivers.
It’s time for that to change and, luckily, price optimization can help you transform pricing into a driver for future growth. Price optimization relies heavily upon the advanced capabilities of machine learning and high-quality data. The valuable and actionable insights you can derive using the right tools will enable you to set the ideal prices for a variety of circumstances and contract agreements that will result in maximized profits.
Best practices for data-driven price optimization
Considering that traditional, manual approaches are no longer sustainable and that data needs a new way to be processed and transformed into actionable insights, you need advanced tools that can help you execute your data-driven pricing strategy.
Enter price optimization software — a solution that leverages artificial intelligence and machine learning capabilities to help you not only tackle the complexities of pricing but also enable you to achieve your P&L objectives.
With price optimization software, B2B companies can set prices that are aligned with each specific selling situation. That’s because this tool accounts for all the factors that drive pricing, simultaneously and rationally aligns the price with the customer, order, and product relationships, measures what drives price response in the market, guides future pricing decisions for multiples scenarios, and it does so in a time-efficient manner utilizing data that is already available.
Most businesses already have transaction data ready to go, including data about the customer, product, order history, and so on. This data is organically captured through regular business activities. In and of itself, it may seem like a necessary but not actionable pile of information. However, the right software or algorithm can turn it into a goldmine of information that drives profits. Besides understanding the power of your data, there are a couple of other key considerations you should make:
- Choosing a relevant pricing strategy and supporting it with data-driven marketing. For example, data-driven marketing can support freemium pricing strategies by enabling companies to track buyers who visit the purchase page but don’t end up buying the premium version. In value-based pricing, data-driven marketing can help companies better communicate their value proposition to certain customer segments and even measure their willingness to pay.
- Implementing the right machine learning models. Working with a data scientist, companies can discern which methodologies are suitable for their price optimization endeavors and build models that take into account consumer behavior, sales history, and an array of other historical and dynamic factors. On top of allowing executives to identify the most relevant prices, this will also enable them to conduct competitive assessments and benchmarking activities. Likewise, it can help them perform tests to determine which factors influence price levels to affect the value that is delivered to the company’s customers.
To develop a price optimization model that truly serves your goals, you need to team up with data scientists and embrace machine learning technology. This way, you will be able to properly analyze demand, internal economics, product architecture, and the competition to define key factors that influence buying and quantify them to determine the ideal price.
Keep in mind that price optimization is an ongoing process that also involves systematically re-setting and managing price changes, revisiting pricing strategies, and stimulating outcomes for the most relevant pricing recommendations. Fortunately, with good software, you can automate this workflow.
In 2020, during the first-ever virtual MindShare pricing and sales conference, attendees were asked to share their biggest hurdle in terms of improving pricing performance. Up to 49% of them have revealed that they lack the necessary tools for optimal pricing.
Achieving pricing excellence in an increasingly dynamic and digital industry is impossible without intelligent pricing solutions that are driven by a data science platform. Without such solutions, companies will continue to experience inconsistent and inaccurate prices, lack of granularity in pricing segments, and significant profit losses.
Machine learning can help you tremendously, but your price optimization endeavors will only be successful with support from real data scientists. Consider partnering up with a professional team that can elevate your data analytics to the next level. Last but not least, utilize all the data you can find from internal and external sources. From stock market information to potentially siloed company-wide data, make sure that you collect enough insights for a reliable price optimization process.