Companies in many industries have traditionally used a cost-based approach to pricing their goods and services. But many are starting to explore a completely different approach called value-based pricing. When combined with data collection and machine learning algorithms, value-based pricing approaches become extremely powerful.
In a traditional cost-based pricing strategy, a seller determines the price of a particular product by adding up the various costs incurred (such as manufacturing, distribution, transportation, marketing, etc.) and applies a fixed price range. Cost-based pricing, also known as cost-plus pricing, is especially common in consumer goods supply chains, where companies may advertise price increases.
Value-based pricing takes a completely different approach. Instead of an inward-looking strategy that focuses on costs and expected benefits, value-based pricing looks outside of the customer to determine what kind of value the customer will receive from a product or service. I'll point it.
according to investmentpedia, Value-based pricing is suitable for more complex products and services, allowing sellers to maximize the price at which they ultimately sell their goods and services, while also promoting customer and brand loyalty. Helpful.
“Value-based pricing is resource-intensive because it requires the collection and analysis of customer data, but it can lead to sales advantages, higher price points, increased customer loyalty, and other benefits. “There is,” writes Andrew Blumenthal in his book. investmentpedia article.
One of the big proponents of value-based pricing is Fabrizio Fantini, vice president of product strategy at ToolsGroup. Mr. Fantini wrote his doctoral thesis titled “Online His Algorithm for Dynamic Pricing'' for his PhD in Applied Mathematics at his ESCP Business School in Paris, France, where companies We help implement sophisticated value-based pricing strategies around the world.
“Frankly, there's nothing complicated about it,” Fantini says. Data Nami In a recent interview. “In a nutshell, the idea is that the right price is something that works at the intersection of you and your client. It's more of a mindset than an algorithm. Once you establish that mindset, it becomes very easy to Become.”
There is no set formula for value-based pricing, and what determines the optimal price in a value-based pricing system varies. They may vary depending on product features or aspects or specific seasons. People in different regions have different values. There's also a psychological aspect, such as people's reluctance to tear up $20 bills.
Value-based pricing also requires more work on the part of the seller. They need to not only analyze their own goals, but also be willing to learn and relearn the lessons the market is teaching them (if they are willing to heed those lessons).
“If you ask any business owner what their goal is, they'll say they want more profits. Well, fine. We all agree. We're all happy,” Fantini says. “It turns out that's not really what companies exist for. Profit is one thing, of course, but they also want more cash, more revenue, more loyalty, better recognition. I want it.”
Machine learning algorithms can be very useful in implementing value-based pricing schemes. According to Fantini, the signals needed to create a value-based pricing system can be found in a combination of common sales data such as products, locations, and people. This data can help companies begin to determine where customers perceive value from their products and where they don't.
Success with value-based pricing is all about framing your questions well, accepting what the data is telling you, and understanding that as the world changes, your current answers will change, says Fan. Tini said.
“These things can only be discovered by being sufficiently humble and learning that aggregated requirements do not necessarily work according to the rational scheme you have in mind,” he says. Masu.
Due to the open-ended nature of value-based pricing, all kinds of data can be collected and analyzed. Fantini says humans have an endless desire for granularity. It can be scary at first. The good news is that businesses can get started without spending a fortune on extensive data collection.
“We don't need that much data. That's counterintuitive,” he says. “First, if you frame your question correctly, you may need surprisingly little data. Data and algorithms are important. I'm not going to ignore that completely. But the truth is that you can use very good frameworks to It means you can solve problems with surprisingly little data, as long as you build on top of it.”
It's important to understand that there is granularity of data on the supply side, such as evaluating product mix across time and space, but also on the demand side, such as how discounts, promotions, and weather encourage people to buy. It is important to understand that there is granularity. These variables should be treated with caution, as there are risks involved in comparing companies with different products and customers.
When using AI to be data-driven, being able to ask the right questions of data is far more valuable than having more data. “Value-based pricing requires a different logic. You have to constantly adjust your thinking based on information you get in the market, and that's very difficult,” Fantini said. says.
Success with value-based pricing requires good data and a good model. But machines don't take into account nuance, so it's more important to have people who can ask the right questions of the data, and even more important to have people who can ask them quickly before market opportunities are lost. says Fantini.
“The gap is in human capabilities,” he says. “We've been taught the wrong skills. The real skill is in framing problems. And machines are really stupid, so we need to ask simple, focused questions.”
Fantini likes the idea of an invisible hand guiding the market and helping buyers and sellers match a win-win price. AI helps the invisible hand work more efficiently by guiding sellers to price points where customers experience the most value.
“This is basically a sustainable source of competitive advantage,” he says. “Those who have mastered that technique are wise to design to price and design to demand. They're not just changing prices.”
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