The reason why "free" doesn't work in the B2B field... Price creates trust

There is a commonly held belief in the entrepreneurial market that lowering prices increases demand. But in real markets—especially in business-to-business (B2B) transactions—“free” often ends up breeding distrust instead. A fintech case study by a serial entrepreneur clearly shows this.

To allow business customers to adopt the service without having to bear the burden of transaction fees, the entrepreneur set the service to be “completely free.” The plan afterward was to generate profit through third-party transaction fees. This was a calculation to remove the initial adoption barrier and quickly acquire customers. Although the company received seed funding in the scale of several million dollars, the market response was rather insignificant. Businesses were slow to register, and potential customers also hesitated.

What changed the situation was an unexpected choice. While maintaining the original revenue model, the entrepreneur added a monthly subscription fee. Everything—product and core value proposition—remained unchanged, but once a price tag was attached, the atmosphere became different. Almost immediately, new business customers started flowing in, and the startup eventually grew into a company worth several tens of billions of dollars.

High prices become a “value signal”

This case shows that price is not only a tool for revenue; it also plays the role of a “quality signal.” Behavioral economics explains this as the price–quality heuristic. The idea is that when buyers cannot fully understand the quality of a product or service, they use price as the basis for judgment.

With goods like wine or electronics—where even if the products are the same, higher prices are often still associated with better evaluations—that’s a typical phenomenon. This principle is even more powerful in B2B markets. For example, if a cybersecurity solution is launched at a price far below the market average, customers are more likely to view it as a “risk” rather than an “opportunity.” Because for enterprises, stability and reliability matter more than cost savings.

Price even changes customer preferences

Lower entry prices usually attract a group of price-sensitive customers. These people tend to care more about cost savings than performance, have lower service loyalty, and are more likely to churn. Cases where support requests are excessively increased are also quite common.

On the other hand, a premium attracts another type of customer. They value not just cheap products, but reliability, performance, and sustainability. Setting a higher price means more than charging extra—it is closer to deciding which customers you will keep company with. In the field of strategic consulting, high prices can also be interpreted as not merely a revenue tool, but a filter for selecting “serious customers.”

Price even determines the competitive landscape

If a company sets its prices at the lowest level in the market, it can easily be categorized as a commodity—“an interchangeable service.” In that case, differentiation weakens, and comparisons are mainly limited to low-price competitors. In the end, it becomes difficult to escape the pressure to cut prices.

Meanwhile, premium pricing requires higher levels of service, professionalism, and reliability. At the same time, the competitive landscape also changes. It can be said that price even determines who you compete with and the criteria by which you are compared. This means that a pricing strategy is not a revenue issue—it is a core variable for defining brand positioning and market identity.

Ultimately, “free” or “low price” is not always a shortcut to growth. The more complex the market is, the more customers interpret quality and risk through price. Especially in trust-focused fields like B2B, low prices may create insecurity rather than appeal. This case again shows that price is not simply a number—it is “strategy” in itself, one that affects customer psychology and market perception.

TP AI Notes for Attention: Use a language model based on TokenPost.ai to summarize the article. The main content of the body may be omitted or may not match the facts.