Report: Outcome-Based Pricing Gains Ground in Tech Market

A small but growing number of tech companies are reportedly offering pricing based on outcomes rather than usage.

With these outcome-based or value-based pricing models, customers pay a percentage of the increased revenue, cost savings, growth in customer count or other benefit they have received from the product or service, The Wall Street Journal (WSJ) reported Wednesday (July 5).

While most tech providers still charge a set subscription fee or a fee based on number of users or amount of computing power used, they are under increasing pressure to provide other pricing models at a time when many customers are looking to lower costs, slow the rising price of subscriptions for business software, and eliminate unexpected spikes in the amounts they are billed, according to the report.

Customers are looking instead for a pricing option that allows them to offset their financial risk when adopting a new product and give a quarter of the cost savings they have obtained to the technology provider, while keeping the other three-quarters for themselves, the report said.

The shift to alternative pricing methods is also being driven by technology that enables customers to compare the price of products to the outcomes that they provide, and by smaller tech companies and startups that see this offering as a way to make inroads against their larger, most established competitors, per the report.

On the other hand, traditional models with a fixed subscription fee provide pricing that is simple and predictable, according to the report.

This report comes at a time when cloud platforms designed for specific industries are gaining ground in the marketplace.

These solutions solve industry-specific problems while also delivering cost savings by enabling users to optimize their operations and pay for only the part of the cloud infrastructure that they need, WSJ reported in December 2022.

Companies are also looking to buy and integrate, rather than build, generative artificial intelligence (AI) solutions.

The sheer computing cost of running large language models (LLMs), the considerable expertise required to build them and the challenge of maintaining the integrity of data and information is leaving most enterprises with little say in the matter, PYMNTS reported in May.