What is a probabilistic value proposition?
What is a probabilistic value proposition?
Imagine if Gmail delivered 95% of emails to the correct person’s inbox and sent the other 5% to a different person’s inbox. Or, imagine if Microsoft Word saved 4 out of every 5 documents successfully but otherwise encoded the file incorrectly, rendering the document unreadable.
A product that only works some of the time — this would be crazy and frustrating for users. No one would buy a product like this. Unless…
Imagine if only 1 out of every 30 shows Netflix recommended was one that you actually wanted to watch, and most were shows you’ve already seen. Or imagine if Amazon’s Alexa only understood 8 out of 10 things you asked of her, often just responding, “I’m sorry, I don’t understand the question.”
A product that only works some of the time — not as crazy in these contexts. What’s different? The last two examples are product features designed to have a probabilistic value proposition.
The hallmark of a probabilistic value proposition is that the benefit a customer expects from the product may or may not occur during any particular interaction. Instead, value is delivered as an aggregate over multiple interactions: one in five, nine in ten, one in a million. These interactions may occur across periods of time, across diverse contexts, given extreme or rare circumstances, or even across different groups of customers.
Products that deliver probabilistic value are rare compared to more traditional products like t-shirts, canned food, and televisions. However, they exist both inside and outside of computing. For example, a seat belt has a probabilistic value proposition. We wear a seat belt during every car ride but experience life-saving benefits only if we get in an accident (ideally never).
Artificial intelligence and machine learning are based on statistics, an entire discipline built on the concept of being right probabilistically — often enough to be useful, but not always. By their nature, all products using artificial intelligence and machine learning have a probabilistic value proposition. Understanding the implications is, therefore, especially important for founders or practitioners in this space.
A probabilistic value proposition means thinking about all possible customer experiences
When we design a product with a deterministic value proposition (the normal kind like Google’s email server and Microsoft Word’s save-to-disk), we focus on maximizing our product's benefits by building new features and functionality. We define these benefits relative to the world where a customer is not using our product or where a customer is using a competing product. Therefore, the value proposition is the sum of all the benefits our product provides relative to those worlds.
When we design a product with a probabilistic value proposition, we still focus on maximizing the benefits of using our product. But, because the benefits do not always occur, we must also consider any costs associated with neutral or bad outcomes that a customer may experience trying to use our product. We need to design two user experiences: one where the benefit is provided and one much less glamorous user experience where our product does nothing or gets the answer wrong. The value proposition of a probabilistic product is the sum of all the benefits of using the product minus the costs associated with using the product when it has bad or neutral outcomes.
Seat belts provide the benefit of safety in the event of a car accident and must be comfortable enough to use the other 99.99% of the time. Gmail can use AI to filter spam from our inboxes and must also provide the functionality to restore real emails to the inbox when the data-driven machine inevitably makes a mistake.
No one likes to imagine bad outcomes from using their product, but probabilistic value propositions require that type of thinking. At some level, the benefits must outweigh the costs. How that balance is achieved can have strategic ramifications up to, and including, the business model.
When the benefits of using your product clearly outweigh the costs, in a way your customer can understand, your team no longer needs to treat the probabilistic nature of your product as a bug to be squashed. AI products built with well-balanced probabilistic value propositions are noticeably easier to operationalize, scale, monitor, and troubleshoot.