If we want pricing decisions to be truly scientific, we have to go beyond surface-level testing. That means choosing the right kind of reasoning - not just running A/B tests, but using data to continuously deepen our understanding.

Price A/B Testing

Deductive statistical reasoning, like A/B testing relies on, asks whether the data is strong enough to reject a null hypothesis. You set up a narrow hypothesis such as “Is $39 better than $49?”and see if the numbers let you rule out a null hypothesis of zero difference in profit. But you never gather evidence for a hypothesis (e.g. “this price is better than that one”), only against it. You don’t build cumulative understanding. Each test is self-contained. Once it ends, the data is thrown away. You’re not learning how pricing works - you’re eliminating fixed options, slowly, one by one.

Price Optimization

We use a better scientific approach: abductive reasoning, through adaptive Bayesian experimentation. Abduction finds the explanation that makes the most sense of the facts. In our case this means using statistical inference to weigh all observed data and select the most  price is most probable in driving the most profit. It weighs all available evidence to infer which explanation best accounts for what we’ve observed so far. It doesn’t just test hypotheses - it builds, updates, and refines them in response to data. This is how real scientific learning works. This allows it to quickly hone in on the very best price.

Our system uses every price experiment-not just the winners-to refine a dynamic model of how pricing affects outcomes. It integrates all data into a single probabilistic framework. Based on that, it selects the next price to test: not randomly, but strategically, based on what’s most likely to yield improvement.

A/B testing asks if one price beats another in isolation.

The Abductive approach asks, “Given everything we’ve seen, what pricing model makes the most sense - and what should we do next?”

That’s why it’s our approach to price optimization with Optifi.

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