In product management, perfect information is just a pipe dream. You hardly ever have the full data you require, and even if you do, it may fail to correspond. Product managers usually encounter a perplexing scenario where every choice appears risky.
This is especially true for senior leaders tasked with the decision to launch a product, modify an existing product, or halt an existing product without the benefits of perfect metrics.
Many product managers struggle with the gap between data and clear implementation, but senior leaders understand that incomplete information is not necessarily a setback but an opportunity to engage in careful reasoning.
This is how Henry Oribe works. He’s a senior product manager skilled at handling high-stakes scenarios in the absence of extensive data. He empowers his teams to move forward with confidence utilizing cognitive approaches and frameworks. He proves that a smart approach to incomplete data positions one a leader.
A primary tool Henry employs is Assumption Mapping. Instead of building a full product, he starts by identifying the key assumptions he has about the user, the market, and the technology. Then he maps these assumptions on a simple two-by two matrix, with risk on one side and proof on the other.
This allows him to quickly identify the assumptions that are high-risk and have limited evidence. These are the “known unknowns” that need prompt validation, guiding the team to run small, focused assessments, like a simple landing page or a single-feature prototype, to gather enough data to move forward.
When he encounters hard, complex problems, Henry uses Bayesian Thinking. This system helps product managers adjust their beliefs about an idea as fresh evidence comes in. It encourages him to start with an assumption about a product’s success and subsequently modify his initial assumption based on data from market research, user talks, and early tests.
By evaluating his confidence and modifying it based on genuine feedback, he avoids merely trying to find evidence to confirm his correctness and ensures the team’s decisions are based on a genuine evaluation of the evidence.
To make informed decisions between alternatives, he uses Decision Trees. This system helps his team visualize all foreseeable results, along with its probability and its potential impact. Through illustrating each choice and its subsequent effects, he can lead his team towards a logical decision. For example, when a product presented conflicting metrics, broad user engagement but low retention, a decision tree allowed him to demonstrate the potential outcomes if the emphasis was on adoption or concentrated on profitability. By estimating the chances for each outcome, he could show the team the optimal path.
Henry is a great leader when he has to make hard product decisions, like discontinuing a product. He is familiar with instances where companies had to implement this. In one case, a product had a small, loyal group of users but didn’t grow, he used these models to show that investing more would negatively impact more favorable outcomes. He could demonstrate, through data analysis, that even if the users voiced strong opinions, the company had to modify its approach for its long-term success.
Henry Oribe’s leadership holds a significant message: good product choices aren’t made with magic, they come from using systems made for handling uncertainty. He empowers his teams to do beyond mere assumptions, but to make confident and logical decisions, ensuring every move is calculated and not just an attempt to test the waters.