"What Product Managers need to know about Product Marketing - Department of Product"

"What Product Managers need to know about Product Marketing - Department of Product"

In the world of product management, the focus is on deciding what to build, for whom, and why. On the other hand, product marketing is all about finding the most effective ways to communicate with potential customers about the product that has been built. These two disciplines may seem separate, but they are deeply interconnected and have a significant impact on each other.

Amazon's approach to product management is a perfect example of how product marketing can influence the decision-making process. Their one-page press release document, known as the PRD (Product Requirements Document), is not just a tool for internal use. It is actually written as a press release to help the product team think from the customer's perspective. By shifting the focus away from internal implications, this approach ensures that the team considers how the new feature or initiative will be perceived by the customers.

Now, let's shift our focus to another important aspect of product management - machine learning. Machine learning algorithms are often seen as black boxes, making it difficult to explain how they work. This poses a challenge when it comes to user experience (UX). Users need to trust the results of the algorithm, even if they encounter occasional errors. Building this trust requires careful consideration of how to present the results in a way that is clear, believable, and actionable.

One approach to building user trust is by backdating. This involves taking historical data and plugging it into the model to produce past predictions that can be verified against known values. By sharing the types of data used in the model, you help users understand that the decisions are based on the same variables they would consider themselves. This transparency builds trust and confidence in the algorithm's output.

Simplifying and selectively showing results is another UX strategy to facilitate decision-making. Instead of overwhelming users with all the details and outputs of the algorithm, consider presenting the results in ranges, deciles, or grades. This provides a less precise measure of value but still gives users a sense of the algorithm's effectiveness. The goal is to ensure that users can understand, trust, and act upon the algorithm's output.

In conclusion, product management and product marketing go hand in hand. The approach taken by companies like Amazon, where the PRD is written as a press release, highlights the importance of considering the customer's perspective in the decision-making process. When it comes to machine learning, the UX aspect plays a significant role in building user trust. Strategies like backdating and simplifying results can help users understand, trust, and act upon the output of the algorithm.

To summarize, here are three actionable pieces of advice:

• 1. Always consider the customer's perspective: Whether it's in the product requirements document or the marketing strategy, thinking from the customer's point of view can greatly influence decision-making.

• 2. Prioritize user experience in machine learning: Transparency and simplicity are key when presenting the results of machine learning algorithms. Make sure the output is clear, believable, and actionable for users.

• 3. Build trust through transparency: Backdating and sharing the types of data used in the model can help users understand the decision-making process behind the algorithm. This transparency builds trust and confidence in the algorithm's output.

By incorporating these insights into your product management and product marketing strategies, you can create a more user-centric approach that resonates with your target audience and fosters trust in your product.