What Product Managers need to know about Product Marketing and Machine Learning

What Product Managers need to know about Product Marketing and Machine Learning

Product management and product marketing are two essential components of any successful product development process. While product management focuses on deciding what to build and why, product marketing is responsible for effectively communicating the value and benefits of the product to potential customers. In order to achieve this, product marketers must understand the needs and preferences of their target audience and develop strategies to reach them.

One important aspect of product marketing is the ability to shift perspective and think from the customer's point of view. Amazon's approach of writing a one-page press release for their product requirements document (PRD) is a great example of this. By framing the features and initiatives in terms of how they would benefit the customer, product marketers can ensure that their messaging is customer-centric. This helps in building trust and credibility with potential customers.

Machine learning (ML) has become an increasingly important tool in product development, but it also presents unique challenges for product managers and marketers. ML algorithms are often seen as black boxes, making it difficult to explain the inner workings and results of the model. This poses a problem for product marketers, as they need to present the results in a clear and actionable manner to build trust with users.

One approach to address this challenge is to backdate the model's predictions using historical data. By showing users the types of data that were taken into account in the model, product marketers can help them understand and trust the decisions made by the algorithm. This transparency builds confidence in the model's capabilities and helps users see that the decisions are based on similar variables they would consider themselves.

Simplifying and selectively showing results is another effective strategy for making ML outputs more understandable and actionable. Instead of presenting the exact output of the algorithm, product marketers can consider presenting results in ranges, deciles, or grades. This provides users with a less precise measure of value, but still gives them enough information to make informed decisions.

In some cases, product marketers may need to define new metrics or scores to effectively communicate the value of the ML model. This requires careful consideration of the target audience and their understanding of the specific metric. By creating a new metric or score that resonates with users, product marketers can make the results more relatable and meaningful.

In conclusion, product managers and product marketers need to work together to ensure that the messaging and communication of the product aligns with the needs and preferences of the target audience. When incorporating machine learning into product development, it is crucial to prioritize the user experience side of the problem. By making the outputs of the ML model clear, believable, and actionable, product marketers can build trust and credibility with users. Here are three actionable pieces of advice for product managers and product marketers:

• 1. Always think from the customer's perspective: Whether it's writing a PRD or presenting the results of an ML model, always consider how it will benefit the customer. This customer-centric approach will help in developing effective messaging and building trust with users.

• 2. Prioritize transparency and explainability: ML algorithms can be complex and difficult to understand. Backdating the model's predictions and showing the types of data considered can help users trust the decisions made by the algorithm. Simplifying and selectively showing results also facilitates decision-making.

• 3. Define meaningful metrics: In some cases, product marketers may need to create new metrics or scores to effectively communicate the value of the ML model. Consider the target audience and their understanding of the metric to make it relatable and meaningful.

By incorporating these strategies, product managers and product marketers can successfully navigate the challenges posed by product marketing and machine learning, ultimately leading to the development of successful and user-centric products.