As a product manager at LinkedIn, your data science team informs you that 64% of outbound messages go unanswered. How do you respond?

This question is designed to assess your capability to utilize data in formulating a strategy as a Product Manager. The data indicates a possible issue with user engagement on LinkedIn, and you are expected to outline your approach to remedy and enhance this situation. You should provide actionable, data-driven, and user-centric recommendations, showcasing how you would work collaboratively with different teams to address the issue. Important aspects to keep in mind while responding: Identifying the issue: What causes 64% of the messages to go unanswered? Creating a detailed plan to address the issue. Working together with other teams.

To begin with, it’s crucial to comprehend why 64% of outbound messages receive no responses. With assistance from the data science team, I would analyze the data to identify specific issues. My goal would be to gather data points such as the timing of messages, content of the messages, and the activities of both the sender and recipient. If the timing of messages is problematic, we could introduce features that suggest the optimal time to message based on the recipient's activity. If the content of the messages is an issue, perhaps they are too generic; we could provide tips for crafting effective messages or offer personalized message templates. In terms of user-related challenges, we could enhance targeting by recommending users to message based on their activities, professions, interests, and more. Additionally, for users who are less active or new to the platform, we could improve the onboarding process or implement features that encourage greater engagement. All of these implementations would necessitate collaboration across different teams, so I would coordinate with user experience, engineering, and marketing teams to ensure the plan is executed effectively.

As a Product Manager, this data offers an opportunity to enhance user engagement. Here’s my plan of action. Initially, I would work with the data science team to perform a thorough analysis to determine the causes of this situation. Are there any patterns among the messages that went unanswered, such as the demographics of the recipients or senders, the content of the messages, or the timing of the sends? With this deeper insight, I could then initiate strategies aimed at addressing the identified issues. For example, if many ignored messages are categorized as 'spam' or 'irrelevant', I would explore ways to improve our matching system to ensure users receive messages that are pertinent to them. If it seems that users are inundated with too many messages, we could consider optimizing the frequency of messages or establishing a system to prioritize and highlight the most important messages. After identifying the initiatives, I would collaborate with the UX, development, and marketing teams to design, implement, and promote the new features or changes. Ongoing monitoring would be essential to evaluate the effects of these adjustments, allowing for further refinements as necessary.