Panel on Leveraging Data Analytics for Effective Product Management
Aug 17, 2023
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Join industry leaders Collin Crowell, Abbie Kouzmanoff, and Ryan Glasgow in a discussion on leveraging data analytics for effective product management. Discover data challenges, redefine success metrics, explore AI applications, and more. Gain insights on understanding 'why' behind data, aligning marketing, growth, and product teams, the importance of experimentation, and leveraging AI for accelerating product analytics and optimization.
Understanding the 'why' behind data trends requires incorporating experience insights and qualitative data.
Adopt a question-first approach for success metrics and involve the team in regularly reviewing results and exploring new insights.
Deep dives
Challenges of using data analytics
One common challenge faced by product managers when using data analytics is the need to understand the 'why' behind the trends and patterns in the data. Simply having analytics and revenue data is not enough to gain a complete understanding. It is important to also incorporate experience insights and qualitative data to truly understand customer sentiment and behavior.
Setting success metrics and integrating data
Product managers should adopt a question-first approach when setting success metrics for their products. Rather than focusing on tracking every single piece of data, it is essential to identify key questions that the team wants to answer and define the specific data needed to address those questions. Integrating data into the product lifecycle involves making the analytics process a collaborative team effort and involving engineers, designers, and other team members. Regularly reviewing results together and exploring additional questions that arise during these sessions can lead to new hypotheses and insights.
The role of A/B testing
A/B testing is a fundamental tool for making data-driven decisions in product management. Some common challenges in A/B testing include stopping work prematurely based on negative metrics or cherry-picking data to support a desired outcome. It is crucial to set metrics that align with the maturity level of a project or initiative and to consider other signals beyond traditional metrics. One pitfall to avoid is trying to experiment with every small change, as feature flagging and slower rollouts can be more effective in certain situations.
The power of AI in data analysis
AI is transforming how teams work with data by accelerating time to value and enhancing data analysis capabilities. AI tools can automate manual tasks, identify trends, and proactively provide insights and suggestions based on data. Integrating AI into product analytics can optimize user experiences and help companies move towards more data-driven decision-making. However, it is important to remember that AI is a tool, and human creativity and understanding are still essential in interpreting and utilizing the data effectively.
Dive into an insightful episode, "Panel on Leveraging Data Analytics for Effective Product Management," with a panel of industry leaders. Moderated by Nicole Breskin, VP of Product at Disney, this discussion features Collin Crowell, Abbie Kouzmanoff, and Ryan Glasgow. Discover data challenges, redefine success metrics, explore AI applications and more. Tune in for enriching conversations and practical strategies that will resonate with aspiring and current Product Managers alike.
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