Blog 81: Tools in product management … (3/7)
- Idea2Product2Business Team
- Jul 29, 2024
- 3 min read
Continued from blog 80.
Product management is a vast field with multiple sub-fields. They range from:
1. Strategy & planning (list of tools highlighted in blog 79).
2. Design (UI/UX) (list of tools highlighted in blog 80).
3. Execution & tactics (list of tools highlighted in blog 81).
4. Go-to-market (list of tools highlighted in blog 82).
5. Metrics & performance (list of tools highlighted in blog 83).
6. Product development & technology (list of tools highlighted in blog 84).
7. Cross-functional (list of tools highlighted in blog 85).
There are several specialised tools catering to each of these sub-fields. In this blog we look at the third sub-field execution & tactics.
PM Tactical / road mapping tools:
· Aha!: Aha! serves as a roadmap and project planning software designed to help teams define their strategy and manage product development projects.
· Productboard: Productboard serves as a centralized platform for product managers to prioritize new features and gather insights.
· Productplan: ProductPlan platform helps organizations to visualize product roadmap and facilitate communication for developing and tracking product strategy and progress.
· Asana: Asana is a work management tool designed to help individuals and teams keep track of tasks, delegate responsibilities, monitor progress, and communicate in real-time.
Project and task management tools:
· Jira by Atlassian: Jira allows teams to plan, track, release and support the development of software.
· ClickUp: ClickUp allows teams to come together, brainstorm, plan, and collaborate on everything from process docs to product designs.
Recent times have seen a rise in the use of AI (artificial intelligence) in product management. AI is helping to derive valuable insights and automate routine tasks. Some key use cases of AI, ML, and analytics in execution & tactics are:
Product documentation. AI, ML, and analytics is used for the following:
- Create comprehensive and precise documents essential for any product development process.
- Generate high-quality, well-developed specifications by feeding AI various descriptions of product specs.
- AI-powered writing assistants to generate ideas and organise thoughts.
Backlog grooming. AI, ML, and analytics is used for the following:
- Provide a systematic approach to backlog management.
- Analyse historical data and user feedback to identify and prioritize the most valuable backlog items based on impact and effort.
- AI can break down high-priority items into manageable tasks, estimate the efforts required and assign them to specific sprints.
Prototyping and testing. AI, ML, and analytics is used for the following:
- Streamline the prototyping and testing phases of product development.
- Simulate various scenarios and predict potential issues.
- AI-driven simulation tools can model real-world conditions, allowing for thorough testing without the need for physical prototypes.
Product lifecycle management. AI, ML, and analytics is used for the following:
- Streamline various tasks, such as demand forecasting, inventory management, and pricing optimisation.
- Examine previous sales data, market trends, and customer behaviour to forecast product demand accurately.
- Software like Siemens Teamcenter and PTC Windchill incorporate AI for lifecycle management.
- Pricing optimization algorithms can dynamically adjust prices based on competitor pricing, demand fluctuations, and customer preferences. Tools like Pricefx and Competera use AI for dynamic pricing optimization.
Customer feedback and support. AI, ML, and analytics is used for the following:
- AI-powered chatbots and virtual assistants assist in product support and enhancement.
- Analyse customer interactions and sentiment. Chatbots can uncover insights such as identifying popular features or common issues.
- Tools and chatbots like Intercom and Drift use AI to enhance customer support.
Financial planning and budgeting. AI, ML, and analytics is used for the following:
- Analyse financial data, predict revenue forecasts, and optimise resource allocation.
- AI-driven predictive analytics to identify cost-saving opportunities and optimize budget allocation.
- Financial planning tools like Adaptive Insights and Anaplan incorporate AI for forecasting and budgeting.
Regulatory compliance. AI, ML, and analytics is used for the following:
- AI systems continuously monitor and ensure compliance with regulatory requirements, reducing the risk of legal issues and penalties.
- Analyse changes in regulations and alert product managers.
- Assess potential risks associated with new product launches.
Manage product launch. AI, ML, and analytics is used for the following:
- AI assistants can automatically generate comprehensive launch plans with integrated timelines.
- AI scheduling tools can optimize launch timelines and marketing campaign activities.
- Generative AI writing tools can assist product managers and their teams in creating marketing collateral efficiently.
- Tools like Asana and Trello use AI to enhance project management for product launches.
Note: The list of companies mentioned above are ONLY a SAMPLE list and NOT an exhaustive list.
Jump to blog 100 to refer to the overall product management mind map.
Source:
Web/Google research as of July 2024
I wish you the best for your journey. 😊