Blog 79: Tools in product management … (1/7)
- Idea2Product2Business Team
- Jul 27, 2024
- 3 min read
Updated: Aug 16, 2024
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 first sub-field strategy & planning.
Tools for product strategy:
· 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 prioritise 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.
Knowledge management tools for product management:
· Confluence: Confluence is a digital workspace created by Atlassian. Teams can create, organise, and collaborate on various types of content, including documents, meeting notes, project plans, and more.
Financial performance & accounting tools:
· Planful: Planful aims to be one-stop shop for Financial Performance Management Software.
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 strategy and planning are:
Innovation and idea generation – AI, ML, and analytics is used for the following:
- Assist with brainstorming and generating new product ideas.
- Analyse vast amounts of data, including patents, research papers, and industry reports, AI can uncover trends and insights.
- AI tools facilitates collaborative ideation sessions.
Market research and analysis – AI, ML, and analytics is used for the following:
- Ingest and process vast datasets from diverse sources like social media, customer reviews, and market reports.
- Identify positive or negative sentiments associated with products or brands.
- Detect patterns and trends within the data to uncover emerging opportunities or threats in the market landscape.
- Tools like Brandwatch, Crimson Hexagon are examples of AI-powered market research platforms.
Requirement gathering – AI, ML, and analytics is used for the following:
- Natural Language Processing (NLP) technologies parse large volumes of unstructured data, such as customer feedback, support tickets, and surveys.
- Tools like MonkeyLearn and Lexalytics can automate the extraction of requirements from unstructured data.
Product strategy – AI, ML, and analytics is used for the following:
- Analyse historical data, market trends, and customer behaviour patterns to identify opportunities and customer preferences.
- Identify correlations, anomalies, and predictive patterns to align offerings with customer needs.
- Tools like Crayon and MarketMuse use AI to analyse market trends and inform product strategy.
Product planning and road mapping – AI, ML, and analytics is used for the following:
- Identify patterns and correlations in data to prioritize features by assessing their potential impact on both customer satisfaction and business objectives.
- AI optimizes product roadmaps by considering market dynamics and resource constraints.
- Tools like Aha! and Productboard incorporate AI to assist in product planning and road mapping.
Competitor analysis – AI, ML, and analytics is used for the following:
- Monitor competitors’ activities, analyse market trends, and identify competitive threats.
- Sift through vast amounts of data to extract actionable insights.
- Provide real-time updates and alerts.
Develop customer personas with collected data – AI, ML, and analytics is used for the following:
- Collate user information from various sources, analyse patterns and trends that may not be immediately obvious.
- Deep learning algorithms refine these user personas based on in-app behaviour and continuous user feedback.
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. 😊