How to do product management lifecycle in 2024
Business landscapes and marketplaces are always growing in competition. Really effective product management plays an understated role in the ultimate success of a company with the responsibility of guiding a product’s development to be something users genuinely need.
Product managers are the ones taking this responsibility by aligning teams to create and deliver products that meet customer needs and deliver real value to the market. But in 2024, Product Managers will rely on their ability (or willingness) to leverage advancements made in AI tools available now to make better product decisions.
In this article, we will be exploring how to manage the product management lifecycle, a structured framework with 8 stages that guides product managers through the journey of developing, launching and iterating successful products from inception right through to discontinuation.
Key Stages of the Product Management Lifecycle
Each stage of the product management lifecycle will need keen foresight and responsiveness to stakehollders in order to properly guide product teams to create products that address problems for users in the best way possible and remain defensible in constantly evolving marketplaces.
1) Identifying User Needs
In this step we prioritise identifying our target market and understanding what their needs really are. You need to do user research to understand the pain points of your customers and the larger market. Ideally, you’ll collect data from diverse sources so you can pattern match and triangulate the biggest pains for your target market. Historically this meant talking to a lot of users and internal teams through user interviews, surveys and feedback forms. That’s still important, but product usage data and qualitative customer feedback is also an extremely important (and relatively new with AI) part of this step.
AI analytics tools can now streamline data collection and analysis with real-time product usage data to provide insights on if people are using your product, which features, where they drop off from key workflows, what different segments do differently, and much more.
Customer feedback, especially feedback captured in-app and at scale with tools like Phospho, is also a great discovery channel. What better way to uncover issues than verbatim ideas and commentary from customers while they are using the product itself?
These are great places to start discovery as it will bring qualitative data into the mix and help us understand if what we’re observing is actual insight or white noise.
For a deeper dive on actioning insights from product data with AI tools, read our previous article going over the 3 steps for product optimisation with Phospho here.
2) Market Research and Analysis
In this second stage, research and analytics are the focus where we try and shift from hypotheses to confident assumptions.
Market research and analysis of trends as well as consumer preferences and the competitors you’re facing gives you insight into any unfulfilled needs and opportunities. Competitor research can also further inform the product’s positioning by identifying any gaps in the market or potential differentiators (USPs).
A firm grasp on this knowledge shapes strategic direction, guides feature prioritisation and informs the overall product direction from an early stage.
3) Product Strategy and Roadmap Development
Common sense dictates that bringing together market research and analysis is a pre-requisite to a well informed product strategy. Product Managers need to take this early understanding and communicate with their teams to translate what is a vision into a more detailed product roadmap. This defines the scope, features, and key milestones or success metrics for the development phase. It also lays out the resource allocation, timelines and clear business objectives for the project at large.
With a clear product strategy and roadmap, this phase sets the tone for the entire lifecycle by providing a structured framework that guides subsequent actions. It’s important to align stakeholders and the wider team on this with effective communication so that everyone understands the product’s overall direction and why.
4) Requirements Gathering and Prioritization
With a vision and roadmap laid out, product teams should have a clear understanding of the path ahead. However, it’s important at this stage for product managers to work closely with engineers, designers, marketers and customer support to build clarity in their understanding of technical constraints and market demands to balance the two effectively.
This collaborative approach brings together multiple perspectives under consideration and can help to identify any challenges and feasibility concerns before they arise so teams can put measures in place to mitigate them.
Furthermore, translating user feedback or customer pains can feed into actionable plans for concrete feature ideas and small improvements. However, prioritisation techniques for stages like these have significantly evolved with AI.
Traditional methods like MOSCOW (Must have, Should have, Could have, Won't have) and RICE (Reach, Impact, Confidence, Effort) are now reinforced with AI driven analytics and predictive forecasting which is extra leverage Product Managers can add to their arsenal. AI tools can also inform product teams about how different features might affect engagement and retention so they can make more informed decisions about prioritisation of backlog items and the development roadmap.
5) Development and Prototyping
The goal of prototyping during the development phase is to create a mockup of the product to test and get feedback on before finalising it.
This stage is where ideas transition from conceptualisation to tangibility, so it’s important to stick to the roadmap and understand we’re testing the product strategy and any assumptions or predictions laid out in the previous steps, while also keeping the focus on delivering the most important features first.
The roadmap serves as a guide but having the flexibility to adapt to any new insights or changes in the market are important too. This is why Agile development methods are growing in popularity and becoming the norm. Key principles of Agile encourage collaboration, rapid testing, iterative development and continuous feedback which helps teams respond quickly to change and reduce the risk of developing features that don’t meet user needs during this build and testing phase.
Managing development adhering to the roadmap with frameworks like Kanban or Scrum can break it down into more manageable chunks for continuous delivery while still allowing for flexibility and adaptation. Regular check ins against the roadmap with this approach to development can help product teams stay on track and make any decisions about potential adjustments as they go which is a far more agile approach.
AI is also growing in popularity in this stage of the lifecycle by allowing product managers to look at predictive bottlenecks in development which speeds up the prototyping phase. AI analyses historical project data, code complexity and previous team performance metrics to flag for any potential issues before they become significant such as tasks that may take longer, dependencies that may cause delays, and even better distribution of tasks among team members.
Agile methods and research can only go so far to mitigate the risks of changing demands or unforeseen issues that come up. By properly leveraging AI capabilities product teams can proactively address more problems before they occur as opposed to reacting to them.
6) Launch Preparation
Before releasing anything, consider whether you want to launch the product in beta or wait until you know its success (based on launch objectives and metrics). Launching in beta can be risky, but it might help you find issues others don’t know how to discover and it will allow you to test different marketing strategies before your final launch.
Next, be clear on who your target audience is and how you can reach them. Have a plan and timeline for getting your message out there and making sure people see it. Aligning all of your sales, marketing, and communication efforts around the product release helps you break through the noise and better reach and influence your target audience. Call it a launch, release, or product readiness, this alignment and consolidated effort is key to a strong market entry for your product or feature.
Finally, be prepared for customer feedback and set up tracking mechanisms for analytics. AI tools can help with real-time launch optimisation by analysing this early feedback and behaviour, allowing teams to adjust their strategies quickly if needed to maximise impact and adoption. Try finding ways to address any concerns voiced by customers, and incorporate those solutions into your final release of the product. This feeds into the next stage with post launch iteration.
For an in depth guide on how to prepare a launch strategy for your AI product, read our previous article here.
7) Post-Launch Feedback and Iteration
Product launches are an important part of the product management lifecycle, but it’s not the end of it. It’s critical to know how to optimise and refine your product after it’s out there based on how users interact with the new product or feature.
Fast and continuous data-driven iteration based on assessing feedback is the cornerstone of product improvement as it either validates or challenges any assumptions made during development. This uncovers any unexpected uses cases, pain points or bottlenecks to identify improvement areas and influence the prioritisation of future development. It also helps with building stronger relationships with your users by demonstrating you value their input and sentiment.
This feedback loop is equally vital for the product team to review its product strategy for any potential risks, issues, or re-adjustment of priorities before moving forward with the rest of the roadmap.
8) Monitoring and Optimization
As we’ve mentioned above, the benefit of continuous data-driven product development is that you can launch and iterate quickly in line with user demands. But in order to iterate effectively, you need to understand on a granular (in-app level) what’s working and what’s not. That means digging into product usage analytics.
Product usage data through AI tools coupled with customer feedback is an unprecedented amount of leverage for gaining insights towards more effective iteration cycles. These tools can process far more data in real-time to identify patterns, anomalies and trends in user behaviour which traditional means or human analysis cannot match.
Having a clearer view of customer behaviour, where people are getting stuck, or what actions users aren’t taking with product usage data is key to evaluating if a product is truly “working” and which areas need the most focus for further optimisation.
Key Trends Influencing Product Management in 2024
The role of product managers during the above lifecycle is a multifaceted one centred around making the right decisions through data to ensure teams build the right thing at the right time.
There are best practices, and for the most part, a general framework that is traditionally followed. But there are trends occurring right now that are reinforcing these for better operations and closer alignment with user and market needs.
AI Integration
There is widely untapped potential in the use of AI tools to reinforce the best practices and provide more rich data for better informed decisions throughout each stage of the product management lifecycle.
In terms of optimisation, AI tools like Phospho can constantly run A/B tests at scale, automatically detecting which features or versions of your product resonate the most with users by analysing data collected through custom KPIs to maximise engagement and performance metrics.
The higher predictive forecasting capability of AI tools also means teams can be more proactive in addressing problems before they occur but, more importantly, while AI concentrates on analysis better than humans can, we can focus on using these insights for creative direction and more strategic planning. This means to say, AI is not replacing human decision making but more so, improving it. A strategic synergy and balance between AI and human judgement is creating an unprecedented competitive advantage for companies adopting these tools now.
Product-Led Growth (PLG)
At its core, Product-led Growth is a business strategy that prioritises the product experience as the primary driver of growth and is hugely popular in the SaaS industry. Under a PLG approach, companies put more effort into making their products better and use data to make smart choices which deliver more value through the product.
The increasing popularity in the shift towards a product led approach indicates a market trend demanding value-driven user experiences as a pre-requisite to conversion and retention. This also provides insights derived from research and development to fuel more aligned sales and marketing efforts in line with what your users and target audience resonate with the most. In fact, Product Benchmark Reports have found that companies with a product led approach are more than twice as likely (100%+ year over year revenue growth) to outgrow sales-led companies.
Agile Methodologies
In a constantly evolving and competitive market, adaptability is a decisive advantage. The product management lifecycle inherently demands and embraces flexibility and isn’t confined to a single endeavour, it embodies a culture of perpetual improvement.
This is a natural reflection of Agile Methodologies which foster the ability to make adjustments and course corrections natively with its frameworks like Kanban and Scrum through collaboration, continuous delivery and iteration. This ensures the product remains relevant and competitive regardless of any unexpected shifts.
Focus on Personal Well-Being
Finally, the role of a product manager has become increasingly demanding in recent years. As the bridge between various teams and stakeholders with the main responsibility over the product’s success, this heightened pressure can be easily overwhelming and take a toll on mental health.
There is a growing understanding that the fast paced nature of product development now, coupled with the need to stay aware and ahead in competitive markets, should necessitate proper self care practices to prevent burnout. By prioritising personal well being, product managers can not only improve the quality of their life but also their effectiveness in their roles to better handle challenges, make decisions, and communicate effectively. This is likely a trend that will continue to become a more important component of successful product management.
Product Management Lifecycle with AI
In order to succeed long term, Product Managers can’t just rely on the same methods to build a great product when in competitive landscapes that incentivise optimisation. Technological advancements and active markets force us to adapt, use the best tools available, and categorically see with data that what we’re building is “working” and meeting success metrics.
The product management lifecycle’s structured approach provides a framework for leveraging these new tools as we have discussed in this article with how its different stages can benefit from AI’s capabilities that can leave you at a handicap unless adopted.
If you want to learn more about how AI tools like Phospho can provide untapped user insights, automate A/B tests with custom KPIs, and let you make better decisions towards iterating your product more effectively, sign up here to simply test it out with your data. It’s as easy as importing a CSV or Excel file!