3-steps to do proper software product Engineering in 2024

The rapidly evolving field of software development is fuelled by key trends and constant advancements in surrounding tools and technology.

For example, one of these is how AI plays a far greater role than it ever has before from better low-code tools to ease of integrating top of the range LLMs such as Chat GPT and Claude. AI is also helping to automate routine tasks allowing developers to focus more on complex and creative aspects of work. In fact, 92% of US based developers use AI coding tools for both personal and professional development tasks, and 34% of companies currently use AI with an additional 42% exploring its implementation.

Whenever we are faced with a fast evolving landscape like we are with AI’s increasing role in software development, adaptability with the use of the right tools and approaches will always comes out on top. So how should we adjust our approach?

The fundamentals for startups haven’t changed, the focus is very much on customers and data, but the question is how customers use software now, and what data should startups be looking for as fuel for effective iteration.

In this article we’ll be going through the 3 key steps are for effective software product engineering in 2024: Ideation and Market Research, Agile Development Methodology, and Deployment and Post-Launch Activities.

We’ll also shed light on the importance and competitive edge startups can gain from monitoring customers’ usage and leveraging the right data to iterate software products effectively by using AI analytics tools like Phospho.

If you want to see further how Phospho is the go-to analytics solution for product engineering on top of AI integrated apps, read this previous article here.

Step 1: Ideation and Market Research

Every product or company starts with an idea, but for that idea to gain any traction it needs to be validated as something that your target market is genuinely in demand for. Therefore, it’s critical to first understand the market’s size, users and competitors. This helps us collect the data for a clearer picture.

One of the hardest parts is initially accurately identifying and understanding your target customer with confidence: demographics, behaviour, goals, pains, desired transformations, previous solutions they have tried, the list goes on as market research ****is a broad term, and this process is never really finished.

But in practical terms a good way to gain some of these insights initially is to look at market trends on google trends and perform surveys, interviews and focus groups with your target customers themselves. Engaging on social media also provides a free way to gauge sentiment and interest in your problem area but also the demographics of the audience it most resonates with to provide a clearer picture of who your target customer really is.

With a clearer understanding of our target customers and the surrounding competitors, your unique value proposition will need to address a specific problem or pain point either for a more specific/niche set of customers, or from a different angle to your competitors. Then it’s just a case of articulating this clearly to your target audience.

The next step in the process is to evaluate your product strategy based on feasibility. Determine if the workload and estimated timeline are possible to achieve. If not, adjust your dates accordingly and request help from additional stakeholders. There are plenty of frameworks to lean on for more in depth feasibility analysis such as DVF (Desirability, Feasibility, Viability), PESTEL (Political, Economic, Social, Technological, Environmental, and Legal), and SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to gain more confidence in the viability of moving forward into actual production with our idea.

The real goal is to ultimately solve a problem that people will pay for, therefore, teams will need to rely on iterative feedback loops to constantly refine understanding to match market needs with their vision during this ideation phase. Getting real feedback from early prototypes, MVPs, and beta testing can all inform you of where changes and fine tuning will be needed to truly address the problem you’re solving.

After clearly defining any assumptions with your early prototypes or MVPs, you can use Phospho to validate or disprove them with real-time monitoring to confirm your hypotheses through detecting patterns and trends in user interactions. By collecting contextual feedback in-app through usage data, product engineers can iterate effectively to focus efforts on the highest leverage points in their product.

With research and validation through early prototypes to gather data covered, we’re left with actually building this idea into a finalised product. For the most effective and iterative process we’ll be looking at Agile Development Methodology.

Step 2: Agile Development Methodology

Agile is a philosophy built upon a set of 4 core values and 12 key principles laid out in the Agile Manifesto. Each of which contributes to the main emphasis on fast and flexible development through iterative work, collaboration, and responding to shifting priorities and requirements quickly. This results in a method for iterative software development where instead of delivering a final working product at the end of the development lifecycle, teams work in small timeframes. These timeframes, called sprints, are usually one to three weeks.

This approach that lends itself to building fast and shipping fast so that the product is constantly meeting the needs and expectations of the customer. The need for speed also means this approach promotes teams to concentrate on producing functional and value adding product development, hence reducing wasted time and energy on non essential work. This allows teams to quickly adjust their approach and stay focused on delivering value.

Agile fosters self-organising, cross-functional teams that communicate effectively to feel more like a unit. To facilitate this in practice, popular agile frameworks for customer centric iterative development include both Scrum and Kanban:

Scrum is a framework for agile software development that involves iterative cycles called sprints, daily stand-up meetings, and a product backlog that is prioritised by the customer. Originating from the dynamics of a rugby match, Scrum advocates for small teams typically comprising 7 to 9 members.

Kanban provides teams with a visual representation of their work, assisting in tracking progress, identifying bottlenecks, and prioritizing tasks. It involves using a board with columns to represent different stages of the development process, and cards or sticky notes to represent work items. By setting limits on work in progress, Kanban promotes a steady pace of development, prevents overload, and promotes focused task completion.

Agile methodologies place a strong emphasis on testing, quality assurance, and continuous improvement. This helps to ensure that software is delivered with high quality and reliability, reducing the risk of defects or issues that can impact the user experience.

Continuous Integration and Continuous Deployment (CI/CD) is the practice of frequently merging code changes into a shared repository, which helps to identify and resolve conflicts early for a smoother development process. This approach streamlines the release process for all builds and simplifies post-launch modifications. Additionally, adhering to CI/CD pipeline best practices eases your product’s maintainability by consolidating it into a cohesive package.

Agile is not a single method, it represents the various collection of methods and practices that follow the value statements provided in the manifesto. Therefore, we need to track key metrics and KPIs to see how agile is working for us. Let’s look at 3 popular metrics:

velocity: is a metric we can track to understand how much work was completed by the team during a fixed time period, usually a sprint. The typical unit of measurement is story points completed and comparisons can be drawn to previous sprints to check for signs of slowing down. It’s important to monitor as it can show signs of any issues and bottlenecks within the team.

sprint burndown: is a visual chart that illustrates the progress made on tasks during a sprint. With this chart you can get a clear overview of the rate and amount of progress made at any given point during the Sprint. As a project manager, this gives you key information about time needed for completion of the agreed workload and helps in forecasting time needed for future sprints.

Lead time: covers all the different processes for delivering a product. It starts when a story enters the backlog (e.g when the client makes a request for a feature) and ends when it is completed in a sprint and then deployed. In this sense, lead time is a measurement of the speed of your team’s value chain. It presents an evaluation of your Agile project management from start to finish. The shorter the lead time gets, the more efficient your processes have become.

Agile methods and practices do not promise to solve every problem present in the software industry (no software model ever can). But they sure help to establish a culture and environment where solutions easily emerge.

Step 3: Deployment and Post-Launch Activities

Ongoing deployment and constant monitoring of your product after launch is equally as important as the development itself. So what are some deployment strategies for minimising downtime and reducing risk of bugs and errors?

Let’s look at blue green deployment, this is a method of using two identical production environments where one is ‘active’ whereas the other is ‘inactive’. Essentially what you do is you deploy updates to the inactive one to perform tests then if all goes well you switch the traffic over to the active one. This allows for really quick rollbacks if any issues come up.

Another effective deployment strategy is canary releases where updates are initially rolled out to a subset of users before a full deployment. In this way, if there are any issues you can catch them early without affecting the entire user base.

These methods allow for fast and constant deployment to get feedback whilst minimising risk from regular changes which agile development and fast moving markets demand.

Regular iterations to gain user feedback is the best way to improve the product and keep its stickiness with customers. Thankfully, leaps have been made in this particular area where AI tools like Phospho have started taking on a larger role.

Phospho is an open source text analytics platform that automates insights extraction from AI integrated products where continuous improvement and evaluation are vital.

Here’s a non exhaustive list of its features to help with tracking and monitoring post launch metrics and KPIs such as user engagement, retention rates, and customer satisfaction:

1. Real-Time Monitoring: This lets you track and log user inputs to identify issues or trends and continuously fine-tune the performance of your LLM app.
2. Custom KPIs Extraction: Create your own KPIs and custom criteria to ‘flag’ for, and you can label whether it was a successful or unsuccessful interaction.
3. Continuous Evaluation: use our automatic evaluation pipeline that runs continuously to keep improving your model’s performance.
4. Easy Integration: simply add Phospho to your tech stack with any popular tools and languages like JavaScript, Python, CSV, OpenAI, LangChain, and Mistral.
5. User Feedback Linking: collect, attach, and analyze user feedback in context to make targeted improvements toward overall app performance.

So if you’re a product engineer or product manager building an AI product, sign up here to integrate Phospho. We have plenty of documentation to support you as well.

AI Tools: The Leverage for Effective Product Engineering

The software product development process is a comprehensive, multi-stage journey that commences with the inception of your idea and continues until it’s finalised into a fully fledged solution to address a real problem. Navigating these 3 steps requires considerable effort but product engineering teams should be aware of the importance of integrating AI’s capabilities with the right approach.

The fundamentals are still the same: customers and data. The only difference now is we finally have the tools to extract the right data for product usage. Leveraging AI tools like phospho to enrich your understanding of users only increases the likelihood of your software succeeding in the market. Sign up here to try Phospho with your own data and see for yourself!