How do you decide what features to build next?

Empathic design thinking starts with deeply understanding your users—not just their overt needs but their emotional landscape and unspoken pain points. It's not about asking users what they want, but observing and feeling what they actually need. This is where AI can be a partner in empathy. Imagine using natural language processing to analyze user reviews or social media mentions, capturing the sentiment and nuance that traditional analytics might miss. Or employing AI-driven user testing tools that go beyond click-through rates to evaluate emotional engagement or frustration levels. By blending empathic design thinking with AI's analytical power, you can create features that resonate on a profoundly human level.

It is quite critical and as well as challenging tasks to add new features in the offering. I also believe that more the features, it will increase the complexity of its applications and moreover proper utilization. Eventually, user experience will be affected and consequently, firm has to bear the loss of losing customer forever. However, it is also supreme to note that how and when to remove existing features which are not up to date as per current technological trends and requirement of the user.

In the context of empathic design thinking, evaluating ideas goes beyond metrics and enters the realm of human experience. It's not just about what's feasible to build, but what deeply resonates with users. Traditional frameworks like Kano or RICE can be enriched by applying design thinking methodologies: observe users in their natural setting, define their pain points in a human-centric way, ideate solutions that address these emotional and functional needs, and then prototype and test. The evaluation is an ongoing, iterative process that continually seeks to understand the user, rather than a one-off 'score' to beat. This approach ensures that you're not just solving problems, but enhancing lives.

In line with design thinking, validating assumptions isn't merely about analyzing metrics; it's a deeply empathetic process. You'll want to use rapid prototyping techniques to get something tangible in front of users quickly, and closely observe their interactions with it. This isn't just to see if a feature 'works' but to understand how it makes the user feel. Are they delighted, frustrated, indifferent? Use these emotional and behavioral insights along with traditional KPIs like retention or revenue to make a holistic assessment. This dual-lens view minimizes the risk of building something that's technically perfect but emotionally or functionally misaligned.

Aligning features with your roadmap isn't just a logistical exercise; it's a strategic imperative that marries vision with execution. Think of your roadmap as a narrative that tells the story of your product's evolution. Each feature is a chapter in that story, and it must logically and emotionally fit. Utilize design thinking to infuse user-centricity into this alignment. AI can further augment this by predictive analytics to forecast feature impact or automating mundane project management tasks, allowing you to focus on strategy. Aim for a roadmap that's not just a sequence of tasks but a cohesive, compelling journey that inspires your team and delights your users.

Collaboration isn't just a buzzword; it's the lifeblood of advanced product development. When deciding on features, think of your team as a think-tank of domain experts. Use agile methods to ensure you're nimble, and consider tools like kanban boards for visualizing work stages. But go beyond tooling. Hold "feature jam" sessions to ideate, employing design thinking exercises to empathize with user needs. Use AI to parse customer feedback at scale or automate A/B test analyses, freeing your team to focus on high-level strategy. This collaborative approach turns feature selection from a gamble into a calculated, collective, and enlightened decision.

Building a new feature, or starting out with a new product is a complex and challenging endeavor. Statistics reveal that most features - perhaps over two-thirds- never or hardly ever get used. So, that is a huge waste of the hard-to-get R&D dollars, but even more importantly, the loss of opportunities in terms of getting the right features in the products that not only create delighted users but even a robust revenue stream. The important things to consider is that this is not a one-and-done process but a continuous process that requires reviewing the "feature mix" inside a product - comparing the key gaps against continuously evolving user needs, and if need be, deprecate the ones that have outlived their purpose.