A guide to building product features users don’t know to ask for

Product managers (PMs) often rely on direct user feedback to guide product development. While this approach leads to valuable incremental improvements, it may overlook opportunities for groundbreaking innovations that address unarticulated user needs.

Users frequently cannot express their deepest pain points simply because they’re unaware of potential solutions. To that end, this article explores strategies to identify and design for these “invisible” user pain points, enabling you to create transformative products and maximize market impact.

The limits of traditional UX research

Conventional user experience (UX) research methods, such as surveys, interviews, and focus groups, rely heavily on users’ ability to articulate their needs and preferences.

However, the Journal of Consumer Research highlights that only 20 percent of consumers can accurately predict which product features they’ll find most valuable in the future.

This finding underscores the limitations of traditional market research methods that rely solely on stated consumer preferences, rather than observing actual behaviors and contextual decision-making.

Alongside this, a 2016 report from the Harvard Business Review found that companies who rely exclusively on user feedback for innovation tend to experience 44 percent lower revenue growth rates compared to those that integrate behavioral insights and predictive analytics into their product development strategy.

The report suggests that while customer feedback is valuable, it’s often reactive and incomplete. Companies that outperform in innovation are those that proactively analyze behavioral trends, anticipate unmet needs, and design solutions before users even realize they want them.

These statistics highlight the well-documented discrepancy between what users claim to experience and what their actual interactions reveal. Observational methods, such as usability testing, heatmaps, and behavioral tracking, consistently outperform direct user reports in uncovering pain points, leading to more effective product design improvements.

Case studies of companies addressing unspoken needs

This section details famous examples of companies who defined innovation instead of just iterating based on user requests.

Apple’s iPhone: Revolutionizing mobile interfaces

Before the iPhone, most mobile users were accustomed to physical keyboards and styluses. In 2006, BlackBerry dominated the market with nine million users worldwide. The idea of a fully touchscreen phone seemed impractical.

However, Apple recognized the need for a more intuitive interface. As of 2023, iPhone sales exceed 2.2 billion units, demonstrating the power of designing for invisible pain points.

Tesla’s Autopilot: Solving driver fatigue before it was expressed

Long commutes and driver fatigue are global issues. According to the National Highway Traffic Safety Administration (NHTSA), drowsy driving causes 91,000 crashes annually in the U.S.

Tesla’s Autopilot, launched in 2014, wasn’t developed in response to user surveys but through behavioral insights. By 2022, the adoption rate for Autopilot surged to 89 percent, showing how addressing unseen pain points can drive mass adoption.

Amazon’s 1-Click checkout: Reducing purchase friction

The inconvenience of repeatedly entering payment details wasn’t a commonly voiced complaint. However, a study by the Baymard Institute (2021) found that 18 percent of cart abandonments result from a lengthy checkout process.

Amazon’s 1-Click Checkout, patented in 1999, led to a five percent increase in conversion rates, which translated to billions in additional revenue.

AirPods and the removal of the headphone jack

Apple’s controversial decision to remove the headphone jack from the iPhone in 2016 sparked criticism. And yet, AirPods became one of Apple’s fastest-selling products, with over 150 million units sold in 2022. Users quickly adapted to the wireless convenience, revealing an unspoken need for seamless connectivity.

Behavioral psychology models for identifying latent pain points

To help you identify potential latent pain points, try implementing these behavioral models with your product team:

Latent pain points and hidden workarounds

Many users create workarounds for inefficiencies, making them less likely to report these issues. Examples include:

• Manually entering credit card details repeatedly instead of recognizing the inconvenience
• Office workers spending hours per week scheduling meetings without considering an automation tool like Calendly
• Healthcare professionals manually logging patient data, even when automated systems exist

Anticipatory design: Solving problems before they surface

Anticipatory design leverages AI and machine learning to predict and address user needs before they become apparent.

• Netflix’s recommendation engine accounts for 80 percent of streamed content, reducing decision fatigue
• Google’s predictive search saves users 200 billion keystrokes per day by suggesting queries in real time
• Spotify’s Discover Weekly feature increased user engagement by 60 percent through AI-driven music curation

The jobs-to-be-done (JTBD) framework

JTBD shifts the focus from what users request to what they’re trying to accomplish. Instead of adding minor improvements, you should ask:

• “What job is the user hiring this product to do?”
• “What pain point exists even if the user doesn’t articulate it?”

Tools for predicting hidden user needs

Besides behavioral models, you can also leverage these tools to help uncover hidden user needs.

Needmining: AI-driven analysis of online conversations

Needmining leverages machine learning to analyze vast amounts of user-generated content. Research published in Decision Support Systems (2022) demonstrated that needmining algorithms in ecommerce forums identified 74 percent of latent customer concerns before companies recognized them.

2. The Painsight framework

Painsight is an unsupervised learning model that scans online reviews and extracts dissatisfaction factors. When applied to 20,000 customer reviews across industries, it successfully identified over 4,500 pain points previously unaddressed.

3. Heatmaps and behavioral analytics

Heatmap and behavioral analytics tools reveal areas of user frustration in digital products:

• A 2021 case study by Booking.com found that adjusting CTA button placements based on heatmaps increased conversions by 12 percent
• LogRocket’s user session recordings identified UI friction that traditional surveys overlooked, reducing churn rates by nine percent

How product managers can implement these strategies

With models and tools out of the way, you can implement these strategies through the following best practices:

1. Move beyond direct user requests to behavioral insights

User surveys and interviews provide valuable input, but they often capture only explicit and surface-level feedback. The real breakthroughs come from understanding user behavior at a deeper level. You can do this by:

• Monitoring behavioral patterns — Track friction points in user workflows. Look for repeated workarounds, abandoned flows, or areas where users take significantly longer than expected
• Analyzing frustration indicators — Session recordings, rage clicks, repeated form submissions, and heatmaps can highlight user frustrations that they may not articulate in feedback
• Using sentiment analysis — AI-driven tools can scan online reviews, forum discussions, and customer support tickets to detect underlying patterns of dissatisfaction

2. Leverage AI and big data to detect emerging patterns

Hidden pain points often emerge before they become mainstream issues. Companies that proactively analyze data can address these issues before competitors even recognize them. Get started by:

• Mining customer support data — Analyze tickets and chat logs for recurring but unreported pain points. Are users frequently asking for workarounds? Do they express confusion about a specific process?
• Tapping into social media and forums — Platforms like Reddit, Twitter, and product-specific communities often surface pain points before they appear in direct customer feedback
• Using predictive analytics — Machine learning models can detect anomalies in user behavior, helping you anticipate friction points before they become widespread issues

3. Validate hypotheses through continuous experimentation

Rather than waiting for direct user requests, proactively test ideas through controlled experiments such as:

• Running A/B tests on small cohorts — Instead of rolling out features broadly, test them with a small subset of users to gauge interest and adoption rates
• Prototyping and shadowing users — Build low-fidelity versions of potential solutions and observe how users interact with them in real-world settings
• Using “painted door” tests — Introduce a feature as if it already exists (e.g., a button that users can click but doesn’t yet function) and measure interest before committing resources

4. Incorporate anticipatory design for seamless user experience

Anticipatory design removes friction before users even recognize it as an issue. The goal is to create intuitive experiences that require minimal cognitive effort by:

• Automating repetitive tasks — Features like auto-fill, smart defaults, and predictive text enhance usability
• Reducing decision fatigue — Instead of offering users an overwhelming number of choices, use AI-driven personalization (e.g., Netflix’s recommendations, Google’s predictive search)
• Leverage contextual triggers — Intelligent nudges, such as proactive notifications or reminders, can guide users toward optimal behaviors

5. Conduct deep-dive ethnographic research to identify hidden needs

Users may not always be able to articulate their biggest challenges, but their daily interactions provide valuable clues. Observing users in real-world environments helps uncover unspoken pain points. To get started, try:

• Shadowing users in their natural settings — Watching users perform tasks (rather than asking them how they do it) often reveals inefficiencies they’ve adapted to but never complained about
• Conducting diary studies — Ask users to document their interactions with a product over a week. This provides a longitudinal view of challenges that aren’t apparent in one-off usability tests
• Leveraging micro-interviews — Instead of formalized, structured surveys, ask users open-ended questions in natural settings to uncover unexpected insights

Final thoughts

As technology advances, your role will shift from responding to feedback to proactively identifying invisible needs. PMs that successfully address hidden pain points will set the standard for innovation.

By integrating AI-driven insights, behavioral psychology frameworks, and predictive analytics, you can design game-changing products that redefine industries. The most successful innovations don’t emerge from what users say they want, but from what they don’t realize they need.

Featured image source: IconScout