Hidden Product Opportunities in User Uncertainty

I’ve been thinking about how we usually go about finding product features.

We look at a user journey, find a problem, and then ask, “What feature can we build to solve this?” It works. However, I think we’re starting one step too late.

Before asking what feature to build, perhaps we should ask a more basic question: What is the user uncertain about at this moment?

That uncertainty often comes from an information gap. The user does not have enough information to predict what will happen, decide what to do, or feel confident about the decision they are making.

Once you start looking for these gaps, you begin to see product opportunities everywhere.

Think about Google Maps

Take Google Maps. I use it almost every day when I drive to work. My problem is not that I don’t know how to get to my office. I’ve been doing the journey for years.

The problem is that I don’t know how long it will take today.

There could be traffic, an accident, road work, or some completely unexpected mess somewhere along the route. Google Maps cannot remove any of those things.

Instead, it reduces my uncertainty.

It tells me how long the journey is likely to take. It shows me where the traffic is. It tells me if something unusual has happened. It can also suggest another route.

The product is not really solving a navigation problem anymore. It is solving a much more important problem:

“How do I make a predictable decision in an unpredictable environment?”

That got me thinking about uncertainty as a useful way to discover product opportunities.

Why uncertainty creates friction

Uncertainty becomes a product problem when it forces the user to guess.

Did my payment go through? Where is my driver? Will my package arrive today? Will I like this movie? Did my colleague see my message?

These are not all the same problem. Still, they have something in common. The user is missing information that would help them understand what is happening or decide what to do next.

Sometimes a product can remove that uncertainty. Often, it cannot. In those cases, the opportunity is to reduce it enough for the user to move forward with confidence.

Let’s take an OTT app

Imagine I open an OTT app at 10 PM. What am I actually trying to do?

I’m not trying to browse content. I’m trying to find something worth spending the next hour or two watching.

And immediately, there is uncertainty.

What should I watch?

I open the app and see hundreds of titles. More choice does not always mean more certainty. So the product shows me recommendations.

Now I have another question: Will I actually like this?

I click on a movie and another uncertainty appears: Is this worth two hours of my time?

Then I notice that the movie is two hours and forty-five minutes long. It is already 10:30. So I have another decision to make: Do I have enough time to watch this tonight?

Suppose I start a series. I now have to decide whether it is worth committing to. Twenty minutes into the first episode, I am still not sure where the story is going.

Should I continue? Or am I wasting my time?

I stop watching.

Three weeks later, I come back. Now I have a different problem: What was happening again?

Finally, I finish the episode. The next question appears: What should I watch next?

If I’m watching with my wife or family, there are even more questions. Will everyone like this? Is there anything awkward in it? Is it too violent? Is there a lot of explicit content?

What looks like a simple journey, open the app, find something, watch, is actually filled with small moments of uncertainty.

This is where uncertainty mapping becomes useful. Instead of starting with features, start by mapping the moments where the user does not know something that matters to them.

For the OTT journey, it might look something like this:

Notice that none of these features were the starting point.

The uncertainty was the starting point.

The feature came afterwards.

More importantly, once we think this way, we can start grouping the different kinds of uncertainty users experience.

Types of user uncertainty

Uncertainty about the future

A large part of user uncertainty comes from not knowing what will happen next.

Will I reach the office on time? Will my package arrive today? Will this movie be worth watching? Will the flight be delayed? Will this product work for me?

This includes outcome uncertainty, timing uncertainty, risk uncertainty, and availability uncertainty.

Google Maps reduces this kind of uncertainty with ETAs and route suggestions. Delivery apps use tracking. Booking platforms show availability. Insurance products explain what happens when something goes wrong.

In each case, the product is helping the user form a better prediction about the future.

Uncertainty about the present

Sometimes the future is not the problem. The user simply does not know what is happening right now.

Where is my Uber? Has my food been prepared? Did my payment go through? Did my files sync? Did my application reach the company?

This includes state uncertainty, process uncertainty, progress uncertainty, and continuity uncertainty.

That is why status indicators are so powerful. They make invisible processes visible.

For example, a delivery that is taking forty minutes feels very different when I can see that the restaurant has finished preparing my food and the driver is on the way.

The situation may not have changed. My understanding of the situation has.

Uncertainty about decisions

Another common problem is knowing the options but not knowing which one to choose.

Which route should I take? Which hotel should I book? Which phone should I buy? Which restaurant should I choose? Which movie should I watch?

This includes decision uncertainty, quality uncertainty, capability uncertainty, and cost or effort uncertainty.

Recommendations, reviews, comparisons, demonstrations and pricing information can all help here.

However, more information does not always reduce uncertainty.

Give me ten choices and I might be fine. Give me a hundred choices and I might become even less certain.

Sometimes the best product experience is not giving me more options. Instead, it is giving me enough confidence to choose one.

Uncertainty about other people

There is also uncertainty that comes from interacting with other people.

Did they read my message? Did the recruiter see my application? Is the buyer still interested? Can I trust this seller? What happens if I make this change?

This includes social uncertainty, trust and identity uncertainty, and consequence or reversibility uncertainty.

Read receipts, identity verification, reputation systems, confirmation messages, undo actions and cancellation policies all reduce this kind of uncertainty.

In many cases, these features are small. Yet they can have a large impact because they reduce the anxiety around taking action.

The same pattern appears everywhere

Once you start looking at products through this lens, a huge number of seemingly unrelated features start to make sense.

Google Maps reduces timing and outcome uncertainty. Uber reduces timing and state uncertainty. Amazon reduces availability and delivery uncertainty. WhatsApp reduces social uncertainty. Banking apps reduce state, risk and trust uncertainty.

The products are different. The underlying job is surprisingly similar.

Products make invisible things visible

A large number of products translate invisible processes into information the user can understand.

A delivery tracker tells me where my order is. A progress bar tells me how much of a process is complete. A flight status tells me whether my journey is still on schedule. A read receipt tells me whether another person has seen my message.

In each case, the product gives me visibility into something I could not otherwise observe.

The underlying job is prediction

A product can help me understand what is happening. It can also help me predict what will happen. Sometimes it helps me understand what happens next. In other cases, it helps me decide what I should do.

That leads to a useful way of thinking about the role of a product.

A product does not necessarily make the world predictable.

It makes the user’s interaction with an unpredictable world more predictable.

How products reduce uncertainty

Once we identify the uncertainty, we can ask what the product can actually do about it.

Eliminate it

Sometimes we can remove the uncertainty completely.

If users constantly wonder whether their payment succeeded, perhaps the problem is the payment experience itself. A clear confirmation may be enough. We may not need another dashboard or another notification.

Reduce it

Sometimes the uncertainty cannot be removed. We can still make it smaller.

Google Maps cannot eliminate Mumbai traffic. It can, however, tell me what the traffic looks like, estimate my arrival time, and suggest another route.

Make it visible

At other times, the best response is simply to show the user what is happening.

If my food delivery is delayed, telling me that the restaurant is still preparing the order does not make the delay disappear. However, it removes the uncertainty around the current state.

Explain it

A product can also explain why something is happening.

A bank can tell me why a transaction is pending. An airline can explain why a flight is delayed. An OTT service can explain why it thinks I might like a particular movie.

Give the user confidence

A product recommendation does not need to guarantee that I will like a movie. It needs to give me enough confidence to press Play.

Similarly, a hotel booking site cannot guarantee that I will love a hotel. Reviews, photographs, location information and transparent policies can give me enough confidence to make the booking.

Help the user act despite uncertainty

Finally, a product can help me act even when uncertainty remains.

Google Maps is particularly good at this. It does not just tell me that there is heavy traffic. It can tell me that my usual route will take 58 minutes while another route will take 41 minutes.

The uncertainty has not disappeared. Instead, it has been converted into a decision.

That, to me, is a much more useful product outcome.

Not every uncertainty should be removed

This is where it gets interesting.

Some uncertainty is actually the point of the product.

If I’m watching a thriller, I don’t want the product to tell me who the murderer is. If I’m playing a game, I don’t want it to tell me everything I’m going to discover. If I’m using a dating app, some uncertainty about the other person is part of the experience.

So the goal is not simply to remove uncertainty.

Instead, we need to identify which uncertainty creates unnecessary friction and which uncertainty makes the experience valuable.

Not every unknown is a problem.

The product opportunity exists when the unknown creates anxiety, hesitation, unnecessary effort, or makes an important decision harder than it needs to be.

Where is the user forced to guess?

This gives me a different way of thinking about feature discovery.

Instead of asking only, “What problem does the user have?”, I think there is another question worth asking:

“Where is the user forced to guess?”

Did my payment go through? Where is my driver? Will my package arrive today? Will I like this movie? Did my colleague see my message? What happens after I submit this? What should I do next?

These are moments where the user is trying to understand something they cannot directly see or control.

That, I think, is one of the most interesting things products do.

They act as an interface between the user and an unpredictable world.

Google Maps cannot control traffic. Weather apps cannot control the weather. Amazon cannot control every truck in its logistics network. Uber cannot control Mumbai’s roads.

Nevertheless, these products can tell us what is happening, what is likely to happen, and what we can do about it.

They make an unpredictable world easier to navigate.

Don’t build a feature just because uncertainty exists

There is an important trap here.

We shouldn’t automatically build a feature every time we find uncertainty.

Sometimes the uncertainty does not matter. The user may be perfectly comfortable with it. In some cases, giving the user more information can even make things worse.

For example, if I’m ordering a pizza and it arrives when promised, I probably don’t care about the exact location of the delivery person every second.

The tracking feature becomes valuable when the uncertainty starts affecting my behaviour. Should I leave the house? Should I call someone? Is the delivery actually coming?

That is the distinction.

Not every unknown is a problem.

The opportunity exists when uncertainty creates anxiety, friction, hesitation, unnecessary effort, or makes a meaningful decision harder than it needs to be.

The feature comes last

We often look at a product and see features.

Google Maps has traffic. Uber has live tracking. Netflix has recommendations. WhatsApp has read receipts. Amazon has delivery tracking. Banking apps have transaction notifications. Fitness apps have progress charts.

But underneath these features is something more fundamental.

They help users make decisions in a world they cannot completely predict.

Where am I? What is happening? What is going to happen? How certain am I? Am I making the right decision? What should I do?

That gives me a different way of thinking about product discovery.

Instead of asking “What feature should we build?”, start with “What is the user uncertain about?”

Then ask whether that uncertainty actually matters. Can we eliminate it? If not, can we reduce it? Can we make it visible? Can we explain it? Can we give the user enough confidence to act?

Only then should we get to the feature.

Because sometimes the best product feature is not the one that gives the user more functionality.

It is the one that gives them one less thing to wonder about.