How to Find Problems Google Analytics Does Not Show

Google Analytics is an essential tool for understanding the performance of a website or e-commerce store. It shows how many people accessed a page, which channels brought traffic, where users abandoned the journey, and which actions generated conversions.

But there is an important limitation: Analytics is very good at showing what happened, but it does not always show why it happened.

You may discover that many users abandon the cart. You may see that a product page has a low conversion rate. You may identify that a checkout step is losing traffic. But this alone does not explain the reason.

Did the user leave because shipping was too expensive? Because they did not understand the delivery time? Because they did not trust the store? Because they could not find the information they needed? Because the button did not look clickable?

These problems often do not appear directly in Google Analytics.

And this is exactly where other types of analysis become useful, such as CES surveys, contextual feedback, and A/B testing.

Google Analytics Shows Behavior, Not Intention

Analytics mainly works with quantitative data. It shows volumes, rates, events, sessions, pages, clicks, and conversions.

This data helps answer questions like:

How many people accessed a specific page?
Which channel brought the most traffic?
At which step did users abandon the journey?
Which product had the most views?
Which campaign generated the most conversions?

These answers are important. But they do not show the user’s perception.

For example, if a product page has many views but few purchases, Analytics may indicate that there is a conversion problem. But by itself, it will not tell you whether the problem is the price, the description, the images, the delivery time, the lack of reviews, or the trust level of the store.

That is why relying only on metrics can lead to incomplete decisions.

The Problem May Be in the Experience, Not the Traffic

Many teams try to solve conversion problems by increasing media investment, changing campaigns, or bringing more visitors to the website.

But in some cases, the problem is not about bringing more people in. It is about what those people experience after they arrive.

In e-commerce, small friction points can directly impact the purchase decision:

The user does not understand the shipping cost.
The delivery time appears too late.
The product page does not answer basic questions.
The return policy is not clear.
The checkout feels long or confusing.
The user does not feel confident enough to complete the purchase.
The main button competes with other elements on the screen.
Important information is hidden.

These problems do not always appear clearly in reports.

You may see that conversion has dropped. But to understand why, you need to combine behavioral data with perception data.

Use CES to Understand User Effort

CES stands for Customer Effort Score. It is a metric used to measure the perceived effort required for a user to complete an action.

Instead of only asking whether the person liked the experience, CES helps you understand whether a task felt easy or difficult.

In an e-commerce context, you can ask:

Was it easy to find the product you were looking for?

Or:

Was it easy to understand the information on this product page?

Or even:

Was it easy to continue to checkout?

These questions help reveal problems that Analytics cannot show directly.

For example, Google Analytics may show that many users visit a product page but do not buy. But a CES survey may show that they did not understand the difference between models, could not find size information, or felt unsure about the return policy.

This type of response turns a suspicion into a much clearer hypothesis.

Ask Questions in the Right Context

A common mistake is using surveys that are too generic.

Asking “How was your experience?” may generate useful answers, but it is usually not as effective as asking something related to the exact moment of the journey.

The question needs to follow the user’s context.

On the search page, you can ask:

Was it easy to find the product you were looking for?

On the product page:

Was it easy to understand the information about this product?

In the cart:

Was it easy to review your items before continuing?

At checkout:

Was it easy to complete your purchase?

After the purchase:

Was it easy to track your order status?

The more contextual the question is, the more useful the answer becomes.

Combine Analytics With Qualitative Feedback

The best approach is not choosing between Google Analytics and user surveys. The ideal path is to combine both.

Analytics helps you find the critical points in the journey. Feedback helps you understand the reason behind those points.

A simple flow would be:

Identify a page or step with low conversion in Analytics.
Analyze where users are abandoning the journey.
Add a CES question or contextual survey at that point.
Read the response patterns.
Turn the learnings into hypotheses.
Test improvements with an A/B experiment.

For example:

Analytics shows that many users abandon the cart.

The CES survey shows that part of the users found it difficult to understand the delivery time.

The hypothesis could be:

If we show the delivery time more clearly before checkout, more users will continue to purchase.

After that, you can create an A/B test to validate whether this change actually improves conversion.

Not Every Problem Requires a Redesign

Another important point: discovering a problem does not mean you need to rebuild the entire website.

Many conversion improvements come from small adjustments:

Changing the position of a piece of information.
Rewriting confusing copy.
Highlighting an important condition.
Simplifying a step.
Removing distractions.
Improving visual hierarchy.
Answering frequent questions earlier.
Testing a new call to action.

The advantage of working with data and experiments is precisely avoiding big changes based on guesswork.

You identify a problem, create a hypothesis, and test a solution.

The Role of A/B Testing in This Process

A/B testing works as a way to validate whether the proposed solution actually improves the result.

Without testing, a change may look good visually but reduce conversion. The opposite can also happen: a simple change that seemed small may have a relevant impact on the journey.

By combining Analytics, CES, and A/B testing, you create a safer improvement cycle:

Metrics point to the problem.
Feedback explains the reason.
Experimentation validates the solution.

This process reduces opinion-based decisions and increases the chances of improving the real user experience.

Examples of Problems Analytics Does Not Show by Itself

Google Analytics may show that a page has low conversion, but it will hardly tell you that:

The user did not understand the product benefit.
The most important information was below the fold.
The page created insecurity.
The button text was not clear.
The user found it difficult to compare options.
The person could not find the return policy.
The shipping cost appeared too late.
The form felt too long.
The product description did not answer an essential question.

These are problems of perception, clarity, and effort.

To find them, you need to listen to the user at the right moment.

Conclusion

Google Analytics is indispensable for any digital operation. But it should not be the only source of truth.

It shows where something happened. But often, you need other tools to understand why it happened.

By combining quantitative data with CES surveys, contextual feedback, and A/B testing, your team can uncover invisible problems, create better hypotheses, and make decisions with less guesswork.

In the end, improving conversion is not only about looking at numbers. It is about understanding the behavior, difficulty, and perception of the person trying to buy.

And the better you understand this journey, the easier it becomes to create experiences that truly convert.