Where the Path to Order Breaks: UX/UI Audit of a Mobile App
We start a UX/UI audit of a mobile app not by redrawing screens but by analyzing the funnel: we reconstruct the user path from first launch to confirmed order and look at where the share of users reaching the end drops most noticeably.
The basis is analytics events, session recordings, and tracking of key steps: cart, delivery method, payment, re-entry.
This is how user path breakpoints are found. Some are visible in the numbers—for example, a drop-off on the address entry screen—while others appear only in recordings: a person spends ten seconds looking for a button, minimizes the app "to think," and does not return.
Drop-off at the payment step is almost always made up of small things: mandatory registration before confirmation, fields with strict validation and no explanations, cart reset when the app is minimized, and a primary scenario that is out of thumb reach on large screens.
Low conversion to order is usually not a traffic problem but the cost of extra steps. Every screen between "I want it" and "I paid" cuts off part of the audience, and in a long scenario losses add up rather than average out.
At the same time, paid acquisition brings people to exactly the same drop-off: reach grows, but the number of orders does not.
We examine how this is resolved: where it is enough to reorder steps and remove extra fields, and where an entirely different payment scenario is needed.
What Changes After the Analysis: Conversion, Retention, Drop-offs
Interface analysis translates "inconvenient" into measurable values: the share of sessions reaching the key action, the share of returns on days two through seven, and the time from hypothesis to release.
The benchmark is not a general assessment that "it got better," but a specific step in the scenario where the most people drop off.
The class of interface drop-off determines which fix will actually help.
Loss on the start screen is treated by restructuring the first scenario; a mid-flow break is most often state loss, when the user returned to the app and the form reset; freezes and empty screens on a poor network are treated with caching and correct offline states.
These cannot be mixed into one task list: the classes have different causes and different costs of correction.
User retention grows from targeted fixes, not a full redesign: we preserve progress between sessions, show meaningful empty states instead of an empty list, and move mandatory fields to the moment when they are actually needed.
The timeline for releasing interface fixes depends on how ready the analysis is for work.
A prioritized list indicating the screen, state, and expected effect allows items to be taken into development without re-analysis—fixes ship in small regular releases rather than once a quarter.
We verify the result against data: we collect funnel events in Firebase, break them down by cohorts, and compare behavior before and after the change. This way, the app's conversion growth is visible for each fix separately.
Work Formats: Full Analysis, Express Check, Targeted Check
The three modes differ not in the thickness of the report but in how many user paths are included in the analysis.
A full interface analysis covers all key scenarios, screen states, and error-handling branches; an express scenario check takes one path to the target action; a targeted screen check covers one area where people are presumably lost.
The choice of work format is determined not by budget but by whether you have a hypothesis about where the loss occurs.
If the hypothesis is formulated—for example, drop-off at the payment step or on the data entry screen—a targeted check is sufficient: we analyze a specific screen, its loading states, empty data, and response to invalid input.
When metrics decline in several places at once, an express check will more quickly show which path accounts for the main loss and which contributes less to it. A full analysis is needed before reworking the app, changing platforms, or entering a new audience.
| Format | When It Fits | Limitations |
|---|---|---|
| Full interface analysis | Before a redesign, platform change, or entry into a new market; when the causes of the decline are unknown | Takes more time and requires access to analytics and user surveys |
| Express scenario check | Metrics drop across several paths, and you need to quickly understand where the loss is concentrated | One path is analyzed; the remaining screens stay outside the analysis |
| Targeted screen check | There is a hypothesis about a specific screen or step; a quick fix is needed before release | Will not identify problems in related screens or in the logic of transitions between them |
The format can be changed during the work: if an express check reveals a systemic cause, we expand it into a full analysis without re-collecting data. Describe the task—we will suggest which mode will give an answer fastest.
How the Analysis Proceeds: From Data Collection to Fix Verification
The analysis proceeds not by taste preferences but by a reproducible sequence: first data, then the scheme, and only then fixes. This order removes the "like it—don't like it" debate: each recommendation has a metric it is tied to and a way to verify it before release.
- Product analytics data collection. We export events, funnels, session depth, and the distribution of entry-exit screens for a comparable period. The goal is to see at which step drop-off is highest, not where it looks most noticeable.
- User scenario scheme. We build a map of screens and transitions, overlay it on the funnel, and mark breakpoints: where the user minimizes the app, goes back, or repeats the same action.
- Interface check. Heatmaps and session recordings show where the thumb actually reaches and which elements remain in the blind zone of large screens.
- Hypothesis testing on a prototype. We assemble disputed areas into a clickable prototype and run it with a focus group or a split test on part of the traffic—before the fix enters the code.
- Prioritization. We keep only what affects the conversion of the step; the rest goes into a separate list with a rationale for why we are not taking it now.
- Handover of fixes to the team. Each point is formalized as a task: screen, scenario, expected metric, acceptance criteria. The designer and developer do not have to guess what was meant.
- Post-release monitoring. We compare the metric of the selected step before and after rollout to confirm the effect or roll back the decision.
At the output, the client is left with a scenario map with marked breakpoints, a prioritized list of fixes, and a set of metrics for verification—a document that work can continue from without our involvement.
Screen Map, Prototypes, and Fix Regulations
The audit ends not with a report but with a set of artifacts that the client's team continues to work with independently. We assemble them so that a new designer or developer enters the project through documents, not through messenger chats.
- App screen map—all states of each screen: loading, empty list, error, offline, partially filled data. It shows where the scenario breaks, not only where it starts.
- Prototypes of key scenarios—the path from entry to target action with loss markers: number of steps, mandatory fields, moments when permissions and authorization are requested.
- Interface fix regulations—what counts as a critical change, who approves it, within what timeframe, how a fix goes from request to release, and how it is rolled back.
- Interface documentation—rules for grid, spacing, typography, and component behavior so that new screens do not diverge from already released ones.
- Component library with states—buttons, fields, lists, notifications in normal, pressed, and disabled states: the developer takes ready-made items instead of handcrafted elements.
- Measurement log—which metrics and on which screens were measured before fixes, so the measurement can be repeated in the same way and the result compared.
- Prioritized hypothesis list—what to test first, in what way, and how to interpret the outcome if the gain is not confirmed.
Each item is tied to a screen and a metric, so the logic of any decision can be reconstructed from the documentation. We update the map and prototypes together with the release—without this, they diverge from the product within two iterations.
Case Study: The Path to Order Was Shortened, Payment Drop-off Was Eliminated
The client is a food delivery service with an app on two platforms.
The audit showed that the funnel loses the order not at the entrance but at the exit: before checkout, a person went through nine screens, four of which were auxiliary—address confirmation, interval selection, repeated phone entry, "check your data." On the payment screen, 38% of carts dropped off: the payment step opened as a separate scenario and lost the cart when the app was minimized.
What It Was Before
Nine screens, 4 minutes 30 seconds from "add to cart" to confirmation, 38% drop-offs at payment, repeat order share—21%. More than half of support requests were "payment failed" and "the cart was lost."
What We Changed
The prototype was assembled in Figma and run in sessions with real users, recording three breakpoints: manual address entry, context break at payment, and lack of order state preservation.
The path was collapsed to four screens: the address is populated by geolocation with manual editing, the delivery interval is selected there as well, cart and payment live in one layer, and the order draft is saved locally and survives app minimization.
What It Became
Checkout takes 1 minute 40 seconds, payment drop-offs—11%, repeat order share—34%, and payment-related requests decreased almost fourfold. Metrics were measured on equal intervals before and after, without seasonal spikes or holiday peaks—the shift holds on both platforms.
How Analytics Analysis Differs from User Interviews
The methods answer different questions, and one cannot be substituted for the other. Product analytics analysis shows where the path breaks and what the cost of the break is: share of completed scenarios, drop-off at a step, distribution of time across screens.
In-depth user interviews explain why a person acts in a particular way and what they expected from the screen.
| Method | What Questions It Answers | Limitations |
|---|---|---|
| Product analytics analysis | Where the scenario breaks, how many users are affected, what changed after release | Sees the action but not the motive—does not explain the cause |
| In-depth user interviews | Why they leave, how they formulate the task, what they find unclear | A dozen conversations do not provide scale, and answers are smoothed over by the desire to appear reasonable |
| Combined analysis | Testing hypotheses from data on real people | Takes longer and requires both competencies on the team |
A comparison of research methods comes down to their limitations: analytics is powerless where the scenario formally passes but the meaning of the step is not understood, while interviews are wrong about frequency—a person remembers the vivid, not the typical.
The choice of research method is dictated by the question, not by convenience: Nielsen Norman Group separates qualitative and quantitative data by the type of tasks they can address.
In practice, we proceed sequentially: data finds the step with the greatest loss and shows the scale, interviews explain the cause, after which fixes are verified with the same metrics.
Common Technical Concerns: Access, Data, Repeat Checks
The audit does not require admin rights in production or access to source code. A read-only role in the product analytics account and exports for key scenarios—login, search, checkout—are sufficient.
If event tracking is incomplete or access cannot be granted, we analyze scenarios using session recordings and test runs of the current build. Below are the concerns that most often come up before the start and how we address them.
What analytics access is needed?
A role with view and export permissions, without changing settings. Events for 2–4 weeks and dashboard screenshots are needed; if security policy does not allow access, we accept file exports according to an agreed list of events.
What volume of data is sufficient for the analysis?
A sample across end-to-end scenarios, not the entire product history. A few thousand sessions on critical paths are usually enough; if traffic is low, we supplement with test runs and session recordings on reference devices.
What should we do if development is fixing the app in parallel?
We lock the baseline build version on which the analysis is performed and do not mix conclusions with new changes. After release, a repeat scenario check on the updated build shows whether the breakpoint has gone away—we compare behavior before and after.
Where are breakpoints more often found—in the interface or in services?
In our experience, it is usually not the screen that breaks but the interface—services link: empty states during network failure, timeouts, repeated submissions, loss of progress on restart. That is why we look at the scenario as a whole, from entry to confirmation of the result.
Let's Discuss the Task: What to Send for an Initial Assessment
To make the initial assessment substantive rather than "we'll take a look and tell you," a minimal set of input data is enough for us.
The more complete the picture of scenarios and metrics, the more accurately we state the scope of work, team composition, and interaction procedure—and the fewer clarifying rounds before the start.
- Link to a test build: internal distribution, closed beta, or demo stand with working access—we go through scenarios on real devices, not from mockups.
- List of target scenarios: what the user should do in the app, which screens you consider key, and where, according to your observations, people are lost.
- Analytics exports for 2-3 months: events, registration and payment funnels, retention, distribution by platform and OS version.
- User feedback: support requests, app store reviews, session recordings, survey and interview results, if available.
- Current design materials: link to mockups with view rights, description of the design system and component library.
- Product context: goal of the changes, metrics by which you will evaluate the result, constraints on release timeline and supported platforms.
- Hypotheses and known issues: where you believe the failure is and what you have already tried changing—this saves an entire diagnostic stage.
The interaction procedure is as follows: within one business day, we confirm receipt of the materials and say what is missing; on the second or third business day, we return with an assessment, work plan, and priorities by breakpoints.
Then there is a short meeting with product and development, 40-60 minutes, to align on the interpretation of metrics and agree on the reporting format. One person on the client side with decision-making authority is enough—the rest join as needed.
Describe the task and send what you already have: even a partial data set allows us to state timelines and scope of work without preliminary guesswork.