How user activity data can transform customer experience in a mobile app 

What’s worth knowing:

• The real challenge in customer experience isn’t measuring what’s visible—it’s uncovering what data doesn’t show at first glance.
• Behavioral analytics—from clicks to moments of frustration—can expose barriers that no UX test or survey would ever capture.
• Data doesn’t replace empathy for the user, but it helps structure it. With the right insights, you can act on customer frustration before it turns into lost revenue.

Data as a compass for customer experience: how not to lose your user in a financial app

In financial applications, the margin for error is extremely narrow. A client who can’t find a key feature within a few taps not only abandons the process but also loses trust. And in a world where customer loyalty is currency, user activity data becomes the best compass for product teams and channel managers driving mobile innovation. 

Event-based analytics: what, where, and when users act

Tools like Amplitude, Mixpanel, or Firebase Analytics allow you to track specific user actions: logging in, browsing offers, starting a loan application, adding a document to a policy, or initiating a payment.

By analyzing these events, you can build conversion funnels and pinpoint where users drop off—whether it’s during onboarding, data confirmation, or authorization.

This type of analytics doesn’t capture any personal or financial data—only interaction details—remaining fully compliant with privacy standards.

What’s worth measuring?

Collected data only becomes valuable when you know what to look for. It’s not about the number of clicks or sessions but about identifying the exact moments when the customer succeeds—or gives up.

Funnel completion rate

A fundamental metric for assessing process effectiveness in your app. You can track it in contexts such as:

• account registration or onboarding,
• loan or credit application,
• insurance policy purchase,
• payment authorization or product activation.

Analyzing conversion funnels helps identify where users drop off most often and which changes make a real difference—for example, shortening a form or adding a progress indicator. In practice, every additional percentage point in funnel completion translates into measurable growth in active customers.

Time-to-value

This metric shows how quickly a user experiences the first tangible value from your app. In banking, it could be making the first transfer; in insurance, activating the first policy or submitting a claim; in investment apps, completing the first transaction.

The shorter the time to that “aha moment,” the higher the likelihood the user will remain active and loyal. It directly correlates with retention and satisfaction.

Drop-off points

These are the moments where users end their interaction or abandon a process. Sometimes it’s a specific screen (e.g., 3D Secure authorization), and sometimes a recurring pattern, such as dropping out after identity verification. 

Identifying these points enables product teams to react quickly and precisely: is the issue technical, due to long load times, or unclear messaging? When combined with qualitative data (like feedback or session recordings), drop-off points reveal where you’re truly losing customers.

Pro tip:

Monitor these three metrics together. A high completion rate might hide the fact that users need multiple attempts (increasing time-to-value). Conversely, a large drop-off halfway through a funnel may signal that users don’t understand the next step. Only by looking at the entire journey can you spot real friction points.

How to track event data in React Native

In most financial organizations, customer behavior data is scattered across multiple systems—analytics tools, CRMs, marketing platforms. As a result, product teams lack a single source of truth about how users actually interact with the app. The first step toward change is streamlining the data flow and standardizing how events are collected—so analytics empower decisions instead of generating noise. 

Define an event schema

Start by creating an event schema that clearly defines which user actions you want to track and what data attributes are assigned to them. In a financial app, this might include:

• screen_view – entering a specific screen (e.g., “Loans,” “Policies,” “Transaction history”), 

• kyc_step_started – starting an identity verification step, 

• payment_initiated – initiating a payment process, 

• claim_submitted – submitting an insurance claim, 

• offer_accepted – accepting a loan or insurance offer. 

Consistency is key: event names, data structures, and trigger logic must remain unified throughout the codebase. This eliminates chaos and delivers data that’s analysis-ready—without the need for manual “Excel cleaning.” 

Build an analytics abstraction layer

Instead of logging events directly in multiple places, create a single source of truth—a simple layer such as analytics.track(eventName, props). This approach ensures:

• cleaner, easier-to-maintain code,
• flexibility to switch analytics providers (e.g., from Firebase to Amplitude),
• improved security compliance (e.g., excluding personal data from payloads).

This layer acts as a filter—passing through only what’s essential for analysis, reducing the risk of exposing sensitive data. 

Ensure compliance and data security

In financial services, there’s no room for accidental data logging. Every event should be designed with privacy by design principles in mind: 

• never store personal identifiers (e.g., email, customer number, SSN),
• if you need to link events to a user, use an anonymous session ID,
• implement consent policies (GDPR/CCPA)—analytics only after user consent,
• never record fields containing payment or policy numbers.

Regular analytics audits—especially after new feature releases—are a strong best practice.

Privacy and regulatory compliance

Before implementing any analytics or session recording tool, ensure it complies with GDPR and your internal security policies. 

In practice, that means: 

• informing users about data collection in the privacy policy,
• obtaining explicit consent for analytics data processing,
• anonymizing identifiers (e.g., hashing user IDs),
• excluding payment or personal data fields such as SSNs or card numbers.

Collaboration with compliance and IT security teams helps establish clear boundaries between behavioral analytics and privacy protection. This is not only a legal obligation—it’s also a powerful trust-building factor. 

Data and security go hand in hand

User data is a powerful source of insight—and a serious responsibility. As cybersecurity threats increase, more institutions are implementing two-factor authentication (2FA) and additional session protection layers. 

If your app combines behavioral analytics with account or payment data, consider integrating with mobile authenticators or SMS codes to enhance security. 

Integrate analytics with product processes

Data alone doesn’t change outcomes—action does. To make data meaningful: 

• combine event data with qualitative insights (from UX research or in-app feedback),
• use analytics during sprint planning and roadmap discussions,
• present key metrics (e.g., completion rate, time-to-value) on product team dashboards.

These insights help you see which features truly enhance the user experience—and which require iteration. 

Putting data to work

Let’s say your data shows that 40% of users abandon the payment screen. That’s not just a number—it’s a story of users who tried but couldn’t complete the process.

What’s next?

• Analyze the context: does the issue occur on a specific payment method (e.g., card, BLIK, transfer) or device type?
• Review user behavior: is the screen slow to load? Is the error message unclear?
• Segment your users: new vs. returning, iOS vs. Android, younger vs. older demographics.
• Test solutions: shorten the payment path, reorder form fields, improve confirmation messaging.

Sometimes a small tweak—like adding a “payment in progress” message or moving the confirmation step closer to the main screen—can boost completion rates by double digits. 

In-app feedback and NPS: what users really think

Quantitative data tells you what happens, but not why. To understand true user motivations, you need more than charts—you need the customer’s voice. In finance, where every interaction builds (or erodes) trust, real-time feedback is UX gold and the foundation of intentional customer experience management. 

When to listen to users

Users rarely complete long email surveys after a process. But if you give them the opportunity to leave a quick, contextual comment inside the app, they’ll respond surprisingly often—especially when something doesn’t work as expected. This type of feedback is particularly valuable because it’s: 

• fresh – the user just experienced the issue,
• contextual – you know which screen and process stage they were in,
• direct – not filtered through a delayed NPS survey.

How to do it right

You don’t need another long-form survey. A short, contextual question appearing after a key flow—like purchase, registration, or activation—is enough.

Example:

After a user completes a policy purchase, a simple modal appears: 
“How would you rate the purchase process on a scale of 1–5?” 
If the score is ≤3, the app follows up: “What went wrong?” 

This simple mechanism provides powerful insight into real issues—from misleading messages to long load times. 

In React Native (or any mobile framework), you can implement this using modal components triggered by specific events—for example, after the third session, transaction completion, or an error.

Always include a session ID with each feedback entry so you can later link comments to contextual data from tools like UXCam, Appsee, or Smartlook. 

What to do with feedback

Collecting feedback is just the beginning. The key is fast analysis and action. If many users report missing payment confirmations, that doesn’t mean you need a new screen—it means your communication at a key stage needs clarification. Sometimes a simple “payment in progress” message solves more problems than a full redesign. 

When feedback clusters around a single stage, like data verification or authorization, prioritize fixes by business impact—not appearance. Address what affects revenue, trust, and retention first; aesthetics can wait. 

Treat feedback as an early warning system. The goal isn’t to react to every signal but to know where to intervene before issues start costing you transactions or customers. 

Case study: when qualitative data saved conversion

In one of Altkom Software’s fintech projects, the mobile app was visually polished, and usability tests went smoothly. Yet sales metrics remained flat. Quantitative data from Google Tag Manager showed where users dropped off but not why. Only after combining it with qualitative insights did the team uncover the root causes. 

Integration with a behavioral analytics tool (UXCam) revealed: 

• session replays showing user frustration—repeated taps on inactive elements (so-called “rage taps”),
• heatmaps indicating that key buttons were positioned outside the visible area,
• funnel analysis showing that most cart abandonments followed a form validation error,
• error logs linking those behaviors to crashes on older Android devices.

After several changes—simplifying the form, moving buttons higher, and adding a “payment in progress” message—the completion rate increased by over 20%. 

This is a prime example of how combining quantitative and qualitative data can drive real business results. 

Summary

Data isn’t just numbers. 

It’s a story about the user—their emotions, needs, and obstacles. Combining quantitative (what happens), qualitative (why it happens), and contextual (under what conditions) data gives you a full picture of customer experience. 

In financial services, that knowledge separates apps that merely work from those that build loyalty, satisfaction, and long-term trust.