Author: Paulina Walkowiak, CEO of CUX
Digital hoarder syndrome – why more data doesn’t mean better results?
I see a recurring pattern: an analytics team is tasked with “instrumenting the app”. Tracking tags for every tap, scroll, and time-on-screen end up in the code. The database swells, costs rise, dashboards multiply—yet you still don’t know why users are abandoning their carts.
The problem lies in a lack of strategy. Analysts often work in isolation from business teams, operating in a binary fashion. They deliver raw, technical data that remains indecipherable to a marketer. In such a situation, it’s only natural to push that data aside. Not because we don’t want it, but because we don’t know how to read it. We prefer to stick to “proven” solutions, even when they’ve stopped working.
From an ROI perspective, the difference between a company that collects no data and one that collects everything but draws no conclusions is… surprisingly small. In both cases, decisions are ultimately made in the dark.
Change requires a shift in perspective. Instead of asking, “What can we measure?”, start by asking, “What business problem do we want to solve?” Only the answer to that question defines which data is actually necessary. If your reports don’t provide a clear answer to “What should I fix in the app tomorrow?”, then you are simply wasting resources.
Cross-Channel strategy: mobile web builds intent, the app builds loyalty
One of the greatest challenges I observe while working with clients is understanding the specific roles of individual channels. For years, mobile websites and apps were treated as nearly identical. It is only when we look at them through the lens of user behavior that we see how distinct their roles are and how significantly this impact conversion rates.
Two worlds, different Goals
Mobile web and mobile apps serve completely different functions in the customer journey. Look at it from a Jobs-to-be-Done perspective:
Mobile Web is an exploration tool. Users arrive from search engines, ads, or social media to browse offers, compare products, and build purchase intent.
Mobile App is a space for loyal customers. Someone who installs your app usually already knows you. This is where you fight for retention and LTV (Life Time Value)—the total value a customer generates for the company throughout their entire relationship with the brand.
Case study: Sinsay and the power of app loyalty
In our e-commerce user behavior analyses, the same pattern repeats regularly: the app gradually takes over the role of a loyalty channel. A prime example is the strategy of the Sinsay brand (LPP Group), which we analyzed at CUX [1]. In 2022, the Sinsay app was already generating 30% of the brand’s total e-commerce orders [2]. Today, it generates an average of 70% of total online sales [3].
What did we discover? Nearly half of all visits to the Sinsay app are loyalty-driven. Users come for coupons, points, scratch cards, and rewards. These are highly engaged customers with a strong purchase intent [4].
This example perfectly illustrates a broader correlation seen across other projects. An app streamlines repetitive interactions and strengthens the brand relationship, rather than taking over the exploratory role of the mobile website.
The cart as an intent marker
In this context, the significance of the shopping cart is also evolving. In mobile environments, it increasingly serves as a signal of intent. In our case study, it proved to be the key metric distinguishing accidental visits from actual shopping sessions.
Products are added to the cart after users view photos and detailed descriptions. The cart view itself is often treated as a “saved items” list, which users return to during subsequent visits. Such a pattern signals engagement and readiness for further action.
The data also reveals users returning to the order confirmation screen after completing a transaction. They are looking for a clear signal that the process was successful. This type of behavior often leads to minor interface adjustments that enhance the user’s sense of control and security.
Inconsistent experience kills LTV
One of the most critical mistakes is treating the mobile website and the app as two separate products with different UX standards. A user who has grown accustomed to browser navigation, only to download the app and encounter a completely different interface logic, will become frustrated.
This frustration directly impacts LTV (Lifetime Value). If your website promises something that the app fails to deliver, the customer feels let down.
The solution is to design both channels as a seamless ecosystem—with a synchronized cart, a consistent chatbot, and intuitive navigation, regardless of the touchpoint.
#AnywhereCommerce is a promise of convenience you make to your customer. If you are building purchase intent on your mobile site and closing the deal in the app, you must ensure these two worlds speak with one voice. Through the synergy of online and offline activities (omnichannel), you create an ecosystem where the customer feels "taken care of," regardless of the device they use.
Experience Metrics: how to detect user frustration before they abandon the cart?
Traditional analytics will tell you how many people left your site. But that’s not enough. You need to know why. On a small screen, emotions are condensed. What might result in a brief hesitation on a desktop often leads to a closed app and a broken process on mobile.
Frustration patterns to monitor
Here are the warning signs that can help you save your conversion rate:
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Rage Clicking – repeated, rapid clicks on the same element. A classic signal that a button is too small, inactive, or that the user expects an interaction that isn’t there. In mobile apps, this is one of the most common issues, often caused by touch elements not being optimized for a finger. Every Rage Click is a sign that you are losing money right now.
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Zooming – if a user has to zoom in on content, it means it hasn’t been properly optimized for mobile devices. This could be due to a font that’s too small, unoptimized product images, or a layout designed with a desktop-first mindset. In the world of Responsive Web Design (RWD), manual zooming is a design flaw.
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Chaotic movement – erratic screen movements, fast scrolling up and down, or jumping through menus. These indicate a lost user who doesn’t know where to click to proceed. Complex app navigation is a frequent trigger for this pattern.
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Backtrack Loops – the user repeatedly moves backward in the purchasing process (e.g., entering the cart, then returning to the product page). This may indicate a technical bug, but more often, it signals a lack of information needed to make a decision. Analyzing these moments often reveals that the customer ended up somewhere they didn’t intend to be.
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Slow element loading – on mobile, every second of delay costs you a conversion [5]. Users are far less patient than on desktop; slow-loading product images or buttons with input lag directly trigger cart abandonment.
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Overlapping elements – across various screen resolutions, UI components can overlap, making key features unusable. This is a particularly painful issue because it often remains invisible during standard testing.
The list of frustration patterns is much longer. Although mobile commerce is over 15 years old, it remains a challenge even for the most experienced teams. The key is systematic monitoring and a rapid response to detected issues.
Analytical workflow in practice: from chaos to actionable insights
Watching thousands of session recordings might sound like a well-planned analysis, but in practice, such an approach rarely leads to clear decisions. That is why a structured process is essential.
Step 1: vVerification of channel goals
Start with a fundamental question: what goals does the mobile website serve, and what happens within the mobile app? Often, organizational expectations are identical for both, yet the customer’s Jobs-to-be-Done are completely divergent. Establish this at the very beginning, before you even touch the data.
Step 2: mapping user flows
Map out the actual paths users take in both channels. This analysis will reveal where the process differs between mobile and desktop and where the most significant drop-offs occur between stages. These friction points require further qualitative analysis.
Step 3: information noise reduction
This is the most critical step. Do you have thousands of session recordings and heatmaps for every screen? Filter them strategically. Focus on the bottlenecks: sessions with errors, abandoned carts, and users exhibiting frustration patterns. Set a filter: “Show me only abandoned cart sessions where a Rage Click occurred.” An overly broad scope of analysis scatters focus and hinders the transition from observation to decision-making.
Step 4: funnel analysis (waterfall)
Compare conversion rates at each stage of the purchase journey between the app and the mobile site. Where does the app perform better? Where does it underperform? This allows you to pinpoint specific areas for optimization.
Step 5: from observation to recommendation
Before implementing costly code changes in the app, formulate a hypothesis based on behavioral data. Verify whether the proposed change actually solves the problem without immediately committing the entire development team.
Business ROI: from data to revenue growth
Transitioning from quantitative to qualitative analytics has a direct impact on revenue.
Reducing Time to Conversion
Every friction point removed on mobile shortens the time to conversion. By analyzing user behavior with CUX, Rossmann identified that 40% of customers failed to notice the “accept terms and conditions” button. A minor adjustment to eliminate this issue increased the conversion rate by 2 percentage points [6]. In another case, it was discovered that a non-clickable ad banner was triggering user frustration (rage clicks). A swift update to a clickable version immediately improved campaign engagement.
Automating insights with insight assistant
Working with behavioral data can take various forms. Some teams rely on manual analysis of recordings and heatmaps, while others build their own rules and interpretation models. Increasingly, solutions that automatically support this process are emerging.
At CUX, one such solution is the Insight Assistant, which acts like an “analyst on demand”—transforming raw data into a concrete action plan [7].
How does it work? Take a heatmap, for example. The visualization alone shows interaction points but doesn’t explain what they mean. Does a cluster of clicks signify interest, frustration, or confusion? Insight Assistant distinguishes between these scenarios and generates a structured report: Observations (what is happening), Insights (what it means for the business), and Recommendations (what to do).
An example? A heatmap shows clicks on a non-interactive image. Insight Assistant diagnoses: “Users treat this element as clickable. This generates frustration and may block conversion. Quick Win: add interaction to the image.”
This approach doesn’t replace the team’s analytical thinking; it simply helps move from data to concrete decisions and actions much faster.
Paradigm shift: Ffrom channel-centered to user-centered
The most important change starts with a mindset shift. Instead of viewing channels as separate entities, we begin to see a user moving seamlessly between them.
Traditionally, organizations think in terms of channels: “We have a mobile site and an app.” A user-centered approach flips this perspective: “We have a user who moves between channels.” This shift forces data integration, shared KPIs, and the design of experiences as a continuous journey rather than isolated silos.
Instead of merely striving to be “data-driven,” let’s ensure we are acquiring insights that we actually put into practice. It’s time to make data finally work for you—delivering answers, inspiring change, and building a competitive advantage based on understanding, not guesswork.
Key Takeaways
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Quality > quantity. Collecting every possible event without a strategy is a cost, not an investment. Focus on data that supports specific business decisions.
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Mobile web builds intent and acquires new users, while the app fosters loyalty and increases LTV. Design both channels according to their unique functions.
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Loyalty drives the app. In many projects, the app serves as a hub for returning users who make purchasing decisions faster. Design your app specifically for return-user scenarios.
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Monitor Experience Metrics. Track Rage Clicks, Zooming, Chaotic Movement, and Backtrack Loops – they detect frustration much faster than sales reports show a decline.
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AI-powered automation. Behavioral analysis reveals recurring patterns across thousands of sessions. Automate their identification instead of analyzing single, isolated events.
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User-centered, Not channel-centered. The user moves seamlessly between channels. Design experiences as a cohesive whole rather than fragmented silos.
*Statista, „Percentage of mobile device website traffic worldwide”, 2024
