Motivation, Not Awareness

Overview

A Fortune 500 financial institution had a problem: their website wasn’t converting visitors into mobile app users at the rate they expected. Some “Download the App” CTAs were performing well. Others were being ignored entirely. The Digital Tools department needed to understand why and what to do about it.

I led this engagement, partnering with the SEO and Content teams to analyze the full user journey from the first landing page to final app download. We delivered actionable-recommendations to help the client make intentional decisions about their CTA strategy and landing page content.

My Approach

Before making any recommendations, I needed to understand where users were dropping off and why. That meant looking at the full journey, not just the download page, and understanding what users were thinking and feeling at each step.

Research question: How can we improve the chances of customers downloading the mobile app based on their behaviors and perception of the mobile app?

I oriented myself to the client’s digital ecosystem, reviewed whatever documentation and site metrics were available, and worked with our SEO team to map the pages most likely to influence app download behavior. From there, we identified which content types and user intents to prioritize for research.

Research Methodology

10 participants, 60-minute moderated remote sessions

Quantitative Funnel Analysis

We analyzed existing site metrics and user behavior data to understand how visitors interacted with the download CTAs to begin the process of downloading the mobile app. Our team did not have access to the client’s primary analytics platform. We partnered with our SEO team to piece together the data we had available, referencing page views, drop-off rates, CTA engagement patterns, and conversion rate from the sources available to us.

In-Depth User Testing Sessions

We conducted 60-minute remote interview sessions with 10 current customers who were unfamiliar with the mobile app. During the sessions, we asked them to review selected blog articles and product landing pages to observe how they engaged with the content and asked follow up questions. This gave us a firsthand look at what information felt confusing, what was missing, and what was keeping them from taking the next step toward downloading the app.

The Finding

Users weren’t ignoring the download CTAs because they didn’t want the app. They were ignoring them because the content around the CTAs wasn’t giving them enough reason to act. The copy was generic, the value proposition was unclear, and there was nothing building trust or reducing the friction of switching from their existing banking habits.

The deeper issue was motivation, not awareness. Users knew the app existed. They just didn’t know why it was worth downloading over what they were already doing.

App Download Drivers & Barriers

Key barriers to adoption

• Feature Ambiguity: Unclear which functions are app-exclusive vs. available elsewhere, reducing incentive to download.
• Low Perceived Value: Many see the app as duplicative of desktop features, especially older users or those who rarely use Bank Client.
• Competitive Gaps: Competitors like Chase/Amex offer standout perks (e.g., tiered cards, robust rewards).
• Practical Concerns: Limited phone storage or a user’s habit of deleting apps they don’t use daily, cause hesitation towards adoption.

Motivators to Convert

• Clear App-Only Perks: Personalized rewards, exclusive features, and unique tools explicitly called out.
• Control & Security: Real-time card management, proactive alerts, and features like the bank’s credit monitoring tool increase trust.
• Seamless Access: QR codes and direct links reduce friction for both mobile and desktop users.
• Social Proof & Incentives: Ratings, reviews, and tailored promotions drive confidence and urgency.

The Output

Our research produced 18 actionable recommendations organized into four categories:

• Improve CTA visibility and placement
• Align messaging with user intent
• Build confidence and reduce friction
• Motivate with incentives and context

We built a prioritization matrix to help the client determine which recommendations to pursue immediately versus which to add to their quarterly roadmap. The matrix also flagged which changes were low-risk to implement directly and which would benefit from A/B testing before a full rollout.

Although we ran out of time during the final presentation to walk through the matrix together, it was included in the deliverable as a framework the client could use independently to sequence their next steps.

Challenges

Keeping the goal in focus

It’s easy to optimize for clicks. But a click on a download CTA doesn’t mean anything if the user doesn’t follow through. We kept the marketing team focused on the actual download as the true measure of success, not intermediate engagement metrics that felt good but didn’t move the needle.

Working within real constraints

Our recommendations had to be actionable for the content team without requiring a full technical overhaul from the product team. We scoped everything with implementation feasibility in mind so our work wouldn’t sit in a backlog indefinitely.

Making sense of messy data

Without access to the client’s primary analytics tool, we had to be resourceful and lean on our SEO team’s experience to help piece together a story that made sense. It took longer than expected, and it’s something I’d approach differently with AI-assisted analysis on a future project to help speed things up at the very beginning. Then spot-checking the findings from the tool.

What I’d Measure

If I were tracking performance post-implementation, here’s what I would have prioritized:

• Overall App download conversion rate on landing pages and article pages to compare against the data for the pages we tested.
• CTA click-through rate by page type and placement, to attribute which CTA is performing better than others.
• Mobile vs. desktop conversion differences across the journey. The engagement and conversion data we received was not segmented by devices.
• Return visit rate from users who clicked but didn’t download.

What I Learned

Data quality shapes everything upstream

Disorganized analytics slowed the project down significantly. On future engagements I’d use AI to run a first pass on pattern extraction before the team invests time in manual analysis.

Presentation timing is a deliverable problem, not just a scheduling problem

Running out of time before the prioritization matrix conversation meant the client left without a clear starting point. In hindsight, I’d restructure the presentation to lead with prioritization rather than ending with it so the most actionable part of the conversation happens while there’s still time to have it.

Recruiting screeners need to be tested

Participants slipped through our recruitment screener, and had already downloaded the app. We immediately stopped the session, but had to spend more time recruiting more participants. Tightening the qualifying questions and testing them before launch is now a standard step in my research planning.

Mobile vs desktop experiences

We tested on both desktop and mobile, but realized that the mobile app experience did not require users to use a separate device; users would get directed to AppStore or Google Play Store. This has real implications for conversion recommendations depending on the device experience.