[the challenge]
The business opportunity
70% of job postings qualified for Premium sponsorship based on hiring complexity, but only 19% were adopting. At $200 additional revenue per Premium job, a 2% lift would generate significant income.
The disconnect: we were pitching Premium after job completion with no connection to what employers told us during posting.
The user experience gap
Employers experienced our funnel as "post for free," spent time crafting their job posting, then were hit with a sponsorship pitch at the end. It felt like a bait-and-switch. We weren't connecting their specific hiring needs to our Premium recommendation, making it feel generic and transactional.
What product wanted
Interrupt users mid-flow with modals or forced interstitials to push Premium earlier. The hypothesis: earlier exposure would drive adoption.
The risk: breaking trust by pitching before we understood their needs.
Active Listening Framework
Phase 1
Explore
Focus on curiosity
Phase 2
Acknowledge
Confirm understanding
Phase 3
Respond
Respond thoughtfully
Translating to Digital
Phase 1
Explore
Collect context through user inputs
Phase 2
Acknowledge
Prove you heard them by reflecting back
Phase 3
Respond
Personalize based on their specific needs
Disruptive modals would break the listening pattern. If we interrupt mid-flow, we would be signaling "we want your money" before proving "we understand your needs". Timing was really important here to build trust with the user.
Influencing Stakeholder Direction
The Challenge: Product wanted disruptive modals. I needed to convince them to wait until review.
My Approach: I walked stakeholders through a real-world scenario. "When you're buying a watch, when do you want the salesperson to make their pitch? If they approach too early, you feel sold to, not understood. Timing determines trust."
The Outcome: Team agreed to test the trust-based approach.
Research Validation
Partnered with UX research to test three variations of Premium recommendations. My trust-based design with contextual right rail messaging outperformed alternatives.
Key Findings:
Users understood the connection between their inputs and our recommendation
Timing felt appropriate (review page, not mid-flow interruption)
Recommendation felt credible and personalized, not generic
Why It Worked
The 8% lift wasn't from one element—it was from designing an integrated trust-building system where every touchpoint reinforced our value proposition.
Remove any piece and you break the trust loop:
Modal would interrupt listening
Earlier timing would feel presumptuous
Generic features wouldn't feel relevant
No acknowledgment would feel transactional
Systems Thinking Over Feature Design
Building trust isn't about perfecting one interaction—it's about designing a coherent system where timing, content, visual design, and interaction patterns all work together. The success came from the integration, not any individual element.
Understanding Before Recommending
This principle, translated from human sales interactions to digital product design, created measurable business value. Users needed to feel heard before they'd trust our advice. The data validated what behavioral psychology suggests: people respond to recommendations they believe are made with their best interests in mind.
Design as Strategic Platform
The right rail isn't just for Premium upselling—it's a strategic asset. By positioning it as "we deeply understand your hiring situation," we created infrastructure for standard sponsorship recommendations, other Indeed product suggestions, richer employer input collection and a consistent "Indeed is listening" experience
Measuring True Success
Premium adoption is a leading indicator, but the real metric is whether employers find enough value to return and sponsor again. Long-term trust drives lifetime value, and that depends on monetization delivering features that actually work.
Iterating Toward Personalization
Future versions should move beyond template-based messaging (inserting variables into stock text) to fully dynamic, contextual content tailored to each unique hiring scenario. The technical lift was too large for V1, but the architecture supports this evolution.