How Emotional Design Boosts Live‑Streaming Gift Revenue: A Practical Framework
This article details a research‑driven approach to building an emotional gift system for a live‑stream dating platform, covering user emotion indicators, competitor analysis, design language, and a semantic intention library that together enhanced user experience and significantly increased revenue.
Preface
Every person is a small universe of emotions; perception and actions are driven by feelings. Emotional experience influences behavior and cognition, yet research and application of emotional design are scarce, motivating the creation of an emotional design system.
Research Background
Previous 0‑to‑1 projects relied on experience. In the live‑stream dating product, emotional experience is a core need, so emotional design must be formally considered.
Research Approach
Extract Scene Emotional Indicators
We defined the live‑dating scenario and identified target users: single or divorced youths in lower‑tier markets using video live‑streaming for dating, passing time, and alleviating loneliness.
We refined coarse‑grained emotional indicators based on the Delft University positive emotion research, selecting five suitable metrics through discussions with designers, researchers, and product users.
Build Gift System Framework
We analyzed five leading competitor products, mapping gift quantity and emotional distribution across value dimensions, and identified emotional priorities.
We also examined competitors' emotional focus to inform our framework.
Validate Emotional Indicators
Through user interviews we segmented users into high, medium, and low spenders, establishing four user personas (leisure, pursuit, fan, group‑party), with leisure and fan types being core.
We compared interview findings with coarse indicators to confirm the emotional factors influencing the live‑dating scene.
Construct Emotional Gift System
We defined design language for each emotion to ensure consistency and accurate emotional expression in gifts.
We built a semantic intention library for gifts by analyzing existing gift data (price, quantity, consumption share, style) and conducting targeted brainstorming.
Conclusion
Combining user motivations and behavior data, we created an emotional gift intention library and design language for the live‑dating scenario, demonstrating significant revenue improvement after launch. Ongoing exploration of emotional design aims to further enhance user experience and commercial value.
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