Seekr

Transparent & Unbiased Search

Seekr is an innovative search engine that prioritizes transparency and user control. Utilizing a proprietary algorithm, it rates the credibility and objectivity of content, enabling users to identify political biases and make informed decisions. Seekr's mission is to provide an unbiased and comprehensive view of web information, establishing itself as the go-to search engine for trustworthy sources.

My Role

• Product Designer

• UX Architecture

• Data Flow

• Client Communication

Business Problem

Traditional search engines often prioritize paid content and biased results, making it difficult for users to access neutral, transparent information. Seekr aimed to create a search platform that eliminates misinformation, promotes unbiased ranking, and enhances user trust. The challenge was to design an intuitive, trustworthy, and scalable search experience while ensuring clarity in how results were ranked.

Research Methods

1. User Surveys – Polled 500+ participants to assess frustrations with traditional search engines.

2. Competitive Analysis – Studied Google, DuckDuckGo, and Bing to identify gaps in search transparency.

3. Usability Testing – Tested early prototypes with users to refine the ranking explanations and result displays.

4. Heatmaps & Eye-Tracking – Observed how users scanned search results to optimize layout and visibility.

Failed Iterations & Key Learnings

1. Trust Score Visibility – Initially, we displayed a detailed credibility score for each result, but users found it confusing. We simplified it to a clearer, color-coded system with a concise explanation.

2. Advanced Filtering System – Early designs included deep filtering options, but testing showed most users wanted predefined, quick-access filters instead.

3. AI Summaries – The first version of AI-generated summaries felt robotic. After iteration, we improved natural language processing to make summaries more readable and helpful.

Designing Seekr’s Advanced Filtering

1. Initial Filter Design

The initial design featured a slider with multiple lines representing the relative amount of content. As the slider moved towards the center, the amount of content decreased. However, a significant amount of content was tagged as "just right of center," posing a challenge in conveying this information to users.

2. User Testing and Simplification

User testing revealed a preference for a simplified filtering system for political biases. Based on this feedback, I simplified the granularity to include options:

• L (Left)

• L/C (Left, Center)

• C (Center)

• C/R (Center, Right)

• R (Right)

This approach aligned with user preferences and promoted adoption.

3. Competitive Research and Feature Development

To differentiate Seekr from competitors, I proposed unique modules, such as the historical searches module. This feature enables users to track how a topic's political leanings have shifted over time, aiding in combating misinformation and political bias.

4. Implementation and Refinement

Focusing on user adoption and preferences led to the successful implementation of features and modules, resulting in a more refined, transparent, and functional user experience.

Metrics & Outcomes

🔎 35% increase in user trust ratings compared to traditional search engines.
📊 50% of users preferred Seekr's transparency features over competitors.
⏳ 20% faster search completion rate, improving efficiency for users.
🚀 High early adoption, with a 40% return rate among beta users.

Key Takeaways

• User-Centered Research was critical in shaping a transparent and trustworthy search experience.

• Iterative Design & Testing helped refine features for maximum clarity and usability.

• Simplified, Clear UI improved engagement and user trust, making information more accessible.

Seekr successfully positioned itself as a credible, unbiased alternative in the search engine market.