{"id":15059,"date":"2024-01-17T14:36:04","date_gmt":"2024-01-17T22:36:04","guid":{"rendered":"https:\/\/imprint.li\/ritsandcompany\/?post_type=avada_portfolio&p=15059"},"modified":"2025-01-22T13:39:42","modified_gmt":"2025-01-22T21:39:42","slug":"design-director-adobe-journey-optimizer","status":"publish","type":"avada_portfolio","link":"https:\/\/ritsandcompany.com\/index.php\/portfolio-items\/design-director-adobe-journey-optimizer\/","title":{"rendered":"SaaS: Adobe Journey Optimizer"},"content":{"rendered":"

<\/div>
<\/div><\/div><\/div><\/div><\/div>
Company<\/strong>: ADOBE<\/h5><\/div>
Role<\/strong>: Design Director, AJO<\/h5><\/div>
Team Size<\/strong>: 10 UXUI Designers, 3 Sr Managers<\/h5><\/div>
<\/div><\/div><\/div>

Adobe Journey Optimizer<\/h2><\/div>

Adobe Journey Optimizer is a single application where you can manage scheduled cross-channel campaigns and one-to-one moments for millions of customers \u2014 and the entire journey is optimized with intelligent decisioning and insights.<\/p>\n<\/div>

Challenge<\/h3>\n<\/div>
\n

Adobe Journey Optimizer (AJO), a cutting-edge SaaS product, required the integration of AI\/ML capabilities to enhance its ability to deliver personalized customer experiences at scale. The challenge was to understand user needs and expectations around AI-powered features, ensuring they were intuitive, trustworthy, and effective for marketing teams and end users.<\/p>\n<\/div>\n<\/div>

Approach<\/h3>\n<\/div>
    \n
  • Stakeholder Alignment<\/strong>\n
      \n
    • Collaborated with engineers, product managers, and data scientists to define key research goals.<\/li>\n
    • Mapped out user pain points and areas where AI\/ML could create significant value.<\/li>\n<\/ul>\n<\/li>\n
    • Research Design & Execution<\/strong>\n
        \n
      • Conducted mixed-methods research, combining qualitative methods (e.g., user interviews, usability testing) and quantitative analysis (e.g., survey data, behavioral analytics).<\/li>\n
      • Led co-creation workshops with marketing professionals and SaaS users to prototype and test AI-driven workflows.<\/li>\n
      • Validated AI-generated recommendations through A\/B testing and real-world scenarios.<\/li>\n<\/ul>\n<\/li>\n
      • AI-Specific Usability Testing<\/strong>\n
          \n
        • Focused on user trust and transparency by testing how users interpret and interact with AI-powered predictions and automated suggestions.<\/li>\n
        • Assessed user sentiment regarding explainability and control over AI-generated actions.<\/li>\n<\/ul>\n<\/li>\n
        • Iterative Collaboration<\/strong>\n
            \n
          • Partnered closely with cross-functional teams to ensure insights were actionable and aligned with technical feasibility.<\/li>\n
          • Provided design recommendations for AI interfaces, focusing on simplicity, transparency, and alignment with user workflows.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>

            Key Impact<\/strong><\/h3>\n<\/div>

            This research drove significant improvements in the usability and adoption of AJO\u2019s AI-powered features. By leveraging mixed methods, the team ensured that the integration of AI\/ML was both technically robust and user-centric, ultimately helping Adobe maintain its leadership in personalized marketing solutions.<\/p>\n<\/div>

            <\/div><\/div><\/div><\/div><\/div>
            <\/span><\/div>
            <\/span><\/div><\/div><\/div><\/div><\/div>
            <\/span><\/div><\/div><\/div><\/div><\/div>

            Software Interface<\/h2><\/div>
            \"\"<\/div><\/div><\/div>
            \"\"<\/div><\/div><\/div><\/div>
            <\/i><\/div>
            <\/i><\/div><\/div><\/div>
            <\/div><\/div><\/div>
            \n\n