{"id":16583,"date":"2024-08-21T15:51:44","date_gmt":"2024-08-21T22:51:44","guid":{"rendered":"https:\/\/ritsandcompany.com\/?page_id=16583"},"modified":"2024-11-04T20:28:05","modified_gmt":"2024-11-05T04:28:05","slug":"computational-product-leadership","status":"publish","type":"page","link":"https:\/\/ritsandcompany.com\/index.php\/computational-product-leadership\/","title":{"rendered":"Computational Product Leadership"},"content":{"rendered":"

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Computational Product Leadership<\/span> for Data-Driven Solutions.<\/div><\/span>
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Blending computer science, data science, and engineering with design.<\/div><\/div>
Jump to Case Studies<\/span><\/a><\/div><\/div><\/div>
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To fully solve product problems requires both systems thinking (ST) and computational thinking (CT).<\/h2><\/div>

Computational Product Management is an emerging approach that integrates computational techniques and tools into the product management process to enhance decision-making, optimize product development, and improve overall product performance. This approach leverages data analysis, algorithms, machine learning, and automation to drive product strategy, design, and execution more effectively.<\/p>\n

Here\u2019s a breakdown of its key elements:<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Predictive Analysis<\/h2><\/div>

Leveraging machine learning models,<\/span> to forecast product outcomes, user engagement, and market trends. This allows for more accurate roadmapping and prioritization of features based on predicted impact.<\/p>\n<\/div><\/div><\/div>

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Algorithmic Design<\/h2><\/div>

Using algorithms to generate design solutions, allows complex and optimized outcomes that would be difficult to achieve manually. This can includes parametric design,<\/span> where designs are defined by parameters that can be adjusted to explore various outcomes, such as preferences that build a personalized and dynamic homepage for every user.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Generative Design<\/h2><\/div>

Using computational tools to create multiple design iterations<\/span> based on defined constraints and objectives. This process can rapidly produce a wide range of design options that meet specific criteria for the team to evaluate on the fly.<\/p>\n<\/div><\/div><\/div>

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Simulation and Analysis<\/h2><\/div>

Incorporating computational tools to simulate and analyze designs<\/span> under various conditions, such as during user task completion. This helps in making data-driven design decisions and optimizing performance.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Data-Driven Design<\/h2><\/div>

Leveraging data from various sources (e.g., user behavior metrics, ethnographic research, JTBD) to inform and drive design decisions.<\/span> This approach helps create more responsive and adaptive designs based on real-world inputs.<\/p>\n<\/div><\/div><\/div>

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Automation<\/h2><\/div>

Using computational tools to automate repetitive or complex design tasks,<\/span> improving efficiency and accuracy. This can include generating design variations, automating design testing, or optimizing design elements.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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Integration with AI<\/h2><\/div>

Combining computational design techniques with artificial intelligence (AI)<\/span> to enhance design capabilities. AI assists in predictive modeling, personalized design solutions, and dynamic adjustments based on real-time data, like Adobe Journey Optimizer’s predictive insights throughout the customer experience.<\/p>\n<\/div><\/div><\/div>

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Let\u2019s connect<\/h1><\/div>
Questions?<\/span><\/a><\/div><\/div><\/div><\/div><\/div>\n
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I work with your\u00a0engineering & product<\/span> teams.<\/h2><\/span>
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(Or bring my own.)<\/div><\/div><\/div><\/div>
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Case Studies<\/div><\/span>
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New Product Launch | Simulation and Analysis<\/h3><\/div>
Contact lens dropout is a significant problem for the category as a whole, and for my client in particular. Incorporating computational tools to simulate and analyze designs for feasibility, desirability and viability, I led the team to make data-driven design decisions and optimize software performance.<\/div>\n<\/div><\/div><\/div>
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Sephora | Data-driven Design<\/h3><\/div>
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Though beauty chain Sephora is widely viewed as a disruptor when it comes to digital retailing, one place where they lagged behind other eCommerce sites was in the poorly designed, badly architected and overall confusing checkout flow. Leveraging data from various sources (e.g., user behavior metrics, environmental factors, best practices for ecommerce checkout) to inform and drive design decisions, I created more responsive and adaptive designs based on real-world inputs.<\/p>\n<\/div>\n<\/div><\/div><\/div>

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Adobe Journey Optimizer | Integration with AI<\/h3><\/div>

Using machine learning (ML) and other artificial intelligence (AI) to enhance software capabilities. My team designed features for predictive modeling and personalized consumer experiences that allowed customers to predict and adjust user journeys based on real-time data.<\/p>\n<\/div><\/div><\/div>

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Enterprise dashboards | Algorithmic & Generative Design<\/h3><\/div>

Employing computational methods to create multiple design iterations based on defined constraints and objectives, \u00a0I created parametric templates where designs are defined by parameters that are adjusted to explore various outcomes and layouts that are user-defined.<\/p>\n<\/div><\/div><\/div>

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