To fully solve product problems requires both systems thinking (ST) and computational thinking (CT).
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.
Here’s a breakdown of its key elements:
Predictive Analysis
Leveraging machine learning models, to forecast product outcomes, user engagement, and market trends. This allows for more accurate roadmapping and prioritization of features based on predicted impact.
Algorithmic Design
Using algorithms to generate design solutions, allows complex and optimized outcomes that would be difficult to achieve manually. This can includes parametric design, 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.
Generative Design
Using computational tools to create multiple design iterations 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.
Simulation and Analysis
Incorporating computational tools to simulate and analyze designs under various conditions, such as during user task completion. This helps in making data-driven design decisions and optimizing performance.
Data-Driven Design
Leveraging data from various sources (e.g., user behavior metrics, ethnographic research, JTBD) to inform and drive design decisions. This approach helps create more responsive and adaptive designs based on real-world inputs.
Automation
Using computational tools to automate repetitive or complex design tasks, improving efficiency and accuracy. This can include generating design variations, automating design testing, or optimizing design elements.
Integration with AI
Combining computational design techniques with artificial intelligence (AI) 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.
I work with your engineering & product teams.
New Product Launch | Simulation and Analysis
Sephora | Data-driven Design
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.
Adobe Journey Optimizer | Integration with AI
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.
Enterprise dashboards | Algorithmic & Generative Design
Employing computational methods to create multiple design iterations based on defined constraints and objectives, I created parametric templates where designs are defined by parameters that are adjusted to explore various outcomes and layouts that are user-defined.