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Algorithmic Feature Prioritization

Question"We are examining ways to prioritize product features. What is the recommended approach in PMTK?"

Prioritizing product features is key to successful product releases and versioning.

The business implications and accumulated costs can be immense if certain product features in the product feature set are unnecessary or less desired by the customers.

Therefore, selecting the best product features and optimizing the product's feature set is critical.

There are different approaches to feature prioritization. They range from debates to formulaic, quantitative, or qualitative scoring.

A prioritization process that uses highly subjective scoring and prioritizing techniques, e.g., MoSCoW, RICE, ICE, Fibonacci Numbers, etc., is unreliable and susceptible to personality clashes, personal biases, and internal politics.

A significant downside of a scale-based prioritization and scoring approach is that participants quickly gravitate to repeatedly selecting the intermediate values of the scale.

Consequently, Blackblot had developed an algorithmic model to consistently and uniformly perform product feature prioritization in a finite number of steps.

Blackblot's Boolean-based algorithmic prioritization model guides product managers through a finite number of product-related decisions.

The algorithm then prioritizes product features based on the answers given.

See the "An Algorithmic Model for Product Feature Prioritization" chapter on page 104 in the Blackblot PMTK Book: Second Edition.

PMTK's algorithmic feature prioritization model is implemented in the PMTK Features Matrix template.