featureoften

Featureoften is a term used in software product analytics and machine learning to describe a framework for evaluating how frequently individual features of a product are used by users. The concept emphasizes using usage frequency to inform development and modeling decisions.

A feature is any functional unit that delivers value, including UI controls, API endpoints, or model inputs. Featureoften metrics come from telemetry data that records events such as clicks, activations, or time spent interacting with a feature.

Data collection uses instrumentation and event logging with anonymization. Frequencies are normalized for audience size and exposure, and may be weighted by recency to reflect current usage patterns.

Applications include prioritizing roadmaps, guiding UX design, and allocating performance resources. In machine learning, high-frequency features can serve as baseline predictors or be used to study feature interaction, while infrequent features may be candidates for experimentation.

Example: in a photo editor, crop and resize are used by many users, while advanced effects are less common. Featureoften analysis highlights core tools for optimization and signals which features might be deprecated or consolidated.

Limitations: frequency does not equal importance; rare features can be critical for niche use cases or compliance. Data quality, sampling bias, and privacy concerns can affect measurements.

Related concepts include feature importance, usage analytics, telemetry, and product analytics.