Analyzing Feature Engagement: Breadth vs. Depth
Breadth vs. Depth analysis plots features on a 2x2 grid: how many people use a feature (breadth) vs. how often (depth). It helps decide where to invest, like improving a popular but seldom-used feature or boosting a niche power-user tool.
Why it exists
To make smart product investment decisions, you need to know more than just raw usage counts. A feature with 1,000 events could mean 1,000 users used it once, or 10 power users used it 100 times. Breadth vs. Depth analysis was created to distinguish between these scenarios and guide strategy.
The mental model
Think of a 2x2 matrix for your product's features. The horizontal axis is Breadth: what percentage of your active users have tried this feature? The vertical axis is Depth: of the people who use it, how often do they come back to it? This grid helps you classify features with more nuance than just 'popular' or 'unpopular'.
How it works
You select a set of features to analyze. The analytics tool calculates the median adoption percentage (breadth) and median usage frequency (depth) for that specific set of features. These medians become the dividing lines for your 2x2 grid, creating four quadrants:
Top-Right (High Breadth, High Depth): High Impact features. Widely adopted and frequently used. These are your core value drivers.
Top-Left (Low Breadth, High Depth): Power features. Used frequently by a small, engaged group. Valuable, but perhaps not easily discoverable.
Bottom-Right (High Breadth, Low Depth): One-Time features. Many users try them, but few return. This might indicate a feature that under-delivers or is only needed for a single task like initial setup.
Bottom-Left (Low Breadth, Low Depth): Questionable features. Not widely adopted, and not used often by those who find them. These are candidates for deprecation or a complete rethink.
When to use it
Use this analysis to guide your product roadmap. It helps identify core features to protect, find 'hidden gems' (power features) that need better marketing, and diagnose popular but underperforming features. It provides a data-driven way to answer 'What should we work on next?'.
When not to use it
Don't use this as the sole input for decisions. A 'low impact' feature might be critical for a small but high-value customer segment. Also, avoid comparing features with fundamentally different purposes, like a daily-use dashboard versus a quarterly reporting tool, as the comparison won't be meaningful.
One canonical example
A product team analyzes three features: 'Dashboard', 'Export to CSV', and 'Advanced Filter'. The 'Dashboard' is top-right (high impact). 'Export to CSV' is top-left (power feature; only accountants use it, but they use it daily). 'Advanced Filter' is bottom-right (one-time; many try it, but it's confusing, so they don't use it again). This tells the team to maintain the dashboard, improve discovery for the export, and fix the advanced filter.
Interview question
When a feature shows high breadth but low depth in a product analysis, what does this typically suggest?
- a.It might be underperforming or serves a specific, infrequent purpose.Correct
- b.It is a power-user tool that needs improved discoverability.
- c.It is a strong candidate for deprecation due to minimal overall use.
- d.It is a core feature driving significant user engagement.
Why? this is the answer
The card defines features with high breadth and low depth as 'One-Time features,' indicating many users try them but few return, suggesting they might under-deliver or are only needed for specific, infrequent tasks. This contrasts with features showing low breadth and low depth, which are candidates for deprecation.
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