Intermediate everything in Analytics & Metrics, page 12
How would you diagnose a flat feature adoption KPI?
This tests your ability to create a diagnostic plan from a single lagging metric. A great answer outlines a funnel (Awareness > Activation > Usage), segments users, and combines quantitative data with qualitative feedback.
Pitfalls of 'Conversion Rate' as a North Star Metric
This tests your ability to see beyond a single metric and understand its second-order effects. A strong answer identifies pitfalls like lower AOV, then proposes counter-metrics (AOV, return rate) and guardrail metrics (page load time).
Evidence-Based Management: Metrics Over Gut Feel
EBM replaces 'I think' with 'I know because the data shows...' It's about making decisions using evidence, not just intuition. Use it to prioritize features with user data or optimize processes by measuring cycle time. The footgun is metric fixation.
RFM Analysis: Find Your Best Customers
RFM analysis segments customers by scoring their Recency, Frequency, and Monetary value. This helps identify your best customers (high RFM), those at risk (low R/F), and new high-spenders.
Bullet Graphs: Packing Context into a Single Bar
A bullet graph packs rich context into one bar, showing a metric against its target and qualitative ranges. Use it on dashboards for single KPIs like sales-to-quota or latency vs. SLA. The footgun is clutter, which defeats its at-a-glance purpose.
Pre-attentive Attributes: How Your Brain Sees Data First
Pre-attentive attributes are visual properties your brain processes instantly, before conscious thought. They're used in data visualization to make key information 'pop,' like using color to highlight an outlier.
GSM: Connect Your Goals to Real Metrics
The GSM framework turns fuzzy goals into concrete numbers by linking what you want (Goal), to observable behaviors (Signal), to a specific measurement (Metric). It's used to define KPIs for new features. The footgun is choosing easy-to-measure vanity metrics.
Color Theory: Guiding the Eye in Data Visualization
Color in a chart is a cognitive shortcut, telling the viewer's brain what to notice and how to feel. Use it to highlight trends (green for growth) or group categories. The footgun is using too many colors, which creates noise and obscures insights.
Data Dashboards: The Single-Page Business Story
A data dashboard is the executive summary for your metrics, telling a story on a single page with key visualizations. It consolidates data from multiple reports, providing a high-level view to monitor business performance.
Analytics CoE: Centralizing Your Data Strategy
An Analytics Center of Excellence (CoE) is an internal data consulting group, centralizing experts to set standards and drive strategy. It helps large organizations standardize data quality and tooling. The footgun: becoming a bottleneck that slows teams down.
Time to Value (TTV): From Signup to 'Aha!'
Time to Value (TTV) measures the time from a user's first touch to their first 'aha moment' of real value. It's crucial for optimizing onboarding and reducing churn. The main footgun is defining value from the company's view, not the customer's.
Data-as-a-Product: Treat Your Data Like Software
Data-as-a-Product (DaaP) treats internal datasets like software, with owners, versions, and SLAs. This provides reliable, self-service data for consumers like analysts or other apps.
Snowflake: Decoupled Storage and Compute
Snowflake decouples storage from compute, acting like a shared-disk system for data management but a shared-nothing system for query performance. This lets you scale compute and storage independently, ideal for variable analytic workloads.
Multivariate Testing: Finding the Best Combination
Multivariate testing (MVT) finds the best *combination* of changes, not just the best single change. It tests multiple elements at once, like three headlines and two button colors, to see how they interact.
Period-over-Period Analysis: Measuring Change Over Time
Period-over-Period analysis answers 'Are we getting better?' by comparing metrics from consecutive time blocks, like this month's sales vs. last month's. The footgun is ignoring seasonality, which can create false signals of growth or decline.
Tracking Schema: Your Analytics Naming Convention
A tracking schema is the shared dictionary for your analytics, defining how you name user actions (events) and their details (properties). It's crucial for ensuring one team tracks "Song Played" the same way as another.
Sessionization: Turning Raw Events into User Stories
Sessionization groups a user's raw clicks and page views into a single "visit." It's used to analyze conversion funnels and calculate metrics like time-on-site. The main footgun: your definition of a "session" is arbitrary and can skew results.
Marketing Attribution: Deciding Who Gets Credit for a Conversion
Attribution modeling decides which marketing touchpoint gets credit for a conversion. It's used to justify ad spend by assigning value to channels like email, social, or search. The biggest footgun is using a simple model that overvalues the final click.
The HEART Framework: Measuring User-Centric Success
The HEART framework measures user-centric success, not just clicks. It provides five categories (Happiness, Engagement, Adoption, Retention, Task Success) to track product health.
AARRR Framework: Pirate Metrics for Growth
The AARRR framework models your business as a five-stage customer funnel: Acquisition, Activation, Retention, Referral, Revenue. It's used to pinpoint leaks in your growth engine. The footgun is tracking raw counts instead of conversion rates between stages.
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