Analytics
494 bites tagged Analytics — interview questions with model answers, and 60-second explainers.
Re-evaluating personas when engagement is low
Bring behavioral evidence, separate persona-wrong from execution-wrong, propose joint research to validate or invalidate. Data-driven challenge to assumptions.
Unify behavior, billing, and CRM data
Ingest events, sync Stripe and Salesforce into a warehouse, resolve identities to one customer, model unified metrics. an ELT pipeline with identity resolution. ignoring identity stitching and the anonymous-to-known join.
Instrument an onboarding flow for analytics
Track each onboarding step plus the activation milestone, define a clean event schema with stable IDs, send reliably via batching or server-side. funnel instrumentation and event design. logging vanity counts with no funnel.
Measuring design system documentation effectiveness
Search-with-no-results, time-to-first-component, support-ticket deflection, tied to analytics and registry data. outcome metrics over vanity page views. citing only page views or NPS with no instrumentation.
Sessionizing clickstream events into sessions
Order events per user, split on inactivity gap, assign session ids, pick event or session grain. grouping events into sessions and grain choice.
Data warehouse vs data lake
Warehouses store structured, schema-on-write data for BI; lakes store raw multi-format data with schema-on-read for exploration and ML. distinguishing two storage paradigms.
What is an OLAP cube and its operations?
A cube pre-aggregates measures across dimensions; operations are slice, dice, drill-down, roll-up, and pivot. multidimensional analysis concepts.
Visualizing long-term trend versus seasonality
A line chart over the full three years, often with a moving average, shows the long-term trend; a seasonal plot overlaying each year by month, or a month-of-year box plot, reveals… matching visualization to the analytical question.
Track scroll depth: Intersection Observer vs scroll events
Place sentinels at depth thresholds and fire once via Intersection Observer, off the main thread; scroll listeners fire constantly and need throttling. efficient scroll-depth tracking.
Build a simple A/B test for a headline
Assign each visitor a sticky bucket, serve the matching headline variant, log impressions and conversions per variant. basic A/B test mechanics. re-randomizing on every load so a user sees both variants.
CSV vs JSON vs Parquet for analytics
CSV and JSON are row-based, human-readable, and bulky; columnar Parquet/ORC compress well and read only needed columns; choose columnar for analytics. file format tradeoffs. defaulting to CSV for large analytical workloads.
Data lake versus data warehouse
Lakes store raw, schema-on-read data of any type cheaply; warehouses store curated, schema-on-write structured data for fast SQL; choose a lake for varied raw data and ML. storage architecture fundamentals.
Calculating Daily Active Users in SQL
Need per-event user_id and timestamp and a clear active definition; count distinct user_id within the day in a fixed timezone. Metric definition plus dedup SQL. Counting rows or fuzzy date-boundary and timezone handling.
Client vs server tracking: pros, cons, examples
Client-side wins on UI context but loses data to blockers and tampering; server-side wins on reliability and trust but misses pure UI events. Tracking placement trade-offs with examples. Picking one for everything.
Building a conversion funnel in SQL
Count distinct users reaching each ordered step, compute step-over-step conversion; the biggest drop-off is the lowest consecutive ratio. Funnel SQL and drop-off reasoning. Comparing each step to the total, or counting events.
Investigating analytics vs database count gaps
Causes include ad-blocker loss, differing identity logic, timezone mismatches, filtering, and pipeline delay; investigate by aligning definitions and tracing one user. Data-quality debugging. Trusting one source blindly.
SQL for a three-step onboarding funnel
Anchor the 30-day signup cohort, count distinct users reaching each later step in timestamp order; conversion is each step over the prior. Funnel SQL with ordering correctness. Counting any occurrence regardless of order.
Visualizing a correlation with a third variable
A scatter plot with a trend line shows the relationship; encode network type by color or facets to expose a lurking variable. Bivariate viz plus confound awareness. Treating the correlation as causal.
Cohort analysis for an onboarding change
A cohort groups users by a shared start trait; compare pre and post Jan-1 signup cohorts on retention by age. Cohort reasoning and clean framing. Comparing calendar periods instead of cohort age, confounding seasonality.
Visualizing a non-linear funnel with re-entry
A linear funnel cannot show branching or re-entry; a Sankey diagram encodes flow volume, splits, and leaks as proportional ribbons. Matching visualization to data shape. Defaulting to a bar funnel or pie chart.
Designing a useful button_click event payload
Include identity, timestamp, and context plus properties like button id, screen, and state; govern with a naming convention. Event instrumentation design. A bare event name, or ad hoc field names per event.
Designing a warehouse model for feature adoption
Star schema with a feature-usage fact table at a defined grain, surrounded by user, feature, date, and device dimensions. Dimensional modeling skill. One giant wide table or modeling without defining the grain.
Migrate a breaking analytics schema change
Dual-write both fields during overlap, backfill history, migrate consumers, then deprecate the old field. safe rollout of a breaking data contract.
Design an analytics event schema
Consistent object-action naming, snake_case, typed properties with units, and shared context like user, session, timestamp. discipline in analytics taxonomy.
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