Contractor-Ready Analytics Pack: Exact Events, Schemas & Dashboards to Ship with Any Mobile Launch

Contractors can move fast, but they can’t fix broken signals that never existed. The most common reason early product questions go unanswered is not the analytics tool — it’s inconsistent events, missing identity, and ambiguous properties. Without an agreed schema, the contractor spends days reconciling event name variants and guessing property meanings instead of shipping dashboards.

A pack reduces friction by turning tribal knowledge into enforceable artifacts: canonical event names, property schemas with types, sample SQL joins, and dashboard widget definitions. That means reliable funnels, cohorting, and revenue joins from day one — and a cleaner handoff back to internal teams when you want to iterate.

bullets:

- Forces a single source-of-truth for event names and properties. - Saves contractor time (and your money) by preventing rework. - Produces immediately usable dashboards and acceptance tests for QA. - Lowers risk of data decay later, when many ad hoc names proliferate.

Pick one naming pattern and enforce it — I recommend snake_case with object_action in past tense (e.g., signup_completed, screen_viewed). Keep the event name short, avoid platform prefixes in the name itself, and push context into properties (screen_name, product_tier). That convention keeps datasets readable and compatible with Amplitude, Mixpanel, PostHog, and BI exports.

Here is a prescriptive minimal event list to include in your pack for most mobile launches. Each event entry in your tracking plan should include: event_name, description, required properties (name:type:example), identity expectation (user_id vs anon_id), and cardinality notes (e.g., high-frequency vs low-frequency). Example events:

bullets:

- session_started — {session_id:string, platform:string, sdk_version:string} - app_opened — {session_id:string, entry_point:string} - screen_viewed — {screen_name:string, screen_id:string} - signup_completed — {method:string, plan:string, user_id:string} - onboarding_step_completed — {step_name:string, step_index:int} - purchase_completed — {order_id:string, amount:float, currency:string} - subscription_renewed — {subscription_id:string, plan:string, next_billing_date:date} - feature_used — {feature_name:string, context:string} - error_encountered — {error_code:string, message:string, severity:string} - share_initiated — {channel:string, content_type:string}

For each property, declare type (string, int, float, boolean, date), allowed values or regex where applicable, and cardinality limits. Example: user_id:string (UUID), allow_null:false; plan:string, allowed_values:[free,pro,enterprise]. Documenting allowed values prevents high-cardinality noise later.

Include one or two sample SQL snippets contractors can drop into a warehouse to validate joins. Example snippets (paraphrased):

bullets:

- Join events to users: SELECT e.user_id, MIN(e.timestamp) as first_seen, u.signup_date FROM events e LEFT JOIN users u ON e.user_id = u.id WHERE e.event_name = 'signup_completed' GROUP BY e.user_id; - Funnel conversion by platform: SELECT platform, COUNT(DISTINCT CASE WHEN event_name='signup_completed' THEN user_id END) / COUNT(DISTINCT user_id) as activation_rate FROM events WHERE event_name IN ('app_opened','signup_completed') GROUP BY platform;

Provide widget specs, not just goals. Each widget should include: title, visualization type, dataset/filter, time range, and acceptance test. Here are 10 recommended widgets every mobile launch needs (wireframe-level):

List the widgets with one-line descriptions and acceptance tests so contractors can implement and you can QA them quickly.

bullets:

- Core funnel (app_opened → signup_completed → onboarding_complete) — funnel visualization; acceptance: each step has >95% properly typed properties. - Daily active users (DAU) by platform — time series; acceptance: platform values match SDK release targets. - New signups by acquisition channel — bar chart; acceptance: at least 90% of signups have non-null channel property. - First-week retention table (D1, D3, D7) — cohort retention; acceptance: cohort sizes > N to avoid noisy rates. - Error rate by screen — heatmap/list; acceptance: errors include error_code and count per 1,000 sessions. - Revenue by day + LTV estimate — time series and table; acceptance: revenue events include order_id and currency. - Feature adoption (top 5 features) — bar chart; acceptance: feature_name cardinality consistent. - Funnel drop-off breakdown by property (device OS, country) — stacked bar; acceptance: device_os non-null for >95% of events. - Sampling indicator & raw event volume — numeric card; acceptance: shows sample rate applied and raw vs sampled counts. - QA event stream (latest 500 events) — table with event_name, timestamp, user_id, raw_properties; acceptance: real-time view within 60s of event emission.

Give contractors a short acceptance test matrix to run during staging and production rollout. The checklist should include identity unification, property typing, allowed values, funnel sanity checks, and end-to-end test events from instrumented devices. Make these checks binary (pass/fail) and require a screenshot for each dashboard widget in acceptance.

Sampling guidance: avoid sampling critical events (signup, purchase, error). For high-volume telemetry (e.g., passive screen metrics), apply deterministic or time-based sampling and record the sample_rate property on each event. That lets analysts reverse-weight counts in SQL and BI. Include a plan for sampling rollbacks: increase sample rate gradually and verify retention/DAU stability.

bullets:

- Staging smoke test: emit test events for every event in the minimal list; verify arrival in warehouse within target SLA (e.g., 60–120s). - Identity test: create a user, complete signup on device A, log in on device B, and confirm user_id unifies across events. - Property validation: run SQL checks that compare allowed_values lists against raw property values and fail if new unknown values exceed threshold (e.g., 1%). - Sampling test: verify sample_rate property exists and reweighted counts match unsampled baselines within acceptable variance. - Dashboard acceptance: contractors must attach screenshots and query SQL for each widget; product lead signs off before launch roll-out starts.