Freightos case study: KPI-driven product execution lessons for platform teams

Hook: Why platform teams should care about Freightos' KPI report

As a platform engineer or product lead, you're under constant pressure to translate telemetry into decisions: prioritize features, protect reliability, and prove impact to the business. When Freightos reported Q4 2025 KPIs that exceeded management expectations, it was more than a finance headline — it was an operational playbook. Their report signals what successful logistics SaaS teams measure, how they interpret engagement signals, and which product metrics correlate with growth and retention. This case study extracts practical telemetry, product, and engagement metrics your enterprise platform team can emulate in 2026.

Executive summary — the most important signals up front

Freightos’ preliminary Q4 2025 KPIs highlight steady engagement from freight buyers and carriers and stronger-than-expected performance across booking and revenue signals. For platform teams, the takeaway is clear: focus on a balanced set of operational and business KPIs — not just requests per second or uptime. Combine reliability metrics (SLOs/SLA attainment), product engagement (cohort retention, stickiness), revenue-driving metrics (gross bookings, take rate), and unit economics (CAC, LTV) to drive prioritized, KPI-driven execution.

Top-level lessons

• Align telemetry to business outcomes: Instrument events that map directly to bookings and revenue.
• Measure engagement across the funnel: Acquisition -> Activation -> Retention -> Revenue
• Use SLOs not just SLAs: Translate user-facing impact into actionable error and latency budgets.
• Standardize event schemas: OpenTelemetry + deliberate naming enables cross-team analytics.
• Implement KPI-driven experiments: Product changes must be tied to measurable cohort outcomes before rollout.

Context: what Freightos reported and why it matters

In late 2025 Freightos published preliminary KPIs for Q4 showing growth and steady platform engagement from freight buyers and airlines. Industry coverage emphasized two things: continued execution of their global freight booking platform, and steady user engagement across customer segments. For platform teams in enterprises and logistics SaaS vendors, this matters because it validates a telemetry-first approach: the metrics Freightos highlighted are the same ones that predict scale and monetization for marketplace and platform businesses.

Mapping Freightos signals to platform KPIs you should track

Below are the practical telemetry and product metrics Freightos implicitly prioritized, translated into implementable KPIs for your team.

1. Demand and revenue signals

• Gross Bookings (GMV): Total value of freight booked through the platform over a period. Core growth indicator for marketplace platforms.
• Take Rate: Percentage of GMV captured as platform revenue. Measure monthly and by cohort.
• ARPU / ARPA: Average revenue per user/account. Track by customer tier and vertical.
• Conversion Rate (quote -> booking): A critical funnel metric for booking platforms.

Actionable telemetry: emit events for quote-issued, quote-accepted, booking-created, and booking-settled. Ensure monetary fields (currency, value) are standardized.

2. Engagement and retention

• DAU / MAU and Stickiness (DAU/MAU): Track platform-level and product-area specific activity.
• Cohort Retention: 1-day, 7-day, 30-day retention for new buyers and carriers.
• Repeat Booking Rate: Percent of buyers who make a second booking within X days.
• Time-to-Activation: Time between sign-up and first successful booking.

Actionable telemetry: capture session_start, user_identified, first_booking, and subsequent_booking events. Use identity resolution to join behavioral data with revenue events.

3. Operational reliability

• Request Latency P95/P99 for booking and pricing APIs.
• Error Rate for transactional endpoints (quote, book, pay).
• SLO Error Budget by service and feature.
• Queue Backlog and worker throughput for asynchronous pipeline processing (e.g., carrier confirmations).

Actionable telemetry: expose tracing spans for critical flows, correlate errors with user impact, and connect SLO burn rate to paging rules.

4. Unit economics and growth efficiency

• Customer Acquisition Cost (CAC) and CAC payback period.
• Customer Lifetime Value (LTV) and LTV/CAC.
• Gross Margin on bookings — differentiate platform revenue vs. passthrough costs.

Actionable telemetry: link marketing campaign IDs to first_booking and LTV calculation windows for accurate attribution.

Instrumentation patterns platform teams should copy

Freightos’ success hinges on readable, reliable telemetry. Here are pragmatic, 2026-ready instrumentation patterns to adopt.

Adopt OpenTelemetry as the canonical event/tracing pipeline

OpenTelemetry is now the de facto standard for distributed tracing and span context propagation in 2026. Use it as the backbone for correlating application traces with business events.

<!-- Example: instrumenting an HTTP booking request (pseudo-code) -->
tracer = opentelemetry.get_tracer("booking-service")
with tracer.start_as_current_span("booking.create") as span:
    span.set_attribute("booking.value", booking_value)
    span.set_attribute("user.id", user_id)
    # call pricing service
    pricing_resp = pricing_client.get_best_rate(...)
    span.set_attribute("pricing.latency_ms", pricing_resp.latency)
    # create booking
    db.insert(booking)

Standardize an event schema (100 fields is not better)

Define a compact, stable event schema for product, billing, and telemetry events. Choose consistent naming (snake_case or camelCase) and required fields: event_type, user_id, account_id, timestamp, session_id, correlation_id, currency, value, region, and experiment_tag.

"If you can't join on keys, your analytics are noise." — practical mantra for data-driven teams.

Correlate traces to events and metrics

Ensure spans include the same correlation_id used in analytics events. This enables fast root-cause analysis: from a drop in bookings (analytics) into slow pricing services (traces).

Examples: Kusto/SQL queries you can run today

Below are example queries to compute key metrics using typical data platforms. Adapt the table/column names to your stack.

-- 1) Conversion rate: quotes -> bookings (SQL)
SELECT
  date_trunc('day', q.timestamp) as day,
  COUNT(DISTINCT q.quote_id) as quotes,
  COUNT(DISTINCT b.booking_id) as bookings,
  1.0 * COUNT(DISTINCT b.booking_id) / NULLIF(COUNT(DISTINCT q.quote_id),0) as conversion
FROM quotes q
LEFT JOIN bookings b
  ON q.quote_id = b.quote_id
WHERE q.timestamp BETWEEN '2025-12-01' AND '2025-12-31'
GROUP BY day
ORDER BY day;

-- 2) 30-day cohort retention (KQL-style)
let start = datetime(2025-10-01);
let end = datetime(2025-10-31);
users
| where first_booking >= start and first_booking <= end
| join kind=inner (
    events
    | where name == "booking.completed"
    | extend event_date = startofday(timestamp)
    | summarize days_active = dcountif(user_id, timestamp > start and timestamp <= start + 30d) by user_id
) on user_id
| summarize cohorts = count() by days_active

Product experimentation and KPI-driven rollouts

Freightos' signal of steady engagement suggests disciplined experimentation: rollouts tied to KPI improvement (not just feature adoption). Platform teams should embed metrics into feature flags and progressive rollouts.

Practical rollout recipe

1. Define the success KPI (e.g., conversion uplift, latency reduction).
2. Design cohorts and guardrails (SLOs, error rate thresholds).
3. Expose experiment metadata in events (experiment_id, variant).
4. Run an A/B test or canary for a statistically significant window.
5. Post-mortem: if success KPI improves without SLO regressions, rollout to 100%.

Snippet: tying feature flags to analytics

// Pseudo-code: ensure feature flag is recorded with every event
event = {
  event_type: "quote.created",
  user_id: user.id,
  timestamp: now(),
  experiment: {
    id: "pricing_v2",
    variant: feature_flag.is_enabled(user) ? "v2" : "v1"
  }
}
analyticsClient.emit(event)

Observability and alerting best practices for platform teams

Observability in 2026 is about multi-signal correlation: metrics, logs, traces, and business events. Align alerts to business impact and SLO burn.

Alerting priorities

• P1: Booking API error rate above threshold and conversion drops > 10% QoQ.
• P2: P95 latency for pricing API above SLO.
• P3: Queue backlog growing > 2x expected processing time.

Use runbooks that surface the correlated business metric (e.g., live bookings impacted) in the alert notification so teams can prioritize remediation against revenue impact.

Data governance, privacy, and 2026 trends to adopt

In 2026, telemetry strategies must respect data privacy while enabling rich analytics. Freightos operates across countries — their approach implies strong governance and consent-aware analytics.

2026 trends you must integrate

• Privacy-preserving analytics: use aggregation, hashing, and differential privacy when handling PII in event streams.
• Server-side tracking and cookieless attribution: cookie limitations and browser privacy changes require server-driven attribution for reliable cohort joins.
• AI-enabled anomaly detection (AIOps): leverage ML to spot dips in conversions or SLO burn earlier than static thresholds — pair AIOps with an observability-first lakehouse.
• OpenTelemetry standardization: reduces vendor lock-in and eases trace/event correlation across microservices.

Governance checklist

• Define allowed PII fields in telemetry and apply schema enforcement.
• Implement retention policies per region.
• Audit who can run cohort queries containing personal data.
• Maintain mapping of business KPIs to telemetry events for auditability.

Case study analysis: what Freightos' metrics implicitly reveal about their platform execution

Freightos’ Q4 2025 indicators — “continued execution” and “steady engagement” — point to disciplined product and platform practices:

• Strong funnel hygiene: steady engagement suggests conversion tracking and optimization across quote and booking stages.
• Operational resilience: exceeding expectations implies SLO discipline and scalability engineering for global peaks.
• Focused monetization: sustained take rate and GMV growth reflect alignment between product features and revenue capture (e.g., differentiated pricing, premium features for carriers).
• Data-driven prioritization: steady engagement signals iterative improvements informed by cohort analysis and experimentation.

How to operationalize these lessons in the next 90 days

Use this 90-day plan to translate Freightos-inspired KPIs into practical execution for your platform team.

Days 0–30: Foundation

• Inventory current events and traces; identify gaps (missing booking/quote events).
• Standardize event schema and implement OpenTelemetry for critical services.
• Define top 10 KPIs mapped to dashboards and alerting rules.

Days 31–60: Measurement and correlation

• Build the core dashboards: GMV, take rate, conversion funnel, cohort retention, SLO burn.
• Instrument experiment metadata in analytics events and set up feature flags for canary releases.
• Establish SLOs for booking and pricing APIs and connect to alerting runbooks and incident playbooks.

Days 61–90: Evidence-driven optimization

• Run at least one KPI-driven experiment (e.g., pricing UI change) with clear success criteria.
• Implement AIOps anomaly detection for conversion drops and SLO anomalies.
• Perform a post-mortem on experiment results and update the roadmap based on impact per engineering hour.

Common pitfalls and how to avoid them

Many teams collect lots of data but fail to convert it into decisions. Avoid these mistakes:

• Over-instrumentation without schema: leads to unjoinable events and analysis paralysis. Standardize names and types first.
• Isolated dashboards: if PMs and SREs use different definitions for “active user,” you’ll get conflicting priorities. Centralize definitions in a metrics catalog.
• Reactionary alerts: paging on raw metrics without business context leads to fatigue. Tie alerts to business-impact signals.
• No experiment tags: changes rolled without tracking make it impossible to attribute wins or regressions.

Advanced strategies for 2026 and beyond

For platform teams operating at scale, adopt these advanced patterns to stay ahead:

• Value-based SLOs: weight SLOs by customer value so incidents affecting top accounts surface higher priority.
• Feature-level SLOs: apply SLOs to features (booking UX, pricing engine) rather than only service-level metrics.
• Hybrid edge/server analytics: combine lightweight client-side signals with robust server-side events to maintain privacy and reliability.
• Experimentation-as-a-service: centralize experiment running and metric computation to reduce errors and speed iterations.

Actionable checklist: telemetry and KPI setup

• Define the top 10 KPIs and map each to specific events/metrics.
• Implement OpenTelemetry tracing across booking and pricing paths.
• Create a metrics catalog with definitions, owners, and alerting thresholds.
• Instrument feature flags and experiment metadata for every release path.
• Establish privacy controls and regional retention policies for telemetry.

Final takeaway

Freightos' Q4 2025 KPI signals are a template: focus on business-aligned telemetry, cohesive event schemas, SLO-driven reliability, and experiment-backed product decisions. For enterprise platform teams building complex SaaS marketplaces, these are the practical levers that convert engineering work into measurable growth and predictable revenue.

Call to action

Ready to turn telemetry into predictable product outcomes? Start with a 30-day audit of your events and SLOs. Download our KPI and telemetry checklist tailored for platform teams, or contact our engineering strategy team for a 90-day implementation plan modeled on Freightos' approach.

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