Campaign Analytics:
How Do I Analyze Multi-Channel Campaign Performance?
Unite data under one identity graph, declare attribution scope, and triangulate impact with MTA, experiments, and MMM. Read channel roles—introducers, scalers, closers—and reconcile with Finance.
Analyze multi-channel performance by standardizing identity & taxonomy, declaring attribution rules, and measuring incrementality. Publish one executive view that ties pipeline, bookings, ROMI/CAC, assist rate, and validated lift. Use path analysis to learn roles and MMM to size upper-funnel/brand when user-level data is sparse.
Principles For Multi-Channel Readouts
The Multi-Channel Analysis Playbook
A practical sequence to see the whole journey and fund what truly moves revenue.
Step-By-Step
- Unify Identity & Events — Implement person/account IDs, UTM & offer IDs, server-side tagging, and CRM sync.
- Publish Taxonomy & Scope — Channels, programs, lookbacks, deduping rules, and sourced vs. influenced logic.
- Run MTA — Start with position-based/W-shaped across first touch, lead create, opp create; monitor assist rate.
- Prove Incrementality — Always-on holdouts for paid and geo A/B for media; document lift and confidence.
- Layer MMM — Quarterly modeling to size brand/upper-funnel and cross-check MTA/experiments.
- Classify Channel Roles — Introducer, scaler, closer; set KPIs and budget caps by role, not just last-click.
- Reconcile & Decide — One executive view: pipeline, bookings, ROMI/CAC, velocity, lift. Shift budget accordingly.
Cross-Channel Views: What Each Tells You
| View | Best For | Key Metrics | Pros | Limitations | Cadence |
|---|---|---|---|---|---|
| Channel Summary | Topline health | Reach, CTR, CPA, ROAS | Fast, universal | Not tied to journey roles | Daily/Weekly |
| Path Analysis | Role discovery | Path length, time lag, assists | Exposes introducers/closers | Descriptive, not causal | Weekly |
| Position-Based MTA | Executive credit | Attributed pipeline/revenue | Balances discovery & conversion | Credit ≠ lift; cookie loss | Weekly |
| Algorithmic MTA | Dense digital journeys | Modeled contribution | Learns patterns across touches | Opaque; needs scale | Weekly |
| Experiments | Channel/program lift | Incremental pipeline/revenue | Causal; offer-level answers | Cost/time; spillover risk | Per Test |
| MMM | Upper funnel & offline | Elasticities, optimal mix | Privacy-resilient; budget tool | Coarse; slower refresh | Quarterly |
Client Snapshot: Roles Clarify Budget
A B2B firm classified paid social as an introducer, paid search as a closer, and webinars as a scaler. With W-shaped MTA, geo holdouts, and quarterly MMM, they shifted 15% of budget to high-lift combos and cut CAC by 16% while maintaining 3.2× pipeline coverage.
Tie your cross-channel analytics to RevOps and visualize the findings in an executive value dashboard so insights drive allocation.
FAQ: Multi-Channel Performance
Clear answers for complex journeys.
Make Channels Work As One
We’ll unify identity, set attribution rules, and prove lift—so you can invest confidently across the mix.
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