Multi-touch attribution assigns credit across the full sequence of touchpoints that led to a conversion. Done well, it estimates the incremental lift from each impression or interaction instead of just splitting credit by a fixed rule. That requires stitching identities across channels, capturing impression and click exposures, and controlling for baseline propensity to convert.

Key ideas to ground your understanding:

• Identity resolution is table stakes: You need a reliable way to link events to people or households across devices and channels. First‑party identifiers, clean rooms, and an identity graph help connect ad exposures, site/app behavior, and conversions without depending on third‑party cookies.
• Data quality drives model quality: Incomplete impression logs, inconsistent campaign metadata, or missing cost data will bias results. Set standards for taxonomy, time zones, and de‑duplication.
• Rules vs. incrementality: Simple models like first/last click, linear, or time decay are fast, but they do not measure true lift. Data‑driven or algorithmic models can estimate causal contribution, but they need strong identity and coverage.
• Privacy reshapes feasibility: Cookie loss and platform restrictions limit user‑level linking. Expect gaps for walled gardens and certain browsers. Use publisher APIs and clean rooms where available and accept more aggregate modeling where not.
• Good MTA is directional: Treat it as a high‑frequency signal to optimize creative, bids, and channel mix, not a single source of truth for total ROI.

Common failure modes to watch for:

• Selection bias: High‑intent users see more touchpoints and get over‑credited. Address with controls, negative samples, or uplift modeling.
• Channel silos and walled gardens: Lack of user‑level export means partial visibility. Use clean rooms or calibrate with aggregate models.
• Attribution leakage: Offline or non-addressable media lift shows up as organic or direct unless controlled for.

Bottom line: MTA is most useful for comparative optimization within addressable channels when you invest in identity, logging, and guardrails.

There is no universal best model. Pick based on data coverage, decision horizon, and risk tolerance. Here is a practical selection and implementation path:

• Linear: Equal credit to all touchpoints. Useful as a baseline when data is thin. Low variance, low insight.
• Time decay: More credit to recent touchpoints. Good when buying cycles are short and recency clearly matters.
• Position‑based (U‑shape or W‑shape): Extra credit to first and last touch (and sometimes mid‑funnel). Helpful when you need to ensure prospecting and conversion tactics both get recognized.
• Algorithmic / data‑driven: Uses statistical or ML methods (e.g., logistic regression with exposure features, Shapley values, Markov chains, or uplift models) to estimate contribution. Best when you have solid identity resolution, impression‑level data, and enough volume.

Implementation checklist:

• Define conversions and windows: Specify events, lookback windows by channel, and inclusion rules for view‑throughs vs. clicks.
• Standardize taxonomy: Consistent channel, campaign, creative, placement, and audience naming enables rollups and benchmarking.
• Normalize and join data: Unify timestamps and currencies, dedupe exposures, and link to spend. Document known blind spots.
• Control for non‑addressable drivers: Bring in seasonality, promotions, and known offline media effects to avoid over‑crediting retargeting.
• Validate: Backtest against holdouts, look for sign flips, and monitor stability when budgets shift.
• Operationalize: Publish recurring reports at tactic and creative levels, with confidence intervals and recommended actions.

Governance and maintenance:

• Quarterly recalibration: Re‑estimate coefficients as channels, tracking, or mix change.
• Change control: Treat model updates like product releases with versioning and release notes.
• Education: Train stakeholders on what the model can and cannot claim about causality.

MTA is strongest when paired with controlled tests and marketing mix modeling (MMM). Together they close measurement gaps and reduce bias.

• Experiments to validate lift: Use geo or audience‑level holdouts to measure incremental impact for key channels. Compare experimental lift to MTA‑estimated lift to detect bias and recalibrate.
• MMM for the full picture: MMM uses aggregated time‑series to quantify both addressable and non‑addressable channels and external factors. Calibrate MTA with MMM so retargeting is not over‑credited and offline media is recognized.
• Unified measurement workflow: 1) Run MMM to understand channel‑level ROI, saturation, and diminishing returns. 2) Run MTA within addressable channels for granular, day‑to‑day optimization. 3) Reconcile the two via calibration factors or constraints. 4) Refresh with new experiments when the mix shifts.
• Decision cadence: Use MMM for quarterly planning and budget allocation. Use MTA weekly to shift spend, rotate creative, and adjust frequency caps.

Outcome you should expect: clearer ROI, faster feedback loops, and decisions that are defensible to finance and compliant with privacy constraints.