Glossary · Marketing Engineering
Multi-Touch Attribution
also: MTA · touchpoint attribution
Definition
Multi-touch attribution assigns credit for a conversion across all marketing touchpoints in the customer journey, using rules (linear, time-decay, U-shaped) or data-driven models. Most MTA implementations confuse correlation with causation and systematically overvalue bottom-funnel channels that intercept already-decided buyers.
MTA attempts to answer 'which channels deserve credit for this conversion?' — but the question itself assumes causality that rule-based models cannot establish. Data-driven attribution (Shapley value, Markov chains) is mathematically more defensible but still builds on observational data. Proper causal attribution requires geo-lift experiments, conversion lift tests, or structural models that explicitly encode the intervention distribution.
Essays on this concept
- Marketing Engineering
Multi-Touch Attribution Is Broken — A Causal Inference Approach Using Directed Acyclic Graphs
MTA models overestimate retargeting by 340% and underestimate display by 62%. The fix isn't better heuristics — it's abandoning correlational attribution entirely in favor of causal graphs.
- Marketing Engineering
Unified Measurement Architecture: Connecting MMM, MTA, and Experimentation Into a Single Source of Truth
MMM says Facebook works. MTA says Google works. The incrementality test says neither works as well as you thought. Three measurement systems, three different answers — here's how to reconcile them into one coherent picture.
- Business Analytics
Bayesian A/B Testing in Practice: When to Stop Experiments and How to Communicate Results to Non-Technical Stakeholders
Frequentist A/B testing answers a question nobody asked: 'If the null hypothesis were true, how surprising is this data?' Bayesian testing answers the question that matters: 'Given this data, what's the probability that B is actually better?'
- Business Analytics
Causal Discovery in Business Data: Applying PC Algorithm and FCI to Find Revenue Drivers Without Experiments
Correlation tells you that feature usage and retention move together. It doesn't tell you which causes which — or whether a third factor drives both. Causal discovery algorithms can untangle this from observational data alone.
- Marketing Engineering
Causal Impact of SEO on Branded Search: A Synthetic Control Method for Organic Channel Measurement
SEO is the only major marketing channel where practitioners still argue about whether measurement is even possible. Synthetic control methods borrowed from policy economics prove it is — and the results will surprise you.
- Business Analytics
Cohort-Based Unit Economics: Why Monthly Snapshots Lie and How to Build a True P&L by Acquisition Cohort
Your company's monthly revenue is growing 20% year-over-year. Your unit economics are deteriorating. Both statements are true simultaneously — and you'll never see the second one in an aggregate P&L.
- Marketing Engineering
Creative Fatigue Detection Using Entropy Metrics: An Automated Framework for Ad Refresh Cycles
By the time your dashboard shows declining CTR, creative fatigue has already cost you weeks of wasted spend. Shannon entropy applied to engagement signals detects fatigue 11 days earlier than traditional frequency caps.
- Marketing Engineering
The Hidden Cost of Optimization: How Over-Fitted Algorithms Destroy Long-Term Brand Equity
Your bidding algorithm gets better every quarter. Your brand gets weaker every year. This is not a coincidence — it's Goodhart's Law applied to marketing, and the compounding damage is invisible until it's too late.
- Marketing Engineering
Incrementality Testing at Scale: A Geo-Lift Framework for Measuring True Campaign Impact
Half your marketing budget is wasted. The classic joke, except now we can identify which half — geo-lift experiments measure what would have happened without the campaign, not just what happened with it.
- Marketing Engineering
Marketing Mix Modeling in the Privacy-First Era: Bayesian Structural Time Series Without User-Level Data
Cookies are dying. Deterministic attribution is shrinking. The irony: the measurement approach from the 1960s — Marketing Mix Modeling — is making a comeback, now powered by Bayesian inference that would have been computationally impossible when it was first invented.
- Business Analytics
Metric Ontology Design: Building a Self-Serve Analytics Layer That Doesn't Collapse Under Ambiguity
Ask five people in your company what 'revenue' means and you'll get five different numbers. The problem isn't the data warehouse — it's that nobody agreed on the definitions before building dashboards on top of them.
- Behavioral Economics
Temporal Construal Theory Applied to Landing Pages: Abstract vs. Concrete Messaging by Funnel Stage
Your top-of-funnel landing page should sell the dream. Your bottom-of-funnel page should sell the mechanism. Construal Level Theory explains why — and the data shows a 34% conversion gap when you get this wrong.
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