Pages tagged "attribution"
8 pages tagged with attribution.
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- Audience Pre-Selection: Buying Pre-Filtered Surfaces Instead of Paying Algorithms to Filter The edge in a world of AI targeting: pick a surface whose nature pre-concentrates your audience (Snapchat=young, a subreddit=one niche, Strava=athletes) so a high fraction of impressions are relevant before optimization — instead of paying Advantage+/PMax to discover who converts.
- Alternative Advertising Channels — Landscape & Ranked Picks Beyond the Meta/Google Duopoly A taxonomy of credible paid ad channels beyond Meta/Google/TikTok, plus a ranked 'try first' shortlist for DTC/ecom. Published entry floors mislead in both directions: Telegram's €2M wall overstates the barrier, Reddit's $5/day floor understates it.
- Engaged-View Conversions (EVC): Why YouTube ROAS Reads Too High EVC credits a conversion when someone watches a YouTube ad without clicking, then buys within the window. It sits inside the main Conversions column — the reason YouTube reports can show more conversions than clicks and inflated ROAS.
- Marketing Analytics in 2026 — The Cookieless Stack Cookie deprecation broke last-click attribution. MMM adoption surged 212% since 2023. Data clean rooms + AI-driven attribution + incrementality testing now constitute the operating stack. The 2026 reality is dual-model: multi-touch for tactics, MMM for strategy, AI reconciles. Plus LTV/CAC cohort analysis as the capital-efficiency layer underneath.
- App Tracking Transparency (ATT): The iOS Privacy Change That Reshaped Paid Media ATT is Apple's iOS 14.5 privacy framework requiring apps to ask before tracking. Most users said no — collapsing ad signal and making creative the new lever.
- Incrementality Testing — The Causal Layer Under Marketing Attribution Incrementality testing measures the *causal* contribution of a marketing channel — what would happen if you turned the campaign off. Distinct from attribution, which is correlational. The three main test designs are geo-holdout, audience-split, and time-based. When MMM and incrementality disagree, incrementality wins. The 2026 best practice: fewer tests that materially change decisions, not more tests.
- Marketing Mix Modeling — Top-Down Statistical Attribution Marketing Mix Modeling (MMM) estimates the contribution of every marketing channel to revenue using aggregate spend and outcome data — no cookies, no pixels, no user tracking. Adoption surged 212% since 2023 because cookie deprecation broke last-click; MMM doesn't need tracking. Google's Meridian (2024) and Meta's Robyn democratized what was a six-figure consulting engagement.
- Multi-Touch Attribution (MTA): What It Is and Why It's Now a Tactical Tool, Not Ground Truth MTA splits conversion credit across the touchpoints in a customer journey. ATT and cookie loss degraded it — so in 2026 it's a tactical signal, triangulated with MMM and incrementality.