Analyze channel contribution with attribution models

Generate multi-touch attribution analysis with model comparison

Marketing leaders defending budget allocation to executives, where multi-touch comparison reveals that last-click dramatically undercredits awareness and consideration channels.

• · Two or more attribution models to compare (first-touch, last-touch, linear, time-decay, position-based, data-driven)
• · Conversion events to attribute (purchases, signups, leads, custom events with optional revenue)
• · Time period (date range or relative period)
• · Conversion window (7/14/30/90 days lookback)
• · Channels to include (or all if unrestricted)

• · Multi-model comparison showing revenue allocated to each channel under each model
• · Attribution bias analysis (reveals positional bias of each model)
• · Conversion path identification and frequency
• · Channel contribution ranking per model (which channel 'wins' under which model)
• · Budget reallocation recommendations based on data-driven vs actual spend
• · Anomaly flagging (channels that look healthy under last-click but weak under multi-touch)

• · Google Analytics MCP (conversion paths, multi-channel funnel reports, assisted conversion data)
• · Google Ads MCP (search attribution reports, cross-network attribution)
• · Meta MCP (view-through and click-through attribution data)
• · CRM MCP (deal stage progression with marketing touchpoint timestamps)

• · Analytics tools are connected via MCPs
• · Conversion events are properly tagged in GA4 and CRM
• · Customer journeys are stitched across platforms (at least within single user ID)
• · At least 100+ conversion paths available for analysis (smaller samples are unreliable)
• · Time period has sufficient data for comparison

• · Data misses cross-platform journeys (GA4 sees web, CRM sees sales, no link between them)
• · Conversion window too short for actual sales cycle (7 days on 90-day B2B cycle)
• · Models compared without understanding which reflects actual customer behavior
• · Budget reallocation recommendations applied without validating on new spend (risk of worse performance)
• · Anomalies misinterpreted (e.g., last-touch looks weak because it captures budget burnout, not failure)

• · Seven attribution models implemented: first-touch, last-touch, linear, time-decay, position-based, data-driven, marketing mix modeling
• · Explicit counterfactual analysis in data-driven model (shows what happens if channel is removed)
• · Channel-level CAC recalculation under each model
• · Conversion window explicitly configurable to match actual sales cycle