Turning a dozen disconnected data sources into one forecasting model
A unified data platform, MMM/MTA stack and audience-uplift engine for a global endurance-race operator
2026
Multi-phase, ongoing
Sports / Marketing Analytics
Starting position
Starting position
- ≈ -25 to -30% vs. actuals
Revenue forecast accuracy
Manual, spreadsheet-based cash forecast
- 15+ disconnected platforms
Marketing & commerce data sources
Paid media, ticketing, CRM, web analytics - no shared warehouse
- Directional only
Channel-level ROI visibility
No marketing mix model or attribution layer in place
- Manual, ad-hoc
Reporting cadence
Management reports rebuilt by hand each cycle
Market size
Multi-million € annually
Paid-media spend under measurement
Approx. budget
200,000 – 999,999 €
Budget breakdown
Budget bucket per Clutch.co project cost category. Exact figures under NDA.
Phased, ongoing engagement
Multi-phase
- 1
Phase 1
Data source audit + unified warehouse design
- 2
Phase 2
Production ETL platform - scheduled pipelines into a central warehouse
- 3
Phase 3
Marketing mix modeling + multi-touch attribution
- 4
Phase 4
Social network analysis pilot for audience uplift
- 5
Phase 5
Statistical forecasting + BI rollout + daily automation
Revenue spread across dozens of markets, tracked in spreadsheets
Highly seasonal ticket revenue, fragmented across paid-media platforms, ticketing systems and regional brands, was reconciled by hand into a single cash-flow forecast - one that consistently disagreed with what the sales data actually showed.
Revenue forecasts built in spreadsheets, disconnected from the sales data warehouse
15+ marketing and commerce platforms with no unified data layer
No systematic view of which channels or campaigns actually drove incremental revenue
Audience growth relying on paid acquisition alone, with no organic-network lever
One warehouse, one model, one forecast
We built a production data platform that pulls every marketing, ticketing and commerce source into a single warehouse on a scheduled cadence, then layered statistical and machine-learning models on top: a marketing mix model to size true channel impact, multi-touch attribution over the unified event stream, a statistical revenue forecast validated against live sales data, and a social-network model that scores and activates high-value audience segments.
Unified data warehouse
Dozens of scheduled pipelines land paid-media, ticketing, CRM and web-analytics data in one warehouse, replacing spreadsheet reconciliation.
Marketing mix modeling
Statistical and Bayesian MMM sizes the real revenue impact of each channel, accounting for adstock windows far longer than typical retail cycles.
Multi-touch attribution
Attribution over the unified event stream shows which digital touchpoints actually convert, reconciled against e-commerce order data.
Social network analysis
Graph-based scoring of the audience network identifies and activates high-value segments, measured against a held-out control group.
How marketing measurement changed
Forecasting and channel measurement moved from manual, spreadsheet-based estimation to an auditable statistical layer running on live data.
Revenue forecasting
Before
Manual cash-budget spreadsheet
After
Statistical forecast validated against live sales data
Forecast error measured and closed quarter over quarter instead of assumed.
Channel measurement
Before
Platform-reported, siloed metrics
After
Unified MMM + MTA across all paid channels
One model reconciles what each channel actually contributed.
Audience growth
Before
Paid acquisition only
After
Graph-scored network activation, A/B measured
Organic network effects tested and quantified, not assumed.
Reporting cadence
Before
Manual, ad-hoc spreadsheet decks
After
Automated BI dashboards + daily data refresh
Numbers refresh themselves instead of being rebuilt each cycle.
Measurable incremental revenue from the first uplift pilot
≈ 3-6x
modeled, first year of the audience-uplift program
≈ 2-5M € / year
projected incremental revenue from the audience-uplift model at scale
200,000 – 999,999 €
Payback period
Within one seasonal cycle
Method
Controlled A/B test (network-activated vs. control audience), revenue-per-user comparison
Confidence
Medium-High - based on a controlled pilot, scaling assumptions applied for the full-year projection
Figures reflect the audience-uplift program only; broader forecast-accuracy gains are not monetized here.
From a forecast that missed by ~30% to one accurate within 1%
The unified platform now runs dozens of scheduled pipelines a day. The statistical forecast, validated against a full quarter of live sales data, closed a forecast gap the previous spreadsheet-based model could not, and the audience-uplift model demonstrated a measurable revenue lift in a controlled test.
Quarterly revenue forecast error
Before
≈ -25 to -30%
After
≈ 1%
vs. previous spreadsheet-based cash forecast
Marketing & commerce data sources unified
Before
15+ disconnected platforms
After
One central warehouse
Production data platform
Revenue per user, network-activated audience
Before
Control group baseline
After
+60-70%
Pilot campaign window
E-commerce orders reconciled to a channel/touchpoint
Before
0%
After
≈ 50%
Post-launch
“We used to close every quarter arguing about whose forecast was right. Now we argue about which channel to fund next - the model already tells us how close we were.”
Head of Marketing Analytics
Confidential - Global Endurance-Race Operator
