AI
For a global sports event organizer, we built a unified data platform that consolidates more than 15 disconnected marketing and sales sources. On top of it run marketing mix modeling, multi-touch attribution, statistical revenue forecasting and social network analysis. Quarterly forecast error fell from around 25–30% to around 1%. A revenue-per-user growth pilot delivered +60–70% in a controlled A/B test.
Key Takeaways
Revenue forecast error: from ≈ 25–30% to ≈ 1%
More than 15 disconnected platforms consolidated into one central data warehouse
Revenue per user +60–70% in a controlled A/B test (network-activated audience)
Attribution coverage of online store orders: from 0% to ≈ 50%
Estimated ROI of ≈ 3–6x in the first year of the program, within one seasonal cycle
Background
Starting point
Revenue forecast accuracy: 25–30% deviation. Manual cash flow forecasting in spreadsheets.
Marketing and sales data sources: more than 15 disconnected platforms. Paid media, ticket sales, CRM, web analytics - with no shared data warehouse.
Visibility into return on investment by channel: Rough estimates only. No marketing mix model or attribution system.
Reporting cadence: Manual, on request. Executive reports are compiled by hand every cycle.
Market size
Several million euros per year - paid media spend covered by measurement.
Scope: Across multiple brands and regions
Source: Internal media spend records from before the engagement began
Estimated budget (NDA)
EUR 200,000 – 999,999
Budget breakdown:
Data platform and data warehouse build: 35%
Marketing mix modeling and attribution: 30%
Social network analysis and revenue-per-user growth: 20%
Reporting, business intelligence and automation: 15%
Budget category based on Clutch.co project cost categorization. Exact amounts are protected by a non-disclosure agreement.
Ongoing phased engagement - phased delivery
Phase 1: Data source audit and design of a unified data warehouse
Phase 2: Production ETL platform - automated data transfer into the central warehouse
Phase 3: Marketing mix modeling and multi-touch attribution
Phase 4: Social network analysis pilot for revenue-per-user growth
Phase 5: Statistical forecasting, business intelligence rollout and daily automation
The challenge
Revenue spread across dozens of markets, tracked in spreadsheets
Highly seasonal ticket revenue, broken down by paid media platform, ticketing system and regional brand, was consolidated by hand into a single cash flow forecast, which regularly diverged from actual sales data.
Revenue forecasts were built in spreadsheets, separate from the sales data warehouse
More than 15 marketing and sales platforms with no unified data layer
No systematic visibility into which channels and campaigns actually drive incremental revenue
Audience growth depended solely on paid advertising, with no use of the organic network
Project
One data warehouse, one model, one forecast
We built a production data platform that automatically feeds every marketing and sales source into a single data warehouse, and layered statistical and machine learning models on top of it:
a marketing mix model to measure the real impact of each channel,
multi-touch attribution on a unified event stream, a statistical revenue
forecasting model validated against actual sales data, and
a social network analysis model that identifies and activates the most valuable audience segments.
Detailed breakdown
Unified data warehouse: Dozens of automated pipelines consolidate data from paid media, ticketing, CRM and web analytics into one warehouse, replacing manual reconciliation in spreadsheets.
Marketing mix modeling: A statistical, Bayesian MMM measures the true revenue impact of each channel, accounting for advertising effects that last far longer than in typical retail cycles.
Multi-touch attribution: Attribution on a unified event stream shows which digital touchpoints actually lead to a purchase, reconciled with online store order data.
Social network analysis: Graph-based valuation of the audience network identifies and activates the most valuable segments, with the impact measured against a control group.
How marketing measurement changed
Forecasting and channel measurement moved from manual spreadsheet estimates to a verifiable statistical system that runs on real data.
Revenue forecasting: Before - A manually maintained cash flow spreadsheet. After - A statistical forecast validated against actual sales data. Forecast error is measured and reduced quarter after quarter, rather than assumed.
Channel measurement: Before - Isolated metrics, with each platform reporting on itself. After - A unified MMM and MTA across all paid channels. A single model establishes each channel's true contribution.
Audience growth: Before - Paid advertising only. After - Graph-based network activation, measured through A/B testing. Organic network effects are tested and measured, not assumed.
Reporting cadence: Before - Manual, ad hoc spreadsheet presentations. After - Automated BI dashboards and daily data refreshes. Figures update automatically instead of being rebuilt every cycle.
Technologies and services
Apache Airflow
AWS Redshift
Python / Bayesian MMM (PyMC)
Statistical forecasting
Social network analysis
Holistics BI
n8n automation
Results
Investment → revenue impact (NDA)
Measurable incremental revenue from the very first pilot:
Ratio: ≈ 3–6x - first-year estimate for the program
Incremental revenue: ≈ EUR 2–5 million per year - estimated incremental revenue from the revenue-per-user growth model at full deployment
Investment: EUR 200,000 – 999,999
Payback: Within one seasonal cycle
Method: Controlled A/B test (network-activated audience vs. control group), comparing revenue per user
Confidence: Medium to high - based on a controlled pilot, with scaling assumptions for the annual projection
Figures refer only to the revenue-per-user growth program; the broader effects of improved forecast accuracy are not expressed in monetary terms here.
Results
Forecast accuracy: from roughly 30% deviation to under 1%
The unified platform now runs dozens of automated pipelines every day. The statistical forecast, validated against a full quarter of actual sales data, eliminated the deviation that the previous spreadsheet model could not, and the revenue-per-user growth model delivered a measurable revenue lift in a controlled test.
Quarterly revenue forecast error: Before ≈ 25–30%, after ≈ 1%. Deviation down by ≈ 30 pp. Validated against a full quarter of actual sales. Compared with the previous spreadsheet-based forecast.
Unified marketing and sales data sources: Before - more than 15 disconnected platforms, after - one central data warehouse. A single source of truth. Dozens of automated pipelines, running daily and hourly. A production data platform.
Revenue per user, network-activated audience: Before - control group baseline, after - +60–70%. +60–70% measured in a controlled A/B test. Pilot campaign.
Online store orders linked to a channel or touchpoint: Before 0%, after ≈ 50%. +50 pp attribution coverage on unified order data. After rollout.
We used to argue every quarter about whose forecast was right. Now we debate which channel to fund next - and the model already shows us how close we were.
- Head of Marketing Analytics, Confidential - global sports event organizer
How does social network analysis increase revenue per user?
Why do companies need a unified data warehouse for marketing analytics?
How can AI and statistical models improve revenue forecasting accuracy?
What is the difference between marketing mix modeling and multi-touch attribution?
What is marketing mix modeling, and how does it help measure marketing ROI?
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