Marketing leaders today face a growing challenge: deliver measurable results in a landscape that’s more complex, fragmented and regulated than ever. Budgets are under pressure, while customer journeys span more channels and more blind spots than before.
With traditional tracking becoming less reliable, it’s harder to understand what’s truly driving performance. That’s where marketing mix modelling comes in. By connecting the dots between campaigns, spend and business outcomes, it brings clarity back to decision-making and replaces assumptions with insight.
The challenge
Today’s marketing landscape is a paradox: more channels, more data, yet less visibility. Tracking customer behaviour has become harder thanks to GDPR, strict consent rules and the looming end of cookies. At the same time, marketing spend is spread across a growing mix of online and offline touchpoints, making it difficult to see what’s really working.
Traditional tracking methods fall short. ROI and performance are often judged by what’s easiest to measure — last-click metrics, web analytics or internal assumptions — rather than by a complete, objective view. The result: fragmented insight, unclear attribution, and growing pressure on marketing leaders to justify budgets without solid evidence.
The solution
LACO helps organisations cut through this complexity with an AI-powered marketing mix modelling (MMM) approach, built on a robust Microsoft Azure and Microsoft Fabric foundation. Rather than relying on user-level tracking, MMM uses advanced statistical and machine learning techniques to connect consolidated marketing inputs with business outcomes like sales, leads or conversions.
A key feature?
Scenario planning. Decision-makers can run what‑if analyses via a conversational interface that acts as an AI agent for the marketing organisation to test budget shifts, channel reallocations or campaign timing. What happens if we boost spend on social and cut TV? Launch a promo earlier? Shift regional targeting? These simulations support smarter, evidence-based decisions before money is spent.
Crucially, the solution is transparent and privacy-friendly. Instead of black-box algorithms built on personal data, LACO’s MMM approach relies on aggregated, governed inputs. Assumptions, sources and model logic are documented, building trust and ensuring compliance, even as regulations evolve.
Finally, MMM models feed into a predictive ROI engine that forecasts the impact of future marketing investments. Budget planning becomes proactive, grounded in data rather than gut feeling. Finance and marketing teams get forward-looking guidance on where to invest, at what level, and with what expected return.
The results
With AI-driven MMM on a strong Microsoft Azure and Microsoft Fabric data platform, organisations move from reactive reporting to confident, evidence-based marketing decisions. ROI attribution becomes transparent across all channels, enabling smarter budget allocation and better performance, often without increasing spend.
Forecasting improves, planning becomes more strategic, and marketing finally earns its place as a measurable growth driver. Because the entire approach is built on governed, aggregated data, organisations stay agile and compliant even as privacy rules continue to change. The end result? Clarity. Control. And a marketing function that moves to knowing.
