Introduction
Marketing Mix Models (MMM) have long been used to identify the drivers of specific segments of a business, whether it is new customer acquisition, upsales, or cross-sales. Advanced approaches even attempt to model churn, with the goal of using insights to minimize or prevent customer loss. Yet one challenge remains stubbornly unresolved: connecting these parts into a single, holistic model.
The Fragmentation Problem
Most MMMs are built as dedicated models, each offering an isolated view of one side of the business. This creates a fundamental limitation, as marketing and media optimizations can be designed either to maximize sales or to minimize churn, but not both at once. As a result, marketing managers are left juggling multiple what-if scenarios, stitching together KPIs, and calculating trade-offs manually. The process is time-consuming, inaccurate, and ultimately incapable of reflecting the true complexity of the business.
Why Holistic Modeling Matters
Business does not happen in silos. Acquisition, churn, brand equity, and customer experience are interconnected forces that shape overall performance. Optimizing one without considering the others risks undermining long-term growth.
Holistic modeling changes the game. By connecting these dependencies into a unified framework, leaders can:
- Optimize for net positive outcomes rather than isolated KPIs
- Forecast with confidence, knowing the model reflects the full business reality
- Run meaningful scenarios that balance acquisition, retention, and brand-building simultaneously
This is not just a technical upgrade, it is a strategic evolution.
Alviss AI: Bridging the Gap
Alviss AI was built to overcome the limitations of traditional MMMs. The platform supports modeling the business as it truly operates, with all dependencies and hierarchical structures intact. The result is true holistic business optimization and forecasting.
And this is not just theory.
A Real-World Example: One Model for Net Growth
We recently implemented a holistic modeling approach for one of our insurance clients. In this case, the model did not just include acquisition and churn as KPIs, it also incorporated intermediaries such as brand equity and customer experience within the hierarchical structure.
The following graph illustrates the structure of the holistic model. Blue nodes are actually modeled while grey nodes are inputs to the models, such as media, price or other factors, for example including competitor activities.
This setup allowed the client to use each component individually, for example optimizing marketing to maximize acquisition or strengthen brand equity, while also combining them to drive net positive growth. This is also a great example how business units can use the same model for their individual purposes:
- The growth department can optimize their activities to maximise new policies,
- The churn department can use the model to derive actions to mitigate churn,
- And the brand team can quantify and act upon maximising brand awareness and consideration.
The outcome was a model that reflects the business as it truly is, interconnected, dynamic, and capable of guiding smarter decisions, across the organization.
Conclusion
The future of MMM lies in holistic modeling. Fragmented models may explain parts of the past, but only holistic models can shape the future. By embracing frameworks that integrate acquisition, churn, brand, and customer experience, businesses can move beyond reactive trade-offs and toward proactive, sustainable growth.
With Alviss AI, that future is already here. If you want to move beyond a siloed approach in your predictive business modeling, let’s get in touch!