TL;DR Summary
Establishing trust in marketing mix modeling (MMM) solutions is critical for marketers making data-driven decisions, especially when encountering the methodology for the first time. AIM (Always-On Incremental Measurement) builds trust by providing short- to medium-term forecasts clients can validate against their raw geo-level data, typically achieving 96% two-week forecast accuracy. The platform uses an ensemble model approach that constructs thousands of individual models for each KPI throughout the day, evaluates each model’s predictions against actual results, and selects only the best-performing models. This continuous real-time learning process ensures that the system doesn’t just look backward at historical data but understands the present and forecasts the future with exceptional precision.
An exploration of how AIM verifies its precision and efficacy
In today’s dynamic marketing landscape, making data-driven decisions is more crucial than ever. However, the introduction of new data modeling techniques can often be met with skepticism, especially when a client hasn’t used them before. One such technique that’s gathering a lot of traction is marketing mix modeling (MMM). How can marketers and key stakeholders gain confidence that MMM can accurately predict future outcomes and guide strategic marketing investments? Let’s explore.
The Challenge of MMM Trust
One of the primary challenges our team encounters when introducing the MMM methodology to a client who’s unfamiliar with it is overcoming trust barriers. The client needs to become confident in the methodology’s accuracy. It’s completely understandable; businesses are often wary of new technologies, especially when significant marketing investments and sales outcomes hinge on their results. Data-driven decisions are only good if the data behind them is accurate. Trust is not given; it’s earned.
Proving the Accuracy of AIM, a Next-Generation MMM Tool
At AIM, we prioritize proving the model’s efficacy from the get-go. Our introductory onboarding step involves presenting clients with short- to medium-term forecasts derived from the model. These forecasts serve as a quantifiable proof of concept. By allowing clients to validate the accuracy of these forecasts against their raw data, we enable them to see the model’s precision firsthand. Once the client has had time to see if the forecasts proved true, we can move forward with the next steps.
To illustrate, the AIM system is designed to provide a forecast of the client’s total key performance indicators (KPIs). Total KPIs include paid media and organic earned KPIs for a specific geo-location over a two-week period. KPIs might include metrics such as the number of installs, registrations, and first-time purchases in the UK. Doing this at a geo-level serves as the source of truth, as the numbers are not manipulated by any other measurement system. The client can verify the accuracy of the forecast by comparing it to their raw geo-level data.
How AIM Achieves High Accuracy: The Ensemble Model Approach
Our modeling approach is not just about building a single model; it’s about creating an ecosystem of models that work in tandem. For each of the client’s KPIs, or marketing goals, we construct individual models. These models, when combined, form what we term an “ensemble model.” This ensemble ensures that the different models inform each other, creating a holistic view of the market forces and marketing activities.
Let’s take an app-based client as an example. For such a client, the KPIs might include app installations, registrations, first-time purchases, and the number of purchase events. We would design a model for each of these KPIs. These individual models then update daily and collectively contribute to the ensemble model.
Ensuring Realistic Outcomes
One of the strengths of our ensemble approach is its built-in checks and balances. This continuous refinement happens in the background, ensuring the final output is both robust and precise.
Here’s How It Works
1. Continuous Learning in Real-Time
Unlike traditional models that rely heavily on historical data, our system is attuned to the present. It learns in real-time from marketing activities as they unfold. This means that while we start by building a robust model based on past data, the real magic happens when our system begins its continuous learning journey from activities happening right now.
2. The Power of Ensemble in Real-Time
The AIM system doesn’t rest on its laurels. Throughout the day, it constructs thousands of models for each KPI. These models make predictions based on data from the past two weeks, but here’s the catch: they make these predictions without being exposed to the actual results from these two weeks. In essence, they’re “blind” to the real outcomes. This approach ensures that each model’s forecast is unbiased and purely based on its understanding of the data.
3. Self-Evaluation and Iteration
After making its predictions, each model is then exposed to the real results from the past two weeks. This allows the model to give itself an error rating based on how accurate its predictions were. This self-evaluation is crucial, as it sets the stage for iterative improvement. The system continuously builds and evaluates model after model, learning and refining with each iteration.
4. Selection of the Best
Out of the thousands of models built throughout the day, only the best make the cut. The system back-checks each model against the actual results, marking its error rate. The model with the least error—the one that’s most attuned to the actual outcomes—gets selected. It is then refreshed in the system, ready for clients to view and use.
The Result: A High Degree of Accuracy
The proof lies in the numbers. Regularly, our ensemble model approach showcases its prowess by achieving an impressive two-week forecast accuracy of 96%. This high degree of accuracy is not just a one-off occurrence but is consistent, reinforcing the reliability of our system. Such precision allows businesses to strategize with confidence, knowing that the data they’re basing their decisions on is both robust and trustworthy.
Conclusion
Our real-time MMM system is a testament to the power of continuous learning and iterative improvement. By consistently building, evaluating, and refining models in real-time, we ensure that our clients always have access to the most accurate and up-to-date marketing insights. It’s not just about looking back; it’s about understanding the present and forecasting the future with unparalleled precision.
At AIM, our commitment to transparency, combined with our ensemble model approach, ensures that clients can have confidence in the marketing mix model’s recommendations before they choose to invest. As we continue to refine and improve our methodologies, we remain focused on providing our clients with accurate, actionable insights to drive their strategic marketing decisions.
Looking for an MMM solution or struggling to trust your current solution? Book a meeting with our experts.
How does AIM prove the accuracy of its marketing mix modeling to build trust?
AIM proves accuracy by providing clients with short- to medium-term forecasts during the introductory onboarding process. These forecasts enable clients to validate the model’s precision against their raw geo-level data, which serves as the source of truth because it’s not manipulated by other measurement systems. Once clients have time to verify that forecasts proved true, they can move forward with confidence in the system’s recommendations.
What is the ensemble model approach and how does it improve accuracy?
The ensemble model approach involves constructing individual models for each of the client’s KPIs (such as app installations, registrations, first-time purchases, and purchase events) that work together and inform each other, creating a holistic view of market forces and marketing activities. These individual models update daily and collectively contribute to the ensemble model, with built-in checks and balances that ensure realistic outcomes. This approach provides more robust and precise final outputs than single-model systems.
How does AIM’s real-time learning process work to ensure accuracy?
AIM’s system constructs thousands of models for each KPI throughout the day, making predictions based on the past two weeks of data without being exposed to actual results—keeping predictions unbiased. After making predictions, each model is exposed to real results, giving itself an error rating based on accuracy. The system continuously builds and evaluates models, learning and refining with each iteration, then selects only the model with the least error to refresh in the system for client use.
What level of forecast accuracy does AIM typically achieve?
AIM regularly achieves an impressive two-week forecast accuracy of 96%. This high degree of accuracy is consistent rather than a one-off occurrence, reinforcing the reliability of the system. Such precision allows businesses to strategize with confidence, knowing that the data they’re basing their decisions on is both robust and trustworthy.
Why is trust a primary challenge when introducing MMM to new clients?
Businesses are often wary of new technologies, especially when significant marketing investments and sales outcomes depend on their results. Clients need to become confident in MMM’s accuracy because data-driven decisions are valuable only if the underlying data is accurate. Trust is not given but earned through proven results, which is why AIM prioritizes demonstrating model efficacy through verifiable forecasts from the outset.
