Top 5 Challenges in Marketing Mix Modeling and How In-Housing Can Solve Them

Introduction

Marketing Mix Modeling (MMM) has been a cornerstone of marketing strategy for decades, guiding brands on where to put their ad dollars for maximum impact. But for many teams, working with MMM has also been a bit of a headache. Between long turnaround times, limited flexibility, and high costs, traditional MMM models often feel more like a burden than a tool. So why do brands keep coming back to it? Because, at its core, MMM has value. It’s just that until recently, brands haven’t had a practical way to harness that value on their own terms.

Here are the top five challenges brands face with traditional MMM and how in-housing their MMM with a tool like Alviss AI can solve these issues.

Time-Intensive Processes

The traditional process of setting up, running, and updating an MMM model can be painfully slow. Many outsourced models are built on fixed cycles, often quarterly or annually, which means brands have to wait weeks or even months to see results. And that’s in a best-case scenario. If there’s a data error or a change in campaign strategy, that timeline can stretch even longer.

In-house MMM changes this dynamic. With a platform like Alviss AI, brands don’t have to wait on third-party schedules or deal with long data processing cycles. They can pull in their own data, run the models, and see results in real-time. This immediate feedback allows marketing teams to adjust campaigns on the fly, capitalizing on trends as they happen rather than analyzing them months down the line.

Lack of Transparency

Most traditional MMM models are built and operated as black boxes. Brands receive reports and metrics, but they rarely see the inner workings of the model. This makes it hard to understand which factors are really driving results and leaves brands at the mercy of their agency’s priorities or methodologies.

When brands bring MMM in-house, they get full transparency. They have access to every part of the model—data, algorithms, and calculations. They’re able to see exactly how their investments are being measured and can dig into the details if something seems off. Alviss AI, for example, offers a customizable interface where teams can adjust parameters and see how changes affect results. Instead of getting pre-packaged answers, brands get insights they can tailor and trust.

High Costs of Outsourced Solutions

Marketing Mix Modeling can be expensive. When brands outsource MMM, they’re often paying not just for the model but for all the overhead that comes with an agency—management fees, consulting hours, and complex software licenses. For many brands, this cost is a barrier to using MMM consistently.

In-housing MMM significantly reduces these costs. By moving MMM to an internal team and using a dedicated platform like Alviss AI, brands can cut out the middleman. They pay only for the tool itself and don’t have to worry about surprise fees or expensive consulting hours. And once they’re up and running, they have the flexibility to run the model as often as they want without incurring additional charges.

Lack of Customization

Every brand is different, but outsourced MMM solutions are often one-size-fits-all. Agencies tend to standardize their models because it’s more efficient, but this approach leaves brands with models that may not fully reflect their unique markets or marketing mixes. Brands with complex data needs, seasonality, or a specific target audience can find that a generic model doesn’t deliver the insights they need.

With in-house MMM, brands have the freedom to customize. They can adjust their model to reflect exactly what matters to them, whether it’s specific marketing channels, regional differences, or seasonal spikes in demand. Alviss AI makes it easy for brands to tailor models to their own strategies, allowing them to adjust the weight of different media types, add unique datasets, or create specialized models for different regions. This customization gives brands a level of insight that no off-the-shelf model can provide.

Challenges with Data Privacy and Security

When brands outsource MMM, they’re also sharing sensitive information about their campaigns, budgets, and customer behavior with a third party. This can raise privacy concerns, especially as data regulations become more stringent. Handing over this data to an external team isn’t always the most secure option, and it can limit how brands use and store their data.

In-house MMM addresses this issue by keeping all data in the brand’s control. Instead of sending data outside the organization, brands can store and process it internally, reducing the risk of data exposure. With Alviss AI, brands retain ownership of their data and can decide exactly how it’s used and shared. This added control makes it easier to comply with regulations and gives brands peace of mind about their data security.

Wrapping up

By bringing MMM in-house, brands are solving old problems with a new approach. They’re no longer waiting on long cycles or accepting a model they can’t see or customize. Instead, they get a tool that’s fast, transparent, cost-effective, and customizable. And with platforms like Alviss AI making in-house MMM easier than ever, brands have the chance to take full control over their marketing insights.

In-house MMM isn’t just about saving money or keeping data secure. It’s about having the power to react, adjust, and experiment at a pace that matches modern marketing. For brands that want to stay competitive, that’s the kind of control that makes all the difference.

This post is part of a 6 part series called “Mastering Marketing Effectiveness with In-Housed MMM”. The posts are outlined below.