Introduction
One of the strangest things about Marketing Mix Modeling is that most companies know they need it but struggle to make it work. The theory makes sense: analyze past performance, measure the impact of different channels, adjust the marketing budget accordingly. But most MMM solutions have a kind of secret flaw. They work, but only if you’ve got endless time and a high tolerance for outsourced complexity.
Alviss AI was built on a different idea: that companies could actually do this themselves. That they wouldn’t just outsource MMM as an obligatory checkbox, but actually in-house it as a practical tool for driving their business. What’s surprising is how fast this approach pays off when companies make the switch. Here are a few stories of brands and agencies who in-housed MMM with Alviss AI and saw real results—not months down the line, but practically out of the gate.
The Retailer Who Cut Waste and Improved ROI
This first example is a retail company with a lot of moving parts—regional campaigns, seasonal variations, and a wide array of product lines. They had always outsourced MMM, but it was a slow and unwieldy process. By the time they’d get results from their vendor, the market had often changed, or new campaigns were already running. So, they’d constantly feel like they were reacting rather than adapting.
When they switched to Alviss AI, things immediately changed. Suddenly, they had real-time insights into their marketing effectiveness. They could run simulations to see what would happen if they shifted budget from one channel to another. And, maybe most importantly, they had the data themselves. They weren’t dependent on waiting for reports from someone else, and they didn’t have to deal with complex data pipelines.
The impact? They cut wasted spend by nearly 15%, simply by reducing investments in lower-impact channels. And because they were running campaigns in real time, they also caught seasonal changes that would’ve otherwise been lost in an annual report.
The Agency That Replaced Guesswork with Precision
Then there was an agency that had always struggled with MMM. Their clients loved the idea of optimization but were frustrated by the practical limitations. Most agencies don’t have the in-house data science resources to set up custom MMM for each client. For this agency, each MMM project took weeks and often relied on dated models and approximations, which meant a lot of guesswork.
Alviss AI solved that problem by giving them a platform they could use with little setup or customization. They plugged in their clients’ data and ran highly targeted models that could be updated at will. Instead of re-running an entire analysis every time a client wanted a change, they could simulate new budget scenarios or see what would happen if they adjusted certain campaign factors.
Now, their clients were no longer making decisions based on guesswork but on clear, concrete data. They saw a measurable increase in customer acquisition rates across nearly all of their accounts. Even more importantly, the agency noticed something surprising: their clients actually trusted them more. Clients could see what was happening in real time and could adjust budgets with far more precision. This allowed the agency to move focus from selling hours to providing value, all because they’d found a way to deliver more than just “ideas.”
The Consumer Brand That Finally Got Transparency
Finally, there’s the consumer brand that had long been at the mercy of opaque marketing reports. They used a traditional MMM provider, and while the results were often helpful, the process was always a bit mysterious. They’d hand over data, wait for weeks, and get back a black-box report with suggestions but no real visibility into how the results were calculated.
Alviss AI didn’t just give them data. It gave them transparency. Now they could see how every dollar in their budget was performing, and they could break down results in ways they never could before. Not only did they gain insight into high-performing channels, but they could also understand why those channels were performing better.
With Alviss AI’s Bayesian modeling, they saw more than just numbers; they saw probability distributions and confidence intervals. They could make decisions with a level of precision they hadn’t had before. Over two quarters, they doubled the accuracy of their predictions, which meant they could push more budget into higher-performing areas without increasing their overall spend.