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The High Cost Of DIY: Why Building A Daily Cohorted MMM/Experimentation Platform Is A Risky Investment

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Let’s be honest, DIY projects can be fun. Building your own bookshelf? Sure. Crafting a custom marketing mix modeling and experimentation platform from scratch? Not so much. The idea of having a custom, in-house platform sounds appealing, however, the reality is that it can also be an expensive, time-consuming, and risky endeavor. Here’s why trying to build a daily cohorted MMM platform yourself might be one of the worst business investments you can make.

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1. Excessive Resource Drain

Creating a daily cohorted MMM platform isn’t a one-person job. You need a dream team of data scientists, engineers, software developers, and statisticians. Meaning that you will be responsible for paying each of these individuals their hefty salaries. Oh, and don’t forget the infrastructure costs. Unlike weekly or monthly cohorted MMM models, daily cohorted MMM exponentially increases the data volume and complexity. More data means more storage, more processing power, and ultimately, more money. Unless you have an unlimited budget at your disposal, this can be a serious financial strain.

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2. Prolonged Time-To-Value

Building a custom MMM platform can take years. Yes, you read that right. YEARS. That’s years of your competitors optimizing their marketing strategies and increasing revenue, while you’re still stuck in development. Marketing moves fast, and by the time your platform is ready it will likely already be outdated. The lost opportunity cost alone makes DIY a risky choice.

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3. Daily Cohorted Data Is A Beast

In theory, daily cohorted data sounds great. More insights should mean more accuracy, right? Not exactly. The reality is that daily data is noisy, volatile, and often difficult to model correctly. Unexplained variance can make your insights unreliable. Plus, seamlessly integrating multiple daily data sources like social media, web traffic, sales, and ad spend is incredibly complex. If your models aren’t precise, you could be making decisions based on flawed data.

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4. Ongoing Maintenance And Constant Updates

MMM isn’t a “set it and forget it” type of deal. Once it’s built, your platform will require ongoing maintenance, updates, and refinements to stay relevant. Advertising platforms frequently update their APIs, and compliance regulations shift constantly. Keeping up with these changes in-house means hiring even more specialists and investing even more resources creating a never-ending cycle.

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5. The In-House Expertise Gap

Even if you manage to build a functioning daily cohorted MMM platform, maintaining it at a high level requires expertise in advanced statistical techniques, machine learning, and data science. The challenge isn’t just hiring top-tier talent, it also means that you have to retain them. Turnover in the tech industry is high, and losing key team members could set you back months or even years.

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6. Security And Compliance Risks

Handling sensitive daily data isn’t just about processing numbers, it’s about security. Keeping up with evolving privacy laws is a full-time job. If you build in-house, your company is solely responsible for updates related to data security, and any compliance slip-ups could lead to hefty fines or reputational damage.

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Why Licensing Is The Smarter Choice

Instead of sinking millions into an in-house platform, consider licensing from a vendor that specializes in MMM and experimentation. Here’s why it’s the better move:

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1. Cost-Effectiveness

Licensing gives you a predictable, subscription-based cost model instead of a massive upfront investment. No need to worry about hiring an entire data science team or maintaining expensive infrastructure.

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2. Rapid Deployment

With a licensed platform, you can start using daily cohorted MMM insights almost immediately. Vendors provide ongoing support, updates, and maintenance so you can focus on optimizing your marketing strategies instead of troubleshooting software.

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3. Access To Industry Expertise

Vendors live and breathe MMM. They stay ahead of API changes, regulation updates, and emerging best practices so you don’t have to. You get access to proven methodologies without having to reinvent the wheel.

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4. Scalability And Security

Licensed platforms are built to scale with your business. They come with built-in compliance measures, security protocols, and the ability to handle massive data volumes without breaking a sweat.

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Final Thoughts

Building a daily cohorted MMM/experimentation platform in-house might seem like a great idea until you realize the financial, operational, and strategic burdens that come with it. The costs, delays, and risks often far outweigh the benefits. Instead, licensing from a trusted vendor allows you to leverage industry expertise, get up and running quickly, and focus on what actually matters: making smarter marketing decisions and driving revenue. At the end of the day, some things are worth DIY-ing. Your MMM platform should NOT be one of them.

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Ready to optimize smarter and scale faster?

+ To learn more about our incrementality measurement platform, visit our Product Page.

+ Read Success Stories from mobile leaders that we have worked with!

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Are Open-Source MMMs for You?

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Marketing Mix Modeling (MMM) has become an indispensable tool for marketers looking to measure the impact of their various advertising channels. As privacy regulations shift and third-party cookies disappear, more businesses are turning to MMM as a privacy-friendly way to analyze marketing performance.

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One option that’s gained popularity is open-source MMM models. These free to use models can seem like a great solution, especially for companies with tight budgets. However, like anything in business, they come with both benefits and challenges. If you're in mobile marketing or a related industry, it’s important to weigh the pros and cons before deciding if an open-source MMM model is the right choice for you. Here is what we’ve come to expect from open-source models:

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Pros of Open-Source MMM Models

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Free to Download: Open-source MMM models don’t require a hefty upfront investment, making them attractive for startups and smaller businesses that may not have the budget for proprietary solutions.

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Transparency and Customization: Since the code is publicly available, businesses can modify and fine-tune the model to fit their unique marketing needs. This flexibility allows for greater control over how data is analyzed and interpreted.

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Privacy Compliance: Unlike methods that rely on tracking individuals, MMM uses aggregated data, making it a privacy-safe solution that aligns with evolving data protection laws.

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Cons of Open-Source MMM Models

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Hidden Costs: While open-source MMM models may not require an upfront payment, they aren’t truly “free.” They require skilled data scientists to set up and maintain, computing power to process large datasets, and ongoing upkeep to ensure accuracy. Without the right expertise, companies may also end up making misinformed decisions, leading to costly mistakes.

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High Technical Barrier: MMM relies on complex statistical models that require a deep understanding of data science. If you don’t have a technical background (or someone on your team who does), implementing and maintaining an open-source MMM model can be overwhelming.

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Time-Consuming Implementation: Setting up an MMM model isn’t quick. Data needs to be collected, cleaned, structured, and tested before any insights can be generated. This process can take months. Time that many marketing teams simply don’t have.

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Limited Granularity and Real-Time Insights: Open-source MMM models rely on historical data, meaning they can’t provide real-time insights. This lag makes it difficult to adjust campaigns quickly, which is especially problematic in fast-moving industries like mobile apps.

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No Built-in Incrementality Measurement: MMM helps measure the effectiveness of marketing channels, but it doesn’t automatically tell you which tactics are truly driving incremental performance. Without proper incrementality measurement, businesses risk misallocating their budgets.

Open-source MMM models can be useful for businesses with in-house data science expertise, but they come with steep learning curves, hidden costs, and limited real-time capabilities. For marketers who want accurate, actionable insights (without the headache of managing an open-source MMM model) MetricWorks offers a next-generation solution that eliminates the complexities while delivering superior results. Here is what we offer:

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Seamless Integration and Experienced Guidance: Our solution is designed for seamless integration with your current MMP and compiles your data into one unified hub, and can be set up within 24 hours. No migrations or SDKs needed. You also get expert guidance from our data scientists who will work with you directly to customize the model for what you need, and serve as an extension of your team.

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Real-Time, Granular Insights: Unlike traditional MMM models that cohort data on a monthly or quarterly basis, MetricWorks delivers daily-cohorted metrics, allowing you to optimize campaigns in real-time instead of waiting for historical reports.

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Built-In Incrementality Measurement: Our platform directly measures incrementality, ensuring that you understand the true impact of each marketing channel rather than just seeing correlations.

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Proven Accuracy: By combining the best elements of MMM with incrementality testing, MetricWorks delivers precise, privacy-safe measurement, giving you confidence in your marketing decisions.

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Want to see how MetricWorks can transform your marketing strategy?

+ To learn more about our incrementality measurement platform, visit our Product Page.

+ Read Success Stories from mobile leaders that we have worked with!

+ Book a meeting with one of our incrementality experts and Get Demo.

Measurement explained by Mike Thomas through a Golden State Warriors basketball analogy

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TL;DR: Last touch attribution ignores direct causation of factors that contribute to successful conversion. MMM offers a more complete understanding of all contributing touchpoints including ones not attributed by last touch. One easy way to describe this is through an analogy of the Golden State Warriors where we compare basketball to marketing. Effective measurement requires the unification of MMM, experiments and last touch to derive the purest source of truth within your marketing campaigns.

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Many performance marketers gravitate towards last touch attribution as a reliable method of measurement. Increased confidence in last touch stems from the security in its output. Last touch provides a definite numeric value that gives marketers a sense of security or comfort in properly allocating budget and ad spend. However, the numeric value of last touch has been proven to be incorrect. Psychologically speaking, one can see why some choose last touch as their measurement method because our human nature seeks reliability in a world where we use constant judgment biases to make key decisions. However, marketers need to realize that last touch is flawed. The output generated by last touch is an assumption that is NOT based on mathematical or scientific reasoning. Last touch fails to correctly credit all channels that led to an effective marketing campaign. It ignores more obscure secondary channels that contribute to successful conversions. It also ignores that a channel can have effects on organics.

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Let’s consider a basketball analogy to illustrate the fallacies of last touch. Imagine you are the manager of the Golden State Warriors. There is an upcoming game versus the Sacramento Kings. The starting lineup consists of Steph Curry, Klay Thompson, Andrew Wiggins, Draymond Green and Kevon Looney with a reserve of seven additional bench players.

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After an intense game, the Warriors end up beating the Sacramento Kings, 110-107 in a close head-to-head game. Stephen Curry hits the game-winning 3-pointer that seals a solid victory for GSW. After the completion of the game, the stat line for the Warriors starting five appears as follows:

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• Steph Curry: 45 points, 4 assists, 4 rebounds, 2 steals, 1 block
• Klay Thompson: 22 points, 4 assists, 11 rebounds, 0 steals, 2 blocks
• Andrew Wiggins: 25 points, 7 assist, 8 rebounds, 3 steals, 0 blocks
• Draymond Green: 5 points, 10 assists, 10 rebounds, 1 steal, 1 block
• Jonathan Kuminga: 0 points, 7 assists, 10 rebounds, 4 steals, 4 blocks

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Only accounting for baskets is an impractical and flawed way of keeping track of performance in basketball. Last touch would only credit the player who scores a basket, ignoring other crucial actions by other players such as assists, rebounds, steals, and blocks. Last touch measurement would only credit Steph Curry (gold trophy with star) for the made basket in the diagram above. Players like Klay Thompson, Andrew Wiggins and Johnathan Kuminga would not receive any credit despite them making valuable contributions that led to the basket. The last touch approach fails to capture all touchpoints (the steal, then the pass, then the drive, then another pass) that led up to Curry shooting the scored basket.  The overall impact of each player on the game is simply ignored to arrive at a convenient but over-simplified and flawed conclusion.

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In contrast to last touch, MMM (Media Mixed Modeling) would credit the entire team for not only the made basket by Steph Curry but the win over Sacramento. Players like Jonathan Kuminga who didn’t score any baskets but contributed significantly with 7 assists, 10 rebounds, 4 steals, and 4 blocks would receive appropriate credit. MMM is comprehensive in accurately factoring the true contributions and performance of each player, even those who didn’t score points.

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Just like basketball, marketing success relies on collective contributions. Marketing effectiveness requires a comprehensive measurement approach that delivers incrementality through a causal learning agenda. This is exactly what MMM is designed to do. MetricWorks has a far superior proprietary version of MMM that is also the only daily cohorted MMM on the market. This means that you can make decisions on a daily basis rather than having to wait for another month or quarter to make a decision. MetricWorks also offers higher accuracy via triangulation that combines the strengths of MMM, experiments and last touch to deliver one trusted source of truth. Change the trajectory of your marketing campaigns today and leave the competition in the dust! It all starts with you scheduling a demo with MetricWorks: https://www.metric.works/demo/