Marketing Mix Modeling (MMM) has long been the go-to method for quantifying the effectiveness of marketing efforts. Many businesses are leveraging MMM to drive better decision-making. But what if you don’t have complete historical datasets? Or what if your data is patchy and incomplete? The good news is that it’s still possible to build an effective MMM with limited data. All you need is pragmatism, a flexible platform, and the right support.
The Challenge of Data for MMM
There’s a common belief that MMM requires years of clean, granular data. While more data does improve predictive power, valuable models can still be built even when historical data is sparse, inconsistent, or incomplete.
Why More Data is Better
MMM methodology thrives on data volume and quality. More data means:
- Greater channel diversity: Insights improve when analyzing 10 channels versus only five.
- Higher granularity: Daily data enables more precise attribution than monthly data.
- Better flight overlap analysis: MMM considers overlapping campaign dates for accurate results.
Unlike last-click attribution models, good MMM models provide a holistic view by treating all channels equally and incorporating variables such as media spend, historical trends, and macroeconomic factors. For example, a QSR selling ice cream could enhance MMM by integrating temperature trends into the model to refine their media strategy and improve targeting.
Arima’s MMM can take sales & marketing data, Non-Media Factors and competitor data into account when creating your models.
How Much Data Do You Really Need?
Ideally, MMM performs best with two or more years of clean data, broken down by weeks and channels. However, many businesses lack this volume. If you have limited data, solutions include:
- Starting with six months of data across key channels and iterating your model over time.
- Handling patchy data by cleaning, adjusting, or aggregating it for insights.
- Using MMM with limited data to still generate directional results for budget allocation and performance optimization.
Strategies for Building MMM with Limited Data
Use Proxy Data
When first-party data is sparse, proxy data can fill gaps, such as:
- Industry benchmarks or brand surveys that provide external reference points.
- Competitor data from sources like SEMrush, Nielsen, Kantar, YouGov and Ubersuggest.
While not perfect, proxy data serves as a surrogate placeholder while internal datasets are built.
2. Aggregate Data Strategically
If detailed data is unavailable, work with broader trends:
- Build multiple smaller models across products or regions.
- Generalize insights from different segments to identify overarching trends.
- Break data into dimensions (e.g., Facebook spend split by placements like feed, stories, and reels).
3. Experiment and Simulate
When real-world data is limited, simulations can help:
- “What-if” scenarios test budget allocation impacts.
- Machine-learning simulations estimate outcomes when complete data is unavailable.
- Tools like Arima can handle both simple and complex modeling.
4. Iterate and Validate
With limited data, MMM should be a living model, continuously refined as more data becomes available:
- Back-testing validates accuracy by comparing predictions to actual results.
- Ongoing validation ensures the model improves over time.
- Stakeholder buy-in increases as early insights demonstrate value.
The Benefits of Starting Early
Starting with limited data has distinct advantages:
- Gain early insights that guide further data collection.
- Encourage buy-in by proving MMM’s value.
- Leverage existing capabilities to showcase what’s possible.
Why It’s Worth It
Traditional MMM required extensive data, but modern platforms have made MMM more flexible, agile and easy to use. Even with limited data, an MMM can:
- Optimize budgets by identifying high-performing channels.
- Provide actionable insights to stakeholders.
- Serve as a foundation for deeper analytics as data grows.
Final Thoughts
Building MMM with limited data requires creativity, focus, and iteration. By using proxy data, prioritizing key variables, and leveraging simulations, businesses can still achieve meaningful results.
With the right mindset and tools, limited data doesn’t have to limit your marketing analytical prowess.
Want to learn more about MMM? Talk to one of our experts.
