7 minutes read
While many marketers understand the importance of a diverse marketing mix, many overlook the importance of accurate insight into the efficacy of their marketing channels as a means to fuel business growth.
Marketing Mix Modelling, or MMM, is a statistical analysis technique that enables businesses to make informed decisions about their marketing strategy, budgets, resources, and more by quantifying the impact of different marketing inputs – such as advertising, promotions, pricing, and distribution channels – on their overall sales or KPI.
You can find our comprehensive guide to MMM here. Because MMM uses less granular data sets, and uses statistical modeling to find correlation, it isn’t reliant on cookie-based attribution models.
Who will be using the software?
First evaluate who within your team will be modeling media using MMM and what they’ll be using it for.
If you’re an ad agency with clients interested in creating Media Mix Models to inform budget decisions, you may want to invest in a software that’s more customizable and client-friendly.
If you’re a brand with an internal measurement team who’s well-versed in the world of MMM, you probably don’t need to pay for any extra frills.
Data collection & Analysis
You’ll also need to consider the data collection process and analysis needs, as these are the backbone of MMM.
If you don’t have many human resources to manually collect data, it may be important for you to choose a software that facilitates seamless integration with various data sources, such as sales, advertising, customer, and marketing data. A good MMM software should also provide robust analytical capabilities to transform raw data into actionable insights.
Now while there are many MMM tools to choose from, let’s compare and contrast two open-source softwares from two of the world’s largest companies: Meta’s Robyn and Google’s Lightweight MMM.
Meta’s Robyn is a commercial tool – meaning users with less experience in MMM may find its streamlined data setup processes and dedicated support more alluring. Additionally, Robyn is an open-source package, so there are no usage fees associated with it, making it an accessible option for businesses and individuals seeking robust modeling tools without financial constraints. And, as an open-source tool, the product is actively developed across an engaged user community on platforms such as Facebook and Github.
Now, Robyn was developed for the R programming language – so effectively using the software does require a certain level of R knowledge as well as experience in statistics. Keep in mind that if you don’t have this knowledge, Robyn may prove difficult to extract useful learnings from.
With that said, Robyn boasts a range of benefits for users; one of Robyn’s biggest pros lies in its comprehensive graphic summaries. With its visual representation of data, Robyn provides an interpretation of results that is (relatively) easily digested by both internal teams as well as clients, making it a great option for analysts using the technology on behalf of a team that may be less familiar with Marketing Mix Modelling.
Additionally, while the technology uses ridge regression, it also allows for calibration of results through experiments, enabling analysts to fine-tune their analyses.
However, creating models with this package can be somewhat time-consuming; without automatic data loading, the manual nature of the process can often require more effort and time investment from the user. Despite this, the package’s benefits, such as its parameterization and active user community, make it a valuable tool for those equipped with the necessary skills and resources to utilize it effectively.
LightweightMMM is a free, open-source package, meaning users can expect to be able to customize and extend its functionalities to suit their specific needs. Users can inspect the underlying code and algorithms, enhancing transparency, and fine-tune the modeling process. Robyn, while also an open-source tool, is not as customizable.
However, this Google-built software was created for Python’s programming language – and it certainly requires its users to have this knowledge as a base. But, its compatibility with popular Python productivity tools (such as Jupyter Notebooks) only enhances the above mentioned usability and flexibility.
A notable advantage of using LightweightMMM is its use of Bayesian probability, which enables it to excel in modeling low-frequency events – such as high-value sales. This approach can provide more accurate estimations and predictions in scenarios where data points are scarce.
Lastly, LightweightMMM is well-known for its ability to generate output swiftly compared to similar products in the market; when working with large datasets or time-sensitive analyses, LightweightMMM has a leg up on competitors such as Robyn, whose output generation speed may vary depending on the complexity of the analysis and dataset size.
While LightweightMMM offers robust functionality, the process of setting up data for analysis can be relatively complex, and will require careful attention to data formatting, structure, and preprocessing. Users need to have a good understanding of the data requirements and may need to invest additional time in preparing the data appropriately. Robyn, since it is a commercial tool, provides more streamlined data setup processes and offers dedicated support to users.
Whilst both packages provide visualizations, the sophistication of the output graphs on LightweightMMM may not be as visually appealing as other tools such as Robyn. If comprehensive visualizations are a priority for your analysis and reporting needs, other tools might offer more advanced graphing capabilities.
LightweightMMM and Robyn are not the only tools and companies in the market. For a comprehensive and detailed breakdown of the market, check out Gartner’s overview of the market.
When choosing a provider, you should consider the specific requirements, resources, and expertise available within your team. Be sure to carefully evaluate the trade-offs and prioritize the features and capabilities that align best with your analysis goals and constraints.
When used appropriately, Marketing Mix Modelling is a powerful tool that can help quantify the relationships between your marketing inputs and outputs and provide insights into the contribution of each marketing element’s influence on key performance indicators such as sales, revenue, market share, or customer acquisition.
If you’d like expert support and guidance for Media Mix Modelling services, get in touch below. We offer a comprehensive and cost-efficient service for businesses looking to measure the impact of their marketing activities.
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