By: Gabriel Mares, Data Analyst at Stellar Search

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In today's complex marketing landscape, understanding the true impact of your marketing efforts is crucial for optimising spend and maximising ROI. Marketing Mix Modeling (MMM) has emerged as a powerful tool for marketers seeking to gain deeper insights into the effectiveness of their various marketing activities. This blog post will explore the concept of MMM, its benefits, and how it can revolutionise your marketing strategy. 

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What is Marketing Mix Modeling? 

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Marketing Mix Modeling is a statistical analysis technique that aims to quantify the impact of various marketing activities on overall business performance, typically revenue or sales. Unlike traditional attribution models that focus on individual customer journeys, MMM takes a holistic approach by analysing aggregate data over time. The primary goal of MMM is to determine how each marketing channel and tactic contributes to revenue, allowing marketers to make data-driven decisions about budget allocation and strategy optimisation. 

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How Marketing Mix Modeling Works 

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At the heart of Marketing Mix Modelling is the goal of finding out how each type of marketing activity contributes to revenue using statistics. For instance, a business might want to know whether to invest £100 more into Facebook or Google, which of course depends on which channel will give the most bang for your buck. One way of solving this without MMM is to invest in the channel that already has the highest ROAS. But this ROAS figure likely depends on the platform’s attribution, which may assume for example that the last ad a user interacted with was entirely responsible for a purchase i.e., last-touch attribution. This might be a useful rule of thumb but realistically, every user is affected by multiple ads across multiple platforms, or things they may have heard from friends about brands or seen on posters and so on, and how marketing affects a person will vary greatly from person to person, so simply attributing credit for a purchase to the last ad is quite crude. So, the platform-reported ROAS is a useful but incorrect estimation of the true ROAS. This is expanded upon later.

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Enter Marketing Mix Modelling. MMM uses a different approach to attribution, one which does not require user data about ads that were interacted with. Instead, MMM tries to relate the spending in each channel to the overall marketing revenue. For example, on some days spending on Facebook might be pushed up due to budgeting decisions, and this would be reflected by a resulting jump in revenue, which can be detected using the statistical algorithm that MMM uses. Alternatively, Facebook spending might jump up while revenue stays the same, which would be suggestive of a low impact on revenue and a small ROAS. The model would track how revenue reacts to every channel’s spending over time, from barely detectable fluctuations to large increases, to ultimately get a measure of how an ensemble of channels affect revenue. This is the first step of MMM.

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In addition, other factors outside marketing activity might also affect revenue, so to avoid building an incomplete picture, some other revenue-drivers are added like holidays, promotions, weather, the wider economy, general brand interest and competitor data. There might also be delayed effects for some channels, in which increased spending may take some time to influence revenue. MMM takes all these factors and uses statistics to track them in relation to revenue.

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In the end, using all the inputted data, the MMM algorithm will output the relation between spend and ROAS for each channel. Using this, we might see that Google has hit a ceiling where it's inefficient to spend more, whereas Facebook has more space to spend – so the clear choice would be to put that £100 into Facebook, which avoids effectively wasting the money in a saturated channel. Wider decisions about the budget can also be made, and MMM can be used to produce optimal spend mixes for budgeting. For this, optimisation is used to find the best possible spend allocations for each channel, such that the overall Revenue is maximised. This is exceptionally useful when there are significantly more channels.

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Why Traditional Attribution Models Fall Short 

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Many marketers rely on platform-specific attribution models or last-touch attribution to gauge the effectiveness of their campaigns. However, these methods have significant limitations. Last-touch attribution oversimplifies the complex customer journey and ignores multiple touchpoints that influence a purchase decision. Each advertising platform has its own attribution model, which may overstate its importance in driving conversions, leading to platform bias. Traditional models often fail to account for external factors like seasonality, economic conditions, or competitor actions, resulting in a limited scope. Moreover, with increasing restrictions on user-level tracking, relying solely on individual customer data is becoming less viable due to data privacy concerns. 

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Key Benefits of Marketing Mix Modeling 

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MMM provides a holistic view, offering a comprehensive understanding of how all marketing activities work together to drive business results. It delivers channel-specific insights, giving a clear picture of the effectiveness and efficiency of each marketing channel. This enables data-driven decisions about where to allocate marketing spend for the highest ROI, facilitating budget optimization. 

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MMM also aids in long-term planning by helping marketers understand the delayed effects of marketing activities. It takes into account external factors that impact business performance, such as seasonality or economic conditions. Importantly, MMM doesn't rely on individual user data, making it a viable solution in an increasingly privacy-conscious world. Practical Applications of MMM Marketing Mix Modeling has numerous practical applications. It's instrumental in budget allocation, helping determine the optimal mix of marketing spend across channels to maximise overall revenue. 

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MMM enables scenario planning, allowing marketers to model different budget scenarios and predict their impact on business outcomes. It's valuable for incremental impact analysis, helping understand the true incremental effect of increasing or decreasing spend in specific channels. MMM can identify cross-channel synergies, showing how different marketing channels work together to drive results. Lastly, it aids in ROI forecasting, predicting the expected return on investment for future marketing activities. 

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Challenges and Considerations 

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While MMM offers powerful insights, it's important to be aware of its limitations. The model requires a significant amount of historical data to produce accurate results, which can be a challenge for newer businesses or campaigns. Implementing and interpreting MMM can be complex and may require specialised expertise. MMM typically provides insights at an aggregate level, which may not capture nuances of specific campaigns or tactics. Additionally, the model needs to be regularly updated to account for changes in the marketing landscape or business environment. 

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Conclusion 

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Marketing Mix Modeling is a powerful tool that can transform how marketers understand and optimise their marketing efforts. By providing a holistic view of marketing effectiveness and enabling data-driven decision-making, MMM empowers marketers to maximise their ROI and drive business growth. As the marketing landscape continues to evolve, embracing advanced analytics techniques like MMM will be crucial for staying competitive and making informed strategic decisions. By leveraging the insights provided by MMM, marketers can create more effective, efficient, and impactful marketing strategies that drive real business results.

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