Marketing Mix Modeling (MMM) is a powerful tool for marketers and decision-makers based on advanced machine learning and predictive algorithms. Its results help find answers to many marketing questions - but certainly not all of them. Let's delve deeper into this topic and discuss where MMM can really help with its insights and where it can't.
Of course - this is one of the key purposes of Marketing Mix Modeling. The results of modeling include weights assigned to every relevant channel or publisher. Moreover, it’s possible to see how that contribution changes over time, especially when certain channels or publishers are inactive. Revenue is not the only target KPI that can be used in modeling - it’s possible to assess the contribution to any target KPI affected by your advertising activity and important to your business. By pairing together the contribution and the amount of advertising spend, MMM also helps assess the effect of each channel on ROI.
The answer is yes - but there are certain preconditions. To obtain meaningful results, you need the spend and reach (or contacts/impressions - depending on the channel) as well as your target KPI data over the same period of time and with the same granularity. For example, if you have daily sales, your advertising budget should also be available on a daily basis - and the same applies to contacts. With digital channels, this should be quite easy; however, with classical media, there are some challenges to overcome: for example, if you place your ads in a magazine and you model on a daily level, you don’t know exactly how many people see your ad every day, so you need some convention how to distribute the numbers over time based on the total print run.
You will also need to take into account external factors. It’s hard to imagine a business that’s completely free from external impacts, such as competitor activity or the weather.
Incorporating such factors in the MMM model and thus capturing additional sources of influence on your target KPI helps improve the model’s accuracy and gain a deeper understanding of how these external factors interact with advertising efforts to drive results.
Based on your experience, you should be able to tell which external factors are affecting your sales, revenues, or other target KPIs. Make a list of those factors and gather the data over the same time period as the target KPI and advertising data. This will enhance the model's ability to accurately attribute the contributions of different channels and help provide a more holistic view of the marketing landscape, leading to more informed decision-making and optimized marketing strategies.
Last-click attribution is a widely used model but it only takes into account the final step of the funnel that leads to the conversion - and only if that step belongs to a digital channel. Remember how many times you converted online after seeing an ad for the first time ever? Probably not so many, if ever at all. Decision-making is a complicated process that normally involves multiple touchpoints and some time in between. Quite often we cannot even remember how many times we saw the relevant ads before we made the decision, or where and when we saw those ads. As a result, the impact of the last step in the funnel tends to be overrated, while the effect of supporting channels gets underestimated or completely neglected. Moreover, assessing the impact of offline media is impossible with last-click attribution.
The essence of MMM is that it compares the dynamics of the target KPI - that can be sales or app installs or registrations or any other conversions - to the dynamics of the advertising activity and budget and derives the correlations, taking into account all the channels involved as well as external factors that could affect the target KPI. MMM results show the whole picture and assess the indirect effects and interrelations, which is impossible with last-click models.
Yes! And Marketing Mix Modeling is actually one of the first-choice alternatives to performance assessment methods based on third-party cookies. The imminent death of third-party cookies is one of the reasons why MMM is becoming so popular. If you still have no experience with MMM and you don’t want to be left behind, it’s about time to jump on the bandwagon!
Indeed, Marketing Mix Modeling should be the key to solving your dilemma. The modeling helps find out how much each channel contributes to the target KPI. To have more accurate results, it’s important that your data cover periods when there was only digital advertising and no TV ads, as well as periods with TV ads and no or very little digital activity. Optimally, you should also include periods without any advertising to serve as a baseline for the model.
By analyzing the relative performance of TV ads in comparison to other channels and accounting for various contextual factors, MMM enables a robust estimation of their true contribution to your target KPI. This empowers you to make informed decisions about the allocation of your advertising budget, optimizing your marketing strategy based on data-driven insights.
Absolutely, this is a perfect use case for Marketing Mix Modeling. Based on the results of the previous flight, you can reallocate your budget more effectively. Another use case is when you need to build the optimal allocation plan with a higher or lower budget - and this is also possible with MMM. Ultimately, modeling can help determine the optimal share of each channel involved in the budget allocation. This way, you can make data-driven decisions that ensure your budget is allocated in the most efficient and effective manner, driving higher returns on your investment and achieving better outcomes for your marketing campaigns.
No, Marketing Mix Modeling is designed to assess the impact of advertising media that have already been part of the company’s advertising campaigns. If there is no past data, the channel cannot be included in the modeling.
Even if you take a daily breakdown, 30 days are not enough to apply MMM and get meaningful outputs. Either you need to rely on the results of previous campaigns or wait until a sufficient amount of data is accumulated. For example, with a weekly breakdown, best practice suggests using at least 2 years of data - which means you need to learn from your past experience.
It is not very likely in real life that an advertising campaign is completely inefficient, but this scenario cannot be completely ruled out - so yes, it’s possible. But when this happens, there must be some really obvious signs and reasons: for example, you will already see that your sales are not growing or growing too slowly. It’s also possible that some external factors outweigh the effect of advertising. For example, think about the poor airlines that were advertising heavily as the COVID pandemic broke out.
Not at this point. Marketing Mix Modeling relies on past data and results, and it doesn’t work from scratch. You will need to start with some trial and error. One possible option would be to learn from competitors’ experience - if you can gain access to their spends and conversions data, maybe in some anonymized form. Depending on what industry your business belongs to, you might be able to find a partner who already runs MMM in this sector and knows which channels work better or which don’t work at all.
No, the purpose of Marketing Mix Modeling is to build optimized budget allocation depending on what KPI is selected as the target. The allocation can vary quite drastically if you model for different KPIs - even if the budget and reach data remain the same. The choice of the appropriate target KPI should be based on your priorities, business experience, and understanding of your market.
Marketing Mix Modeling is a valuable tool but in order to really benefit from it, it’s essential to understand its capabilities and limitations. After all, every tool is only good for its purpose and only when applied properly.
The MMM process is based on complex machine-learning algorithms and therefore it might be daunting for business professionals without a sound mathematical background to start out on this journey alone - and even more daunting to make actual decisions based on the outputs of MMM. That’s why it’s important to have an experienced navigator to guide you in this way and help you build your own expertise in this field.
If you didn’t find an answer to your question in our article or if you are still not sure if Marketing Mix Modeling could help you cope with your challenges, feel free to contact us directly!