Mastering Marketing Mix Modelling: Your Roadmap To Success

Marketing Mix Modelling (MMM) is the practice of analysing an organisations multi-channel marketing efforts to establish which elements are driving the most success. In turn, this enables you to better allocate resources based on the channels that are driving the most ROI, so you can continue to optimise performance and invest the right level of spend.

Marketing mix models use aggregated data to determine trends in seasonality and then predict channel attribution. These types of statistical models have been used historically, however they were phased out due to the rise of individual tracking. 

We’ve now seen a return of MMM’s due to changes in legislation, such as GDPR, 3rd party cookies and Googles privacy sandbox, which has reduced the ability to use individual tracking, forcing organisations to look for alternative ways to track and predict channel performance and attribution.

Marketing Mix Models are designed to answer questions like:

  • Am I spending money in the right places?
  • Am I overspending in some channels?
  • How much money should I be spending?
  • How should I split my marketing investment across the marketing mix?
  • How much money will I make in the next quarter?
  • What is the point of diminishing return?


Getting the most out of your marketing mix model

In order to achieve these insights, it’s important to feed the model with high quality data so you can obtain the optimal output. You need to consider factors such as:

  • How much marketing spend do I have access to?
  • Are there other factors that will affect revenue? Such as stock shortages, changes in pricing or macroeconomic factors?
  • What type of data do you have at your disposal? For example sales data, marketing spend data, stock data.
  • How much data do you have access to, and how granular is this data? For example do you have 1 years worth of data, or 8 years worth of data? The more data the better.
  • What are your goals? E.g. do you want the model to optimise ROI, or generate the most awareness, or drive the most traffic to your website? MMM can only prioritise one goal at a time.
  • What marketing channels are within your remit?

Once you’ve input the data and parameters into the MMM, the model will then output:

  • A selection of different combinations of marketing spend, based on your goals and budget
  • The diminishing return curves for each channel based on current data
  • The decay rates for each channel
  • Current vs optimised return / revenue estimation
  • Current channel spend vs suggested optimised spend


Benefits of Marketing Mix Modelling

As we’ve touched upon earlier in this blog, marketing mix models can bring a wealth of benefits to your business, mainly by steering your decision-making towards investing in the perfect blend of marketing channels to drive the optimal output. However, further to this you can also benefit from:

  1. A clear foundation for ongoing data-driven insights

Marketing mix modelling provides a quantitative foundation for decision-making, rather than relying on gut instinct or intuition. It also enables you to regularly analyse your marketing investment, performance and ROI over time, so you can uncover trends and patterns across your marketing mix.

  1. Greater level of insights

Marketing Mix Models also enables you to dig deeper into your performance, so you can understand how your multi-channel marketing campaigns work together, which channels drive the highest attribution, how seasonality impacts your campaigns, customer channel preference and changing user behaviour. 

This level of insights means you can tailor your marketing campaigns based on different audience segments – for example if one type of demographic typically has a higher conversion that can be attributed to one marketing channel, and a different demographic typically responds more positively to another channel, you can use MMM to create the perfect blend of activity based on the value of each audience segment.

  1. Capability for predictive analytics

By examining the results of previous marketing campaigns and their influence on business outcomes, businesses can enhance their ability to predict future success more accurately. This predictive capability aids in making well-informed decisions and crafting effective marketing strategies, enabling businesses to optimize their decision-making process and develop impactful marketing strategies.


Challenges of Marketing Mix Modelling

Whilst there are many benefits to leveraging marketing mix modelling, it does not come without it’s challenges, and it’s important to carefully consider these before you begin using your MMM. These challenges include:

  1. Getting your data right in the first place

The first hurdle in setting up your marketing mix model is ensuring that the data you’re inputting is high quality, clean and in the right format. You also need at least 3 years of data in order for the model to churn out recommendations – anything less than this would be an unreliable output, so ensuring that you have a data collection and data cleaning process in place is critical. Ask yourself if you have the right data systems in place, from data warehouses and lakes to data visualisation.

  1. Complexity of the data

With so many different factors to consider, it can be difficult to ensure that the analysis is accurate and comprehensive, and different industries may require different approaches for analysis. Therefore, before you start using your marketing mix model, you need to ensure that you’re equipped to handle this complex data with varying parameters and limitations that may impact your models output. 

  1. Ongoing management of the MMM

Marketing mix models are a complex form of statistical analysis, and given that they are steering you on financial investments for your marketing activity, you need to be 100% confident that the data you’re inputting, the model performance, and the output delivered by the model are all performing seamlessly. 

It’s also natural for an organisation to alter their level of investment, marketing channel preference and goals on a frequent basis, so you need to ensure the model is set up to satisfy your ever-changing goals. This requires a specialist skillset from analysts who have experience working with marketing mix models, and can be a challenge if you don’t have this skill set available internally.


How Ipsos Jarmany can support you

At Ipsos Jarmany, we build marketing mix models that combine the power of machine learning and statistical analysis to uncover the best way to invest your marketing resources. These models can be tailored to your businesses goals, marketing budget and parameters.

Once your data has been inputted into the model, it will run approximately 2000 times, each time changing the spend and the channels to maximise ROI. Our model will then output the top 100 optimised spends, based on your current / defined spending patterns to show the variety of different approaches that can be taken to solve the same problem. We’ll then work with you, and your business knowledge, to select the option that is best suited to your organisation.

Further to this, our model feeds the outputs into an interactive Power BI report so you can visualise the optimal approach, whilst also giving you the ability to alter the spend for each channel to review how this impacts return, decay curves and other factors.

If you’d like to find out more about how you can use marketing mix modelling to uncover the best way to allocate your marketing spend, or if you’d like to see a demo of our model, then reach out to the team today.

Data-driven decision-making, made easy with Jarmany