Marketing Mix Modelling is a powerful analysis of sales and marketing data that helps estimate the impact of marketing activities on sales. It’s a popular tool with businesses, who use it to measure and predict the effectiveness of their marketing efforts.

Often referred to as MMM, Marketing Mix Modelling is an excellent tool for identifying which parts of your marketing contribute the most to overall performance.

In this blog post, we’ll delve into exactly what MMM is, how it’s used and how you utilise it to better understand marketing performance.


What is Marketing Mix Modelling (MMM)?

Marketing Mix Modelling (MMM) is a statistical analysis technique used by businesses to quantify and evaluate the impact of various marketing channels and elements on sales or other key performance indicators. The goal is to understand how different elements of the marketing mix contribute to the overall success of a product or service.

As businesses face tighter marketing budgets and use omnichannel strategies, they are searching for efficient ways to spend money, and so often turn to MMM to help them achieve this.

MMM is a statistical tool that helps figure out how each part of the marketing mix affects overall sales or profits. It helps identify which marketing activities work best so businesses can spend their resources wisely.


How does Marketing Mix Modelling (MMM) work?

  1. Data Collection: Collect data on various marketing variables such as advertising spending, sales promotions, product features, pricing strategies, and distribution channels. Additionally, external factors like economic conditions may also be considered.
  2. Analysis: Statistical models are then developed to analyse the relationships between these variables and the outcomes, typically sales or revenue. This involves using regression analysis or other statistical techniques to identify patterns and correlations.
  3. Attribution: The models aim to attribute a quantitative value to the impact of each marketing element. This helps businesses understand which components of the marketing mix are most effective in driving sales or achieving the desired outcomes.
  4. Optimisation: Once the impact of each element is understood, companies can use this information to optimize their marketing strategies. For example, they might reallocate budget to the most effective channels or adjust pricing strategies based on the findings.
  5. Forecasting: Marketing Mix Modelling can also be used for forecasting future outcomes based on different marketing scenarios. This helps businesses make informed decisions about future marketing investments.

It’s worth noting that Marketing Mix Modelling is a complex process and requires access to comprehensive and accurate data.


How is marketing mix modelling used for digital marketing?

Marketing Mix Modelling (MMM) can evaluate and optimise digital marketing channels in the following ways:

  1. Identify and understand marketing KPIs:
    • Spend on channels like PPC, Paid Social and Display
    • Assess the impact of SEO efforts on website traffic and conversions.
    • Analyse the contribution of email campaigns to overall sales or conversions.
    • Measure the effectiveness of social media platforms in driving brand awareness and sales.
  2. Cross-Channel Analysis:
  • Assess how different digital channels work together. For example, evaluate how social media advertising and email marketing complement each other in driving conversions.
  1. Attribution Modelling:
  • Help produce attribution models to understand how different touchpoints contribute to conversions. This is particularly important in digital marketing, where a customer’s journey may involve multiple online interactions before a conversion
  1. Optimisation:
  • Once the impact of each digital marketing element is understood, adjust strategies accordingly. This could involve reallocating budget to the most effective channels, refining targeting strategies, or optimizing the timing and frequency of campaigns.
  1. Forecasting:
  • Use the insights gained from MMM to make informed predictions about the potential impact of changes in digital marketing strategies. This helps in planning future campaigns and budget allocations.
  1. Experimentation:
    • Implement controlled experiments or A/B testing to isolate the impact of specific changes in digital marketing strategies. This helps in validating the findings from the modelling process.


If you’d like to find out more about how ASK BOSCO® fits into your marketing strategy, just drop us an email at and we’ll be happy to help.