Skip to main content
Contact
Get in touch
Posts What is Marketing Mix Modelling (MMM)?

What is Marketing Mix Modelling (MMM)?

Harsh Tandon

In this Article

Benefits of marketing mix modelling (MMM)

For any marketing activities to be successful, understanding consumers’ behaviours and whether a channel is oversaturated is essential. While data and analysis play undeniably important roles in this, marketing mix modelling (MMM) plays an even greater one, representing the merging point of data and analysis with the psychology of consumer understanding.  

Marketing mix modelling (MMM) is a statistical tool that enables an understanding of how each part of an organisation’s marketing activity impacts consumers’ behaviours, sales, return on investment (ROI) and more. Through MMM, an organisation’s performance can be broken down by channel and various types of data can be incorporated to evaluate the effectiveness of marketing activities and determine which are making the most substantial differences to the organisation’s overall performance. 

  • Enables organisations to quantify and measure marketing channels effectively to assess which drive the most sales and return on investment 
  • Equips organisations with long-term insights that will bolster planning through effective forecasting and marketing campaign generation based on previous performance  
  • Helps organisations allocate budgets according to the best performing channels due to measuring growth based on investments
  • Instils confidence due to its statistical reliability and being privacy-safe, both of which are particularly important in a post-cookie world
  • Offers organisations a holistic view of the impacts that various factors will have on achieving specific KPIs, ensuring marketers can make more informed decisions based on how and when marketing activities will impact KPIs. 

How do marketing mix modelling (MMM) & commercial mix modelling (CMM) work?

Marketing mix modelling (MMM)

Marketing mix modelling (MMM) is used by organisations aiming to understand how marketing activities impact KPIs being measured. Its ability to measure the impact that certain pricing choices, promotional offers, product launches or advertising campaigns may have on sales makes it a game-changer for organisations. 

In MMM, the dependent variable used to assess the relationship between sales and marketing activities is usually:  

  • Sales volume: to assess the impact of different marketing activities on sales 
  • Revenue: to track the amount of money generated by sales 
  • Competitor analysis: to understand how your organisation’s marketing activities are affecting your position in the market. 

In contrast, the independent variables in MMM are the marketing activities or factors that might drive those results, such as: 

  • Advertising spend: the amount invested in promotion across various channels. 
  • Price: to explore the impact of price adjustments on sales 
  • Promotions: discounts, coupons, or offers that could increase sales 
  • Distribution: the potential impact of product availability across various locations on sales. 

Commercial mix modelling (CMM)

Commercial mix modelling (CMM) is an analytical approach that examines a variety of commercial factors that drive an organisation’s performance. It begins with collecting data from across the organisation on pricing, promotions, distribution channels, products and more, combining the resulting data into a cohesive dataset.

The insights presented within the dataset help organisations gauge which factors contribute most to performance and where investments result in the highest returns. It also enables organisations to test various scenarios— price changes, promotional adjustments, changes within distribution channels— to assess the potential impact on performance. Through this, organisations can optimise their overall commercial mix to grow and become more profitable.  

How does commercial mix modelling (CMM) differ from marketing mix modelling (MMM)?

While both commercial mix modelling (CMM) and marketing mix modelling (MMM) are granular approaches that help organisations analyse the impact of marketing activities, their scope, methodology and applications differ.  

Scope

CMM offers a broader approach when it comes to evaluating the marketing activities that would impact an organisation’s performance, integrating various functions to optimise revenue and profitability. It encompasses external, non-marketing data sources such as weather, seasonality, competitor pricing, interest rates, etc.  

MMM, on the other hand, is more partial, purely marketing data that offers a more detailed and expansive result. As a statistical analysis method, it quantifies the impact that marketing activities— campaigns, paid advertisements, promotions, etc.— have on specific KPIs. Focusing more on media and investments rather than a wider marketing strategy, its granularity is what marks its stark contrast to CMM.  

Despite the broad scope of CMM, it is just as granular and technical as MMM. 

Methodology

CMM blends analytics, business intelligence and strategic insights, considering both internal and external factors that can affect an organisation’s growth. The approach entails: 

  • Scoping & data auditing:
    • Understanding the KPIs and defining whether the model should target revenue, acquisitions, renewals or some combination form the scoping basis. Data auditing includes tech and journey mapping to determine the stages comprising the funnel for lead gen and closing, as well as the tools and tech used at each stage. 
  • Data collation & cleaning:
    • This includes a data request to outline the full scope of what can be used in the model, with cleansing, organising and playback taken into consideration to check for completeness and broad accuracy. During this stage, data is also combined and reaggregated for ingestion into the model. 
  • Exploratory analysis & feature configuration:
    • Plotting all the raw data to understand distribution and periodicity and exploring this raw data to identify gaps and anomalies is conducted during this stage. Correlation analysis helps find feature relationships and possible collinearity, feature types are configured for use in the model and decay is applied (AdStock) to channel features to simulate the memory effect of advertising.
    • Diminishing returns to channel features simulate channel saturation and other transformations such as smoothing or feature combination.  
  • Pre-processing & feature engineering:
    • Calendar and dummy variables can be included to represent milestones and seasonality, with each variable transforming across a range of parameters to find the most realistic behaviour. 
  • Commercial mix modelling (an iterative process with pre-processing & feature engineering):
    • Once the model for the approach is scoped (e.g. logistic vs. linear, pooled, nested, hierarchical) and fit for processed features to optimise accuracy and generalising power, it is then checked against existing commercial knowledge and external priors and returned to feature processing to refine variables and tune parameters accordingly.
    • All candidate variables are imported and tested from the pre-processing stage. Finally, the model is refined continually by adjusting variables to optimise statistical measures of accuracy. 
  • Optimisation & simulations:
    • The present channel saturation is analysed, the optimal channel mix is delegated for specific budgets and results are presented from scenario simulations to understand which channels have headroom and which are oversaturated.
    • A budget guide is provided for optimising revenue and the ability to plan for different scenarios: mitigating headwinds, capitalising on opportunity and planning contingencies. 
  • Next steps & recommendations:
    • Recommendations are given based on budget optimisations and added value. 

MMM, in comparison, focuses on econometric modelling and regression analysis to determine the contributions made by various marketing channels on an organisation’s outcomes. Econometric modelling is a statistical, mathematical approach that quantifies the relationship between marketing activities and business outcomes, built with historical data. Regression analysis is a technique used within econometric modelling to measure the impact of independent variables (marketing activities) on dependent variables (sales or revenue). 

Application

Senior executives and C-suite employees may use CMM for longer-term strategic planning and decision-making, whereas MMM would be used by marketing teams to optimise spending and budget allocation towards campaigns or advertisements.  

The broader scope of CMM enables senior executives and C-suite employees to gain a complete picture of the various commercial drivers and their impact on marketing rather than isolated results. On the other hand, the granularity of MMM ensures marketing teams strategically plan and forecast how changes in spending across channels might impact sales and plan scenarios accordingly. 

How to build a marketing mix model

The first step in building a marketing mix model will be to collate and prepare your data. This will involve collecting historical data on sales and marketing spend across different channels and should go back far enough in time to effectively capture market conditions and seasonality fluctuations. 

Next, selecting the appropriate model to facilitate this will be crucial. Selecting the model can come from its robustness or flexibility, catering to your organisation’s unique needs. 

Building the model will come after this. This will include defining the relationship between marketing spend and sales or other KPIs and considering carryover effects, saturation or external factors. 

Furthermore, fitting the model will use your historical data to estimate the parameters of the MMM. Once the model is fit, the results can be analysed to precisely determine their contributions towards each marketing channel. 

Finally, the insights gleaned from these results can help you adjust marketing strategies accordingly, increase budgets within the highest-performing channels and reduce it in those underperforming. 

Examples of marketing mix modelling (MMM)

Organisations across a variety of industries can apply marketing mix modelling (MMM) to lead to improved outcomes. A few of such examples include:  

  • Consumer Packaged Goods (CPG): Gathering data on sales, advertisements, campaigns and pricing can help CPG organisations understand which channels—digital advertising, TV campaigns, etc.— drive the most overall return on investment. 
  • Retailers: From seasonal promotions to discounts and the influence of both in-store and online presence, retailers can leverage MMM to understand peak performance periods, digital sales and foot traffic to allocate budgets accordingly or reassess promotional calendars.  
  • Financial Services: Financial institutions can use MMM to evaluate their multi-channel advertising efforts and ensure they are reaching the appropriate audiences, encouraging sign-ups.  

Why businesses should choose CACI to carry out CMM 

CACI supports businesses in their delivery of optimised marketing efficiency by: 

  • Determining the value and performance of activity through evolved multi-touch and econometric modelling 
  • Producing results to sustain and increase growth through targeted investment and improved marketing performance 
  • Delivering improved accuracy, consistency and availability of marketing performance insights 
  • Enhancing capability by evolving data, technology and process 
  • Supporting the provision of ongoing strategic and delivery resource 
  • Helping businesses dig into bespoke segments and utilise in-house data products to unlock insights 
  • Offer businesses location-based insights into the effects that marketing has at various levels, from stores to regions.  

Find out more about the impact that marketing mix modelling can have on your business by contacting us today. 

Click here to read our short infographic to learn how CACI’s Commercial Mix Modelling can transform your business strategy.

Sources:

Case study

Using FUSION to power and project manage major upgrade work

Summary

CACI’s FUSION methodology is designed to deliver projects on time and in budget. Following three key phases – shape, create and utilise – FUSION sets out the vision, goals and timelines of all customer delivery and internal development work. This case study looks at how CACI’s Cygnum workforce management and scheduling software went through a major enhancement project to deliver a new release, Cygnum 2020, overseen internally using FUSION. This included improving the software, ensuring future compliance for users and the release of a mobile app to support enhanced usability of Cygnum.

Industry

Technology

Products used

Solution

As with all projects, it is important to fully understand, as a team, why we are creating a project and what it needs to achieve. “We had been working alongside our customers for a number of years to deliver software development projects which were often bespoke,” explains Luke Brown, project manager for Cygnum 2020. “Components of these projects were actually beneficial to all our customers, so a new release made it possible to amalgamate these as standard for everyone. Research and development we had been working on internally was also included, as was any ongoing compliance updates, be they for data and security regulations or third-party compatibility.”

This would bring benefits to everyone by delivering enhanced functionality and the creation of a common platform for future development. A release like this also makes customer support of Cygnum more straightforward by having commonality in use of the system and satisfying compliance concerns for all.

“Once we understood what the project needed to achieve, it enabled us to focus on the mobilisation and discovery stages of FUSION,” says Luke. “A good example of this is the mobile app that we wanted to create for Cygnum. Extending the usability of Cygnum made a great deal of sense and was something that customers were keen to see. We identified the need to cover mobile forms via the app, essentially making the capturing of data for field-based workers available in real-time, so that they could log data on the move. This would then bring more benefits to more of our customers.”

“Once we had established an agile approach to the FUSION methodology that was to drive the Cygnum 2020 project, we went into a phase of building and testing, building and testing,” says Luke. “This helped the Cygnum team to ensure that everything was coming together as planned.”

Since Cygnum was undergoing a number of functionality enhancements, it was important that the team then started working on the Cygnum user guides to incorporate the changes that Cygnum 2020 would introduce. This started with a number of internal sessions to upskill the team to help them prepare for the launch of Cygnum 2020.

“Education of staff internally was hugely important,” stresses Luke. “Not only did it help team members to prepare on an individual basis, we were able to listen to their feedback and establish areas where we could perhaps be a little clearer in our use of language and terminology.“

“At the same time this supported the verify stage of the project. We were using QMETRY, a Jira add-on, which was supporting the constant process of building and testing that underpinned the create stage of Cygnum 2020. This helped us with the all-important stress testing, too, which consisted of two rounds of regression testing.”

Results

Once the shape and create stages of any FUSION project are completed, it is time to focus on the utilise aspect of the project. “A major aspect of the utilise stage for Cygnum 2020 was awareness,” says Luke. “We had to make everyone in our wider team aware of Cygnum 2020 and what it meant, before engaging with our marketing colleagues to establish materials and campaigns to help us with customer awareness.

“This included updating user guides, a webinar which was recorded by Cygnum solutions architect, Kristen Butler, supporting marketing materials including a teaser campaign and glossy, detailed release notes that our customers could use to generate interest in the upgrade work amongst their teams when they came to implementing Cygnum 2020. This relied heavily upon the work completed in the create stage of the project, since the team was able to draw upon its own internal education to help inform the awareness element for clients.”

Image of an office worker typing on laptop and updating software. A progress bar is shown superimposed above the keyboard.

This then turned into the transition phase of the project, with a go-live date for release established and a number of customer upgrade orders to fulfil immediately! The transition phase is ongoing, as is the final element of the project, evolve.

“The evolve phase has already started,” explains Luke. “We are already getting feedback from customers and our own internal processes which are informing the future roadmap for Cygnum; we’re not resting on our laurels and patting ourselves on the back for a job well done, we’re looking for ways to continue to evolve Cygnum and make it even better for the next release.”

Cygnum 2020 went live in May 2020. Using the proven project delivery methodology of FUSION, the team was able to operate the process smoothly, on time and in budget. FUSION is the delivery methodology that CACI uses for all of its projects, both internally and customer facing.

Case studies

Turning strategy into success: enabling the right customer experience to deliver on your growth targets

Richard Vaughan

In this Article

As businesses grow, so do the expectations of their customer experiences and those delivering it. Acquiring more customers, more products and, therefore, more data comes with increased complexity and an increasing demand on those in marketing, data and IT to enable growth.  

A successful growth agenda must consider not just the business goals, but the actionable steps to achieve them. These steps should include understanding the data, the insights it will deliver and the technical capabilities of how to scale. This can be a daunting challenge given the complexity and scope of the market.  

In this blog, we will explore how an enhanced customer experience can be delivered alongside business growth and the common issues businesses face, from operating at scale and delivering high-value experiences with limited technical resource to optimising technology for growth.   

The challenges of operating at scale

Businesses experiencing rapid growth on the journey from start-up to breaking into the mid-market and beyond find themselves in a complex landscape. With growing volumes of customer data, pressure to deliver an effective customer experience and legacy technology from early in the business’ trajectory, the challenge of maintaining that growth can be significant.  

These challenges can be broken out across the pillars of Data, Technology and People and Process. However, these are fundamentally reliant on each other, with each requiring attention to enable an impactful customer experience. 

Data is critical for understanding your customers and provides the foundation for your customer experience. Leveraging your customer understanding enables personalisation and brings you closer to a 1:1 relationship. The challenge is drawing insight from your customer data, considering things like segmentation and modelling to understand behaviour, then making that understanding actionable and available to be used for personalisation.  

This brings us to Technology. Underlying martech is the engine for customer experience, fuelled by data. Businesses often hold on to legacy systems and processes, which can become limiting factors when experiencing growth. Scaling existing technology can create bloat and operational inefficiencies as the aspirations are built on unsteady foundations.  

With Data and Technology in place, there is still the strategic element to consider. Mature, data-led businesses treat their customer experience as an iterative process. By monitoring campaign performance and understanding their customers, their communications are personalised across content, timing and channel, and they are constantly assessing how they can be more relevant and engaging for their customer. 

Enabling this iterative cycle requires a nimble customer experience, paired with the breaking down of silos between marketing, data and IT functions to enable your team to work efficiently and keep up with consumer demands.  

By putting customer understanding and activation along with the right tools in the hands of the marketer, they can identify and deliver high-value activities across customer acquisition, retention and win-back.   

Delivering high impact solutions with limited technical resources

The answer to the question: “What are the high-value activities of our brand?” is often hidden within the data. Focused insight gathering and customer understanding can reveal where in the process customers churn, what marketing is effective and what could be done better.  

Often the challenge here is again one of resource: finding someone with the right skills and resources to draw insight from data, present it clearly for marketers, data experts and C-suite, and then use this to inform the customer experience.  

This process is ongoing and iterative. A one-off solve will always be limited as customer needs, products and demographics evolve over time. Therefore, effective growth requires a dedicated measurement framework to ensure the customer experience is driving results.  

By taking a customer-first approach and prioritising your high-value activity, you can articulate what you need to deliver the experience effectively, whether that is more understanding of who your customers are, better capacity to automate and personalise, or more substantial reporting to continue iterating to grow closer to your customer.  

Optimising technology for business growth

High-value activities depend on the right technology. Legacy systems often hinder growth by limiting access to real-time data and advanced capabilities required for superior customer experiences. 

Growing businesses often outgrow legacy platforms, which lack the sophistication needed for modern demands. Upgrading to advanced marketing platforms allows companies to enable personalised, multi-channel journeys, automate tasks at scale and leverage AI-powered insights, helping marketers meet evolving customer expectations with greater efficiency. 

Selecting and implementing the right technology can be a challenge, however. The martech space is incredibly crowded with, at time of writing, over 14,000 products (per Scott Brinker’s State of the MarTech report), all with their capability, functionality and requirements. To select the right tool, businesses must consider what their aspirational customer experience is, how the tool integrates with the rest of their stack and how they are going to deliver value quickly after embarking on implementation.  

What should brands do and how can CACI help?

To successfully scale up your business without compromising your customer experience, CACI suggests considering your data, technology and operational processes ahead of making major changes. The key to growth is working backwards from your aspirational state to construct an actionable maturity roadmap. This ensures you are dedicating time and effort to the immediate priorities that will bring value back to the business while working towards your goal state. 

Our tried-and-tested approach of bringing together experts on Data, Technology, People and Processes has delivered results for complex brands like EasyJet and ASOS. CACI’s data-led, customer-centric approach focuses on enabling the customer experience by understanding the overall business vision and customer needs, considering market positioning and the steps a brand can take to sustainably and effectively deliver on their ambition. 

If you are looking to accelerate customer data or technology changes by connecting and activating your insight, please get in touch to discuss what strategies and solutions that our team of experts can help you deliver.

Related case studies

Case study

Delivering data & insights to provide Bright Horizons with a new approach to childcare

Summary

Trusted by families to look after their children for over 30 years, Bright Horizons is an award-winning nursery provider. The company operates over 300 community and workplace nurseries throughout the UK — each is individually designed to serve the needs of its community. Bright Horizons provides tailored childcare for corporate clients and for families, at home, at work and in local settings.

Company size

10,000+

Industry

Education

Products used

Challenge

Bright Horizons initially approached CACI for data to support their new site opening and acquisition insight programme.

Reliable data that was quick and easy to interpret for new site and location decision making was needed

Access to demographic data to support proposition development

Gain a better understanding of existing potential catchments

Solution

CACI provided Acorn demographics, profiling and mapping, giving insight into specific postcodes and communities. High-level demographic maps are instantly visible in InSite’s Locator tool.

Marketing Manager Eddie Thorogood explains: “The blend of data creates reliable and up-to-date information about the demand for our services, to support decision-making about how and where we can expand our operations so we can deliver high quality childcare where it’s needed. It also helps us improve our business model, so we can manage our portfolio and flex and balance our sites to meet changing needs.”

Results

Bright Horizons’ three pillars are ‘people, quality, growth’. Eddie explains, “We’re not about just growing for the sake of it. We always want to be where we are needed – where parents can find us and our services will be useful. With this data insight at local level, we can provide a clear picture of community and workplace need to our senior leadership team, so they can sign off new facilities.”

Learn more about Acorn and InSite.

Case studies

Maximising customer engagement marketing with AI

Sagar Limbu

In this Article

How Adobe, Braze, Bloomreach, Optimove and CACI lead the way together

In today’s fast-paced digital world, customer engagement has evolved into a sophisticated science that requires real-time analysis and personalised interactions across multiple channels. Businesses are increasingly leveraging Artificial Intelligence (AI) to optimise these processes, ensuring that customers receive relevant, timely, and personalised communication at scale.

Leading the charge in AI-driven customer engagement are platforms such as Adobe, Braze, Bloomreach, and Optimove. Each provides distinct features to make customer engagement more efficient and impactful. At CACI, we work alongside these providers, helping organisations across all sectors to integrate and fully leverage the capabilities of these technologies.

Navigating the latest AI features, ensuring seamless data integration, and delivering hyper-personalisation can quickly become overwhelming—especially when marketing teams are already stretched. Knowing where to begin, or how best to align new capabilities with business objectives, often proves challenging without expert guidance.

That’s why below I share an overview of how these leading platforms are incorporating AI to shape the future of customer marketing—and how our team of MarTech experts can support you in effectively putting these powerful features into action.

Adobe: The power of Predictive Analytics with Sensei

Adobe’s suite of marketing solutions, particularly within the Adobe Experience Cloud, utilises AI to enhance customer journeys. At the heart of this is Adobe Sensei, Adobe’s AI and machine learning framework. Sensei powers several predictive analytics and personalisation features across Adobe’s platforms.

CACI can help you integrate Adobe Sensei with your existing marketing activities, enabling you to forecast customer behaviour, tailor content, and automate workflows. The result is a more personalised experience at scale for your customers, backed by the Adobe ecosystem’s powerful insights.

Braze: Real-Time optimisation with Intelligence Suite

Braze’s Intelligence Suite is a collection of AI-driven tools designed to make marketing more adaptive and responsive. Its Intelligent Channel feature automatically selects the best communication method based on customer behaviour, while Intelligent Timing determines the optimal time for engagement.

We can support you in adopting Braze by helping you configure Braze’s AI tools to match specific business objectives, ensuring that you maximise the return on investment in Braze. We will work closely with you to configure Braze’s AI tools, ensuring your multi-channel campaigns connect with customers in the right place, at the right time.

Bloomreach: Hyper-Personalisation with Bloomreach

Bloomreach combines commerce and marketing in one platform, with Bloomreach Engagement a leading player in AI-driven personalisation. Bloomreach Engagement in tandem with the commerce capabilities of Discovery continuously analyses customer intent and behaviour to adjust content and recommendations in real time.

We can assist you in seamlessly deploying Bloomreach Engagement, ensuring that the AI engine is fine-tuned to your unique needs. Through key data integrations we can enable you to create highly personalised digital experiences that improve conversion rates and customer engagement, leveraging Bloomreach’s AI to achieve fast, tangible results.

Optimove: Customer Data Platform meets AI with Optibot

Optimove’s Optibot is an AI-powered recommendation engine that helps marketers optimise their engagement strategies by providing actionable insights from customer data. It allows companies to predict customer behaviour, like churn or purchase intent, and adjust marketing tactics accordingly.

We can help you integrate Optimove and ensure you’re utilising Optibot’s AI-driven capabilities to their fullest potential. Through data analytics support and customer journey optimisation, we enable your organisation to fine-tune your outreach, engage customers more effectively, and enhance customer lifetime value through intelligent, data-driven marketing.

How CACI can help you to effectively apply AI tools

While each of these platforms offers powerful AI tools, integrating them into your organisation’s marketing efforts requires deep technical know-how and strategic insight.

We provide end-to-end support if you are looking to implement AI-driven marketing technologies. With a focus on data integration, customer journey mapping, and multi-channel engagement strategies, our team of experts will ensure that these AI tools align with your specific objectives and business model.

Whether it’s implementing AI-powered personalisation, automating decision-making processes, or scaling customer communications, we’re here to help you get the most out of your investment in AI.

By partnering with us, you gain access to a wealth of knowledge in deploying these technologies effectively, along with ongoing support to continuously optimise AI-driven marketing initiatives. We empower your organisation to move beyond manual processes, enabling you to focus on strategic growth while delivering personalised, relevant customer experiences at scale.

How can you get ahead?

The future of customer engagement marketing lies in AI, and companies like Adobe, Braze, Bloomreach and Optimove are leading the charge with innovative tools that make marketing more efficient and impactful.

CACI’s role is to help you harness these platforms effectively, tailoring them to your specific objectives and ensuring you capitalise on AI’s potential for personalisation, automation, and meaningful customer connections.

Whether you’re already using AI features in your existing MarTech stack or considering new solutions, we can guide you towards maximum return on your technology spend. If you’d like to explore your options or unlock more value from your current set-up, please get in touch—our team is here to help you navigate the next steps.