CACI Ltd is delighted to announce it has been selected by Amazon Web Services (AWS) as an official launch partner for the AWS European Sovereign Cloud (ESC), a major AWS initiative designed to help organisations meet stringent European digital sovereignty, security, and compliance requirements.

This appointment further reinforces CACI – a global AWS Premier Tier Partner – as a trusted advisor for organisations looking to adopt sovereign cloud solutions while leveraging the scale, resilience and innovation of AWS.

The European Sovereign Cloud is purpose-built to ensure the highest levels of governance and assurance, making it particularly suited for mission-critical and highly regulated sectors such as public services, national security, defence, financial services, healthcare, and critical infrastructure. This is also essential in supporting large commercial organisations navigate regulatory landscapes, protect sensitive data, and maintain customer trust at scale.

Why are the AWS ESC Principles Important?

The AWS ESC applies the principles above in the European context, giving organisations absolute confidence that their data and operations remain under tight European control, while enabling innovation without compromise.

Key capabilities include:

• EU-only operations: managed exclusively by EU-based personnel, ensuring governance and operational independence.

• EU data residency: all customer data – including metadata – remains within the EU, supported by isolated service environments.

• Independent European infrastructure: physically EU-based facilities with separate control systems including independent billing, security, and multiple Availability Zones for resilience.

What Being an AWS ESC Launch Partner Means for CACI Clients

CACI brings proven expertise in cloud transformation, security, and compliance. Becoming an ESC launch partner further enables CACI to:

• Guide organisations through sovereign cloud adoption using AWS best practices.

• Deliver secure and compliant solutions tailored to EU regulatory requirements.

• Enable innovation without compromise, by combining sovereignty with AWS scalability and resilience.

To prepare for this milestone, CACI has invested in advanced training for its teams on AWS Digital Sovereignty competency and principles, ensuring clients receive expert guidance in planning, migrating to, and operating sovereign cloud environments.

Tracy Weir, Chief Executive of CACI Ltd, comments: “We’re proud to be named an AWS launch partner for the European Sovereign Cloud. This partnership reinforces our dedication to helping organisations across public and private sectors meet stringent sovereignty requirements, whilst leveraging the power of AWS. It also underlines our commitment to delivering excellence and best practice across every stage of AWS cloud adoption.”

CACI AWS Credentials and Sovereign Cloud Expertise

CACI pairs deep AWS expertise with secure cloud delivery experience across defence, public services, finance, healthcare, and critical infrastructure. Our powerful capabilities include:

• First AWS Trusted Secure Enclave Vetted Partner the UK providing trusted National Security & Defence sensitive solutions

• Other AWS Competencies including Migration, DevOps and Government Consulting

• A partner ecosystem of 36+ strategic partners across all verticals

• Jezero Landing Zone Accelerator: AWS validated secure cloud LZA enabling rapid deployment on AWS, and compliance with global security standards

• 400+ AWS certifications: held by expert CACI engineers.

AWS ESC launch timeline, locations, and investment

AWS ESC begins its roll out from January 2026, starting with its first region in the State of Brandenburg, Germany, expanding capabilities and coverage to additional regions over time. This phased approach reflects AWS’s commitment to supporting European organisations with scalable, sovereign cloud solutions.

AWS has also committed €7.8 billion in investment in Germany by 2040 as part of this initiative, reinforcing its long-term support for European digital sovereignty and innovation.

With over five decades of delivering complex programmes across commercial and public sectors including highly regulated, mission-critical industries, CACI is well-positioned to help organisations adopt secure, compliant cloud solutions on the AWS European Sovereign Cloud.

For help with ESC or any AWS or other cloud projects, get in touch today.

As attitudinal segmentations are usually based on surveying a smaller sub-group and not based on data which can be easily applied to customers on your database, bridging attitudinal segmentations can be a challenge and is not always a straightforward process. However, it is a great way to provide a consistent customer experience.

So, what is attitudinal segmentation and what considerations should an organisation have when it comes to their approach for bridging an attitudinal segmentation?

What is attitudinal segmentation & how to bridge an attitudinal segmentation

Attitudinal segmentations are typically created using data from quantitative surveys. They can be a powerful tool for delivering rich insights into customer and prospect mindsets and provide a valuable framework for organisations to engage customers effectively through an in-depth understanding of their needs, attitudes and motivations.

Being able to treat customers consistently throughout the marketing funnel helps to establish a relationship with them and deliver resonating messages that will drive increased engagement. Once someone becomes a customer, they will expect to see the same messages that originally struck a chord with them reflected and developed in their ongoing journey with you.

The economic and social disruption since the pandemic has permanently changed consumers and their expectations of brands, so ensuring your online messaging aligns with these changes is increasingly important. We consistently see organisations that are personalising messaging for their customers increasing their market share, net promoter scores, return on investment and profitability. With this in mind, being able to make your attitudinal segmentation actionable on your database should be a key part of your customer engagement strategy.

Key questions to address the challenges of bridging an attitudinal segmentation onto your customer base

There are no two ways about it – data is key to tackling this challenge and making it actionable. To achieve this, you should ask the following five questions to get started:

• Where and who created the segments? Were the segments created by your organisation or a media/research partner? This is pertinent to understanding if you can get to the raw data or in understanding the level of granularity of data you can obtain.
• What data is there? Do you have access to the responder level data or tables by segment or Pen Portraits? The data you can reach will determine the method of bridging that can be used.
• Were questions only posed to your customer base or to the wider population? What types of questions were asked and were they personal to the organisation or more generalised? This can impact the resulting solution.
• Are there any behavioural traits reported within the data that were part of the same survey? Wider data beyond pure attitudes can be helpful to model this back to the database.
• Were any demographic questions asked or was postcode captured? This can help the process of creating the link between segments and customer base.

While bridging an attitudinal segmentation can be challenging, these questions will help identify how simple or complex the solution will be.

Key techniques for bridging attitudinal segmentation

Depending on the granularity of the data your organisation has access to, the following techniques can be leveraged:

• Responder level data: As this is the most granular form of data, it produces the most accurate results. Techniques here include modelling each of the segments by using a mix of the responder data and CACI’s own data to score this up against a customer database before validating this against the responder panel.
• Tables by segment: We can compare each customer’s results to the segment averages based on a combination of multiple data points. Validation is key through profiling and sense checking the segment distribution.
• Pen Portraits: Here we would use a rules-based approach to recreate segments based on high-level views of the segment to capture the different blend of information that you have to bridge the data. As before, the final step of validation is key to ensuring the solution’s accuracy.

If raw data is inaccessible or unavailable, the following alternative methods can support:

• Adding golden questions to market panels: This will provide more demographic and behaviour traits which support the bridging process.
• Surveying the whole customer base with golden questions: Responses can often be skewed to particular segments, however, and some consumers may be more inclined to answer than others.

Considerations at the start of an attitudinal segmentation journey

Including key customer traits

When beginning an attitudinal segmentation, our first recommended consideration would be to include some key customer traits. Including additional questions such as demographic markers (postcode, gender and age band) will support segmentation mapping on to the database.

Cross-team engagement

Cross-team engagement will be invaluable to ensure the segmentation meets goals and drives value. This will help flesh out what the segmentation will be used for now and in the future, as well as gauging what you need from the segmentation and building it accordingly. It is also pertinent in getting buy in as early as possible to ensure teams are engaged when the solution is rolled out.

Backing segmentations with research

Another solution would be to build the segments first and then use research to enhance them with attitudinal values. This solution can work well with one of the benefits of running focus groups to bring life to the segments rather than using the attitudes to drive the segmentation.

Ultimately, it is about finding the right balance that works for your organisation based on wants and needs. Attitudinal segmentations can bring excellent insights but are limited in their applications across a database. Fundamentally, it is a process of ensuring that through engaging the whole organisation, your solution is optimised to meet strategic aims.

How CACI can help

CACI is in a unique position with a UK-wide dataset on all adults, encompassing over 800 variables that we can use to profile and create proxy variables to support the possibility of a successful bridging exercise. We help solve the challenges associated with bridging attitudinal segmentation for leading organisations many times each year.

To learn more about getting the most out of your segmentation and how CACI can support you through this journey, get in touch and we can discuss your challenges in more detail.

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:

• https://www.ruleranalytics.com/blog/analytics/marketing-mix-modelling/  
• https://www.ruleranalytics.com/blog/analytics/marketing-mix-modelling/ 
• https://kleene.ai/marketing-mix-modelling/ 
• https://www.ruleranalytics.com/blog/analytics/marketing-mix-modelling/ 
• https://gaintheory.com/marketing-mix-modeling/ 
• https://medium.com/@nialloulton/building-a-marketing-mix-model-a-guide-for-marketers-37cc5e41e459