What is subscription fatigue?

Subscription fatigue refers to consumers’ deteriorating interest in a subscription or service, resulting in their cancellation. This is often due to feeling overwhelmed by their numerous subscriptions or losing sight of the value each subscription brings. It goes hand-in-hand with churn, where uncertainty, mental exhaustion and subscription overload leads to diminished satisfaction with the subscription experience.  

What is causing subscription fatigue? 

With the ever-increasing number of subscriptions consumers have, decision overload is inevitable. Mounting costs, managing multiple accounts and the pressure to maximise each subscription all contribute to declining satisfaction. When value is unclear, questioning a subscription’s worth surfaces. 
 
Value must therefore be constantly reiterated and subscriptions models must be flexible enough to meet consumers’ unique needs. Signs of fatigue must be identified early on and actions to mitigate fatigue must be taken.  
 
CACI understands the challenge: people want convenience and personalisation, but they also want affordability and control. 

Over-subscription

Subscribing to and managing multiple subscriptions can be mentally draining. The simple fix in consumers’ minds is typically to unsubscribe, even if the service itself is not the problem.

Inability to reinforce value

If consumers feel that they are paying for a service they do not use, the feeling will quickly lead to subscription fatigue. When it comes to subscriptions, low perceived value or service underutilisation are often the driving factors behind cancellations. If value cannot be demonstrated, even your most loyal subscribers may be lost.

Lack of flexibility

When feelings of frustration or overwhelm creep up among the plethora of subscriptions a consumer has, offerings that do not feature flexibility are likely the first to go. Rigid plans will not appeal to already-fatigued consumers. If subscribers feel as though they maintain control over their subscription, they will be easier to retain and keep satisfied. Establishing tiered memberships, flexible pricing, pause options, add-ons or various payment plans can help rectify this.  

How can brands fight subscription fatigue? 

Subscription fatigue may be inevitable within an oversaturated subscription landscape, but understanding the origin of fatigue and the strategies that your organisation can implement to combat this will make a tremendous difference. Leveraging predictive modelling, customer insights and data and segmentation are among the most effective approaches.

Use predictive modelling

AI-driven predictive models forecast customer behaviours and guide the next best actions. Proactive retention and upsell strategies can therefore be developed, resources can be prioritised towards customers with the highest potential and a measurable performance uplift can be seen in metrics like LTV, conversion and engagement. 

Focus on customer insights 

By integrating transactional, behavioural, attitudinal and external data, CACI helps you attain a comprehensive view of your subscribers that will improve your decision-making across acquisition, retention and product development. 

These insights help you:

• Build strategic confidence by grounding it in real customer behaviour  
• Identify high value customers 
• Understand churn drivers 
• Uncover growth opportunities 
• Benchmark performance against your competitors 
• Better understand your position within the market  
• Spot underperforming segments or categories where competitors are gaining share

Grounding strategic decisions in external evidence also improves internal storytelling and stakeholder alignment. 

Focus on acquisition through segmentation

Poor segmentation drains budget by targeting low-value audiences. Without precise targeting, campaigns miss the mark and media mix decisions lack data-driven optimisation.  

CACI’s bespoke segmentation capabilities give you intuitive, data-rich segments reflective of the diversity of your customer behaviours, values and attitudes. This enables personalised marketing and CRM journeys, enhances media targeting and campaign ROI and bolsters strategic planning by revealing which segments to grow, retain or re-engage across three core areas: 

• Data: Curated, high-quality foundational data with diverse input lenses and no personally identifiable information (PII).  
• Segment simulation and validation: Segment-level data layer, validation to assess predictive accuracy with guardrails in place and performance audited.  
• Persona enhancement: Defined by segment characteristics and enriched with psychological and behavioural traits, every step is tested by experts to ensure it is structured, auditable and iterative.

Through this tailored approach, CACI equips you with segmentation that reflects your customers, leading to better decision-making, campaigns and long-term growth.

How CACI can help you overcome subscription fatigue

CACI helps subscription brands unlock growth by transforming fragmented customer data into actionable insight. Through advanced data science and AI-powered decisioning, we support acquisition, retention and personalisation at scale. 
 
We can help you:

• Build deeper customer understanding and target the right audiences 
• Forecast behaviour, improve retention and justify investment 
• Turn insights into action across media and CRM 
• Simplify data and bridge capability gaps

To find out more about how your organisation can successfully overcome subscription fatigue, get in touch with us.

“Our digital transformation is failing because it is fragmenting”. This was the defining statement from a recent roundtable with C-suite leaders from global enterprise organisations, met with nods and echoes of agreement across the room.  

Many of these leaders went through mergers and acquisitions, regional expansion and business proposition changes. The end result was the same: hundreds of disconnected tools and platforms, masses of digital sprawl, rising inefficiencies, disjointed customer experiences and a tangled web of overlapping technologies.  

If this sounds familiar, you are not alone. Over 40% of organisations now operate four or more separate systems, and while multiple platforms can signal maturity, the lack of integration between them often introduces operational friction—slowing delivery, increasing costs, limiting personalisation and constraining AI adoption.  

This is where ecosystem orchestration becomes strategically imperative in designing how your entire digital ecosystem works together. 

What is ecosystem orchestration?

Ecosystem orchestration is the discipline of designing, connecting and governing all digital platforms, experiences and data as a unified system rather than a disparate collection of isolated tools and journeys. It defines how these technologies should work together to deliver efficient operations, connected customer experiences and AI-ready foundations. 

For most organisations, this ecosystem spans experiences, content, data and their supporting platforms. 

Ecosystem orchestration focuses on: 

• How data flows across your CRM, CDP, CMS, analytics and personalisation 
• How experiences are assembled across channels, regions and brands to make them seamless 
• How your platforms integrate, scale and evolve alongside your organisation  
• How governance, security and performance are embedded by design. 

What is digital fragmentation? 

Fragmentation rarely appears as a single problem. Instead, it develops gradually as new platforms, regions and business needs are layered on existing digital estates. If one layer is weakened, it reduces the effectiveness of the entire structure and ultimately damages both your business outcomes and perceived value to your customers. This inefficiency prevents your organisation from reaching its potential.

Fragmentation tax: The unwanted cost of disconnected systems

When digital ecosystems grow without orchestration, the impact compounds over time. You may start to see: 

Operational inefficiencies rise 

When your teams jump between multiple systems, duplication and manual work skyrocket. Delivery slows and administrative load increases. 

Maintenance outweighing innovation 

Technology teams spend more time maintaining integrations, bug fixing and patching software than building new value-generating features. 

Data reporting inconsistencies

Inaccurate data creates reporting inconsistencies and data teams spend more time reconciling data than generating insights.  

Personalisation becoming impossible

Disconnected CMS, CRM and data platforms mean your organisation does not have a single customer view. This leads to segmentation being non-existent or superficial. 

AI-readiness severely constrained

AI requires unified data, modern architecture and consistent governance. Poor data hygiene and siloed insights create unstable foundations for predictive modelling and limit automation at scale. 

Brand and experience consistency breaking down

Multiple regions and brands lead to inconsistent UX, duplicated content and disconnected customer journeys. 

Costs quietly increasing

Duplicated platforms, unnecessary licences, security vulnerabilities and inefficient workflows inflate spend. 

Leadership is struggling to make data-driven decisions

Fragmented data erodes trust, making it harder for leaders to drive strategy or prove ROI. 

What ecosystem orchestration will enable

Fragmented digital estates can derail even the most ambitious digital transformation plans. Ecosystem orchestration is the solution to ensuring your business is future-ready, laying the foundation for scalable experiences, operational efficiency and AI-ready growth. 

If the challenges described here feel familiar—from disconnected journeys to rising operational effort—it may be time to reassess how your ecosystem is designed to work together.  Speak to our team about simplifying your digital ecosystem. 

What is subscription churn?

Subscription churn refers to the number of subscribers or customers that stop their subscription with your organisation within a specific period, measured against the overall customer base. Churn can be interpreted in several ways and organisations may have their own method of calculating churn depending on what suits them. However, the principle remains the same: churn shows how effectively you retain customers. 

A high churn rate means that customer retention may present difficulties, whereas a low churn rate is indicative of successful retention. 

Why is churn important in the subscription sector?

Subscriptions have embedded themselves into consumer behaviour, with 4 in 5 UK adults now signed up for at least one subscription service and nearly one-third subscribed to a subscription box delivery service. While this shows how appealing the convenience of subscriptions is, cost is a key barrier. As the cost of living rises, subscriptions are often the first thing customers look to cancel. 

In the subscription sector, churn directly affects revenue predictability, customer acquisition, lifetime value (LTV), growth and brand reputation. Even small churn rises can lead to longer-term financial instability. Understanding churn is therefore essential to uphold customer and subscriber satisfaction and retention. 

Types of customer churn

To mitigate churn, organisations must distinguish between its two types: voluntary and involuntary. Each provides a unique lens on customer behaviour and organisational performance, also requiring their own prevention and combative methods. 

Voluntary churn

Voluntary churn is when customers choose to end their relationship with a service or product. These are instances when they no longer recognise a service’s value, have opted for a competitor’s service, can no longer afford the service or other considerations.

Involuntary churn

Involuntary churn happens when customers unintentionally end their subscription with a service due to reasons beyond their control. Financial pressures are one of the most substantial driving forces behind churn, especially for discretionary spend on products that are optional rather than essential. 

Average churn rates for subscription sector

Customer churn can be expected to an extent but determining the amount of churn that your organisation can withstand and the maximum length of time in which losses can be made up will be critical for long-term growth. 
 
Churn rates also vary by customer segments. Through Acorn, our geodemographic segmentation, we found that younger Acorn groups like Tenant Living might avoid long-term subscriptions as cost is a hugely influential factor in their circumstances. Customers within Acorn’s Commuter Belt Wealth group might enjoy the convenience of subscriptions, but busy and irregular schedules can complicate commitment. We also found that subscription drop-off after discount periods is common across different segments. 
 
By recognising these behavioural differences, your subscriber retention strategies can be more effective.

Subscription churn reduction

To counter the effects of churn, organisations may turn to offering incentives that attract price-sensitive customers who churn post-offer. While this may remedy the situation to an extent, the following approaches will bolster your understanding and reduction of churn by combining proactive and reactive strategies with data. 

Bespoke segmentation

Poor segmentation leads to wasted budget on low-value audiences. Campaigns miss the mark without precise targeting and media mix decisions lack data-driven optimisation. 

CACI’s bespoke segmentation capabilities enable you to create intuitive, data-rich segments reflective of the diversity of your customer behaviours, values and attitudes. This powers personalised marketing and CRM journeys, improves media targeting and campaign ROI and supports strategic planning by revealing which segments to grow, retain or re-engage in three capacities:

• Data: Curated, high-quality foundational data with diverse input lenses and no personally identifiable information (PII). 
• Segment simulation and validation: Segment-level data layer, validation to assess predictive accuracy with guardrails in place and performance audited. 
• Persona enhancement: Defined by segment characteristics and enriched with psychological and behavioural traits, every step is tested by experts to ensure it is structured, auditable and iterative.

Predictive modelling

Through predictive modelling, AI-driven models forecast customer behaviours and guide the next best actions. This enables proactive retention and upsell strategies, prioritises resources towards customers with the highest potential and drives measurable performance uplift in metrics like LTV, conversion and engagement. 

Customer insights

CACI’s data offers a holistic view of customers that helps organisations better understand churn drivers. Customer insights are divided among: 

Core demographics

• Affluence 
• Disposable income 
• Age band 
• House size 
• Occupation 
• Number of children

Key behaviours

•  Price sensitivity 
• Loyalty 
• Motivated by premium/value 
• Convenience 
• Environmental attitudes

Digital behaviours

• Posts/reads ratings & reviews 
• Social networks 
• Influencers 
• Newspaper & magazines read

Brand engagement

• Websites visited 
• Loyalty cards 
• TV channels 
• Newspapers 
• Streaming sites 
• Magazines

An understanding of customers’ lifestyles is enriched through additional layers of their interests and hobbies, lifestyle attitudes and shopping behaviours. For subscription brands, this reveals not just who your customers are, but why they subscribe. Our insights showed that customers tend to be mindful of ethical and environmental issues and are concerned about their online security. They also tend to focus on provenance when it comes to shopping, considering where products are made/grown, the value they place on quality goods and those that make life easier. These motivations influence a subscription’s perceived value, a customer’s loyalty to a subscription and brand and what may sway their thought process in terms of staying or cancelling. 
 
Through this holistic view, you can also benchmark your organisation’s performance against competitors to gain a clear view of market position and competitive dynamics. This helps you understand where you stand in the market, who you are winning with, where you are losing and why. It identifies underperforming segments or categories where competitors are gaining share, enabling focused interventions. It also supports internal storytelling and stakeholder alignment by backing up strategic decisions with external evidence.

How CACI can help you navigate churn reduction

CACI helps retail subscription brands unlock growth by transforming fragmented customer data into actionable insight – driving acquisition, retention and personalisation at scale through advanced data science and AI-powered decisioning. 
 
We can support you in:

• Building deeper customer understanding and targeting the right audiences 
• Forecasting behaviour, improving retention and justifying investment 
• Turning insights into action across media and CRM 
• Simplifying data and bridging capability gaps

To find out more about how your organisation can successfully navigate churn reduction and strengthen customer loyalty, get in touch with us. 

In my first blog of this two-part series, I broke down the five automation metrics and principles I rely on most to help leadership demonstrate value. This second blog builds on that thinking. In my e-book, ‘Network automation in 2026: building resilience, assurance and future-ready networks’, I explained that one of the biggest challenges that network and operations leaders face today is making every change safe. 

Automation is not just about efficiency, but maintaining control within modern networks that are dynamic, distributed and tightly-connected to cloud platforms and third-party services. While automation is essential, speed without control creates risk. By unifying the three capabilities of assurance, observability and lifecycle management, it becomes possible to execute network changes in a safe and repeatable way.

Assurance: Validate before and after every change

For me, assurance is the foundation. Validate every change is safe and compliant before it goes live, then confirm it behaves as intended after deployment. Continuous validation before and after every change is now expected, helping to ensure changes are safe and compliant. Streaming telemetry and service mesh architectures provide real-time visibility, making it easier to spot issues and respond quickly

How to implement assurance:

• Define policies as code and embed them in your pipeline. 
• Run intent checks to catch misconfiguration and drift early. 
• Use change windows that include automated validation and safe rollback paths.

Outcome: Fewer failed releases and emergency fixes and better audit outcomes because evidence is generated as part of normal work. 

Observability: Real insight from streaming telemetry

In my first blog, I covered MTTR and MTTD with the time it takes you to detect issues and restore normal service. Observability is what drives this. Move beyond static, device-centric health checks to provide continuous visibility across paths, services and users.

How to implement observability: 

• Stream telemetry from network and edge assets into a common model. 
• Use service mesh patterns where appropriate to trace requests end-to-end. 
• Align dashboards to service objectives, not individual devices. 

Outcome: Faster detection, clearer root cause and performance data that stakeholders can actually trust. 

Lifecycle management: Remove tech debt as you modernise

Teams often try to automate on top of legacy risks. Lifecycle management prevents that. You plan upgrades, renewals and retirements proactively to prevent new changes from piling risk onto legacy.

How to implement lifecycle management: 

• Maintain an accurate inventory and map controls to business risk. 
• Standardise on reference designs that are easier to secure and support. 
• Budget for renewal and decommissioning alongside new projects. 

Outcome: Lower exposure, simpler operations and a platform that adapts as the business evolves. 

How to implement a safe automation framework

To bring assurance, observability and lifecycle management together for safe automation, I recommend organisations consider the following best practices:  

1. Start with responsibility: Assign clear owners for providers and controls. Everyone should know who approves what. 
2. Use reference designs: Build simple patterns that map known threats to specific controls, then reuse them. 
3. Automate safely: Codify configuration and policy, prevent drift and escalate recovery with tested rollbacks. 
4. Adopt Zero Trust: Assume breach, verify access and enforce least privilege across sites and clouds. 
5. Strengthen monitoring: Track performance, changes, access and compliance in one place. 
6. Keep governance practical: Set standards that teams can follow, measure them and iterate. 

What to measure

To make progress visible and defensible, you can refer back to the core metrics from my e-book and previous blog:  

• Change success rate and rollback avoidance 
• MTTR and MTTD
• Compliance score and drift
• Latency and packet loss against service objectives.

These metrics will help you determine whether your automation is actually making change safer.  

Two quick wins for the first 30 days

If you want to quickly build momentum, I recommend: 

• Pre-change validation on one high-traffic service: Add automated checks for policy compliance and performance impact, then track the effect on change success rate. 
• Drift detection with weekly remediation: Choose a critical domain, enable drift alerts and close gaps to raise your compliance score. 

Where SD-WAN and SASE fit

At the edge, SD-WAN and SASE extend consistent policy and observability to every site. They simplify operations, support identity-led access that aligns to Zero Trust and reduce risks from technical debt and legacy systems so networks can adapt securely as business needs evolve. 

How we can help

In my work with clients, I see the same challenge time and again: network change needs to move faster, but it also needs to be safer and more predictable. At CACI, we help organisations bring structure, visibility and governance to complex networks so change can happen with confidence. 

We support teams in putting practical assurance and observability in place, improving lifecycle management and reducing configuration drift, without slowing delivery. That means fewer regressions, clearer accountability and a more predictable change pipeline.
 
If you’d like to explore how this approach could work in your environment, visit our Network Automation page to start the conversation with our specialists. 
 
You can also download my new Network Automation in 2026 eBook for a deeper dive into how assurance and automation work together to build resilient, future-ready networks. 

In my new e-book ‘Network automation in 2026: building resilience, assurance and future-ready networks’, I uncover how network automation is no longer just about speed, but about reducing operational risk, strengthening compliance and stabilising services when the unexpected strikes. To meet the expectations of leadership, network automation must clearly demonstrate its ability to deliver on outcomes.  

This first blog in a two-part series breaks down five automation metrics and principles I rely on to help advise leadership: practical, executive-friendly and aligned to how boards evaluate resilience, risk and customer experience.

1. Change success rate and rollback avoidance 

What it is: This is the proportion of changes that complete as planned without causing incidents or requiring rollback. 
Why it matters: In my experience, this is one of the fastest ways to prove to leadership that automation is about increasing safety and predictability, not just throughput. 

How to improve:  

• I always begin with applying pre-change validation, policy gates and standardised reference designs that map controls to threats with simple, repeatable patterns. These give teams simple, repeatable patterns that map controls to threats. 
• Instrument your pipelines to capture change outcomes automatically.
• Assign clear ownership to execute each change and align teams.  

What good looks like: A steady rise in successful, first-time changes and a consistent fall in rollbacks over consecutive release cycles. 

2. Mean time to detect (MTTD) and mean time to repair (MTTR)

What it is: The time it takes you to detect issues and restore normal service. 
Why it matters: I find that detection and recovery are very important for leadership, especially because automation and observability deliver measurable business value. 

How to improve:  

• Stream all of your telemetry into a single view, then use intent checks to highlight drift or policy violations and automate first line remediation where safe.  
• Strengthen monitoring by tracking network performance, changes, access, compliance and security events.

What good looks like: Faster detection windows followed by runbook-driven recovery that is measured in minutes, not hours.

3. Compliance score and configuration drift

What it is: A combined indicator of how closely your estate aligns to policy and how far it strays from approved configurations. 
Why it matters: Boards and auditors need confidence that controls are enforced consistently across hybrid estates. 

How to improve:  

• Treat policies as code and run continuous checks.  
• Block non-compliant changes before they land.  
• Generate audit evidence automatically to save a huge amount of time.  
• Keep governance practical by setting clear standards, control owners and measurable policies. 

What good looks like: A rising compliance score with drift trending down. Exceptions are documented and time-boxed. 

4. Alert volume reduction

What it is: A measure of how many alerts actually correlate to meaningful incidents. 
Why it matters: High alert volume hides real risk and drains team capacity. 

How to improve:  

• Consolidate tooling, de-duplicate at the source, only measuring what maps to user or service objectives.  
• Safely automate by applying Infrastructure as Code and Policy as Code to prevent drift and speed up recovery.

What good looks like: Fewer alerts, higher signal quality and a clear link between alerts and customer impact. 

5. Latency and packet loss against service objectives

What it is: End-to-end performance measured against the targets that matter most for your services. 
Why it matters: User experience is the ultimate goal. Device health means little if transactions stall. 

How to improve:  

• Set service-level objectives (SLOs) for your priority journeys, instrument path visibility and factor network changes into performance reviews.  
• Adopt Zero Trust principles to assume breach, verify access and enforce least privilege.  

What good looks like: Stable or improving latency and loss for your top services, even during high change periods. 

How to get started 

I recommend teams start small when adopting these metrics, but take the following into consideration: 

1. Select two high impact metrics that you can measure today. 
2. Automate the collection and reporting so data is timely and trusted.
3. Share a simple scorecard with trend lines and short commentary.
4. Only add more metrics when the first set is stable. 

How we can help

In my work with CIOs, one of the biggest challenges I see is turning network automation into something that’s measurable, governed and trusted. At CACI, we help organisations align automation with business goals, reduce operational risk and create real clarity around performance and compliance. 

We bring proven architectures, practical operating models and clear measurement frameworks, so teams can track success rates, reduce configuration drift and improve incident response. We also help teams build simple, outcome focused scorecards that connect day-to-day network activity to executive priorities. 

If you’d like support establishing a metrics baseline or shaping an automation roadmap around the principles in this blog, visit our Network Automation page to learn more or get in touch with our specialists. 

You can also download my Network Automation in 2026 eBook for a deeper look at the frameworks and metrics that high performing organisations are using today. 

In the next blog in this series, I’ll explore how assurance, observability and lifecycle management work together to make every network change safe. 

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.

Cloud innovation trends: Why optimisation must come first

In the race to modernise, many businesses make a critical mistake: innovating before optimising their cloud infrastructure. It’s an easy trap to fall into – new technologies promise speed, agility and competitive advantage. However, without a solid foundation, those promises can quickly unravel.

So, what difference will optimisation make to cloud innovation? How do complex hybrid environments affect optimisation and what are the repercussions of innovating too soon?

Why optimisation should come first

Cloud optimisation isn’t just a technical exercise – it’s a strategic imperative. Before you invest in AI-driven tools, advanced analytics or multi-cloud deployments, you need to ensure your existing environment is efficient, secure and cost-effective. Otherwise, innovation becomes a gamble rather than a growth driver.

How the complexity of hybrid environments affects optimisation

Modern IT landscapes are rarely simple. Most organisations operate in hybrid environments, combining:

• Cloud-native workloads
• Semi-native applications
• Containerised services
• Legacy systems migrated via IaaS.

This mix introduces complexity that can quietly erode ROI and performance. Without optimisation, you risk inefficiencies that undermine every future initiative.

Common pitfalls of innovating too soon

When businesses rush to innovate without first optimising, they often encounter:

Duplicated workloads

Hybrid setups frequently lead to duplication of environments or services, especially when containerised and legacy systems overlap with cloud-native tools. This consumes bandwidth and burdens IT and DevOps teams with managing multiple versions of the same workload.

Latency issues

Poor workload distribution across cloud environments increases latency, slowing response times and masking compliance or security issues. For customer-facing applications, this can directly impact user experience and brand reputation.

Security saps

Unoptimised containerised and legacy workloads are vulnerable to governance and compliance risks. Differences in data storage and flow between environments complicate tracking, while unresolved legacy issues can carry over post-migration.

Mounting costs

With up to 30% of cloud spend wasted, inefficiencies inflate monitoring and security costs, draining budgets that could fund innovation.

Why this matters now

Cloud strategies are under pressure to deliver more – faster, cheaper and greener. Without optimisation, businesses risk inefficiency, higher costs and vulnerabilities that stall progress. In an industry where every second counts, building on shaky ground isn’t just risky, it’s expensive.

How to get started

Before chasing the next big trend in cloud innovation, take time to:

• Audit your current architecture: Maintain visibility by understand what’s running, where and why.
• Identify duplicated workloads and inefficiencies: Determine whether any services or resources are the cause behind draining budgets.
• Align resources with business priorities: Ensure any spending on cloud innovation drives value for the business.
• Implement governance and security best practices: Establishing best practices early on will ensure that innovation is scaled effectively.

This foundation ensures innovation is sustainable, not just a short-term fix.

The CACI approach: Building a cloud that enables innovation

Ready to build a cloud foundation that enables innovation?

Don’t leave your cloud strategy to chance. Our specialist cloud architects and optimisation experts have helped leading organisations modernise, streamline and unlock innovation without compromise. Contact us today to start your cloud optimisation journey.

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