Capability: Data Strategy

Multi-touch attribution (MTA) vs marketing mix modelling (MMM)

Posted on: 24 July 2025
Multi-touch attribution (MTA) vs marketing mix modelling (MMM)

What is marketing mix modelling (MMM)?

Marketing mix modelling (MMM) is a statistical tool that helps organisations understand and quantify the impact of marketing activities on consumers’ behaviours, sales, return on investment (ROI) and more. It breaks down an organisation’s performance by channel, incorporating various types of data to evaluate effectiveness and determine which marketing activities are most heavily influencing the organisation’s business outcomes, which we explore further in our blog on marketing mix modelling.  

Based on a series of steps, MMM begins with data collection of marketing variables, followed by an analysis of the data collected to identify relationships or patterns and building a customised model to showcase actions and results. Finally, scenario testing can be conducted to gauge possible outcomes, leveraging the results to optimise marketing strategies and bolster decision-making. 

What is multi-touch attribution (MTA)?

Multi-touch attribution values each customer touchpoint leading to conversion, with its goal being to decipher the marketing channels or campaigns that should be credited with the conversion. The intention of this is to measure the effectiveness of each channel or touchpoint so that marketers are aware of where they should focus efforts and resources and allocate future spend in the most effective ways possible to enhance customer acquisition efforts.  

Through multi-touch attribution, a more comprehensive view into customer journeys can be gained, enabling organisations to create better strategies or optimise their ad spend in line with market shifts. The ability to see how each touchpoint impacts a sale is what allows organisations to dissect customer journeys and allocate budgets accordingly.

What are the differences between multi-touch attribution (MTA) vs marketing mix modelling (MMM)?

Aggregated versus disaggregated data

Aggregated data is statistical data used in MMM that is grouped into channels, regions or times to assess trends in terms of how channels contribute to sales. Disaggregated data, on the other hand, is behavioural data that is used in MTA to gain the most detailed insights possible at user or individual level.  

Organisations require aggregate information for visibility into external trends that may be affecting marketing efforts and conversions. In comparison, the precise level of detail available through disaggregated data is critical in MTA as it is required for assigning multiple touchpoints within a customer journey.

Objective and impact assessment

MTA uses trackable customer interactions to understand the importance of each touchpoint. As a result, one of the most substantial differences between these two is their objective. MTA focuses on the impact of specific, individual touch points and their sale or conversions impact, whereas MMM focuses on the overall impact of your marketing mix and how that combination influences sales or other outcomes.

Choosing the right approach for your company

MMM’s main goal is to help organisations deduce overall business outcomes and MTA helps organisations understand the contributions of individual touchpoints to conversions or actions. MMM includes both online and offline channels, whereas MTA only includes digital channels that track individual user behaviours. 

While MTA may not be easy to implement due to ever-changing customer journeys paired with uniting all touchpoints across various devices, channels and platforms, it does enable flexibility and offers a more granular understanding of what does and does not work within marketing initiatives. This flexibility and granularity equips organisations with insights that allow for informed, data-driven decision-making for digital marketing campaigns.

When to use multi-touch attribution modelling (MTA)

Multi-touch attribution has become a staple for organisations requiring tactical insights and are focused on short-term optimisation by measuring and quantifying the impact that their digital marketing campaigns are having. The visibility that multi-touch attribution modelling provides into the success of touchpoints across a customer’s journey is unparalleled. This insight is critical for organisations to consider amidst consumers’ increasing wariness of marketing messaging. Through this, the right audiences and their respective marketing preferences can be identified across channels, enabling customised messaging to be created and the right consumers on the right channels at the right times to be reached. 

Maximising ROI can also be made possible through multi-attribution modelling by engaging with consumers in fewer though more frequent and impactful marketing messages that ultimately shorten sales cycles. 

When to use marketing mix modelling (MMM)

Marketing mix modelling should be used when needing to understand the combined impact of advertising spending, promotions, pricing and distribution channels. It can be particularly impactful for organisations that are well-established and have a plethora of data over the course of many years to work with. From media activities to external variables including macroeconomic factors and competitors’ activities and internal variables like product distribution, product changes and price changes, countless categories can be monitored for organisations to analyse data and understand the relationship between sales and these elements. Its [immunity to the everchanging privacy landscape] is also a key advantage.

How to use both approaches together

Both MTA and marketing mix modelling MMM are key approaches in the realm of marketing analytics. When used together, MMM can offer macro-level views into marketing impact on revenue, while MTA can supply granular insights into the effectiveness of specific marketing channels. Organisations that understand when and how to use both approaches will find themselves transforming their marketing strategies and maximising their ROI.  

Combining these two approaches when building an attribution strategy is often recommended. However, MMM will ultimately be most effective for gaining long-term, strategic insights that can bolster planning and financial outcomes, whereas MTA is best suited for short-term, tactical insights that can enhance day-to-day optimisation, campaigns and decision-making. 

How CACI can help

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

  • Determining the value and performance of activity through evolved multi-touch & econometric modelling
  • Producing results to sustain & increase growth through targeted investment & improved marketing performance
  • Delivering improved accuracy, consistency and availability of marketing performance insights
  • Enhancing capability by evolving data, technology & process
  • Supporting the provision of ongoing strategic & delivery resource.

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

Watch a session from our recently event on how to optimise marketing performance through Commercial Mix Modelling.

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Data activation: turn insights into impact in real time

Posted on: 2 July 2025
Data activation: turn insights into impact in real time

Turn insights into impact: activate your data in real time for faster results

In a world that moves at digital speed, timing is everything. It’s not enough to know your customer—you need to act on that insight while it still matters.

Data activation is the process of turning insights into timely, targeted actions, across channels and in real time. It enables brands to move from passive analysis to proactive engagement, driving relevance and results.

For many brands, the challenge isn’t collecting data. It’s activating it. Right person, right message, right moment? That’s the dream. But a lack of integration, channel connectivity, and data agility turns that vision into delay.

At CACI, we help brands bridge the gap between insight and impact by enabling real-time data activation—powering better experiences, smarter marketing, and faster growth.

The reality: data without action

Your analytics team knows who abandoned their basket. Your CRM knows who hasn’t opened an email in months. Your loyalty system knows who just hit VIP status.

But if these systems don’t talk to each other—or worse, talk too slowly—then those valuable signals go unused or arrive too late to make a difference.

The consequences?

  • Delayed personalisation
  • Missed conversion moments
  • Campaigns running on outdated data
  • Frustrated teams with siloed tools

The CACI approach: real-time activation, delivering real business impact

We help brands unlock their data’s full potential by ensuring insights flow seamlessly into the systems that matter, when they matter most.

Here’s how we do it:

  • Connected ecosystems:  We build and enhance integrations across your marketing, CRM, loyalty, and analytics platforms to ensure data can move fast and freely.
  • Dynamic audience segmentation:  We enable you to build and refresh customer segments in real time, based on behaviour, triggers, or lifecycle changes.
  • Channel activation: Whether it’s email, app push, social, web estate or in-store, we make sure your data feeds directly into the tools that deliver experiences.
  • Event-based triggers: From cart abandonment to product interest, we help you automate actions at the moment they matter—not a day later.

Why real-time matters

Today’s consumers don’t wait—and neither should your data. The brands winning in customer experience are those who’ve moved from “reporting” to real-time reacting.

By activating data instantly and intelligently, you can:

  • Increase conversion rates
  • Improve customer retention
  • Reduce media waste
  • Boost ROI on your technology stack

Want to see where you’re losing time (and value)?

Let CACI help you uncover how your digital estate is performing. We’ll identify where your data flows (or doesn’t) and show you how to speed up your insight-to-action journey. Don’t let valuable signals sit idle—activate them in real time with CACI.

Use our Digital Analytics Self-Assessment Checklist to evaluate your current capabilities and uncover opportunities for growth. It’s a practical first step toward unlocking the full potential of your digital strategy.

Breaking down data silos in analytics

Posted on: 30 June 2025
Breaking down data silos in analytics

In today’s experience-driven world, customer journeys are anything but linear. They begin on mobile, pause on desktop, skip to in-store, loop through social media—and often, they leave behind a trail of fragmented data that brands struggle to unify. This in turn leads to frustration and a poor customer experience.

Data silos in marketing analytics often arise from disconnected systems, departmental boundaries, and legacy technologies that don’t communicate with each other. These silos prevent a unified view of the customer, making it difficult to deliver consistent, personalised experiences across channels.

At CACI, we see this disconnected data challenge as a pivotal opportunity. We support brands in breaking down data silos and rebuilding customer understanding with clarity, purpose, and precision.

CACI’s approach: connecting the disconnected

We enable brands to identify, intercept and connect fragmented users and journeys across their entire online and offline estates.

Here’s how:

  • Unified customer view: Our data solutions stitch together disparate identifiers and touchpoints to create a single, accurate view of each customer. One profile, all channels.
  • Journey analytics: We map real customer journeys—not just clicks or page views. Our insights reveal where users drop off, what triggers conversion, and where friction hides.
  • Omni-channel attribution: Our Commercial Mix Modelling solution allows brands to understand what truly influences customer decisions, from ATL and OOH sighting through to more targeted, 1-2-1 channels. We help brands attribute value accurately and make smarter decisions about where to place marketing spend, with a channel, segment and customer lens.
  • Real-time interception: Powered by advanced analytics, we empower brands to recognise a fragmented user in the moment and serve the right message, at the right time, in the right place.

Transforming data into decisions

Disconnected data isn’t just a technical problem—it’s a customer experience problem. When brands can’t “see” their customers clearly, the customer feels it. Poor targeting. Broken journeys. Irrelevant messaging.

At CACI, we see data as the fuel for transformative brand experiences. By helping brands unify, enrich, and activate their data, we unlock meaningful personalisation, higher ROI, and lasting loyalty.

Ready to connect the dots?

Ready to break down your own data silos and unlock the full potential of your analytics? Start by identifying where you stand. Use our Digital Analytics Self-Assessment Checklist to evaluate your current capabilities and uncover opportunities for growth.

Data harmonisation: speak the same language across teams and systems

Posted on:
Data harmonisation: speak the same language across teams and systems

Unifying your data story: speak the same language across teams and systems

In the world of digital analytics, there’s a silent struggle happening behind the dashboards: inconsistency. Different teams. Different tools. Different definitions. And the result? A fragmented data story that no one can confidently act on.

This is where data harmonisation comes in. It’s the process of aligning data from disparate sources into a consistent structure, using shared definitions, taxonomies, and metrics. By harmonising data, businesses can eliminate confusion, reduce duplication, and ensure that insights are accurate and actionable across the organisation.

At CACI, we work with brands to bring order, clarity, and alignment to their data foundations—because without consistent taxonomy and standardised metrics, even the most advanced analytics can mislead rather than inform.

The hidden cost of inconsistent taxonomy & metrics

When marketing, digital, product, and analytics teams define success differently, it creates confusion. What counts as a “conversion”? How is “engagement” measured? Is “bounce rate” the same across platforms?

Without a common language:

  • Reports contradict each other
  • Decisions are delayed
  • Cross-channel comparison becomes unreliable
  • Teams lose trust in the data

In an era where fast, data-led decisions drive competitive advantage, this kind of friction is a blocker to growth.

CACI’s approach: building a solid analytical foundation

Our approach simplifies  complexity and align on the fundamentals. Here’s how:

  • Taxonomy harmonisation: Through collaboration with stakeholders, we design and implement a unified taxonomy that reflects your brand’s goals, customer journey stages, and platform specifics.
  • Metric standardisation:  Consistency in metric definitions ensures everyone speaks the same data language. We bring consistency to how key metrics are defined, calculated, and reported—so that insights are trusted, comparisons are accurate, and decisions are aligned across the organisation.
  • Governance frameworks:  We help embed processes, guardrails, and tools that ensure ongoing data integrity, even as teams scale and evolve.
  • Enablement & training: A good taxonomy isn’t just a document—it’s a mindset. We deliver practical enablement to ensure adoption and understanding across your organisation.

Why it matters

When taxonomy and metrics are consistent, analytics becomes a true strategic asset. Brands can compare performance across campaigns, channels, and regions. They can move faster, with confidence. And perhaps most importantly—they can trust the story their data is telling.

Struggling with inconsistent metrics and siloed definitions? Let’s bring clarity to your analytics and confidence to your decisions. Use our Digital Analytics Self-Assessment Checklist to evaluate your current capabilities and uncover opportunities for growth.

What is Marketing Mix Modelling (MMM)?

Posted on: 9 April 2025
What is 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. 

Benefits of marketing mix modelling (MMM)

  • 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.

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

Posted on: 6 March 2025
Turning strategy into success: enabling the right customer experience to deliver on your growth targets

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.

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Introducing Mood’s unique approach: Agile digital twins

Posted on: 30 January 2025
Introducing Mood’s unique approach: Agile digital twins

In our previous blog in this series, we uncovered the key characteristics of digital twins, their advantages and challenges and what organisations that adopt a digital twin can expect to gain from it. Today, we’ll examine Mood’s unique approach to constructing digital twins and how it can support organisations. 

What is Mood and what approach does it take with digital twins? 

Mood’s platform addresses the challenges of creating digital twins by offering a highly flexible and customisable solution that caters to specific organisational domains. Mood’s approach is centred on three key pillars:   

Agility and flexibility   

Mood enables the creation of agile digital twins that can be rapidly adapted to an organisation’s unique requirements. Whether it’s a specific industry, business model or operational process, Mood’s platform provides the tools needed to build a digital twin that accurately represents the organisation’s domain in the virtual world.   

Integrated data and consistency  

Mood’s platform integrates data from multiple sources, ensuring that the digital twin is truly reflective of the real-world state. This integration is key to maintaining clarity and consistency across the organisation, allowing for more accurate analysis and decision-making.   

Rapid deployment and optimisation 

Mood offers services that accelerate the deployment of digital twins, allowing organisations to start benefiting from their virtual models in a shorter timeframe. Its continuous monitoring and real-time analysis capabilities also enable rapid optimisation of operations, providing a significant competitive advantage.   

Common questions about digital twins 

1. How is a digital twin different from a simulation or a 3D model?  

While simulations and 3D models are static representations often used for specific scenarios or time points, a digital twin is a living, dynamic model that continuously updates based on real-time data. Digital twins provide a more comprehensive and accurate view of the current state of a system and allow for ongoing monitoring, predictive analysis and decision-making, far beyond what static models or simulations offer.  

2. Do digital twins require IoT (Internet of Things) technology?  

While IoT technology is a common and effective way to gather real-time data for digital twins, it is not strictly required. Digital twins can also be built using other data sources, such as enterprise systems, manual inputs and historical data. However, IoT devices enhance the digital twin’s ability to reflect real-time changes where physical assets are critical, making them particularly valuable in dynamic environments.  

3. Are digital twins only applicable to manufacturing and physical assets?  

No, digital twins are not limited to manufacturing or physical assets. They can be applied across a range of industries and domains, including healthcare (e.g., patient monitoring), urban planning (e.g., smart cities), logistics (e.g., supply chain management) and even service-oriented sectors. Any process or system that can benefit from real-time data integration and analysis can potentially utilise a digital twin.  

4.How difficult is it to create and maintain a digital twin?  

The difficulty of creating and maintaining a digital twin depends on the complexity of the system being modelled, the availability and quality of data and the technology stack used. While some digital twins can be complex and resource-intensive to develop, there are also more straightforward and scalable solutions available. With Mood, your digital twin can start small, returning instant value and iteratively scaled based on priority.  Maintaining a digital twin requires ongoing data integration, model updates and regular performance evaluations to ensure it remains accurate and relevant, so a single platform acting as the lynchpin can be hugely beneficial.   

How Mood can help 

Mood’s platform and professional services offer a unique solution by providing the flexibility, integration and agility needed to develop and maintain effective digital twins. By leveraging Mood’s capabilities, organisations can achieve a new level of operational clarity and efficiency, ensuring they remain resilient and competitive in the face of ongoing challenges.  

For organisations lacking the confidence to build their own digital twin from scratch, our consultants work directly with our customers to help them, ensuring they have the skills they need moving forward. Contact Mood today to begin your journey towards an agile, data-driven future.  

 

Understanding the key characteristics & outcomes of a digital twin

Posted on: 27 January 2025
Understanding the key characteristics & outcomes of a digital twin

Digital Twin

In our previous blog in this series, we examined a real-life example of where a digital twin helped drive outcomes for an organisation and the overarching importance of digital twins amidst the ever-changing technological landscape. Today, we’ll explore the characteristics comprising digital twins, including their advantages, challenges and what organisations can expect from them. 

What are the key characteristics of a digital twin? 

A digital twin, in its most basic form, is a virtual representation of a physical entity or group of entities, such as the machines and their systems on a manufacturing shop floor. However, in the context of organisations, digital twins go beyond simply replicating physical assets. They represent the entire organisational structure, including processes, workflows, systems and even human behaviours. Some of the key characteristics of a digital twin include: 

Real-time data integration (H3) 

  • Dynamic and continuous synchronisation: A digital twin constantly updates its virtual model based on data from its physical counterpart or the processes it represents. This real-time integration allows the twin to accurately reflect the current state of the system, asset or organisation it models.   
  • Data sources: It incorporates data from various sources, including IoT sensors, enterprise systems, operational data stores and external data feeds, ensuring a comprehensive and up-to-date virtual representation.   

High fidelity and accuracy

  • Detailed and precise representation: A digital twin provides a high-fidelity model that captures the complexities and nuances of its subject. This includes both physical characteristics (e.g. dimensions and materials) and operational parameters (e.g. performance metrics and environmental conditions).   
  • Scalability: The accuracy of a digital twin can scale from a single asset (e.g. a machine) to complex systems (e.g. an entire manufacturing plant or organisational process, including its external factors).   

Two-way interaction 

  • Bidirectional communication: A digital twin supports two-way communication, allowing not only the updating of the virtual model based on physical world changes, but also enabling the virtual model to influence its real-world counterpart. For instance, adjustments made in the virtual model can be implemented in the real-world system.   
  • Predictive and prescriptive capabilities: Beyond mere replication, a digital twin can predict future states and prescribe actions based on simulations, scenario analysis or machine learning algorithms.   

Comprehensive lifecycle representation

  • Lifecycle coverage: A digital twin spans the entire lifecycle of the system, organisation or asset it represents, from design and development through to operation, maintenance and even decommissioning. This ensures that insights can be derived at any stage, supporting continuous improvement and adaptation.   
  • Change management: It adapts to changes in the physical environment, evolving over time as the real-world counterpart undergoes modifications, whether in design, operation or environment.   

Simulation and scenario analysis 

  • What-if scenarios: A digital twin enables the simulation of various scenarios and potential changes before they are implemented in the physical world. This includes testing new designs, operational strategies or responses to hypothetical events, all within a risk-free virtual environment.   
  • Optimisation: By analysing different scenarios, the digital twin helps in optimising performance, reducing costs, improving efficiency and enhancing risk mitigation.   

Advanced analytics and machine learning  

  • Data-driven insights: A digital twin leverages advanced analytics, including predictive modelling, machine learning and AI to extract meaningful insights from the vast amounts of data it processes. This allows organisations to predict outcomes, prevent failures and optimise operations.     
  • Learning capability: The digital twin can “learn” from the data it receives, continuously improving its accuracy and predictive capabilities over time.   

It’s important to note, however, a digital twin can still function effectively and add value without ML and AI, instead relying on real-time data integration, simulation and rule-based systems, until enough data is generated to create ML models.   

Contextual awareness 

  • Environment and ecosystem awareness: A digital twin understands the context in which the physical asset, organisation or process operates, including its environment, external influences and interdependencies with other systems, enhancing the relevance and precision of the insights generated.   

Interoperability and integration 

  • Seamless integration: Digital twins are designed to integrate seamlessly with other digital systems, tools and platforms within an organisation. This interoperability ensures that the digital twin can act as a central hub for data and insights, interacting with various enterprise systems like ERP, CRM and PLM.   
  • Modularity and scalability: The architecture of a digital twin should allow it to be modular, enabling different components to be updated, replaced or scaled independently, which is critical for adapting to evolving organisational needs.   

Visualisation and user interaction 

  • User-friendly interface: A digital twin often includes advanced visualisation tools such as 2D & 3D models, dashboards or even augmented reality (AR) interfaces, simplifying users’ interactions and interpretations of the virtual model. The use of these depends on the need, however.   
  • Interactive decision support: Users can interact with the digital twin to perform analyses, run simulations and explore different operational strategies, all through an intuitive and accessible interface.   

Security and compliance   

  • Data security: Given that a digital twin deals with real-time and potentially sensitive data, robust security measures are a fundamental characteristic. This includes data encryption, secure communication protocols and compliance with industry standards and regulations.   
  • Governance and compliance: Digital twins must adhere to governance frameworks and compliance requirements, ensuring that the data and operations they manage meet regulatory and ethical standards.   

What are the advantages of digital twins for organisations? 

Proactive maintenance  

The system sent automatic notifications when machines required attention, whether due to routine maintenance, in response to a negative trend or as a response to an unexpected incident. This minimised downtime and ensured continuous production with a higher utilisation rate. 

Trend analysis 

The digital model tracked stats over time, allowing for trend analysis. This feature was invaluable in predicting when a machine might require more significant intervention or identifying when a production line was consistently underperforming.  

Quality assurance  

By integrating the testing processes into the digital twin, the system provided real-time feedback on the quality of the fire detectors being produced. Engineers could react quickly to any deviations, ensuring that only high-quality products left the facility.    

Enhanced decision-making

Digital twins provide a comprehensive view of organisational operations, enabling decision-makers to visualise the impact of changes before they are implemented. This leads to more informed and strategic decisions, reducing risks and improving outcomes.   

Operational efficiency 

By simulating processes and workflows, organisations can identify inefficiencies and bottlenecks in real-time, allowing for continuous optimisation and therefore improved productivity, reduced costs and agility to change.   

Predictive maintenance and risk management  

Digital twins can predict potential failures or risks by analysing data trends and patterns, minimising downtime, preventing costly disruptions and enhancing resilience.   

Scalability and flexibility 

Organisations can use digital twins to model and test new business strategies, products or services without disrupting existing operations, enabling businesses to innovate and adapt to changing market conditions with minimal risk.   

Employee and resource optimisation  

By simulating human behaviours and interactions within the organisation, digital twins can optimise resource allocation, improve workforce planning and enhance employee engagement.   

What challenges arise when creating digital twins? 

Complexity and customisation  

Developing a digital twin for an organisation is inherently complex due to the need to capture and integrate diverse data sources, processes and systems. Additionally, each organisation has unique requirements, complicating the creation of a one-size-fits-all solution.   

Data integration and quality  

A digital twin’s accuracy and effectiveness depends on the quality and integration of data. Inconsistent, incomplete or siloed data can compromise its ability to provide reliable insights, leading to suboptimal decision-making.   

Scalability of platforms    

Most existing platforms for creating digital twins are rigid and domain-specific, limiting their applicability across different industries or organisational needs and potentially hindering organisations from fully leveraging the potential of digital twins.   

High development costs and time

The process of designing, developing and deploying a digital twin is often time-consuming and expensive. This can be a significant barrier for organisations, particularly those with limited resources.  

How Mood can help 

For organisations lacking the confidence to build their own digital twin from scratch, Mood consultants work directly with customers to equip them with the necessary skills to progress towards an agile, data-driven future. Contact Mood today to begin your journey. 

Stay tuned for the next blog in this three-part series, where we’ll explore the unique approach to digital twins offered by Mood and how organisations that leverage Mood’s capabilities can enhance their digital twin experience. 

 

How digital twins drive real-world outcomes for organisations

Posted on: 21 January 2025
How digital twins drive real-world outcomes for organisations

Digital twins have emerged as a transformative concept that offers unprecedented opportunities for organisations to monitor, analyse and optimise their operations. However, the term “digital twin” is often misunderstood or oversimplified, leading to confusion about its true value and application. In this blog series, we will demystify the concept of digital twins, particularly in the organisational context, explore their advantages and challenges, and assess Mood’s innovative approach to creating agile digital twins that enables organisations to achieve enhanced clarity, consistency and rapid optimisation.   

Real-life example: Creating an early digital twin of a manufacturing shop floor 

Early in my career, I embarked on a project that would essentially become a digital twin of a manufacturing shop floor and associated processes. This experience was a formative one, laying the groundwork for my current understanding of how digital representations can drive efficiency, insight and optimisation in real-world operations.   

The challenge: Optimising production, maintenance & testing processes 

The manufacturing facility I worked at produced fire detectors, and the shop floor was a bustling environment where efficiency and quality were paramount. However, managing the maintenance of machines and the rigorous testing of the manufactured products presented significant manual processes and thus challenges. The facility needed a system that could not only track and manage these processes but provide insights into potential issues before they became critical.   

The solution: A digital model using Microsoft Visio, SharePoint, InfoPath & Raspberry Pis 

Visualisation with Visio

To tackle these challenges, I created a visual model of the shop floor using Microsoft Visio. This model detailed the layout of the shop floor, with the various machines and their specific roles in the manufacturing process. The visual representation served as a foundation for what would later evolve into a more sophisticated digital twin.   

Data management with SharePoint 

To bring this model to life, I used Microsoft SharePoint to create data lists that held critical information about the machines, maintenance schedules and test results. These data lists became the backbone of the system, feeding data into the Visio model, allowing it to be more than just a static diagram.   

Interactive user interfaces with InfoPath

For the maintenance and test engineers, I developed user interfaces using Microsoft InfoPath. These interfaces enabled them to input data related to maintenance schedules, findings, test results and general information. Engineers could also report incidents such as unexpected machine downtimes directly into the system. This data entry was crucial, as it provided the real-time updates necessary for the model to reflect the current state of the shop floor accurately.   

Data capture with Raspberry Pis 

To further enhance the system’s capabilities, data collected directly from the machines using Raspberry Pis, such as throughput rates, machine performance metrics and any deviations from expected operation was fed into the SharePoint lists via CSV files periodically. This integration of what was essentially an early form of IoT devices was a critical step towards creating a more responsive and accurate digital representation of the shop floor.   

The outcome: A digital twin of the shop floor processes 

What emerged from these efforts was, in essence, a digital twin of the manufacturing shop floor. This system provided near-real time dashboards that displayed the status of the machines and their key metrics. Engineers could gauge which machines were approaching tolerance levels for throughput or which production lines were close to failing quality tests.    

Reflection: Realising the concept of a digital twin 

By visualising the shop floor, integrating near-real time data and enabling interactive user interfaces, I was able to create a system that mirrored the physical world and provided actionable insights to improve efficiency, quality and maintenance in the form of what is now known to be a digital twin. This early project taught me the importance of digital representation in driving real-world outcomes and laid the foundation for my ongoing work in developing and advocating for a flexible, agile platform that can be adapted to any organisational domain and enable rapid turnaround without the need to cobble together several tools.  

How Mood can help 

For organisations lacking the confidence to build their own digital twin from scratch, Mood consultants work directly with customers to equip them with the necessary skills to progress towards an agile, data-driven future. For further insights, download our full whitepaper “Understanding Digital Twins” or Contact Mood today to find out more.

Stay tuned for the next blog in this three-part series, where we’ll dive into the characteristics of digital twins including their advantages, challenges and what organisations can expect from them. 

 

Why effective project prioritisation in consultancy is crucial

Posted on: 10 October 2024
Why effective project prioritisation in consultancy is crucial

When it comes to consultancy, project prioritisation is critical so that customers receive urgent or important work first before less vital items. In straightforward projects with one product owner and a finite backlog, you can approach this issue by working through the backlog and asking them to label them using MoSCoW, the prioritisation technique used in project management and business analysis to help stakeholders understand the importance of various requirements, for example. It’s when you move to a project with multiple product owners and an ever– extending backlog that the problems appear, however.  

So, what are the common project prioritisation challenges arising in consultancy nowadays and what solutions are available to consultants to solve them? 

Common challenges in consultancy around project prioritisation

Within each project, each stakeholder (this could be from multiple products, multiple product owners or stakeholders without a product owner) will bring their own backlog, each believing that their demands are the most important and that all your resources are theirs to use. Negotiating between these product owners can be difficult, especially as they may have their own deadlines that they’ve committed to, perhaps only needing your resources for part of their project and a delay could cause their entire project not being delivered on time.  

While earlier and clearer communication would undoubtedly help with these issues in the long run, where do consultants start in the meantime? 

How consultants can improve project prioritisation

Consultants that refer to a categorical prioritisation list for each project (such as the example below) will notice immediate and significant improvements. By scoring each project against a list of categories, with the resulting score used to order the backlog and any incoming items, their respective priority and importance will be illustrated to the wider business.  The category list is:  

Once a project has been scored on each of these points, the total score is calculated. This is then used to rank projects against each other. It’s important to reassess the time rating approximately every three months, as this rating will need to be increased to reflect the real-world situation.  

Benefits of this approach

The advantage of adopting this approach is that it enables you to provide an explanation as to why certain projects are higher priority than others rather than using a more subjective approach. It’s possible to add a higher rating to categories so that the calculation better represents the company’s priorities.  

Potential difficulties of this approach

Some of the issues we’ve noticed so far are that these categories don’t necessarily work as well for enabling items such as a pilot, which won’t deliver any benefit to the system on its own but is required before the new feature can be started. To bolster this, we had to consider the ultimate deliverable being enabled, otherwise, the supporting item would score too low.  

 Technical debt is another type of work that doesn’t quite fit into these categories, which is why we ultimately decided to remove it and prioritise it separately.  

 Despite all the categorisation and discussions, you can end up with a list that doesn’t quite correspond with your gut feeling based on market trends. To mitigate this, a review was organised every few months to monitor scoring accuracy.  

Conclusion  

For projects without a finite backlog where upfront prioritisation isn’t possible, this approach allows you to prioritise against existing work in flight. However, it’s important to account for the amount of time, effort and morale downturn it takes to pause and restart.  

This method of prioritisation is ultimately particularly a useful tool for prioritising the constant stream of incoming projects from multiple product owners. The conversations that come out of the prioritisation are also of substantial value, and to some extent, enable the prediction of what will be delivered in the near future.  

To learn more about project prioritisation in consultancy, speak to one of CACI’s experts today.