Capability: Data Strategy

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.

SOURCES:  

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. 

Adopting AI into organisations: carrying past lessons into the future

Posted on: 11 July 2024
Adopting AI into organisations: carrying past lessons into the future

Reflecting on projects and organisations I’ve worked with in the customer strategy and marketing technology space, there are new challenges daily, but how organisations harness data feels like Groundhog Day. From changes of funding models and head counts to the centralisation and decentralisation of teams, it feels like the value of the Data and & Analytics (D&A) functions is still in a phase of being scrutinised heavily, with some businesses unable to unlock the magic that was promised many years ago.

In 2024, AI is everywhere, creating both excitement and fear. The big question I often hear is: “How do we use AI to stay ahead of the curve?”. The lack of knowledge around the limitations has created a sense of infinite opportunity. The rate of change is rapid and big players are getting involved. It’s easy to see how organisations can feel like a deer in the headlights on what to do, afraid to be left behind.

The risk is empty promises, money wasted and no results. There are lessons to take from the D&A revolution to help guide us in the AI era, however. Some of the key themes from successful D&A transformations I’ve been part of at CACI that are relevant for AI adoption by analytics and data professionals in the customer space have been:

  • Establishing value
  • Data literacy
  • Data modernisation
  • Evolving the process

Establishing value

Establishing value is pivotal to getting the business along the journey by helping stakeholders understand how data and analytics helps their bottom line. Stakeholders don’t want a handful of numbers, they want the capability to make better decisions, execute more efficiently and deliver greater impact. Therefore, if a predictive model has been created to determine when is best to target a customer sale, the job is not done. The next steps are to substantiate what this can mean for the business in terms of opportunity, followed by activating it to drive and prove that the sale has happened.

This will be similar with AI. It is key to start by defining the use cases and business challenges to be addressed. Once this is understood, AI initiatives can have buy-in and be driven more easily. It doesn’t require a large roadmap, a series of proof points and steps to prove value is more than enough. Establishing what value is and demonstrating it unlocks the licence to move forwards with smaller, incremental steps.

Data literacy

Increasing data literacy is key to establishing a two-way conversation between stakeholders and D&A ambassadors. For stakeholders, it allows them to define their ambition and utilise D&A outputs to deliver to that ambition. For D&A ambassadors, it’s talking the language of the business, contextualising the day-to-day impact. Interestingly, working on this in the past has ended up with stakeholders mentioning “data” less.

For example, I worked with marketers who wanted to understand the opportunity in terms of how many customers they were going to reach. “What about the data?” was banded around, which can mean different things to different people. After helping educate them on the role of understanding counts and what that means for volumes, the language shifted to “volumes of unique eligible customers who will receive the campaign”. The less the conversation becomes about “data” and more what it means for customers while knowing the considerations with respect to data, the more effectively the business can reach its outcomes with less confusion and at a greater pace.

With AI, the role of ambassadors for data, analytics and AI is to always be translators, empowering users to understand and carry the conversation in all directions. That means fostering a culture where there is specific training for different stakeholders, tailoring how you talk to the stakeholder’s world and keeping at the forefront of developments to help people understand what AI means for both their day-to-day and future.

Data modernisation

I’ve often seen organisations leapfrogging with their technology capabilities or implementing data science models only to realise that the integrations were not set correctly and the data itself was not fit for purpose. There is the assumption of quality of data and that all tools are fit for purpose, however, data management and governance practices that have not evolved to meet requirements risk creating low quality data, which will affect outputs and create a lack of trust in the data and models. Furthermore, low data accessibility, exasperated by poor data management, can increase latency and make the speed to value slow and painful. These areas are typically not what stakeholders are thinking about and often results in large-scale data transformations becoming dead in the water.

Data modernisation requires reviewing infrastructure and governance so that processing and storage happens closer to where decision-making happens, improving speed, reducing cost and closing silos. Focusing on access, quality and efficiency will enable AI to be integrated in a way that is usable and scalable. Moreover, as AI application increases, AI-focused data management practices will significantly improve accuracy and performance of the models, which is crucial when productionising AI.

Although it may not be pretty or exciting for the end users, addressing data modernisation must be a key priority for D&A and AI ambassadors. There will be challenges in helping organisations understand the ramifications of substandard data management and governance practices. Tackling these issues head on will improve the time to value for AI and mitigate issues with quality, cost and output. Beyond this, modernisation of data governance must venture further– with a strict focus on ethics and compliance– by assuming the role of PII within the organisation and how that is used with AI, if using external technology.

Evolving the process

Once there is buy-in and a return on the D&A initiatives is recognised, interest and further investment will then be generated from the organisation. The next part, scaling, is sometimes the hardest step. In my experience, those who reach this point likely had smaller, autonomous teams tackling the D&A transformation. Moving forwards requires ongoing attention and adaptation, with the trend being to create specific roles and departments. While this can make sense, the risks include siloing teams and shifting focus from business outcomes to becoming more about delivering tasks.

The same will apply to AI, where it’s tempting to have an ‘AI department’. The balance that must be struck is the ability to deliver business outcomes versus the need to nurture specialisms to ensure that there is growth for the individuals, a combined view on the future and enforcement of best practice. This will emulate cyclical trends of centralisation and decentralisation of teams. This is not a bad thing– it’s okay to constantly evolve and adapt operating models around business needs. AI is unique in that it will become more pervasive in the day-to-day, so while AI technology may be centralised, its use will seep through the whole of an organisation.

How CACI can help?

Despite AI feeling like the next revolution, for some, it’s an evolution of data and analytics. We are in a period where D&A is being scrutinised in terms of its value, but the question is not being asked of AI just yet. It will be, and the themes above will gear you towards being able to drive ROI.

To learn more about how CACI can drive value with AI in driving value from your customers, contact us today.

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New Year, New me? Reset, face out to the market, connect, and engage

Posted on: 3 January 2024
New Year, New me? Reset, face out to the market, connect, and engage

There has been a lot written in the past 18 months about how the pandemic realigned the way we were living. A moment in time that stretched into a phase that was different for literally everyone, affecting us all in different ways; young and old, key workers through to shop staff, blue collar workers and office employees. Just think for a moment about how different life is now, how you think it might have affected the above groups and how such a seismic shift defines who we are today, what we want from our lives, our relationships, our jobs and our future.

Key reflections in the new year

As we move into a New Year, even if we don’t make wholesale changes, I always find it to be a good time to reflect on where you are at, force a change – even a small one – and move into the New Year in a new gear.

In the last few years, those changes for me have been about reflecting on how we are conducting ourselves on a day-to-day basis and resetting into what is needed in the year ahead rather than drifting along with an adopted behaviour that you inherited post-Covid. I have been very keen to try to get colleagues to do the same.

Go out and see clients face to face, walk around the places we work on and have delivery meetings on client sites rather than on the dreaded Teams. While I’m fully aware of the benefits in time and travel that Teams has brought, I think it is incumbent on us all to ensure we use the channel in the right way, not just because it is the easiest thing to do. There will always be instances where face to face does make a lot more sense – think about it and make the effort!

Understanding the impact of consumers’ changing values and priorities

Working in consumer understanding at CACI, I’ve found myself reflecting a lot over the past 18 months, not just about the behavioural changes the pandemic has instilled in us, but how it has altered our values. In general, we have become much more particular about what we choose to do with our time away from home. Some groups, because of what home is to them (singles in smaller shared accommodation versus families in larger, out-of-town homes for instance), will have wildly different values based on that home set up, their life stage, affluence, etc. which maybe, or may not, be like their pre pandemic selves. However, how they value their time, effort and disposable income has definitely shifted.

The impact of the Cost of Living crisis has further evolved everyone’s position, with disturbing situations becoming everyday concerns. Simple things such as keeping warm, having a hot shower, or saving on electricity bills are driving the younger cohorts back to the office now more than the Boomer generation. That, and the realisation that without real face time with their peers and seniors, their careers may be stunted.

Gen Z Millennial Boomer office stats
CACI Cost of Living tracker 2023

Applying these learnings in real time: Revo takeaways

Beyond this change in ourselves, we have seen huge changes in the businesses and organisations we work with.

In this last year, I became a board member of the industry body, Revo – an organisation that has gone through wholesale change, not because of Covid per se, but because of what the market needs out of such industry groups. In the past, it was famed for a large-scale conference, held over three days in a regional city, overlapping with many other similar events and organisations. Today, it is very much reset as a not-for-profit organisation, run by the members, for the members.

It is focused on providing a community platform to connect next generation Revo Hub members with those who have a few years under their belts. Instead of a huge annual conference, we now provide smaller events, including the very recent Revo Awards ceremony at Control Room A in Battersea Power Station. On this night, we celebrated the achievements of the best in the business across marketing, asset management, regeneration and leasing. As Revo evolves, those members who contribute will do the same, with a focus on getting out into the market to explore and learn from these winning best practice examples.

Predictions for the future of our working world

So, thinking about the future changes; in our work world, 2023 was centered around the birth of generative AI, albeit over thirty years after the business world started using all forms of AI (under a different name). I no longer struggle to answer the question ‘What do you do for a living?’. While our world at CACI isn’t as straightforward as saying you work in Finance or Retail, with Generative AI for the masses now, I can (relatively) easily explain that I work in consumer data to support businesses like banks, using natural language in AI to categorise large volumes of calls data to better direct enquiries. Or, using AI on satellite imagery to create spatial wealth distribution indices for far flung places. Or, put more simply, use behavioural data (like GAI can) to enable better actions and interactions with customers and prospects.

My biggest goal moving into 2024, and one I would encourage colleagues and friends alike to adopt, is to just get out there and see places again. Make sure you are putting a value on that travel and time, but also make a concerted effort to get away from your screen (office or home), force a new experience, and share that. In a world where AI will take away the mundane tasks, it is even more important to enjoy the new experiences that these new repurposed places bring.

How Transport for Greater Manchester increased value from data to understand the people behind travel patterns

Posted on: 19 September 2023
How Transport for Greater Manchester increased value from data to understand the people behind travel patterns

The challenge

  • Increase the proportion of journeys made by active travel and public transport
  • Understand variations in the customer profile across different modes of travel, and specific Bus, Metrolink, and cycle routes
  • Understand barriers to take-up for different user groups (e.g. geographic location, affordability)
  • Identify appropriate ways to engage with existing customers and target new users

The solution

To overcome these challenges, Transport for Greater Manchester partnered with CACI on the following solutions:

  • Use Acorn Postcode, Workforce Acorn, Paycheck, and Retail Footprint to enhance their own datasets, including survey data (at the sampling, weighting and analysis stages)
  • Use with GIS systems to identify spatial patterns and trends
  • Postcode-level analysis provides a granular understanding that allows for targeted intervention

The benefits

“CACI’s Acorn, Acorn knowledge base and supporting products (Paycheck, Retail Footprint), used in combination with our own datasets, increase the value we can get from our data and help us to understand in more depth the people behind the travel patterns.”

Rosalind O’Driscoll, Head of Policy Insight and Public Affairs – Transport for Greater Manchester

Read the case study

You can access and download the full case study here. If you have any questions or want to learn more about CACI’s solutions, please get in touch with us.

What is InView? 

Posted on: 26 July 2023
What is InView? 

InView is CACI’s data platform that is specifically designed for the NHS. It is modular by design with over 30 modules out of the box, and makes data sharing for ICS simple and efficient through its standardisation and safety in data management. The flexibility, maintenance and content provided by a standard data platform built in-house can only go so far. Considering the many pressures faced by NHS Trusts daily, they need a data platform that supports—rather than hinders– them.  

InView empowers NHS Trusts nationwide to enhance their reporting and unlock the potential of their data by ensuring that all data reporting is correct, consistent and complete within a singular integrated solution that will transform patient care outcomes. 

But how exactly does InView work? And what makes it so beneficial for the NHS? This blog will dive into everything you need to know about InView so you can make informed decisions about your own data platform. 

How does InView work? 

To meet the high volume of mandated statutory changes and local reporting requirements, your Trust should be equipped with a solid data platform that is easy to use and fully maintained. InView is risk-free*, robust and easily maintained, ensuring that you and your Trust can meet these requirements by providing all key statutory outputs and fully maintaining them in line with NHS change notifications as part of core product releases.  

Designed and built in a way that promotes rapid implementation of a solution within a Trust, InView secures you with plenty of pre-built content from all disparate data sources in one unified, trustworthy solution. Each of InView’s 30 modules is built from a sophisticated, layered design that will keep future maintenance costs down and future proofing up. Its layers include: 

  • Acquisition Layer: This layer accepts the data from incoming data sources and is designed to accept data in a raw format prior to any data checking.  
  • Integration Layer: As the middle ground between the Acquisition layer and the upcoming Translation layer, this layer moves data from one source to the other and performs matching between data sources. Trust-specific business rules are implemented and dictate how incoming data affects the information stored in the data warehouse.  
  • Translation Area: Data quality and integrity checking are carried out during this layer. This part of the processing also restructures data into a “star schema” model.  
  • Data Model: The aforementioned “star-schema” model is created at this layer, which is optimised for ad-hoc querying and historical data storage. It supports the historical storage of fact data, manages changes to dimensional data and hierarchical structures and ensures historical reporting is conducted effectively.
  • Serving Layer: This layer interacts with the InView user graphical user interface (GUI) to simplify configuration. Database views can be created at this layer to support reporting with minimal effort required from the end user. Real time data can also be presented at this layer, and non-InView data can also be combined to supplement any data you need to report on. 
  • Compliance Layer: This layer is where all statutory outputs are maintained and released to the Trust. 

Where can InView be deployed? 

InView can be deployed either on premise, in the Cloud, or hosted in CACI’s HSCN environment. Once deployed, our highly skilled technical experts forming the Managed Services team will work alongside you to ensure that you and your Trust are constantly supported after InView goes live. We will support you throughout the entire project implementation through fully transferring the necessary skills that will help you and your Trust feel more self-sufficient when using InView. 

Benefits of using InView 

NHS Trusts need accurate, reliable and readily available data for critical reporting and decision making. While this is crucial, it can be one of the biggest challenges for data professionals across the NHS to overcome. InView’s range of benefits can help you and your Trust overcome these challenges through its: 

  • Consistency: As a proven in-house solution that promotes a single version of the truth 
  • Availability: As a maintained product that can supply end-to-end reporting and can be implemented with all local rules correctly applied to incoming data
  • Efficiency: As a partner that is committed to continuously enhancing its solution
  • Flexibility: As an easy-to-use, extendable solution that is tailored to your Trust’s requirements and ensures your Trust will adapt to changes quickly 
  • Reliability: As a modern, interactive solution that allows for sharing not only within your organisation, but with ICS partners and NHSE too.  
  • Volume: As a solution that reduces the onus of statutory changes on the Trust 
  • Low cost of ownership: As a low total cost of ownership solution with maintained product content and changes that a Trust can action themselves. 

InView use cases 

InView produces a single, governed version of the truth that will drive consistent numbers that will enhance decision making, financial measurement, forecasting and information sharing across your Trust. By leveraging InView, you can present data for all purposes from one cohesive source to your Trust’s existing BI Toolset, which will simplify the reporting process and minimise the training needed for your Trust’s analysts.  

To get a sense of just how streamlined these processes within your Trust can be, take a look at some our of client case studies: 

CACI as your InView provider 

CACI has been providing Trusts with a solution that evolves and meets the demands of NHS reporting for over 20 years. Our very own data platform, InView, integrates all disparate source systems to optimise reporting across your Trust. By removing the statutory maintenance burden and time-consuming running of mandated reporting datasets, you and your Trust can focus on achieving priorities while meeting requirements and responding to any ad-hoc or urgent changes as they arise. To top it off, you will gain access to a user community for collaborative content and idea generation and learn how you can further enhance your own InView experience through other users’ takeaways. 

To learn more about InView and how our data warehouse solution could help your organisation, visit our InView page.  

*Risk-free for mandated statutory requirements