AI-Powered Digital Twins: Marketing’s Marvel

Published August 14, 2024

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Past Writings – 1

Digital Twins will transform marketing. Powered by AI, Segment Twins and Singular Twins these twins will help marketers bridge the ga between adtech and martech, achieve the twin objectives of increasing customer LTV (lifetime value) lowering CAC (customer acquisition cost), and help with profitable growth. Let’s begin by reviewing some of my recent writings on Digital Twins.

How Agentic AI will Transform Digital Marketing:

The advent of Agentic AI is poised to revolutionise the way businesses approach customer relationships and lifetime value maximisation. At the core of this transformation lies the concept of “Digital Twins” – AI-driven virtual representations of individual customers that possess a profound understanding of their behaviours, preferences, and interactions.

Digital Twins are not mere static customer profiles; they are dynamic, continuously evolving models that mirror the real-world customer’s journey with a brand. Powered by ML algorithms, large customer models (LCMs), and vast troves of customer data, Digital Twins become living embodiments of the customer experience.

The creation of these Digital Twins begins with the aggregation and analysis of data from various sources, including CRM systems, purchase histories, browsing patterns, social media interactions, and demographic information. This could even be augmented with real-world news and events which could impact shopping behaviour. This data is then fed into sophisticated LCMs, akin to the large language models used in natural language processing, enabling them to learn and adapt to the unique characteristics of each customer.

As customers interact with a brand across various touchpoints, their Digital Twins continuously analyse and incorporate these interactions, refining their understanding of the customer’s preferences, needs gaps, pain points, and potential future actions. This deep, personalised understanding empowers Digital Twins to anticipate customer needs, make tailored recommendations, and guide the customer journey, thus enabling brands to maximising their lifetime value.

By leveraging the insights and recommendations provided by Digital Twins, brands can create highly targeted and effective marketing campaigns, offering products and services that resonate deeply with each customer’s unique needs and preferences. This level of hyper-personalisation (N=1) not only drives increased sales and customer loyalty but also fosters a sense of trust and connection between the brand and its customers.

Furthermore, Digital Twins empower brands to optimise customer journeys, ensuring that every touchpoint is tailored to the individual’s preferences and needs. From personalised content and messaging to dynamic promotions and predictive recommendations, Digital Twins enable brands to deliver a truly seamless and engaging customer experience.

The integration of Digital Twins and Agentic AI into marketing strategies represents a significant step towards realizing the true potential of customer relationships and maximizing lifetime value. By continuously updating customer profiles in real-time and providing actionable insights, Digital Twins ensure that every marketing message is highly personalized and timed to perfection, improving customer satisfaction, and enhancing the efficiency of marketing spend.

Generative Journeys: Digital Marketing’s New Core: Digital Twins in marketing create detailed virtual models of individual customers, allowing for highly personalised marketing strategies. By understanding and predicting customer behaviour, Digital Twins enable marketers to tailor their approach to each customer’s preferences and behaviours, enhancing engagement and maximising the customer lifetime value. Digital Twins have two interfaces – to a Co-Marketer and the real customers whom they is modelled on. They empower hyper-personalised marketing (N=1) by predicting needs, recommending relevant products, and optimising individual customer journeys. They operate in real-time, updating with each brand interaction to ensure perfectly timed, relevant communications and experiences. Within simulated “mirror world” environments, Digital Twins interact with Co-Marketers to test and identify winning strategies for each customer. By unlocking this granular, AI-powered understanding of customers, Digital Twins also enhance loyalty and trust. They herald a new era of truly customer-centric, profitable marketing.”

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Past Writings – 2

CEO Memo: How Agentic AI can Power the Profipoly Quest: “Start by imagining what this future can be for your customers. For eCommerce, imagine every shopper’s digital twin interacting with a Co-Marketer to eliminate the tyranny of irrelevant messages and offers to get the right product recommendations at the right time. For a stock broking business, imagine a Co-Broker who can assimilate all the incoming news, distil trends, and then make portfolio suggestions. For a travel business, imagine a travel assistant for every customer being their advisor during the decision-making process and a guide during the actual travel. What’s common to these scenarios are three things: N=1 Hyper-personalisation, which ensures that the recommendations and pathways are unique for every customer; Agent-to-Agent interaction, which creates efficiency in the engagement process, by focusing on the best options and filtering out the irrelevant; Value-maximising journeys, which are composable, omnichannel, generative, shoppable, to ensure faster conversion… As your Large Customer Model learns from real-world interactions (and events), your Mirror World simulations become more accurate. As your Digital Twins interact with your Co-Marketer, they uncover new insights and strategies. And as you deploy Generative Journeys, you’re continuously learning and adapting to each customer’s evolving needs… With Agentic AI, engagement isn’t just about sending the right message at the right time. It’s about creating a seamless, adaptive, and value-adding experience across every touchpoint. Your Co-Marketer tests messages with the customer’s Digital Twin, and then engages the real customer with the right content and recommendations to create a “hotline” where no message from your brand is ignored.”

Email, CPaaS, and Martech: New Profit Pools: “Martech companies can significantly enhance customer Lifetime Value (LTV) by evolving from predictive and generative AI to agentic AI. This advanced approach involves transforming the Customer Data Platform (CDP) into a Large Customer Model (LCM), enabling the creation of digital twins and a co-marketer. These components can work together in a “mirror world” to drive “generative journeys,” resulting in N=1 hyper-personalisation for every customer. This next-generation software platform provides a competitive edge, allowing martech companies to deliver unprecedented engagement, conversion, and retention – thus eventually reducing the need for wasteful adtech spending, and enabling brands on their “profipoly quest.””

Martech’s 10+1 Foundations in the AI Age: “With the Unistack and Unichannel foundation in place, martech companies will need to rebuild their stack for AI. This will mean moving from predictive and generative AI to “Agentic AI.” This new stack will comprise five layers: Large Customer Models (LCMs), Digital Twins, Co-Marketer, Mirror World, and Generative Journeys…These AI-powered replicas of individual customers predicting needs and actions for 1:1 personalisation at scale. With Digital Twins, AI agents can simulate millions of individualised interactions, tailoring pitches and follow-ups to each customer’s preferences. This approach scales personalised engagement, creating virtual sales companions that provide bespoke experiences, effectively merging sales and marketing into a seamless, white-glove service.”

Large Customer Model: Foundation for AI-first Martech: “In the Mirror World, Digital Twins (virtual replicas of individual customers) interact with a Co-Marketer (an AI-powered marketing assistant) to identify the most effective strategies and personalized experiences for each customer… Large Customer Models,  along with their applications in Generative Journeys, Co-Marketers, and Digital Twins, herald a new era of AI-driven, customer-centric marketing that fosters stronger, more meaningful connections between brands and their customers, driving long-term growth and competitive advantage.”

Marketing’s AI Triad: Large Customer Model, Digital Twins, Co-Marketer:

The Large Customer Model allows for the creation of two types of digital twins: Segment Twins and Singular Twins.

Segment Twins are AI-powered models representing groups of customers who share similar behaviours, preferences, and demographic characteristics. By leveraging data from various sources such as customer data platforms (CDPs), marketing automation tools, and adtech platforms, Segment Twins analyse vast amounts of behavioural and interaction data. This includes interests, engagement patterns, and demographic details from platforms like Google, Meta, Instagram, and TikTok. These twins provide a comprehensive view of customer segments, enabling marketers to tailor strategies more effectively. By simulating these segments, brands can craft targeted and impactful marketing campaigns, ensuring higher relevance and engagement. More interestingly, marketers can converse with these twins to better understand the mindset. [See this post by Gautam Mehra.]

Singular Twins are AI-powered replicas of individual customers, capturing detailed insights into their preferences, behaviours, and interactions. These dynamic models continuously evolve, reflecting real-time customer journeys. By aggregating data from sources like CRM systems, purchase histories, browsing patterns, social media interactions, and demographic information, Singular Twins provide a comprehensive understanding of each customer. Operating within a simulated environment, or “mirror world,” Singular Twins interact with the brand’s Co-Marketer, an AI-powered assistant, to test various scenarios and identify optimal engagement strategies. This facilitates true 1:1 personalisation, allowing brands to deliver highly targeted and effective marketing campaigns by anticipating next actions. The integration of Singular Twins enables brands to anticipate customer needs, make tailored recommendations, and optimise customer journeys, ensuring every touchpoint is personalised.

The integration of AI-powered Segment Twins and Singular Twins will revolutionise marketing by providing unparalleled insights and personalisation. Segment Twins enable marketers to understand and target customer groups with precision, crafting campaigns that resonate deeply with specific demographics. Singular Twins, on the other hand, offer a granular view of individual customers, allowing for real-time, 1:1 personalised interactions. Together, these digital twins transform marketing from a broad, impersonal approach to a highly tailored strategy, driving engagement, customer satisfaction, and ultimately, higher conversion rates and loyalty. This shift not only enhances marketing efficiency but also fosters stronger, more meaningful customer relationships, positioning brands for sustained success in the AI era.

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More – 1

Digital Twins have a long history and widespread use across industries. Here is some background and recent news on Digital Twins.

Digital Twin Consortium defines a digital twin: “A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Digital twin systems transform business by accelerating holistic understanding, optimal decision-making, and effective action. Digital twins use real-time and historical data to represent the past and present and simulate predicted futures. Digital twins are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT/OT systems.”

Technology Review: “The concept of digital twins was introduced in 2002 by Michael Grieves, a researcher whose work focused on business and manufacturing. He suggested that a digital model of a product, constantly updated with information from the real world, should accompany the physical item through its development. But the term “digital twin” actually came from a NASA employee named John Vickers, who first used it in 2010 as part of a technology road map report for the space agency…Since those early days, technology has advanced, as it is wont to do. The Internet of Things has proliferated, hooking real-world sensors stuck to physical objects into the ethereal internet. Today, those devices number more than 15 billion, compared with mere millions in 2010. Computing power has continued to increase, and the cloud—more popular and powerful than it was in the previous decade—allows the makers of digital twins to scale their models up or down, or create more clones for experimentation, without investing in obscene amounts of hardware. Now, too, digital twins can incorporate artificial intelligence and machine learning to help make sense of the deluge of data points pouring in every second.”

Celonis: “Digital twin technologies began life in manufacturing. The digital representation of a physical object, product, or component has allowed manufacturers and product developers to assess the suitability of designs and any areas for optimization, as well as gauge resource demands and construction requirements. This can all happen before production begins, but even after production is underway digital twins can be used to improve the manufacturing process. They can even be used to decide what to do with products when they reach the end of their lifecycle…Digital twin technology can be applied to more than just a physical asset. Businesses are using process mining to construct a digital twin model of the processes that support their entire organization, using tasks, activities, and transactions drawn from system data (in much the same way a physical digital twin uses sensor data). This process digital twin, or Digital Twin of an Organization, is a living, breathing virtual replica, fed with real-time data, that businesses can use to explore and scrutinize processes at a granular and high-level view. Everything from bottlenecks to duplicated processes and disconnects can be inspected, diagnosed, and triaged.”

The Verge: “[Zoom founder] Eric ]Yuan] really wants you to stop having to attend Zoom meetings yourself…He thinks one of the big benefits of AI at work will be letting us all create something he calls a “digital twin” — essentially a deepfake avatar of yourself that can go to Zoom meetings on your behalf and even make decisions for you while you spend your time on more important things, like your family.” Eric: “Your digital twins, multiple digital twins, are different based on your training. One digital twin is really more like a sales expert; another digital twin of yourself is more like an engineering expert. Again, you manage that. Whenever you send a digital twin of yourself to join any other meetings, any other digital context, we know that they’ll be authentic given AI-based authentication.”

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More – 2

Solutions Review: “Digital Twins use realistic representations of physical objects to help businesses better understand the behavior of real-world assets, systems, and processes. By collecting vast amounts of real-world data, often in real-time, organizations can model the behavior of industrial machines to study the precise conditions under which they’re likely to fail. They can simulate physical assets like wind turbines and aircraft engines to identify minute inefficiencies. They can analyze complex systems to identify opportunities to lower costs, improve quality, and proactively prevent problems…A Digital Twin is a virtual replica of a physical object or system that’s designed to mirror its real-world counterpart as closely as possible. Digital Twins typically start with a conceptual model, such as one generated from computer-assisted design (CAD), building information modeling (BIM), or geographic information system (GIS) tools. Rather than staying static, however, this model continually evolves as organizations collect data about their physical counterparts from sensors, real-time data streams, or any other data source. They can then apply insights gleaned from the Digital Twin back to the physical world in a continuous feedback loop—such as recalibrating robots on an assembly line to improve quality or rerouting traffic in a Smart City.”

Bloomberg: “Digital twins have been used for decades in industrial sectors such as aerospace engineering and more recently in car manufacturing. With blueprints and real-time data from sensors, engineers can approximate performance in extreme conditions — a jet engine in severe weather, for example — and predict maintenance needs. Though definitions are evolving, digital twins typically require a two-way flow of information: Real-world data from a physical object goes into a model, then the model forecasts outcomes for the real-world object.” The article discusses a very specific application: “Glioblastoma is one of the most common, aggressive and deadly forms of brain cancer. The prognosis — with median survival of 15 to 18 months after diagnosis — hasn’t changed in more than half a century. A major challenge in treating glioblastoma is that tumors vary widely among patients and the cells within them aren’t uniform. Too often, selecting the right combination of invasive surgeries, radiation and chemotherapy amounts to high-stakes guesswork. A “digital twin” of the brain tumor could help. Thanks to the leaps forward in artificial intelligence and mathematical modeling over the past decade, doctors can now create a virtual copy of the affected area in a specific patient using genetic information, medical records, blood tests and scans. With a digital twin, doctors can detect abnormalities before physical symptoms emerge, project how tumors will develop, and determine which combination of surgeries and treatments will deliver the best outcomes.”

Wall Street Journal: “The U.S. Air Force announced [recently] its latest initiative to unleash the power of “digital twins”—computerized simulators that mimic real-world systems with almost perfect accuracy. This bold effort, dubbed Model One, integrates 50 top military simulations into a unified system to adapt to the ever-evolving landscape of digital warfare… The rise of virtual technology, including digital twins, hasn’t been as flashy as videogaming’s virtual realities, but it’s quietly reshaping how industries conceive physical technology, from race cars to artificial hearts. As accurate simulations become stand-ins for physical testing, innovation becomes faster and less expensive. According to a recent Allied Market Research report, the global digital-twin industry is projected to grow 20-fold, from $6.5 billion in 2021 to $125.7 billion by 2030. Some pundits predict it will spark a new industrial revolution… Imagine billions of future technologies being digitally designed, tested, even certified each year…all without the time, cost, and environmental degradation of physical innovation. Now, picture AI learning to improve these designs, operate them, and even train humans. Then the hoped-for future of personalized medicine, sustainable energy, abundant agriculture and space colonization may become attainable.”

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Fusion Strategy

A seminal book that discusses digital twins (among other things) is “Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future ”  by Vijay Govindarajan and Venkat Venkatraman. Here are a few excepts from the book:

It all starts with data, the crux of fusion. Not just any data, but real-time data on products in use. By systematically accumulating such data, companies can develop datagraphs, which capture the relationships, links, and interrelationships between a company and its customers through product-in-use data and are the fundamental building blocks of the fusion strategies… The concept of the datagraph is inspired by social networks and graph theory, and powered by AI and machine learning (ML). Datagraphs are derived from data network effects, which are triggered when a company’s product gets smarter as it gathers more data from users of the product.

…Industrial companies typically use one or more of three types of digital twins. A product twin represents the product in a virtual environment during the design and development stage (product as designed). A process twin is a digital representation of the end-to-end manufacturing process, including the role of suppliers and distributors (product as manufactured). And a more recent version, the performance twin, digitally represents the product in use to track and collect data on the factors that impact how the product performs in the field (product as deployed).

Often, industrials use digital twins in separate functions, delegating product twins to R&D units and design groups, assigning process twins to supply chain and operations teams, and leaving performance twins to the marketing and service functions. The benefits accrue along narrowly defined metrics if the twins are proposed, funded, and operated independently with boundaries. Industrial data network effects are created by linking the three digital twins: design, manufacturing, and deployment. We refer to this as a tripartite digital twin, or a tripartite twin for short.

The book discusses how Tesla uses digital twins: “Tesla has created a performance twin of every car it has made, not just a digital twin of every model as it was designed or a twin of the assembly line that created it. The sensors in each car provide data in real time about how the vehicle is performing on the road; the company’s AI/ML systems study the data in real time; and Tesla uses the most relevant data-driven insights to continuously improve all its cars’ autonomous driving systems. Moreover, the AI interprets the data and determines whether a vehicle works as intended or needs maintenance. With many issues, Tesla’s digital experts ensure that problems can be fixed with software updates; for instance, they can adjust the regenerative braking levels to reduce collision risks and adjust door rattling via over-the-air software updates. On average, it issues a major software update once every month. The company’s tripartite twins help it optimize future products with generative design, the emerging technology that applies AI to optimize designs. By aggregating real-time data from thousands of products in the field, the digital twins simulate the performance and conditions faced by the fusion product over its lifetime. Armed with this data, generative design software tweaks Tesla’s product designs and simulates performance in real-world situations until it arrives at a solution that satisfies the company’s goals.”

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More Books

The Digital Twin by Noel Crespi, Adam T. Drobot, and Roberto Minerva (Editors): “Imagine the future of the global economy: imagine factories that produce experiences, with data flows replacing part supply. Imagine being able to represent and cure a human body as efficiently as we model and test a plane or a car. Imagine a clinical trial involving virtual patients and being evaluated using an AI engine. Digital twins – or virtual twin experiences as I rather name them – are unparalleled catalysts and enablers of this global transformation of the economy. Digital twins are representations of the world that combine modeling, simulation, and real-world evidence: in other words, they make it possible to understand the past while navigating the future… As they provide a multi-scale, multi-discipline, generative, holistic, and inclusive approach to innovation, virtual twin experiences based on collaborative platforms provide an inspiration for new offerings. Improving global health requires an inclusive perspective across cities, food, and education. Developing global wealth in a sustainable manner involves new synergies between data and territories. Digital twins make it possible to collaborate at scale through immersive virtual reality. They provide a continuum across disciplines and organizations. They connect biology, material sciences, mechanics, electronics, and chemistry. They allow to improve use through data intelligence. This translates into continuous improvements towards more sustainable industrial processes, enhanced and customized treatments, and the development of new services from the lab to the hospital nearby or the street downstairs. Combining the real and the virtual world thus leads to new ways of inventing, producing, treating, and learning.”

Digital Twins: Internet of Things, Machine Learning, and Smart Manufacturing by Yogini Borole, Pradnya Borkar, Roshani Raut, Vijaya Parag Balpande, and Prasenjit Chatterjee:

Digital twins come in a variety of forms depending on how magnified the product is. These twins most significantly diverge in the area of application. It is typical for various kinds of digital twins to coexist in a system or process.

Component twins or parts twins: Component twins are the core unit of a digital twin and the most basic representation of a functioning component. Parts twins are virtually the same thing as identical parts, despite the fact that they refer to far less important parts.

Asset twins: An asset is formed when two or more components perform well together. Asset twins allow you to investigate the interactions between these factors, generating a wealth of performance data that can be analysed and turned into insightful information.

System or unit twins: System or unit twins, which show how diverse assets work together to form a complete, usable system, are the next degree of magnification. System twins offer visibility into how assets interact and may make performance suggestions.

Process twins: Process twins describe how several systems interact to construct an entire production plant. Are all of those systems coordinated for maximum efficiency, or will delays in one system affect others? The specific timing schemes that eventually affect overall efficiency can be found with the use of process twins.

Artificial Intelligence-Augmented Digital Twins by Abdalmuttaleb M. A. Musleh Al-Sartawi, Anas Ali Al-Qudah, and Fadi Shihadeh (Editors): “Digital Twinning (DTW) aims to create a highly accurate virtual model of a physical system or process to simulate system behavior, condition monitoring, detection of abnormal patterns or errors, a reflection of system performance, and future trend prediction, which is highly beneficial for various sectors such as finance, banking, accounting, manufacturing, agriculture, and healthcare. Moreover, it helps to map between virtual and real-world resources, which can in turn support to identifying known unknowns as well as also help to discover unknowns. More specifically, advancements in Artificial Intelligence are transforming what they can do in the fields of accounting, finance, and banking. DTW is also made increasingly more intelligent by AI agents, to the point where it can prescribe actions in the physical world, so that banks and other financial institutions can change, in real-time, with fact-based choices, alternate processes to prevent bottlenecking issues for example. By deploying both AI and DTW, banks and accounting firms can obtain insights into their operations, allowing them to achieve significant benefits such as improved efficiency, cost savings, and enhanced sustainable development efforts. Therefore, DTWs are not only helping these firms to optimize operations and decision-making; they also play a critical role in enabling them to realize their ESG roles…By replicating the physical world, digital twins can support companies to better utilize resources, optimize supply and logistics networks, reduce carbon emissions, and improve employee safety.”

Handbook of Digital Twins by Zhihan Lyu (Editor): “A personal digital twin has been defined as a virtual version of an individual that is built from her digital footprint. Slightly different flavors of personal digital twin can be distinguished from different disciplines: in healthcare, the personal digital twin is steered toward a merger of all health data, supported by live sensor devices. The resulting human digital twin can be applied to visualization, simulation, and prediction. The approach in retail, digital marketing, and e-commerce is similar: by accumulating data from consumer behavior, a digital twin of the customer can be built and applied to produce better-targeted recommendations.”

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In Marketing

Digital Twins, initially conceived for industrial applications and manufacturing, are now poised to revolutionise marketing. With the convergence of artificial intelligence (AI), big data, and advanced analytics, the marketing industry stands on the cusp of a transformative era. Here’s why now is the right time for digital twins in marketing and how marketers can harness this groundbreaking technology.

Why Now?

1. Data Proliferation: The explosion of data from various sources – social media, CRM systems, purchase histories, browsing patterns, and IoT devices – provides a rich foundation for creating detailed digital twins (and also the “datagraphs”) of customers. This wealth of data enables marketers to build comprehensive, dynamic models that mirror real-world customer behaviours and preferences.
2. Advancements in AI-ML: AI-ML technologies have matured significantly, allowing for the development of sophisticated algorithms that can process and analyse vast amounts of data. These advancements enable digital twins to evolve continuously, learning and adapting to each customer’s unique characteristics and interactions in real-time.
3. Integration of Martech and Adtech: The integration of martech and adtech creates a seamless ecosystem where digital twins can thrive. This integration facilitates the flow of data across platforms, enhancing the accuracy and relevance of customer profiles. Customer segments can be enriched with data from adtech to create better content and campaigns.
4. Rise of Agentic AI: The era of Agentic AI is upon us, characterisd by intelligent agents capable of interacting autonomously with other agents. This advancement will empower a Co-Marketer to engage in dynamic dialogues with digital twins, allowing for the continuous refinement of strategies and next-best actions. This interactive capability ensures that marketing efforts are not only highly personalised but also adaptive to real-time changes in customer behaviour and preferences, significantly enhancing the effectiveness and efficiency of marketing campaigns.

How Marketers Can Leverage Digital Twins

• Building Detailed Customer Profiles: The first step in leveraging digital twins is to aggregate and analyse data from various sources to create detailed customer profiles (at individual level or for customer clusters). This involves collecting data from multiple touchpoints into a Large Customer Model (LCM). AI algorithms then process this data to construct dynamic, evolving models of individual customers.
• Hyper-Personalisation: Digital twins enable marketers to achieve N=1 hyper-personalisation, tailoring marketing strategies to the unique needs and preferences of each customer. By understanding individual behaviours and predicting future actions, digital twins can recommend personalised products, services, and content, driving increased engagement and loyalty.
• Optimising Customer Journeys: With digital twins, marketers can optimise customer journeys by ensuring that every touchpoint is tailored to the individual’s preferences and driven by their actions. From personalised content and messaging to dynamic promotions and predictive recommendations, digital twins enable brands to deliver a seamless and engaging customer experience.
• Implementing Generative Journeys: Digital twins can interact within simulated “mirror world” environments, testing various strategies and scenarios to identify the most effective approaches for each customer. This process of generative journeys allows marketers to continuously learn and adapt to evolving customer needs, ensuring that marketing efforts are always relevant and impactful.
• Enhancing LTV and Reducing CAC: By leveraging the insights provided by digital twins, brands can drive increased sales and customer loyalty, and also improve marketing efficiency. Consequently, brands can maximise customer lifetime value (LTV) while minimising customer acquisition cost (CAC).

The Path Forward

To successfully implement digital twins in marketing, brands need to invest in AI (predictive, generative, and agentic) technologies, integrate their martech and adtech platforms, and build a robust data infrastructure. Collaboration between data scientists, marketers, and technologists is crucial to develop and maintain accurate and dynamic digital twins.

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AI-First Marketing

By creating AI-powered virtual representations of customers, businesses can unlock unprecedented levels of personalisation, efficiency, and engagement. Let’s explore the stack that underpins the implementation of digital twins in marketing.

• Foundational Large Language Models (LLMs): At the base of the stack are Foundational Large Language Models (LLMs) such as ChatGPT, Gemini, and Claude. These models are the building blocks for understanding and generating human-like text based on vast datasets. LLMs facilitate natural language processing, enabling the creation of detailed customer profiles from various data sources. By interpreting customer interactions and behaviors, LLMs provide the initial insights needed to build accurate digital twins. But they are not customised for marketing.
• Foundational Marketing Model (Martech+Adtech): The next layer integrates martech and adtech, creating a cohesive ecosystem that spans multiple channels. This foundational marketing model includes channel-specific SLMs (small language models) for email, SMS, WhatsApp (WA), and offsite platforms such as Facebook, Google Ads, and Instagram. With training data from these diverse sources, marketers can create better messages on channels..
• Fine-Tuned Enterprise Model: Building on the foundational marketing model is the a layer which acts as an enhanced Customer Data Platform (CDP) enriched with Personally Identifiable Information (PII) about customers. This model aggregates data across properties, products, channels, and performance metrics from the different channels, thus enabling the model training programme to incorporate real-world feedback on content. This layer completes the Large Customer Model, bringing together customer data, channel content and performance, clusters (segments), catalog (product information), and conversations (from social media).
• Co-Marketer: At the heart of the stack is the Co-Marketer, an AI-powered marketing assistant that leverages insights from digital twins to enhance creatives, messaging, campaigns, and offers. The Co-Marketer can be considered a marketer’s digital twin, a co-intelligence of sorts. It interacts with digital twins to test and fine-tune strategies, ensuring that every customer touchpoint is optimised. This agentic AI capability allows for dynamic, real-time adjustments to marketing efforts, leading to more effective and engaging campaigns.
• Digital Twins: At the pinnacle of the stack are the AI-powered Digital Twins, which come in two forms: Segment Twins (N=M(any)) and Singular Twins (N=1). Segment Twins represent customer groups with shared behaviours and preferences, enabling marketers to craft targeted content and strategies for specific segments. Marketers can also have conversations with personas representing each of the segments. For example, a segment twin could be asked to rank email creatives in terms of likely performance. Singular Twins, on the other hand, are individual customer replicas that provide personalised recommendations and guide customer journeys. They become more powerful with accumulation of individual data (both first- and zero-party, and derived attributes) and the progression of journeys in the lifecycle. These twins continuously evolve, integrating new data and interactions to remain current and accurate.

The integration of these layers creates a seamless pathway from raw data and generic experiences to highly personalised marketing experiences. By continuously refining strategies based on real-time insights, businesses can achieve N=1 hyper-personalisation, delivering tailored experiences that drive engagement and loyalty. In the words of Jack Carr quoting a quantum computer (almost an “artificial general intelligence”) named Alice in “Red Sky Mourning”, the Digital Twins can “in real-time, analyse the past and evaluate the present to predict the future.”

This is the coming world of AI-powered Digital Twins, which I think of as “marketing’s marvel.” How does it look like?

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Story – 1

I gave ChatGPT and Claude the following prompt: “Write a story describing this world. You can call the marketer as Maya and take the example of a company called A1Books, an omnichannel book seller.” Here is ChatGPT.

Maya is the Chief Marketing Officer at A1Books, an omnichannel book seller renowned for its personalized customer experiences. Her day begins with a fresh cup of coffee and a glance at her dashboard, powered by the latest advancements in digital twin technology.

The Foundation: Large Language Models

At the core of A1Books’ marketing stack are Foundational Large Language Models (LLMs) like ChatGPT, Gemini, and Claude. These models process and interpret vast amounts of customer data, ranging from purchase histories to social media interactions. As Maya reviews the latest customer insights generated by these LLMs, she appreciates how they transform raw data into actionable insights, enabling a deeper understanding of A1Books’ diverse customer base.

Integrating Martech and Adtech

Maya’s next task involves leveraging the Foundational Marketing Model, which integrates martech and adtech data across multiple channels such as email, SMS, WhatsApp, and social media platforms like Facebook and Instagram. This integration ensures a seamless flow of data, enhancing the accuracy of customer profiles. Today, Maya notices a trend: customers who engage with A1Books’ Instagram ads are more likely to purchase non-fiction titles. She decides to refine the ad campaigns accordingly.

Enhancing with Fine-Tuned Enterprise Model

With a solid foundation in place, Maya turns her attention to the Fine-Tuned Enterprise Model, an enhanced Customer Data Platform (CDP) enriched with Personally Identifiable Information (PII). This layer aggregates data from various properties, products, channels, and performance metrics. By incorporating real-world feedback, Maya can see how different campaigns are performing across segments. She notes that personalized email campaigns featuring exclusive author interviews are generating higher engagement and decides to expand this initiative.

The Co-Marketer: Maya’s Digital Twin

One of Maya’s most powerful tools is the Co-Marketer, an AI-powered assistant that interacts with digital twins to optimize strategies. Today, the Co-Marketer suggests a new campaign focusing on a recently released fantasy series. By conversing with the Segment Twins, Maya’s Co-Marketer fine-tunes the campaign to target specific customer groups who have shown an interest in similar genres. The Co-Marketer’s ability to dynamically adjust messaging ensures that each interaction is perfectly timed and highly relevant.

AI-Powered Digital Twins

At the pinnacle of A1Books’ stack are the AI-powered Digital Twins. Segment Twins represent groups of customers with shared behaviors and preferences, while Singular Twins are detailed replicas of individual customers. Maya reviews the recommendations generated by these digital twins, noting that a customer, Alex, frequently browses fantasy novels but hasn’t made a purchase recently. The Singular Twin suggests a personalized offer on a new fantasy release, anticipating Alex’s interest.

Optimizing Customer Journeys

Maya implements the personalized offer through a generative journey, where the Singular Twin tests various scenarios within a simulated environment. The twin interacts with the Co-Marketer to identify the most effective approach, ensuring that Alex receives a timely and relevant recommendation. This level of personalization not only enhances customer satisfaction but also drives engagement and loyalty.

Real-Time Adjustments and Insights

As the day progresses, Maya continuously monitors the performance of various campaigns. The digital twins evolve with each customer interaction, integrating new data to remain accurate and up-to-date. Maya appreciates how these twins enable real-time adjustments to marketing strategies, ensuring that A1Books can respond swiftly to changing customer behaviors and preferences.

The Future of Marketing

Maya reflects on the transformation digital twins have brought to A1Books. By integrating advanced AI technologies with a robust data infrastructure, A1Books can deliver hyper-personalized experiences at scale. The seamless interaction between Segment Twins, Singular Twins, and the Co-Marketer has revolutionized their marketing approach, driving profitable growth and customer loyalty.

As she wraps up her day, Maya envisions the future possibilities of digital twins in marketing. The continuous refinement of strategies, real-time personalization, and dynamic customer engagement are just the beginning. With AI-powered digital twins, the world of marketing is set to achieve new heights, creating deeper, more meaningful connections between brands and their customers.

In the world of A1Books, every customer interaction is a step towards a more personalized, engaging, and satisfying experience, thanks to the marvel of AI-powered digital twins.

10

Story – 2

Here is Claude’s story.

Maya leaned back in her chair, her eyes fixed on the holographic display hovering above her desk. As the lead digital strategist for A1Books, she was examining the latest insights from the company’s AI-powered marketing system.

“Co-Marketer, show me the performance of our latest campaign for mystery novels,” Maya commanded.

Instantly, a friendly voice responded, “Certainly, Maya. The mystery novel campaign has seen a 15% increase in engagement across all channels. Would you like me to break down the performance by segment?”

“Yes, please. And let’s focus on the ‘Thriller Enthusiasts’ segment,” Maya replied.

The display shifted, revealing a detailed breakdown of how the campaign resonated with different customer groups. Maya’s attention was drawn to a pulsing icon representing the Segment Twin for Thriller Enthusiasts.

“Interesting,” Maya mused. “Co-Marketer, can you ask the Thriller Enthusiasts Twin to rank our top three email creatives for the upcoming summer reading promotion?”

“Of course,” the Co-Marketer replied. After a brief pause, it continued, “The Thriller Enthusiasts Twin has ranked the creatives. Would you like me to display the results and explain the reasoning?”

“Yes, go ahead,” Maya nodded.

As the Co-Marketer began explaining the twin’s analysis, Maya marveled at how far marketing technology had come. Just a few years ago, this level of insight and personalization seemed like science fiction. Now, it was her daily reality at A1Books.

Suddenly, an alert flashed on her screen. “Maya, I’ve detected an unusual pattern in customer behavior for user ID 78291,” the Co-Marketer announced. “This customer’s Singular Twin is showing a 78% probability of churning in the next 30 days. Would you like me to generate a personalized retention strategy?”

Maya sat up straight, her interest piqued. “Absolutely. What can you tell me about this customer?”

The Co-Marketer quickly pulled up a detailed profile. “User 78291, Sarah Thompson, is a 32-year-old professional who typically purchases business and self-help books. However, her recent browsing history shows an increased interest in travel guides. Based on her Singular Twin’s analysis, I believe she may be planning a career break to travel, which could explain the potential churn.”

Maya nodded, impressed by the depth of insight. “Alright, let’s craft a personalized offer for Sarah. Maybe a bundle of travel guides with a loyalty discount?”

“Excellent suggestion, Maya,” the Co-Marketer responded. “I’ll create a few options for you to review, optimized for Sarah’s preferences and past behavior.”

As Maya reviewed the proposed strategies, she couldn’t help but reflect on how digital twins had transformed A1Books’ approach to customer engagement. The ability to understand and anticipate customer needs with such precision had not only boosted sales but had also fostered a deeper connection with their audience.

“Co-Marketer,” Maya said, a new idea forming, “let’s simulate how our top 1000 customers would respond to a new ‘Personalized Reading Journey’ feature. Use the Singular Twins to predict engagement and potential revenue impact.”

“Certainly, Maya. I’ll run the simulation in our Mirror World environment and have the results for you shortly,” the Co-Marketer replied.

As the simulation began, Maya smiled. With AI-powered digital twins at her fingertips, she felt like she had a superpower – the ability to understand and serve A1Books’ customers better than ever before. The future of marketing, she thought, was not just about selling books; it was about creating unique, personalized experiences for every reader, one digital twin at a time.

11

Additional Ideas

I asked ChatGPT and Claude and Gemini for additional ideas to what I had written.

ChatGPT

Enhanced Customer Support

1. Proactive Customer Service:

1. • AI-powered digital twins can predict when a customer might need support based on their interactions and behavior patterns. This allows companies to proactively reach out and offer assistance before the customer encounters an issue, improving customer satisfaction and loyalty.
   • Example: If a customer frequently searches for troubleshooting information, their digital twin can prompt a support agent to reach out with solutions before the customer files a complaint.

2. Personalized Customer Support:

1. • Digital twins can provide customer service agents with a detailed history and preferences of the customer they are assisting, allowing for more personalized and efficient service.
   • Example: When a customer contacts support, the agent can immediately see the customer’s recent interactions, purchase history, and any previous issues, enabling them to provide tailored assistance.

Dynamic Pricing and Promotions

3. Real-Time Pricing Adjustments:

1. • AI-powered digital twins can analyze market trends, competitor pricing, and customer behavior to dynamically adjust prices in real-time, ensuring competitive pricing and maximizing revenue.
   • Example: If a customer shows high interest in a product but hasn’t purchased it, the digital twin can trigger a personalized discount to encourage conversion.

4. Targeted Promotions:

1. • Digital twins can help in creating highly targeted promotions based on individual customer behavior and preferences, ensuring that promotions are relevant and effective.
   • Example: A digital twin can identify customers who are likely to respond to a limited-time offer and tailor the promotion specifically to their interests and buying patterns.

Product Development and Feedback

5. Customer-Driven Product Innovation:

1. • Digital twins can gather and analyze customer feedback in real-time, helping companies to quickly adapt their products to meet customer needs and preferences.
   • Example: If a digital twin identifies a recurring complaint about a product feature, the company can prioritize updates to address the issue.

6. Virtual Product Testing:

1. • Companies can use digital twins to simulate how customers might interact with new products or features before they are launched, reducing the risk of product failures and improving the overall customer experience.
   • Example: A1Books could use digital twins to test different layouts of their online bookstore to see which one provides the best user experience.

Cross-Selling and Up-Selling

7. Intelligent Cross-Selling and Up-Selling:

1. • Digital twins can analyze purchase patterns and customer preferences to suggest relevant complementary products or upgrades.
   • Example: If a customer buys a book, the digital twin might suggest related books or audiobooks based on the customer’s reading history and interests.

8. Subscription and Membership Programs:

1. • Digital twins can identify customers who are likely to benefit from subscription services or loyalty programs, and personalize the offers to increase adoption rates.
   • Example: A1Books can use digital twins to identify frequent buyers who might be interested in a subscription service for exclusive content or early access to new releases.

Customer Retention Strategies

9. Predictive Churn Analysis:

1. • Digital twins can predict which customers are at risk of churning based on their behavior and interactions, allowing companies to take preemptive measures to retain them.
   • Example: If a customer’s engagement drops significantly, the digital twin can trigger a retention campaign with personalized offers to re-engage the customer.

10. Loyalty Programs Optimization:

1. • By analyzing customer behavior, digital twins can help optimize loyalty programs to ensure they are rewarding the behaviors that contribute most to business goals.
   • Example: A1Books can tailor its loyalty program rewards based on what motivates each customer, such as discounts, exclusive content, or early access to new books.

Enhanced Analytics and Insights

11. Real-Time Marketing Analytics:

1. • Digital twins can provide real-time analytics and insights into the effectiveness of marketing campaigns, allowing for agile adjustments and optimization.
   • Example: If a marketing campaign isn’t performing as expected, the digital twins can provide insights into why and suggest modifications to improve results.

12. Behavioral Segmentation:

1. • Digital twins can help in segmenting customers based on detailed behavioral data, allowing for more precise and effective targeting.
   • Example: A1Books can segment its customers into groups based on their reading habits, purchase frequency, and engagement levels, and tailor marketing strategies accordingly.

Claude

1. Ethical considerations: Discuss the potential privacy concerns and ethical implications of using such detailed digital representations of customers. How can marketers ensure they’re using this technology responsibly?
2. Integration with IoT devices: Explore how digital twins could incorporate data from Internet of Things (IoT) devices to create even more accurate customer profiles. For example, smart home devices or wearables could provide insights into daily habits and preferences.
3. Predictive inventory management: Digital twins could be used to optimize inventory across physical and online stores based on predicted customer behavior and external factors like weather or local events.
4. Virtual product testing: Discuss how digital twins could be used to simulate how customers might interact with new products before they’re launched, allowing for better product development and marketing strategies.
5. Cross-industry applications: Explore how the concept of digital twins in marketing could be applied to or integrated with other industries like healthcare or finance for a more holistic customer experience.
6. Augmented and Virtual Reality integration: Consider how digital twins could enhance AR and VR experiences for customers, creating immersive and personalized shopping environments.
7. Dynamic pricing strategies: Explain how digital twins could inform real-time pricing decisions based on individual customer behavior and market conditions.
8. Customer service applications: Discuss how digital twins could be used to enhance customer service by predicting issues and personalizing support interactions.
9. Regulatory challenges: Address the potential regulatory hurdles that might arise as this technology becomes more prevalent in marketing.
10. Impact on traditional marketing roles: Explore how the advent of AI-powered digital twins might transform traditional marketing job roles and required skill sets.