Rajesh Jain

Published June 16, 2024

1

New Martech

One of the biggest pain points that comes in my conversations with marketers is how hard martech is as compared to adtech. Adtech is simple: call an agency, give a budget, set the RoI/ROAS expectations, and then sit back and watch the traffic come in. Martech, in contrast, is hard – put together an internal team, buy one or more martech solutions, collect data from customers, create segments and journeys, run campaigns with regular updates to creatives and copy, work across multiple channels each with their own quirks, do analytics and continuous improvement, and then repeat this process ad infinitum. Little surprise then that most of the digital budgets are targeted to adtech and not martech, even though retention and growth from existing customers is what will power profitability.

Generative AI is making some part of the process simpler by bringing speed and efficiency to the creative process. An article in Wall Street Journal has more: “In fields like marketing, the technology is helping transform tasks such as copywriting. Dan Bettinger, a principal product-marketing manager in tech, says using large language models almost daily has helped him craft better copywriting, build out outlines for articles and blog posts, and come up with interesting ideas and ways to approach certain topics “that maybe I wouldn’t have seen.” It’s also helping him do all these things more efficiently. “I’m saving at least an hour a day through the use of AI,” he says. Bettinger, who took a four-day online course on AI last July to learn more about the technology and how he could apply it to his career, gives the model he uses instructions like “pretend you’re a chief marketing officer” or “pretend you’re an expert copywriter” to help draft materials faster. He also uses AI to help develop scripts for video presentations, to analyze competitive websites for SEO purposes and to summarize data and research. Another way he uses AI is to “pressure test” copy he has written. He instructs the model to “pretend you’re a skeptic and you’re presented with this stuff. What would you not like about it? And then how would you fix it?” This helps him get additional ideas and see things he might have missed, he says.”

Much more needs to be done. What if AI could essentially become a Co-Marketer where the marketer would just give the outcomes rather than oversee a team doing tasks? As I wrote in Co-Marketer: Martech meets AI: “Think of the co-marketer not as a tool, but as a person. Co-Marketers…will take on goals rather than do simple request-reply chats. This is the next generation of AI where an AI agent can spawn and supervise other agents, much like a manager can. From segmentation to content to channel orchestration, AI agents can do the work of a marketing department to assist the CMO. What the CMO needs to do is to give the broad direction. For example, the CMO could say, “For my Best Customers, I need to ensure 20% revenue growth and an increase in frequency of purchase.” The Co-Marketer then should be able to identify the right products and the persuasion messages specific to each individual (N=1) to work towards achieving the outcome.”

In other essays, I have discussed Autonomous Agents and Agentic AI, wherein I have envisioned martech’s new future built using Co-Marketers, Digital Twins, Mirror World, and Large Customer Models. In this series, I will introduce a new concept – Generative Journeys, which will underpin the next-generation of martech.

2

Recap

Let’s do a quick recap on the four ideas I had mentioned about martech’s future.

Co-Marketer: A Co-Marketer, powered by Agentic AI, acts as a virtual collaborator in marketing departments, streamlining operations from data analysis to campaign execution. By automating routine tasks and harnessing vast amounts of data for decision-making, a Co-Marketer enables CMOs to focus on strategic initiatives, fostering a more efficient and creative marketing environment. It collaborates with the human team through seamless conversational interfaces to plan campaigns, generate personalised content, spot trends, and enable more efficient and innovative marketing processes. Continuously learning and adapting, the Co-Marketer becomes an indispensable ally in making informed decisions, optimising marketing ROI, and building strong customer relationships. It represents a transformative shift in marketing, augmenting human capabilities with AI to navigate complexities and drive superior results.

Digital Twins: 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.

[From a recent article in Wall Street Journal: “AI systems can take in data on a person’s individual characteristics—such as appearance, shopping preferences and health profile—then predict how they would look in an item of clothing, how they would answer a question or be affected by a disease. This AI content [is] sometimes referred to as a person’s digital twin… Consulting firm Gartner refers to the technology as “digital humans”—and estimates that in five to 10 years companies might even have digital twins for every single one of their customers.” Also see this essay by Gautam Mehra about an “AI Twin”: “What if we could take all the vast amounts of data we have access to and create a digital twin that one could literally converse with… Brands can now finally have an always on version of their best customers and be able to converse freely. Asking about the brand, product, their thoughts, their motivations as well as their inhibitions.”]

Mirror World: The Mirror World is a virtual “sandbox” environment where the Co-Marketer and Digital Twins interact to simulate, test and optimize marketing strategies and customer experiences. In this AI-powered digital realm, countless scenarios and hypotheses can be played out, with Digital Twins reflecting real customers behaviours. The Mirror World enables rapid experimentation and refinement of hyper-personalised journeys, content, offers and interventions for each individual customer. Successful approaches from these simulations are then deployed in the real world. By providing a space for AI agents to collaborate, learn and evolve solutions, the Mirror World accelerates marketing innovation and effectiveness. It gives marketers a powerful testing ground to perfect N=1 personalisation at scale before engaging actual customers. The Mirror World is thus an essential component powering the future of predictive, highly relevant, and agile AI-driven marketing.

Large Customer Models: LCMs are the foundational AI architecture that powers intelligent agents like Co-Marketers and Digital Twins. Similar to large language models but focused on customer understanding, LCMs are trained on vast volumes of first-party and external customer data. They learn patterns and predict behaviours to create rich, holistic customer profiles. Fed with real-time data from marketing interactions, LCMs continuously refine their understanding of each customer’s unique traits, needs and proclivities. They are the “brains” that guide personalised content creation, next-best-action recommendations, and journey optimisations. Akin to a “Customer Data Platform on steroids”, LCMs unify and activate customer data to an unprecedented degree. By serving as the always-learning customer intelligence engine, LCMs enable true 1:1 marketing at scale and help drive measurable increases in customer retention, monetisation and lifetime value.

3

Campaignless Future

A few months ago, I had written the following (which I never got around to completing and publishing).

Much of digital marketing today is centred around what I call ABC – acquisitions, branding, and campaigns. Focus and spending is perhaps in the ration 80:10:10. My belief has been that half of the spending on acquisition is being wasted because of wrong acquisition and reacquisition. If marketers can recognise this AdWaste, they can then redirect some of that spending towards building better relationships with their existing customers and by doing so, significantly boost profitability.

Through the past few years, I have written almost 80 essays discussing new ideas in marketing and evolved a new vocabulary for this next-gen digital marketing: Martech 2.0, Velvet Rope Marketing, Email 2.0, Loyalty 2.0, Inbox Commerce, Email Shops, Magic Carts, Profipoly, Progency, Unistack, Unichannel, Microns, Atomic Rewards, Mu, Muniverse, Micronbox, iDarpan, AMPifier, AMPlets, Gamelets, Dynamic Email Footers, Action Ads, Chief Profipoly Officer, Large Customer Models, Digital Twin (for Marketing), EnCoRe Triad, Funnel Frictions, Red and Green Journeys, Profipoly Score, BRTLNG (Best-Rest-Test-Left-Next-Guest customers), Best Customer Genome, Profishare.

I want to take one of these ideas and expand on it: how digital twins and software agents could usher in a world of a world without campaigns.

**

Campaigns have been at the heart of martech. Marketers need to bring their current customers back to their properties (websites, apps) to complete the transaction. While some customers may come on their own thanks to brand recall, in most cases it is the push messages whose purpose it is to persuade customers for the next action. So, a digital marketer’s day is spent on segmentation and creating campaigns which are then delivered across multiple channels (email, SMS, push notifications, and in recent times, WhatsApp, and RCS). These are largely broadcast campaigns – a set of offers with enticing creative sent to “segments of many”. Because these campaigns have a single purpose – a clickthrough to the brand’s property, they are not highly personalised. While channels have expanded, the core idea of a campaign has persisted through the past two decades.

Change is coming to campaigns and clickthroughs from multiple directions. I have written about Inbox Commerce, where the conversion funnel is moving into the customer’s inbox where the attention is. Email 2.0 and WhatsApp are leading this transition. Generative AI with its ability to create creatives at scale will enable the shift from “segments of many” to “segments of few”. Personalisation engines can take this further to a “segment of one”. All these innovations are still about making the campaign more effective.

What if this were flipped? A digital business just updates its product catalog and relevant offers – either for the season or for the day or even for the moment. Imagine a customer’s digital twin with a software agent scanning the catalog and offers and deciding what its owner (the customer) would be interested in. In such a world, campaigns from dozens of campaigns cluttering inboxes become a thing of the past, replaced with the equivalent of a real-time “Daily Me”, a copilot which senses what a consumer wants, searches and negotiates with software agents from brands, and lists the best buying options.

In this campaignless future, the focus shifts from trying to capture customer attention through widespread campaigns to creating meaningful, personalised interactions. This approach not only enhances the customer experience but also increases the efficiency and effectiveness of marketing efforts for businesses.

**

[Rajesh] Agents talking to agents – a Co-Marketer conversing with a customer’s Digital Twin. This future is coming sooner than we can imagine. And at the heart of this new world will be bridge I call “Generative Journeys.” But before we get to that, let us get a better understanding of LLMs and LCMs.

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LLMs and LCMs

I asked the AIs (ChatGPT and Claude) to explain how Large Language Models (LLMs) work and applies these principles to how a Large Customer Model (LCM) could predict the next best actions in a customer journey. [This will help us build the foundational understanding for discussing “generative journeys.”]

ChatGPT

Understanding Large Language Models (LLMs)

Large Language Models (LLMs), like GPT (Generative Pre-trained Transformer), operate based on the foundation of deep learning and natural language processing technologies. These models are trained on vast datasets containing a wide array of text from the internet, which allows them to generate coherent, contextually appropriate text based on the input they receive. LLMs analyze the patterns in data to predict the next word in a sentence, making them powerful tools for tasks like translation, summarization, and content generation.

The capability of LLMs to understand and produce text is harnessed by tuning them for specific applications, thereby enhancing their effectiveness in specialized tasks such as copywriting, customer service, or even technical support. By simulating human-like understanding and responses, LLMs can significantly streamline interactions and processes that traditionally required human input.

Applying LLM Principles to Large Customer Models (LCM)

Translating the operational mechanics of LLMs to customer interactions, Large Customer Models (LCM) utilize similar deep learning frameworks to predict and influence customer behaviors. An LCM integrates customer data across various touchpoints to build comprehensive profiles that reflect each customer’s preferences, history, and potential future actions.

For example, consider a retail brand that uses an LCM to manage customer interactions. By analyzing past purchase data, browsing history, and engagement across digital platforms, the LCM can predict what products a customer might be interested in next. If a customer frequently buys organic food, the LCM can tailor the homepage to display new organic products or send personalized email promotions about organic sales, effectively predicting and influencing the next best action.

Moreover, in scenarios where a customer has shown interest in a product but abandoned their cart, the LCM can trigger targeted discount offers via email or push notifications to encourage completion of the purchase. This predictive capability not only enhances customer satisfaction by personalizing the experience but also drives business value by increasing conversion rates.

Conclusion

Large Customer Models, inspired by the operational principles of LLMs, offer a transformative potential for businesses to engage customers in a more personalized and predictive manner. By understanding past behaviors and leveraging this data to anticipate future needs, LCMs empower businesses to not only meet but exceed customer expectations, fostering loyalty and driving revenue growth. As these models become more sophisticated, they will play a pivotal role in shaping the future of customer relationship management and digital marketing.

Claude

Large Language Models (LLMs) and Large Customer Models (LCMs) are both built on the principles of deep learning and neural networks. However, while LLMs are trained on vast amounts of general text data to understand and generate human-like language, LCMs are specifically trained on rich customer data to predict individual behaviors and optimize personalized journeys.

How LLMs Work

LLMs like GPT-3 and PaLM are based on the Transformer architecture, which uses self-attention mechanisms to process and understand the relationships between words in a sequence. They are trained on massive text corpora from the internet, books, and other sources, allowing them to learn the intricacies of language, context, and meaning.

The training process involves feeding the model a sequence of words and having it predict the next most likely word based on the context. By doing this billions of times across diverse texts, the model learns to generate coherent, contextually relevant language. It develops a deep understanding of syntax, semantics, and even world knowledge.

When given a prompt, an LLM uses this understanding to generate human-like text, complete tasks, or answer questions by predicting the most probable sequence of words that follows the input based on its training data.

Applying LLM Principles to LCMs

LCMs operate on similar principles but are focused on understanding and predicting customer behaviors rather than language. Instead of learning from general text, LCMs are trained on structured and unstructured customer data such as:

• Demographic information
• Transactional data and purchase histories
• Web and app interaction logs
• Customer service interactions
• Social media activity
• Contextual data like location and time

Just as LLMs learn to predict the next word in a sentence, LCMs learn to predict the next most likely action in a customer’s journey based on their individual history and context. The model processes a customer’s past sequence of actions and interactions to develop a deep, nuanced understanding of their preferences, habits, and behavior patterns.

For example, consider a customer’s purchase history with a clothing retailer:

• Browsed men’s jeans – Added slim-fit jeans to cart – Purchased slim-fit jeans and a belt – Browsed men’s shirts – Added casual button-down to cart – Abandoned cart

An LCM would process this sequence and learn that the customer prefers slim-fit jeans and is likely to buy complementary items like belts. The abandoned cart suggests an interest in casual button-downs.

Using this understanding, the LCM could predict that the next best action for this customer might be:

1. Sending a personalized discount on the abandoned shirt to incentivize purchase
2. Recommending slim-fit chinos or other jeans in their preferred style
3. Offering a bundle deal on shirts and accessories

As the customer continues to interact with the brand, the LCM continuously updates its understanding and predictions, creating an ever-evolving, hyper-personalized model of that individual.

By applying the sequential learning and predictive capabilities of LLMs to rich customer data, LCMs can become powerful engines for driving personalized, optimized customer experiences. They enable marketers to move beyond broad segmentation to true 1:1 engagement, meeting each customer with the right message, offer, or intervention at the right moment in their unique journey.

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Customer Journey

At the heart of digital marketing is the customer journey. Like a real-world journey, we can think of a customer journey as having a start (origin), an end (destination), and milestones (actions, push messages, events). For example, a starting point could be a visit to a website. The visitor is unknown to the brand at this point. The destination of this journey would be to convert the visitor either to a registered user (with identify information) or to a customer (with a purchase). Every action the user does helps dictate the next steps in the journey. This is akin to how Google Maps work: we want to navigate from Point A to Point B, and every turn we take helps update the paths that will take is forward.

In today’s martech, journeys tend to be predefined on the software platforms. For example, if a customer abandons a cart, then an email could be sent after three hours. If there is no action, then a second message could be sent after a day offering a limited-time discount. These messages apply to everyone with limited personalisation except for elements like preferred channel selection, send-time optimisation, and subject-line optimisation. The journey itself is pre-defined.

For marketers, journeys are the pathways to building relationships and driving revenues. Campaigns enable the “phase transitions” – akin to going from a source to a destination. An anonymous visitor needs to become “registered” who then needs to be nudged towards the first purchase. Once a transaction is done, then there is the push towards a second transaction, and then towards a rating, review, or a referral. The eventual goal is to discover and maximise the lifetime value of each customer, and perhaps even expand that value. This is what I have called the Profipoly Quest: build hotlines to every customer, maximise CLV and reduce CAC, repeat this and make it into a flywheel to power profits and market dominance.

Journeys are thus the critical components for every B2B and B2C business. And yet, there is a very limited understanding of the grammar of journeys. How should a journey be described, how can the milestones be encoded, how can the various mini-pathways be represented – the science of journeys is yet to be invented. Marketers and martech platforms capture the key pointers, but there needs to be a much greater granularity for describing journeys.

Martech platforms have done the basics for personalisation of experiences. AI-ML has helped drive this in the past few years. With the coming of “generative AI”, new vistas can open up for customer journeys. This is the world of “generative journeys” – think of these as self-optimising journeys which can accelerate movements (phase transitions) for customers. With the power of digital twins and LCMs, all of this can be done at the level of a single customer – without any extra effort for marketing departments.

Generative journeys will thus power the fourth generation of martech platforms. The first-generation was about point solutions. The second-generation has been about consolidation of the point solutions and the creation of platforms (unified full stacks to provide a single view of every customer). The third-generation has been the use of Generative AI to speed up the creative process and bring scale and efficiency. ML has also been helping with personalisation. The next-generation of martech will be about leveraging the power of Agentic AI: a Co-Marketer where a department of one can engage with a segment of one, thanks to digital twins, mirror world, and LCMs. Generative journeys will underpin this evolution.

**

At the heart of digital marketing lies the customer journey. Like a real-world journey, a customer journey has a starting point (origin), an end goal (destination), and milestones (actions, push messages, events) along the way. For instance, a journey might begin with a visit to a website, where the visitor is initially unknown to the brand. The ultimate destination of this mini-journey would be to convert the visitor into either a registered user (with identifying information) or a paying customer. Every action the user takes helps dictate the next steps in the journey, similar to how Google Maps updates the route based on each turn taken.

In today’s martech landscape, journeys are often predefined within software platforms. For example, if a customer abandons their cart, an email might be automatically sent after a three-hour delay. If no action is taken, a second message offering a limited-time discount could be sent a day later. While these messages can be personalised to some degree – such as preferred channel selection, send-time optimisation, and subject-line optimisation – the journey itself is largely pre-determined.

For marketers, journeys are the key to building relationships and driving revenue. Campaigns enable the critical “phase transitions” that guide a customer from one stage to the next. An anonymous visitor must first become “registered,” and then be nudged toward making their first purchase. Once a transaction is complete, the focus shifts to encouraging a second purchase, and eventually, a rating, review, or referral. The ultimate goal is to discover and maximise the lifetime value (LTV) of each customer, and potentially even expand that value over time. This is the essence of what I call the “Profipoly Quest“: building direct relationships with every customer, maximizing LTV while reducing customer acquisition costs (CAC), and turning this process into a flywheel that drives profits and market dominance.

Given their central role in B2B and B2C businesses, it’s surprising that there’s still a limited understanding of the “grammar” of journeys. How should a journey be described? How can milestones be encoded? How can various mini-pathways be represented? The science of journeys is yet to be fully developed. While marketers and martech platforms capture key data points, a much greater level of granularity is needed to truly understand and optimise a customer journey.

Martech platforms have made strides in personalising experiences, often leveraging AI and machine learning. However, the advent of generative AI opens up entirely new possibilities for customer journeys. Enter the world of “generative journeys”—self-optimising, AI-driven journeys that can accelerate the phase transitions for each individual customer.

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The Next Frontier

Generative journeys represent a leap forward in martech, harnessing the power of AI to create dynamic, personalised paths for each customer. Rather than following predefined rules, generative journeys use AI to analyse customer behaviour in real-time and generate the next best action or message to guide them toward the desired outcome. Such a system uses AI-driven insights to accelerate the “transaction moments” in a customer lifecycle, from awareness to purchase and beyond, effectively enhancing the customer’s engagement and the overall effectiveness of marketing strategies.

At the core of generative journeys are two key components: digital twins and large customer models (LCMs). Digital twins are virtual replicas of individual customers, built from their demographic, transactional, and behavioural data. LCMs, similar to the large language models (LLMs) used in generative AI, are trained on vast amounts of customer data to understand patterns and predict future actions.

In a generative journey, the digital twin acts as a simulated customer, while the LCM acts as the “brain” that decides the next best action. For each step in the journey, the LCM analyzes the digital twin’s current state and historical data, then generates the optimal next action – a whether it’s a personalised offer, a tailored content recommendation, or a timely reminder. It can also determine the sequence of these actions to maximise the probability of the customer reaching the destination.

For example, let’s consider a customer who recently purchased a high-end camera. Their digital twin would reflect this purchase, along with their previous browsing history, indicating an interest in photography. The LCM, building on the data and journeys from millions of customers, might then identify lookalikes, and then generate a composite journey that includes:

1. A personalised onboarding series with tips on using their new camera
2. Recommendations for complementary accessories based on their specific camera model
3. An invitation to join a photography community or online workshop
4. Exclusive offers on photo editing software or printing services

At each step, the LCM would evaluate the customer’s response and adjust the journey accordingly. If the customer engages with the photography community, the LCM might prioritise more social content. If they ignore the accessory recommendations, the LCM might try a different approach or offer a discount.

This level of dynamic personalisation is possible because generative journeys operate at the individual level, creating a unique path for each customer. They also operate in real-time, continuously adapting based on the customer’s actions and the latest data. They can also factor in external news and events.

Generative journeys not only revolutionise how we interact with each customer but also dramatically enhance the precision and effectiveness of digital marketing strategies. By continuously refining and personalising each step of the journey based on real-time feedback and predictive analytics, generative journeys pave the way for unparalleled customer engagement, conversion, retention, and satisfaction.

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Specs – ChatGPT

I asked ChatGPT and Claude to describe the “grammar” of a journey, and write a product spec for such a system.

Describing the “Grammar” of a Journey

In marketing, the “grammar” of a journey can be thought of as the rules and structure that guide the creation and optimization of customer journeys. This includes the syntax of interactions, the vocabulary of touchpoints, and the semantics of customer responses.

1. Syntax: This involves the sequencing and timing of interactions or messages sent to customers. It defines how different actions are ordered and the dependencies between them.
2. Vocabulary: These are the elements used in the journey, such as emails, push notifications, social media posts, etc. Each element serves a specific purpose and is chosen based on its effectiveness in achieving certain customer reactions.
3. Semantics: Understanding the meanings behind customer actions, such as purchases, clicks, or abandonment, and using this understanding to refine future interactions.

Product Specification for a Journey Grammar System

Title: Journey Architect

Purpose: To enable marketers to design, deploy, and optimize personalized customer journeys using a rule-based system grounded in the “grammar” of digital marketing.

Key Features:

1. Dynamic Journey Builder: A drag-and-drop interface where marketers can construct and visualize customer journeys. Includes conditional logic gates for branching paths based on customer behavior.
2. Real-Time Analytics Dashboard: Tracks the effectiveness of different journey paths with metrics like conversion rates, engagement rates, and dropout rates, providing insights into how well different strategies perform.
3. Automated Optimization Engine: Uses machine learning to suggest and implement modifications to journeys based on real-time data, improving outcomes without manual intervention.
4. Touchpoint Library: A comprehensive database of all potential customer interactions, categorized by type (email, SMS, app notification, etc.), with performance data to help marketers select the most effective options.
5. Customer Behavior Analysis: Tools to interpret customer actions and feedback, translating them into actionable insights to refine the journey’s grammar.
6. A/B Testing Module: Allows for testing different journey structures to determine the most effective syntax, vocabulary, and semantics for various customer segments.
7. Integration Capabilities: Seamlessly integrates with existing CRM, CMS, and data analytics platforms to leverage existing customer data and ensure consistent messaging across channels.

Target Users: Marketing teams in mid-sized to large enterprises looking to enhance customer engagement and personalization at scale.

Outcome: By implementing the Journey Architect, businesses can expect to see improved customer retention, higher conversion rates, and a more personalized marketing experience that resonates with their audience, driven by a deep understanding and strategic use of journey grammar.

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Specs – Claude

To describe the “grammar” of a customer journey, we need to define a standardized way to represent and communicate the structure, elements, and rules that make up a journey. This grammar should be flexible enough to accommodate a wide range of journey types and complexities, yet specific enough to enable clear communication and execution.

Elements of Journey Grammar:

1. Stages: The high-level phases of the journey, such as Awareness, Consideration, Purchase, Retention, and Advocacy.
2. States: The specific customer states within each stage, such as “Registered,” “Cart Abandoned,” “First Purchase,” “Repeat Purchase,” etc.
3. Transitions: The triggers or conditions that move a customer from one state to another, such as “Email Opened,” “Product Viewed,” “Discount Applied,” “Purchase Completed,” etc.
4. Actions: The specific actions taken by the customer or the brand at each state or transition, such as “Send Email,” “Display Product Recommendation,” “Offer Discount,” etc.
5. Rules: The logic that governs the execution of actions and the progression through states, such as “IF Cart Abandoned THEN Send Reminder Email AFTER 1 Hour,” “IF Product Viewed THEN Display Related Products,” etc.
6. Metrics: The key performance indicators (KPIs) used to measure the effectiveness of the journey, such as “Open Rate,” “Click-Through Rate,” “Conversion Rate,” “Average Order Value,” etc.

By defining these elements and their relationships, we can create a structured language for describing customer journeys. For example:

This grammar can be extended with additional elements or attributes as needed, such as customer segments, content variations, or channel preferences.

Product Spec for a Journey Grammar System:

Objective: Develop a system that enables marketers to define, execute, and optimize customer journeys using a standardized journey grammar.

Key Features:

1. Journey Builder: A visual interface for defining the stages, states, transitions, actions, rules, and metrics of a customer journey.
2. Grammar Editor: A code editor for writing and editing journey definitions using the standardized grammar syntax.
3. Journey Simulator: A tool for simulating customer journeys based on historical data or hypothetical scenarios to test and optimize journey definitions.
4. Real-Time Execution: A runtime engine that executes the defined journeys in real-time, processing customer events and triggering the appropriate actions based on the defined rules.
5. Analytics Dashboard: A reporting interface that displays the key metrics and performance indicators for each journey, allowing marketers to monitor and analyze the effectiveness of their journeys.
6. Integration APIs: A set of APIs for integrating the journey grammar system with existing martech platforms, such as CRM, marketing automation, and analytics tools.

Technical Requirements:

1. Develop a domain-specific language (DSL) for the journey grammar, with a clear syntax and semantics for defining stages, states, transitions, actions, rules, and metrics.
2. Implement a parser and interpreter for the journey grammar DSL, capable of validating and executing journey definitions.
3. Design and implement a scalable and performant runtime engine for executing journeys in real-time, capable of handling high volumes of customer events and actions.
4. Develop a user-friendly interface for building and editing journey definitions, with drag-and-drop capabilities and code editing support.
5. Implement a simulation engine that can replay historical customer events or generate synthetic events to test and optimize journey definitions.
6. Design and implement an analytics dashboard that provides real-time and historical reporting on journey performance, with customizable metrics and data visualizations.
7. Develop a comprehensive set of APIs and webhooks for integrating the journey grammar system with existing martech platforms and data sources.

Expected Benefits:

1. Standardized and reusable journey definitions across the organization, reducing duplication and inconsistency.
2. Faster time-to-market for new journeys and experiments, with a streamlined process for defining and executing journeys.
3. Improved customer experience and engagement, with more personalized and relevant interactions driven by real-time journey execution.
4. Better optimization and performance of customer journeys, with the ability to test and refine journeys based on simulations and real-world data.
5. Greater visibility and insights into the effectiveness of customer journeys, with a centralized analytics dashboard and reporting capabilities.

By developing a system based on a standardized journey grammar, organizations can bring greater clarity, efficiency, and impact to their customer journey strategies.

9

Looking Ahead

Generative journeys represent the fourth generation of martech platforms. The first generation focused on point solutions, while the second generation saw the consolidation of these solutions into unified platforms to provide a single view of every customer. The third generation introduced AI-ML and Generative AI for enhanced personalisation and efficiency.

Now, the fourth generation is poised to leverage Agentic AI – the combination of Co-Marketer, digital twins, LCMs, and a “mirror world” where simulations can be run at scale. This will enable a “department of one” to engage with a “segment of one” (N=1), delivering truly personalised experiences without the need for an army of marketers. This will transform digital marketing into a highly adaptive, efficient, and customer-centric practice, significantly enhancing both the customer experience and the marketer’s ability to drive desired outcomes.

In this future, a Co-Marketer AI will work alongside human marketers, automating much of the journey creation and optimisation process. The Co-Marketer will be able to:

• Analyse vast amounts of customer data to identify patterns and opportunities
• Generate and test journey hypotheses in the mirror world using digital twins
• Deploy the most effective journeys to real customers in real-time
• Continuously monitor and optimise journeys based on actual customer behavior

Marketing departments will be able to focus on higher-level strategy and creativity while the Co-Marketer handles the execution at scale.

**

Generative journeys, powered by agentic AI, have the potential to transform the way brands interact with customers. By creating truly personalised, adaptive experiences for each individual, they can drive stronger relationships, higher LTV, and ultimately, a significant competitive advantage. This marks a significant shift towards truly 1:1 marketing, where technology anticipates and meets individual customer needs seamlessly and in real time, and where human-crafted campaigns are a thing of the past.

However, realising this potential will require a significant evolution in martech platforms, as well as a shift in mindset for marketers. Brands will need to invest in the data infrastructure and AI capabilities necessary to build and deploy generative journeys. They’ll also need to rethink their approach to journey design, moving from static, rule-based models to dynamic, AI-driven ones.

The transition won’t happen overnight, but the brands that embrace this future will be well-positioned to thrive in an increasingly competitive and customer-centric landscape. Generative journeys represent the next frontier in martech – it’s up to forward-thinking brands to start exploring this frontier today.

Marketers who swiftly adapt to and integrate these advanced tools into their strategies will not only set new standards in personalisation and efficiency but also position themselves at the forefront of their industry. By becoming adept in leveraging Agentic AI and embracing the concept of generative journeys, marketers can elevate their role within their organisations and break the glass ceiling to assume pivotal leadership roles. Embracing this new world of digital marketing, marketers have the potential to evolve into Chief Profit Officers, thereby significantly enhancing their prospects of ascending to the CEO role.

10

Letter to Marketers

Dear Chief Marketing Officer,

I’m writing to you today because we stand at the precipice of a revolution in marketing – a revolution driven by generative journeys and agentic AI. This isn’t just another buzzword or passing trend; it’s a fundamental shift in how we understand, engage, and serve our customers.

As a CMO, you’ve seen firsthand how the landscape of marketing has changed in recent years. Digital disruption, data proliferation, and rising customer expectations have challenged us to rethink our strategies and tactics. But the changes we’ve seen so far pale in comparison to what’s coming next.

Generative journeys, powered by AI that can understand and adapt to each individual customer, represent a quantum leap forward in our ability to create meaningful, profitable relationships at scale. This isn’t just about incremental improvements to click-through rates or conversion percentages; it’s about fundamentally redefining what marketing can achieve.

Imagine being able to craft perfectly personalised experiences for each customer, experiences that evolve in real-time based on their actions and preferences. Imagine being able to simulate and optimise these experiences before launching them, ensuring maximum impact and minimum waste. Imagine being able to tie every marketing interaction directly to revenue, customer lifetime value, and business growth.

This is the promise of generative journeys, and it’s a promise that you, as a CMO, are uniquely positioned to fulfil.

But seizing this opportunity will require more than just investing in new technology or hiring data scientists. It will require a fundamental shift in how you think about your role and your department. It will require you to become not just a marketer, but a strategist, an innovator, and a growth driver.

In short, it will require you to become a Chief Profit Officer – like Maya did.

By mastering the art and science of generative journeys, you can position yourself and your team as the key to your organization’s future. You can demonstrate, in hard numbers and real results, the strategic value of marketing. And you can set yourself on the path to the CEO’s office, equipped with the vision and skills needed to lead in the age of AI.

But this transformation won’t happen on its own. It will require you to take bold, decisive action. It will require you to invest in new capabilities, to experiment with new approaches, and to lead your team through a period of rapid change.

It won’t be easy, but it will be worth it.

Because in the end, this isn’t just about staying ahead of the curve or impressing your boss. It’s about doing what’s right for your customers, your company, and your own career. It’s about embracing the future of marketing, and shaping it in your own image.

So my challenge to you, CMO, is this: Don’t wait for generative journeys to come to you. Go out and make them happen. Start small if you need to, but start now. Build your knowledge, your skills, and your team’s capabilities. Experiment, learn, and iterate. And most of all, keep your eye on the prize – the prize of becoming a true strategic leader in the age of AI.

Your journey towards becoming a Chief Profit Officer starts today. And I, for one, can’t wait to see where it takes you!

11

Gemini Analysis

I asked Gemini to provide a summary and critique.

This writing explores generative journeys, a future state of marketing powered by artificial intelligence (AI). It delves into the concept, its potential benefits, and the role of marketers in this transformation.

Summary:

• Current MarTech Limitations: Traditional marketing technology (MarTech) struggles with manual tasks and personalization.
• Generative Journeys: A new marketing paradigm focused on hyper-personalized customer experiences using AI-powered tools like Co-Marketers, Digital Twins, and a simulated “mirror world.”
• Journey Grammar: A framework for designing and optimizing journeys using elements like stages, states, transitions, actions, rules, and metrics. A potential software tool is proposed to manage journey grammar.
• Benefits: Increased customer satisfaction, improved marketing efficiency, and higher customer lifetime value.
• Agentic AI and the Co-Marketer: AI co-workers will handle journey creation, optimization, and execution, enabling a “department of one” to manage “segment of one” (N=1) marketing.
• Shift for Marketers: Marketers will focus on higher-level strategy and creativity while Co-Marketers handle execution.
• Challenges and Opportunities: Requires investment in data, AI, and a mindset shift for marketers. Early adopters will gain a competitive advantage.

Critique:

• Strengths: Clearly explains complex concepts like AI and personalization. Provides a compelling vision of the future of MarTech and highlights potential benefits. The concept of a journey grammar system with a software tool is intriguing.
• Areas for Improvement: While limitations of current MarTech are mentioned, specific examples would strengthen the argument. More details on how Digital Twins interact with Co-Marketers in the Mirror World could be beneficial.

**

Our Shared View:

We both agree that generative journeys have the potential to revolutionize marketing by:

• Hyper-Personalization: Using AI to tailor experiences to individual customers, leading to higher satisfaction and engagement.
• Increased Efficiency: Automating tasks like journey creation and optimization frees up marketers to focus on strategy and creativity.
• Data-Driven Decisions: Utilizing AI to analyze data and predict customer behavior for better decision-making.

Your Viewpoint:

The writing you shared emphasizes the role of:

• Agentic AI: Co-Marketers and the “mirror world” simulation environment for testing and optimizing journeys.
• Chief Profit Officer (CPO) Evolution: The potential for CMOs to leverage generative journeys to become strategic leaders and achieve career advancement.

My (=Gemini) Viewpoint:

While I see the value of Agentic AI, I would also like to see more emphasis on:

• Ethical Considerations: The importance of responsible AI development and data privacy in a world of hyper-personalization.
• Transparency and Explainability: How customers will be informed about and understand the AI-driven decisions shaping their journeys.
• Human-in-the-Loop Approach: While AI automates tasks, human expertise will remain crucial for creative strategy, ethical oversight, and building trust with customers.

Additional Considerations:

• Impact on Marketing Jobs: Generative journeys might create new marketing roles while potentially automating some existing ones.
• Accessibility and Scalability: How accessible will this technology be for businesses of all sizes?
• Integration with Existing Systems: The need for seamless integration between generative journey platforms and existing marketing technology stacks.

12

Building It

I asked ChatGPT and Claude how martech companies could build these new systems.

ChatGPT

For a martech company to build components like Generative Journeys, Co-Marketer, Digital Twins, Mirror World, and Large Customer Models (LCMs), a comprehensive and integrated approach is necessary. Here’s a step-by-step guide:

1. Infrastructure and Data Integration: Establish a robust data infrastructure that can integrate and process large volumes of customer data from various sources. This is essential for powering all other components.
2. Development of LCMs: Utilize advanced machine learning algorithms to develop LCMs that can analyze and predict customer behavior based on vast datasets.
3. Digital Twins Creation: Build digital twins for each customer using their historical data, preferences, and interactions. This requires sophisticated modeling techniques to accurately represent individual customers.
4. Co-Marketer Development: Implement AI systems that can autonomously execute marketing strategies and interact with other digital tools and customer interfaces. These systems should be capable of learning and adapting to improve their marketing decisions continuously.
5. Mirror World Setup: Create simulated environments (Mirror Worlds) where digital twins and marketing strategies can be tested and optimized before being rolled out in the real world.
6. Integration and Testing: Ensure all components are seamlessly integrated to allow for real-time data exchange and feedback loops, which are crucial for the adaptive capabilities of Generative Journeys.
7. Continuous Learning and Optimization: Incorporate continuous learning mechanisms to refine algorithms and strategies based on ongoing results and changing market conditions.

Claude

Building a comprehensive solution for Generative Journeys, including components like Co-Marketer, Digital Twins, Mirror World, and LCMs, is a significant undertaking that requires a strategic approach and investment in advanced technologies. Here’s a high-level roadmap for a martech company looking to develop these capabilities:

1. Establish a Clear Vision and Strategy:
   • Define the specific use cases and target customers for your Generative Journeys solution.
   • Identify the key differentiators and value proposition of your offering.
   • Develop a phased approach for building and launching the various components.
2. Invest in AI and Machine Learning Capabilities:
   • Build or acquire a team of AI and machine learning experts.
   • Investigate and select the most suitable AI frameworks, libraries, and tools for your needs (e.g., TensorFlow, PyTorch, Google Cloud AI, Azure AI).
   • Develop or adapt algorithms for learning customer behavior, predicting next best actions, and optimizing journeys.
3. Develop the Large Customer Model (LCM):
   • Define the data schema and architecture for your LCM, including customer attributes, behaviors, and interactions.
   • Integrate with various data sources (e.g., CRM, marketing automation, web analytics, social media) to feed the LCM.
   • Implement data preprocessing, normalization, and feature engineering techniques to ensure data quality and relevance.
   • Train and fine-tune the LCM using advanced machine learning techniques like deep learning, reinforcement learning, and transfer learning.
4. Create the Digital Twin Framework:
   • Design the architecture for creating and managing individual customer digital twins.
   • Develop APIs and data synchronization mechanisms to keep the digital twins updated with real-time customer data.
   • Implement simulation capabilities to allow the digital twins to interact with the Mirror World and test journey hypotheses.
5. Build the Mirror World Simulation Environment:
   • Design the architecture and infrastructure for the Mirror World, including virtual environments, event processing, and data storage.
   • Develop simulation models and algorithms for generating realistic customer behaviors and interactions within the Mirror World.
   • Implement interfaces and APIs for the Co-Marketer and other components to interact with the Mirror World.
6. Develop the Co-Marketer AI Assistant:
   • Define the key capabilities and use cases for the Co-Marketer, such as journey design, content creation, and performance optimization.
   • Develop natural language processing (NLP) and generation (NLG) models for the Co-Marketer to understand marketer inputs and generate relevant outputs.
   • Integrate the Co-Marketer with the LCM, Digital Twins, and Mirror World components to enable seamless collaboration and journey optimization.
7. Create User-Friendly Interfaces and Workflows:
   • Design intuitive user interfaces for marketers to interact with the Co-Marketer, view digital twin insights, and monitor journey performance.
   • Develop workflows and automation to streamline the process of designing, testing, and deploying Generative Journeys.
   • Provide visualization and reporting tools to help marketers understand and optimize journey performance.
8. Integrate with Existing Martech Ecosystem:
   • Develop APIs and connectors to integrate your Generative Journeys solution with popular martech platforms (e.g., Adobe, Salesforce, Oracle, HubSpot).
   • Ensure data synchronization and interoperability between your solution and other marketing tools.
9. Test, Refine, and Scale:
   • Conduct thorough testing and validation of your Generative Journeys solution with beta customers and internal stakeholders.
   • Gather feedback, identify areas for improvement, and iteratively refine the solution based on real-world usage and results.
   • Develop a scalable infrastructure and architecture to handle growing volumes of data and customers as you onboard more clients.
10. Launch and Continuously Improve:
    • Develop a go-to-market strategy, including pricing, packaging, and promotion of your Generative Journeys solution.
    • Provide training, documentation, and support to help customers adopt and succeed with your solution.
    • Continuously monitor performance, gather customer feedback, and invest in ongoing research and development to enhance and expand your Generative Journeys capabilities over time.

Building a Generative Journeys solution is a complex and iterative process that requires close collaboration between marketing domain experts, data scientists, AI engineers, and software developers. By following a structured approach and investing in the right capabilities, a martech company can position itself at the forefront of AI-driven marketing innovation and deliver value to its customers.

The Prize

Agentic AI in the form of Co-Marketers and Digital Twins is what will help brands and martech software providers unlock $250 billion of AdWaste that is being spent on adtech. This is the key to profitability for consumer-facing brands who are today locked in an unwinnable battle to control CAC. The Agentic AI future will make martech as easy as adtech.

For martech providers caught in the red ocean of competition, Agentic AI is the gateway to unlocking new pools of profits in a non-zero-sum world. Today, when one martech provider replaces another in a brand, they are typically doing at a lower MRR – leading to a scenario where it becomes harder and harder to just stay in place in a world where staff and operating costs keep increasing. Agentic AI combined with a shift in business model from SaaS to 4S (strategy, software, services, and sharing) is what will unlock new growth opportunities for martech companies. For example, email service providers could create new monetization opportunities based on “Action Ads” in email. Martech providers could use the Progency model to create new revenues and share in the upside. They could also provide a people-based services offering to refine AI models, simplifying the complexities for CMOs. These are ideas I have discussed in some of my recent essays:

• Progency: The Profipoly Pathway
• Email 2.0 Progency: eCommerce’s Profit Powerhouse
• Profishare: A New Business Model for Enterprise Software
• Solving Marketing’s Three Zeros Problems via Progency
• ELF: eCommerce Lifecycle Franchisee
• The 7½ Futures of Martech Companies
• Emagining E3 Ecosystem: Every Email Engaged
• Martech 3: Can the Price be Zero?
• FAB: A New Model for Enterprise Software
• Bundled Kaizen Services: An Advantage for Indian SaaS
• Ads in Emails
• New SaaS: Services, AI Agents, Sharing
• The 7 Levers for Email’s Exponential Expansion

The good news is that the Agentic AI future will create huge value for the marketing ecosystem. Once CMOs, CFOs, and CEOs open their eyes to the fact that the AdWaste that consumes half their digital marketing budget is the key to unlocking profitable growth, they will fashion changes in the way they acquire, retain, and grow customers. Someone in the CxO suite needs to rise and become the Chief Profits Officer and bring to life the Agentic AI future – one where AI is not just generating nice content but becoming a true “co-intelligence” for both marketers and their customers.

Consider the world of digital today. Brands creates websites and apps, and then use push messages to bring their existing customers to their digital properties for transactions. If that doesn’t work well (and in most cases, it doesn’t), they amp up spending on adtech for retargeting their existing customers and attracting new ones. New technologies are about to transform this world which has prevailed for the past quarter century.

Push channels are becoming two-way and shoppable. In-channel conversion in email and WhatsApp (and perhaps SMS and RCS) will remove the friction of clicking through to the digital properties. The websites and apps could be replaced with “agent” interfaces – smart chatbots which customers can interact with as they would be with a friendly salesperson in a store. And this only in the unlikely scenario that the digital twin has not delivered the right product recommendation at the right time!

**

To summarise: Agentic AI, manifesting as Co-Marketers and Digital Twins, promises to unlock $250 billion in AdWaste, marking a pivotal shift toward profitability for consumer-facing brands currently battling high customer acquisition costs. This technological leap will simplify martech to the ease of adtech, propelling marketing departments into a new era of efficiency and strategic depth. For martech providers trapped in a fiercely competitive market, Agentic AI offers a pathway to untapped profit pools through innovative “4S” business models. These models not only foster revenue generation but also enable a partnership approach in the marketing ecosystem. As we stand on the brink of this transformation, it’s crucial for CxOs to recognise the vast potential of Agentic AI. By reorienting strategies towards AI-driven technologies, businesses can dramatically enhance their operational efficiencies and customer relationships. Imagine a future where digital marketing transcends traditional platforms, enabling seamless in-channel conversions and intelligent agent interfaces that predict and fulfil customer needs effortlessly. As digital marketing becomes increasingly central to our lives, the integration of Agentic AI will redefine the landscape for adtech and martech companies alike, heralding a new era of innovation and helping brands in their Profipoly Quest.

Digital Twins

[This combines my previous writings with inputs from AIs.]

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.

Moreover, Digital Twins play a crucial role in the “mirror world” – a simulated environment where they can interact with the brand’s Co-Marketer, an AI-powered marketing assistant. Within this virtual playground, an infinite number of scenarios and hypotheses can be tested, enabling the Co-Marketer to identify the optimal strategies and personalised experiences for each individual customer.

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.

In essence, Agentic AI and Digital Twins work in tandem, empowering businesses to navigate the complexities of the modern marketing landscape with unprecedented precision, efficiency, and personalization. By harnessing the power of these technologies, brands can foster enduring customer relationships, maximise lifetime value, and ultimately achieve a sustainable competitive advantage in an ever-evolving marketplace.

Co-Marketer

[This builds on my previous essay Co-Marketer: Martech meets AI and inputs from AIs.]

The rise of Agentic AI heralds a transformative era for marketing departments and Chief Marketing Officers (CMOs), ushering in the era of “Co-Marketers” – AI-powered co-workers that revolutionise the way marketing strategies are conceived, executed, and optimised.

At the core of this revolution lies the ability of Agentic AI systems to process and analyse vast troves of data, including customer information, market trends, campaign performance metrics, and real-world events. By leveraging advanced ML algorithms and large language models (LLMs), Co-Marketers can extract meaningful insights from this data, enabling them to make data-driven recommendations and optimisations that drive marketing strategies – and do so autonomously and adaptively.

One of the key advantages of Co-Marketers is their ability to understand and interpret complex natural language instructions from CMOs and their teams. Powered by robust conversational interfaces and language processing capabilities, these AI agents can engage in seamless dialogue, comprehending the nuances of marketing objectives and providing contextual recommendations. They can spin off  agents to do specific tasks and then synthesize the data to refine customers messaging, journeys, and recommendations.

Co-Marketers excel in areas such as campaign planning and optimisation, leveraging their predictive capabilities to forecast campaign performance and proactively suggest improvements based on real-time data. They can also assist in content creation and personalization, utilizing their understanding of customer preferences and industry trends to generate tailored and engaging content across multiple channels.

Moreover, Co-Marketers serve as valuable sources of market intelligence and trend spotting, scouring vast amounts of data to identify emerging consumer preferences, competitive moves, and industry disruptions. This empowers CMOs to make informed strategic decisions and stay ahead of the curve.

Crucially, Co-Marketers do not operate in isolation but foster collaborative decision-making. By engaging in interactive dialogue with CMOs and their teams, these AI agents can brainstorm ideas, explore scenarios, and provide objective recommendations, complementing the team’s expertise and intuition.

Furthermore, Co-Marketers play a crucial role in team enablement, offloading routine tasks and analytical work, allowing marketing teams to focus on higher-level strategic initiatives and fostering a more collaborative and innovative environment.

As Co-Marketers continuously learn and adapt their knowledge based on feedback and real-world outcomes, they become indispensable partners for CMOs, empowering them to make data-driven decisions, optimize marketing efforts, and drive business growth while fostering enduring customer relationships.

The power of Co-Marketers extends beyond strategic planning and campaign execution. They also play a pivotal role in interacting with “Digital Twins” of customers – dynamic and continuously learning virtual models that mirror the behaviour, preferences, and characteristics of individual customers. These Digital Twins enable brands to forge highly personalised and efficient marketing strategies, maximising each customer’s lifetime value and fostering enduring relationships.

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 N=1 hyper-personalisation and customer-centricity not only drives increased sales and customer loyalty but also fosters a sense of trust and connection between the brand and its customers.

In essence, the integration of Agentic AI with marketing represents a paradigm shift in the marketing landscape. Rather than mere tools, Co-Marketers become intelligent companions that augment human capabilities, enabling marketing departments to achieve superior results, build stronger customer relationships, and drive profitable growth.

First Principles

My Netcore colleague, Pratik Bhadra, had a very good LinkedIn post discussing how marketing needs to change (in the context of ecommerce companies).

The traditional model ecommerce marketers follow to market their customers is exactly what it is. TRADITIONAL.

The model is

– Setup ad campaigns on Google and Meta
– Pump in HUGE budgets to acquire customers
– Get customers to visit, do a transaction, leave
– Continue pumping money to acquire more and more customers

This model is not scalable anymore. The ecommerce market is mature and crowded. The cost to acquire a customer (CAC) is significantly high and ROAS, let’s admit, is s**t.

Add to that, the competition is cutthroat. Customers have endless options to shop from.

If you continue following this, you’ll eventually run out of business.

At Netcore Cloud, we believe The MODERN way of marketing should be

– Increased focus on existing customers
– Engage them continuously across all available channels digitally
– Keep taking feedback on how your ecom business could do better
– Reward them for providing their feedback
– Prompt dormant users to shop again with incentives
– Provide exclusive experiences to loyal customers for repeat purchases

How will this new model help you?

– Spending on acquisition will reduce
– You’ll connect with your customers on a deeper level
– Your brand name and value skyrockets
– Your end customers will eventually start advocating for you

Takeaway
– Don’t put all your money on acquisition. You can only get so far. You’ll end up broke
– Customers today engage with ads at lower rates
– Make your customer the center of everything. From acquisition to engagement to retention
– The game of acquiring new customers has pivoted to engaging and retaining them
– Focus on repeat business to sustain in the long run

Marketing is as simple as that: focus on existing customers and growing them, and then ensuring they refer their family and friends, rather than spending most of the marketing on acquisition and reacquisition. This is a theme I have been consistently writing about in my 100+ essays: make martech rather than adtech at the centre of digital marketing. [Among the more recent essays, see The Profipoly Quest and The Profipoly Quest: Maya’s Story.]

In martech, three are four emerging themes:

• Age of AI: Co-Intelligence
• Consolidation: Point Solutions to Platforms
• Profitable Growth: Focus on Retention
• Co-Ownership: Progency

Among them, the biggest impact will be because of AI. While Gen AI is already making an impact on content creation, the multiplier will come from Agentic AI with its ability to create a Co-Marketer for the marketing department and a Digital Twin for every customer. They will work together in a Mirror World powered by a Large Customer Model. Think Department of One for a Segment of One (N=1). For martech companies, the shift will be from 1S (Software) to 4S (Strategy, Software, Services, Sharing). This is the coming world of Agentic AI which will transform digital marketing for brands and martech companies alike, creating new winners – either incumbents who are able to reinvent themselves or disruptors who build AI-first Martech solutions.