AI Twins: The Future of Marketing Intelligence

Published October 11-18, 2024

When Machines Speak

Welcome to the coming world of AI Twins, a revolutionary concept that extends beyond the realm of industrial Digital Twins. While Digital Twins are software replicas of machines used to monitor and optimise physical systems, AI Twins aim to mirror human traits, behaviours, and decision-making processes. These advanced digital replicas leverage AI to simulate human-like interactions and provide personalised insights through conversational interfaces.

Unlike their industrial counterparts, which rely on sensor data and IoT devices to optimise machine performance, AI Twins harness behavioural data, preferences, and digital interactions to replicate human behaviour. This enables them to engage in natural conversations, offer personalised recommendations, and enhance user experiences across various domains.

As Gautam Mehra puts it, the AI Twin is built with “deterministic and observable data.” This means that the AI model is trained on concrete, measurable information rather than subjective or speculative data. This approach ensures that the AI Twin’s responses and insights are grounded in factual, verifiable information.

Deterministic data refers to information that is certain and produces consistent results when analysed. This could include recorded behaviours, documented outcomes, or measurable metrics. For instance, sales figures, customer interaction logs, or performance evaluations would be considered deterministic data. Observable data refers to information that can be directly seen, measured, or recorded. This contrasts with inferred or hypothetical data. Examples of observable data include customer purchase history, time spent on specific tasks, or quantifiable achievements.

By using deterministic and observable data, AI Twins can provide more reliable and actionable insights. This approach helps to minimise biases and reduces the risk of the AI making unfounded assumptions or predictions.

Here is an example of how Gautam built an AI Twin of a Samsung Galaxy S24 customer: “We used our 3 core reports: Behaviour, Intent & Mentions. Behaviour reports help us understand who our customers are. We do this by unpacking 1st party data (cohorts of their own customers) of our clients. However, for this interview, we use “interests” which are cohorts/groups of people on audience platforms like Meta, TikTok, Pinterest which you can understand better in our platform. We create a brief that represented people who are consuming content and interested in Samsung Galaxy S24. We used several data points from this behaviour report to create the core “character”. Things like age, gender, location, profession, topics they (collective they refer to engages with Samsung Galaxy S24 on digital media) are interested in and several other data points to feed and create this character. We also fed it with memories. This memory was derived from several thousand conversations that people have had on the social and open web (Mentions Report) about Samsung Galaxy S24, what they search for, hundreds of questions they ask and how they evaluate the competition (Intent Report).”

Note: In previous essays, I used the phrase “Digital Twins” when I should have referred to “AI Twins.” My thanks to Gautam for highlighting the distinction between these two types of twins.

Additional Reading:

Examples

Imagine a future where AI Twins assist in various applications requiring human-like decision-making.

A product manager is developing a new smartphone model. She consults an AI Twin of successful product launches, built on data from past sales figures, customer reviews, and feature adoption rates. The Twin helps her prioritise features and predict market reception.

A business professional is learning Mandarin for an upcoming international project. He engages with an AI Twin made from fluent Mandarin speakers, practicing conversations, receiving real-time feedback on pronunciation, and learning cultural nuances that are crucial for business interactions in China.

A chef planning a new restaurant menu interacts with an AI Twin of culinary trends, trained on data from popular recipes, ingredient pairings, and diner preferences. The Twin suggests innovative dishes and helps optimise the menu for profitability and customer satisfaction.

A city planner is redesigning a busy intersection. She collaborates with an AI Twin of traffic flow experts, using data from traffic patterns, accident reports, and pedestrian movement. The Twin helps simulate various design options to improve safety and reduce congestion.

An e-commerce manager wants to reduce cart abandonment rates. She works with an AI Twin of customers, built on data from user clickstreams, purchase histories, and session durations. The Twin suggests personalised strategies to improve checkout completion rates.

An individual looking to improve their overall health engages with an AI Twin of fitness experts and nutritionists. Through regular conversations, the Twin helps create a personalised workout routine, offers dietary advice, and provides motivation by simulating the encouragement and knowledge of top trainers.

A professional considering a career change consults an AI Twin representing successful individuals who have made similar transitions (sourced from LinkedIn data). The Twin offers insights into the challenges and opportunities of the new field, suggests skill development strategies, and helps create a roadmap for the career pivot.

A retail chain creates an AI Twin of its top-performing stores, analysing data on layout, inventory, staffing, and sales. Store managers interact with this Twin to optimise their locations. For example, a manager of an underperforming store could ask about ideal product placement or effective promotional strategies. The AI Twin provides recommendations based on successful practices, helping replicate top store performance across the chain, thereby improving overall efficiency and profitability.

A stock exchange develops AI Twins for all public companies using data from financial reports, press releases, and public statements. Investors and analysts interact with these Twins to gain insights into company operations and strategies. For instance, an investor could ask about a tech company’s R&D focus or market expansion plans.

A content creator is planning her YouTube strategy. She interacts with an AI Twin of successful YouTubers, trained on data from view counts, engagement rates, and trending topics. The Twin advises on content ideas, optimal posting times, and audience growth strategies.

A farmer is planning crop rotation for the next season. He collaborates with an AI Twin of agricultural experts, using data from soil composition, weather patterns, and crop yield histories. The Twin recommends optimal crop selections and planting schedules to maximise yield and sustainability.

A cybersecurity team leader is developing a new threat detection system. She consults an AI Twin of security analysts, built on data from past breach attempts, common attack vectors, and system vulnerabilities. The Twin helps prioritise security measures and predict potential new threats.

A hotel manager wants to improve guest satisfaction. She works with an AI Twin using data from guest reviews, booking patterns, and service request logs. The Twin suggests personalised amenities and service improvements to enhance the guest experience.

A game developer is designing a new mobile game. He consults an AI Twin of successful games, using data from player engagement metrics, in-app purchase patterns, and retention rates. The Twin suggests game mechanics and monetisation strategies to maximise player satisfaction and revenue.

A language learning app developer is creating a new course structure. He works with an AI Twin of polyglots and language acquisition experts, using data from learner progress rates, common mistakes, and engagement patterns. The Twin helps design an adaptive learning path that optimises language retention and fluency development.

Marketing – 1

AI Twins in marketing, particularly Segment Twins, offer a unique advantage by synthesising data from various sources, including customer data platforms, marketing automation tools, and ad platforms. They analyse behavioural patterns, engagement metrics, and demographic details from major platforms like Google, Meta, and TikTok, effectively bridging the gap between martech and adtech insights. This comprehensive view of customer segments enables marketers to develop highly targeted strategies and personalised experiences.

The true game-changer lies in the conversational interface of AI Twins. Marketers can directly interact with these digital entities, asking questions, testing hypotheses, and gaining deeper insights into customer mindsets. This level of interaction facilitates a degree of customisation and personalisation previously unattainable in marketing strategies.

As I wrote in Digital Twins in Marketing: Magical Minions: “Customer Twins take two primary forms: Segment Twins and Singular Twins. Segment Twins represent groups of customers with similar behaviours, preferences, and characteristics. These virtual cohorts allow marketers to simulate responses to campaigns across different customer segments, test messaging and offers on virtual groups before real-world deployment, and identify trends and patterns within specific customer cohorts. On the other hand, Singular Twins are AI-powered replicas of individual customers, enabling hyper-personalised marketing at scale – true N=1 marketing. These individual twins facilitate predictive modelling of customer journeys and real-time optimisation of interactions across all touchpoints, ensuring each customer receives a tailored experience.”

The Twins idea can be expanded further as this slide shows:

The power of AI Twins to, in the words of Jack Carr, “in real-time, analyse the past and evaluate the present to predict the future” makes them an extraordinarily powerful asset for marketing teams. This capability fundamentally transforms the way marketers approach decision-making and strategy development.

By analysing past data, AI Twins can uncover patterns and insights that might be missed by human analysts, providing a deep understanding of historical trends and campaign performances. Their real-time evaluation of present conditions allows marketers to stay agile, responding quickly to emerging trends, sudden market shifts, or changes in consumer behaviour.

Most crucially, the predictive capabilities of AI Twins offer marketers a glimpse into potential futures, enabling them to anticipate market changes, forecast campaign outcomes, and pre-emptively address challenges. This foresight allows marketing teams to be proactive rather than reactive, crafting strategies that are not just responsive to current conditions but are prepared for future scenarios.

Moreover, the conversational interface of AI Twins democratises access to these complex analytics, allowing marketers at all levels to engage with data in intuitive ways. This accessibility fosters a data-driven culture within marketing teams, where decisions are increasingly backed by robust analysis and predictive insights.

In essence, AI Twins serve as a bridge between the vast sea of data available to marketers and the actionable insights needed to drive effective strategies. They augment human creativity and intuition with data-driven precision, enabling marketing teams to craft more targeted, personalised, and effective campaigns.

Marketing – 2

I asked ChatGPT and Claude for some examples for the use of AI Twins in marketing.

Marketing Campaign Optimisation: A marketer needs to decide among multiple messages for a holiday season campaign targeting the brand’s loyal customers. She consults an AI-powered Segment Twin to rank the content and collaborates with it to refine the campaign’s messaging and timing, ensuring higher engagement and conversion rates. The Segment Twin uses deterministic data such as past campaign performance, customer feedback, and demographic information to provide its insights.

Product Launch Strategy: A Chief Marketing Officer (CMO) is planning a new product launch. Instead of relying on gut instinct or historical data alone, she consults a Segment Twin – an AI model representing her brand’s most loyal customers. Through a natural conversation, she asks the Twin to rank three potential messaging strategies. The Twin not only provides rankings but offers insights into why certain messages resonate more strongly, drawing from vast amounts of behavioural data. Together, the CMO and the Segment Twin refine the campaign’s timing and content, ensuring higher engagement and conversion rates.

Customer Retention Analysis: A marketer wants to understand why a segment of customers churned. She engages in a conversation with an AI Twin representing that group. The Twin analyses data such as churn rates, customer complaints, and usage patterns to provide detailed insights. It identifies the main pain points and suggests specific improvements to retain customers, helping the marketer develop targeted retention strategies.

Market Research and Insights: A market researcher is tasked with understanding consumer attitudes towards a new product. They interact with AI Twins representing different segments of the target market. The Twins uses data from surveys, focus groups, and social media mentions to provide comprehensive insights. These insights help the researcher identify potential barriers to adoption and opportunities for product improvement.

Personalised Content Creation: A digital marketing team is developing a content strategy for a diverse audience. They consult an AI Twin representing different customer personas. The Twin analyses data from content engagement metrics, user behaviour on the website and app, and social media interactions to suggest tailored content topics, formats, and distribution channels for each persona. This enables the team to create highly relevant and engaging content that resonates with specific audience segments, improving overall content performance and ROI.

Dynamic Pricing Optimisation: An e-commerce company wants to optimise its pricing strategy across various product lines. They utilise an AI Twin that simulates market conditions and consumer behaviour. The Twin incorporates real-time data on competitor pricing, demand fluctuations, and historical sales patterns to recommend optimal pricing strategies. It can also predict how different customer segments might respond to various pricing models, allowing the company to implement dynamic pricing that maximizes both sales volume and profit margins.

Influencer Marketing: A brand is looking to enhance its influencer marketing strategy. They employ an AI Twin that analyses data from social media platforms, engagement rates, and audience demographics of potential influencers. The Twin helps identify the most suitable influencers for the brand’s target audience and predicts the potential impact of different influencer partnerships. It can also suggest optimal content formats and posting schedules for each influencer, maximising the effectiveness of influencer collaborations.

McKinsey offers an interactive example of how combining a digital twin with generative AI brings added value to the customer journey.

From the post: “Digital twins and generative AI (gen AI) heighten each other’s impact. Gen AI enables access to digital twins for the common, nontechnical user through a conversational interface, unveiling a new realm of synthesis, analysis, and insights. Gen AI can also be leveraged to collect, transfer, and augment data input to digital twins. When large language models are integrated with digital twins, they can be fine-tuned to enable up-to-date, real-time responses and even serve as an optimizer for solving specific tasks using domain knowledge. Specific use cases for gen AI–powered customer digital twins (CDTs) include simulating and predicting what product a customer purchases next, predicting each customer’s churn propensity, analyzing customer behavior to predict future purchasing patterns, and improving customer experience across the customer journey. Together, these technologies leverage real-time data to identify and act on various scenarios, helping organizations efficiently analyze vast quantities of data.”

Conversations

I asked Claude and ChatGPT to create two conversations with Maya, CMO of A1Beauty, an omnichannel beauty and skincare products seller, and Segment Twins of the most loyal customers. and that of churned customers.

Claude

Maya and the Loyalist Twin

Maya: Hello Loyalist Twin, I’m planning our holiday campaign and I’d like your insights on our most loyal customers.

Loyalist Twin: Hello Maya. I’m here to help. What specifically would you like to know about A1Beauty’s most loyal customers?

Maya: What do you think would be the most effective promotional offer for this group?

Loyalist Twin: Based on analysis of past purchasing patterns and engagement data, our most loyal customers respond best to exclusive early access to new products rather than discounts. They value feeling special and ‘in the know’.

Maya: Interesting. Any specific product categories they’re most interested in lately?

Loyalist Twin: There’s been a growing interest in eco-friendly and sustainable skincare products. Searches and purchases in this category have increased by 30% in the last quarter among loyal customers.

Maya: Great insight. How about messaging? What tone resonates best with them?

Loyalist Twin: Our loyal customers respond well to messaging that emphasizes A1Beauty’s commitment to quality and innovation. They also appreciate content that educates them about ingredients and skincare routines.

Maya: Thank you, Loyalist Twin. This will really help shape our holiday campaign.

Maya and the Churned Twin

Maya: Hello Churned Twin, I’d like to understand why we’ve lost some customers recently.

Churned Twin: Hello Maya. I’m here to provide insights on customers who have stopped purchasing from A1Beauty. What would you like to know?

Maya: What’s the primary reason customers have been churning?

Churned Twin: Analysis shows that the main reason for churn has been perceived lack of value. Many churned customers felt that A1Beauty’s products were priced higher than competitors without a clear justification for the premium.

Maya: I see. Were there any specific product lines that saw higher churn rates?

Churned Twin: Yes, the anti-aging serum line saw the highest churn rate. Customers reported that they didn’t see the promised results quickly enough.

Maya: That’s concerning. Did churned customers mention any competitors they switched to?

Churned Twin: Many churned customers mentioned switching to NaturalGlow, citing their transparent ingredient lists and lower prices as key factors.

Maya: This is valuable information. What do you think could bring these customers back?

Churned Twin: A combination of strategies could work. Offering a satisfaction guarantee on the anti-aging line, more transparent communication about ingredient efficacy, and a loyalty program that provides increasing value over time could address the main pain points of churned customers.

Maya: Thank you, Churned Twin. This gives us a clear direction for our customer retention efforts.

ChatGPT

Conversation with Loyalist Twin

Maya: Hello, Loyalist Twin. I need to decide on the best message for our upcoming holiday season campaign targeting our loyal customers. Can you help me rank the content?

Loyalist Twin: Certainly, Maya. Based on data from past campaign performance, customer feedback, and engagement metrics, I have ranked the three messages as follows: Message A has the highest potential for engagement, followed by Message B and then Message C.

Maya: Great. Can you provide insights into why Message A resonates more with our loyal customers?

Loyalist Twin: Absolutely. Message A highlights exclusive rewards and personalized offers, which align well with the preferences and behaviors of our loyal customers. They value recognition and exclusivity, which this message effectively communicates.

Maya: That makes sense. How can we refine the timing of this campaign to maximize its impact?

Loyalist Twin: I recommend launching the campaign during the first week of December, as our data shows that this is when our loyal customers are most active and likely to engage with promotional content.

Maya: Excellent. Thank you, Loyalist Twin. Your insights are invaluable.

Loyalist Twin: You’re welcome, Maya. Happy to assist.

Conversation with Churned Twin

Maya: Hello, Churned Twin. I want to understand why some of our customers have churned. Can you provide insights based on your analysis?

Churned Twin: Of course, Maya. By analyzing deterministic data such as churn rates, customer complaints, and usage patterns, I’ve identified several key reasons for churn. The primary issues are lack of personalized offers, slow customer service response times, and limited product variety.

Maya: That’s very insightful. How can we address these issues to improve retention?

Churned Twin: To improve retention, I recommend implementing more personalized offers tailored to individual customer preferences. Additionally, enhancing the efficiency of our customer service and expanding our product range to include more variety will address the key pain points.

Maya: These suggestions are very actionable. Can you also help me develop a targeted retention strategy?

Churned Twin: Certainly. I suggest creating a segmented retention campaign that focuses on re-engaging customers who have churned in the past six months. Use personalized email offers, loyalty program enhancements, and proactive customer service follow-ups.

Maya: Thank you, Churned Twin. Your detailed analysis and recommendations will be very helpful in improving our customer retention strategy.

Churned Twin: You’re welcome, Maya. I’m here to help ensure we can win back our valued customers.

Twinning

I asked ChatGPT to explain how a brand could create AI Twins, more specifically, Segment Twins.

Data Collection and Integration

A. Leverage Martech Data:

Customer Data Platforms (CDPs): Collect and unify data from various sources such as website interactions, email campaigns, CRM systems, and purchase history.
Marketing Automation Tools: Gather data on customer engagement with marketing campaigns, including open rates, click-through rates, and conversion metrics.
Loyalty Programs: Extract data on customer participation in loyalty programs, reward redemption, and engagement levels.

B. Integrate Adtech Data:

Social Media Platforms: Collect behavioral and demographic data from platforms like Facebook, Instagram, and TikTok. This includes likes, shares, comments, and ad interaction data.
Advertising Platforms: Use data from Google Ads, programmatic advertising platforms, and other ad networks to understand customer interactions with ads, click-through rates, and conversion metrics.
Third-Party Data Providers: Supplement your data with external data sources that provide insights into customer interests, behaviors, and demographics.

Data Processing and Cleansing

A. Data Cleansing:

Remove Duplicates: Ensure that the data is free of duplicates to maintain accuracy.
Standardize Data: Convert all data into a standardized format for easy integration and analysis.
Handle Missing Values: Address any missing values in the dataset by imputing or removing them.

B. Data Processing:

Segmentation: Group customers into segments based on shared characteristics such as age, gender, location, purchase behavior, and engagement levels.
Feature Engineering: Create relevant features from the raw data that will be used to train the AI models. Examples include average purchase value, frequency of purchases, and engagement score.

Model Training

A. Select AI Algorithms:

Machine Learning Algorithms: Use algorithms such as clustering (e.g., K-means) for segmentation, and classification (e.g., decision trees, random forests) to predict customer behavior.
Natural Language Processing (NLP): Utilize NLP techniques to analyze textual data from customer reviews, social media interactions, and support tickets.

B. Train the Models:

Supervised Learning: Train the models using labeled data where the outcome (e.g., purchase behavior, churn) is known.
Unsupervised Learning: Use unsupervised learning techniques to identify patterns and segments within the data without predefined labels.

C. Validate and Optimize Models:

Cross-Validation: Use cross-validation techniques to ensure the models are robust and generalize well to new data.
Hyperparameter Tuning: Optimize the model parameters to improve performance metrics such as accuracy, precision, and recall.

Deploying Segment Twins

A. Create Digital Personas:

Build Segment Profiles: Develop detailed profiles for each customer segment, including demographic information, behavioral patterns, preferences, and engagement metrics.
Memory Integration: Feed the AI Twins with “memories” derived from historical data, conversations, and interactions to make them more contextually aware and responsive.

B. Implement Conversational Interfaces:

Chatbots and Virtual Assistants: Integrate the Segment Twins into chatbots or virtual assistants to enable natural conversations with marketers.
API Integration: Provide APIs to allow seamless integration of the AI Twins with other marketing tools and platforms.

Continuous Learning and Improvement

A. Real-Time Data Integration:

Live Data Feeds: Continuously feed the AI Twins with real-time data from ongoing campaigns, social media interactions, and customer behavior.
Feedback Loops: Implement feedback mechanisms to update the models based on new data and changing customer behaviors.

B. Performance Monitoring:

Key Performance Indicators (KPIs): Track KPIs such as customer satisfaction, engagement rates, and conversion metrics to assess the effectiveness of the Segment Twins.
Model Retraining: Periodically retrain the models to incorporate new data and improve accuracy and relevance.

Two Multipliers

Two interconnected concepts, Agentic AI and Kaizen, have the potential to significantly amplify the power of AI Twins in marketing. This synergy creates a new paradigm that we can call “Kaizen Marketing,” which combines the strengths of AI and human expertise to drive continuous improvement at unprecedented speed and scale.

Agentic AI in Marketing

Agentic AI introduces the concept of multiple AI agents interacting with each other to achieve complex goals. In the context of marketing, this manifests as a Co-Marketer working in tandem with various AI Twins. These twins can include Customer Twins (both Segment and Singular), Product Twins, Store Twins, Location Twins, and others. The Co-Marketer, an AI-powered entity itself, orchestrates these twins, analysing their insights, coordinating their actions, and making strategic decisions.

For instance, the Co-Marketer might engage with Segment Twins to identify broad market trends, consult Singular Twins for personalised customer insights, interact with Product Twins to optimise product recommendations, and leverage Location Twins to refine geotargeting strategies. This multi-agent system operates at both macro and micro levels, balancing broad market strategies with individual customer experiences.

The power of this approach lies in its ability to process vast amounts of data, identify patterns, and make decisions in real-time, far surpassing human capabilities. Once trained, the Co-Marketer can take over many of the CMO’s routine tasks, allowing human marketers to focus on high-level strategy and creative direction. This dynamic interaction not only enhances the efficiency of marketing efforts but also ensures that strategies are continuously optimised based on real-time data.

Kaizen in AI Marketing

Kaizen, the Japanese philosophy of continuous improvement, can be applied to this AI-driven marketing ecosystem. Both the Co-Marketer and the AI Twins can be designed to improve incrementally on a daily basis. This improvement can come from various sources:

Learning from each interaction and decision made
Analysing the outcomes of marketing campaigns and customer interactions
Incorporating new data and market trends
Adapting to changes in customer behaviour and preferences

The concept of 1% daily improvement, compounding to a 37X increase over a year, is particularly powerful when applied to AI systems. Unlike humans, AI doesn’t tire and can consistently apply learnings across all operations. This continuous improvement can manifest in various ways:

More accurate customer segmentation
Better prediction of customer behaviour
More effective personalisation of marketing messages
Improved product recommendations
More efficient allocation of marketing resources

For martech companies, this confluence of Agentic AI, AI Twins, and Kaizen principles presents a significant opportunity. They can package this as “Kaizen Marketing” – a thin services layer that combines the power of AI with human expertise to deliver continuous marketing improvement.

Kaizen Marketing

In Kaizen Marketing, a human “services” team would work alongside the AI systems. Their role would be to:

Monitor the performance of the Co-Marketer and AI Twins
Provide domain expertise to enhance the AI models
Identify areas for improvement that the AI might miss
Ensure the AI’s decisions align with broader business goals and ethical considerations
Translate AI insights into actionable strategies for human decision-makers

This human-AI collaboration ensures that the system benefits from both ML capabilities and human intuition and creativity. It also addresses potential concerns about fully automating marketing decisions, providing a level of human oversight and strategic direction.

The Bundled Kaizen Services concept fits perfectly into this model. By offering this as a bundled service, martech companies can provide a comprehensive solution that includes both the AI technology and the human expertise needed to maximise its potential.

This approach offers several advantages:

Continuous Improvement: The combination of AI’s learning capabilities and human expertise ensures ongoing enhancement of marketing strategies.
Scalability: The AI can handle vast amounts of data and multiple tasks simultaneously, while the human team can focus on strategic oversight.
Personalisation at Scale: The system can deliver highly personalized marketing at a scale impossible for human-only teams.
Competitive Advantage: For Indian SaaS companies, this bundled service leverages their cost advantage in providing high-quality human expertise alongside cutting-edge AI technology.

Thus, the integration of Agentic AI and Kaizen principles in marketing, facilitated by AI Twins and human expertise, represents a powerful new direction for Martech. It promises to deliver continuous improvement in marketing performance at a scale and speed previously unattainable, potentially transforming how companies approach their marketing strategies and customer engagement.

In summary: AI Twins represent a revolutionary concept that extends far beyond traditional digital replicas. These advanced AI-powered entities mirror human traits, behaviours, and decision-making processes across various domains, from healthcare to urban planning. By harnessing behavioural data, preferences, and digital interactions, AI Twins enable natural conversations, personalised recommendations, and enhanced user experiences.

In marketing, AI Twins offer unparalleled insights and capabilities, seamlessly bridging the gap between martech and adtech, thereby shaping the future of marketing intelligence. Powered by deterministic and observable data, these digital doppelgangers represent different customer segments, products, and market dynamics, allowing marketers to achieve highly personalized and dynamically optimised strategies in real-time. The integration of Agentic AI and Kaizen principles further amplifies their power, ensuring marketing strategies are data-driven and continuously refined for maximum effectiveness.

For martech companies, this presents a significant opportunity to offer comprehensive solutions combining cutting-edge AI technology with valuable human expertise. As we move forward, AI Twins will redefine marketing practices, enabling hyper-personalisation at scale, predictive analytics, and unprecedented customer understanding. This AI-powered future of marketing not only optimises customer engagement but also paves the way for exponential forever profitable growth – a Profipoly. In an increasingly complex and competitive digital landscape, AI Twins can set new standards for marketing efficiency, effectiveness, and delightful customer experiences.

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