Digital Twin of a Customer (DToC): How It Can Help Marketers

Artificial intelligence and machine learning systems are evolving faster than ever. We have so many new tools that allow us to do things we couldn’t before. For instance, the digital twin technology. It uses advanced analytics and ML models to create an interactive digital twin of customers (DToC) using historical and real-time customer data.

Unlike legacy tools like static entities or customer personas, digital twins are continuously updated with real-life user data. They reflect an individual’s current state of mind rather than their past actions, and you can talk to them and ask for their feedback on your latest business, marketing, or sales plans. These virtual avatars not only simulate customer behavior but also provide context and predictions for future marketing decisions.

With cookieless marketing and data privacy regulations, DToCs are a great way to understand prospects and implement data-driven marketing strategies. In this article, we look at the origins of digital twin technologies, learn about the concept of the digital twin of customers, and ensure personalization at all touchpoints with Delve AI’s Digital Twin of Customer software.

What Is a Digital Twin of a Customer (DToC)?

A digital twin of a customer (DToC) is a virtual representation of a customer or customer group that mimics, analyzes, and anticipates customer behaviors. It leverages first-party data and other consumer data sources to replicate customer experiences in a digital environment. Like regular twin technology, DToCs use advanced analytics, AI, and machine learning algorithms to construct a digital version of your buyer.

Gartner says, “Instead of merely collecting data points, [digital twin of a customer] provides context and predictions of future behaviors. It uses both online and physical interactions and it’s dynamic, updating as new information comes in and recognizing that a single person can exemplify more than one persona…”This is because people, and in turn, personas can change over time.

DToCs build on work currently being done by famous brands like Google, Amazon, and Netflix, i.e., using AI and ML algorithms to interpret behavioral data and tweak product experiences.

Still a nascent technology, 70% of C-suite tech executives at large enterprises are already investing in digital twins. Result-wise, global and technology decision-makers agree that implementing the digital twin technology increases customer engagement (63%) and acquisition (73%).

And why should it not?

Now you can not only replicate in-store and online stores with digital twins, but also the customers who visit them. This gives you insights into how specific customers or shopper groups will respond to changes in the customer journey before they are implemented, and rule out detrimental additions. With virtual customers in a virtual store, DToCs can suggest personalized offers and make shopping a seamless affair.

How do customer digital twins work?

The process of creating customer digital twins can be broken down into four steps:

• Collect data personalized to your product/object/use case.
• Feed it into data models built with advanced mathematics and statistics.
• Use AI and machine learning for data assimilation – combine your data and models.
• Maintain a continuous flow of information to and from the digital twin.

The DToC ingests customer data from multiple sources, often including behavioral, transactional, and customer interaction data (support transcripts, social media engagement, NPS scores). This data is then fed into advanced analytical models that generate a representation of users’ behavioral patterns, interests, and decision triggers.

Customer digital twins are different from static customer intelligence models in that they maintain a continuous two-way data flow. So every new transaction, page visit, or social interaction updates the twin in real time and keeps it in line with the customer's current state of mind.

Once built, the virtual model will help you make predictions and provide personalized recommendations.

Digital twins vs. simulations, CDPs, and personas

A digital twin of a customer isn’t a static buyer profile. It doesn’t just store customer contact data, like a CRM system. It cannot be stated enough, but digital twins are vastly different from other customer intelligence tools like buyer personas, customer data platforms (CDPs), and simulations.

To start with, simulations represent a single process at a point in time and don’t use current data. On the contrary, digital twins simulate multiple interconnected scenarios and processes, using real-time data. It’s not a one-way workflow either – digital twin systems simultaneously receive and transmit information. It’s kind of the same case with other systems.

To give you a better understanding, here’s a table that highlights the major differences between digital customer twins, traditional personas, CDPs, and simulations.

Across all comparisons, you can see that the fundamental difference is that DToCs can simulate, predict, and interact with customers or users, and not just collect and visualize data in dashboards. Plus, the systems are automatically updated with fresh data.

Digital twins vs 360-degree view of the customer

A digital twin of a customer (DToC) differs from the 360-degree view of the customer mainly due to the type of data collected and used. For those who don’t know, a 360-degree view of the customer involves collecting and merging data from various touchpoints and customer data platforms into one place. It helps organizations fully understand their prospects and needs.

You need multiple tools and services to develop a 360-degree view of your customer.

Extensive data is required, like user behavioral data, transaction history, interests, and attribute data, to successfully create an all-around view of the customer. Doing so is expensive and time-consuming, hence out of reach for many small and medium-sized businesses. In fact, Gartner has found that only 14% of organizations have achieved a 360-degree view of their customer.

With third-party cookies going out of commission in the future, you’ll no longer be able to use them to gather interest and targeting ideas. It will get significantly harder to drive revenue based on this data alone.

DToCs are unaffected by this.

A digital twin of a customer largely uses first-party data as seed data to offer consumer insights and personalizations throughout the customer journey. It’s a dynamic, digital buyer that encapsulates the ever-changing nature of your prospects without compromising data quality and trust.

A Brief History of The Digital Twin Technology

The global digital twin market was valued at $35.82 billion in 2025 and is projected to reach $328.51 billion by 2033, growing at a CAGR of 31.1% from 2026 to 2033. It’s an astounding growth rate. But do you know how digital twins came about?

Heard of the Apollo 13 accident, NASA’s spaceflight that was supposed to land on the moon but didn’t? The organization was able to rescue its astronauts from crashing by using digital twin technology. Fast forward to 2002, Michael Grieves introduced the concept of digital twins as part of product lifecycle management.

The term was formally introduced by John Vickers, a NASA principal technologist, in 2010.

According to IBM, “A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It spans the object's lifecycle, is updated from real-time data, and uses simulation, machine learning, and reasoning to help make decisions.”

It is a virtual replica of an original object.

Digital twins integrate Industry 4.0 technologies, like automation, artificial intelligence, big data, and 3D printing, to create simulations, solve performance problems, and enhance customer experiences. As such, they are primarily used in design, manufacturing, construction, maintenance, architecture, healthcare, and engineering.

But it’s not that easy to create digital twins. This is because of data scarcity and system monitoring. Disruptions at a very fine level cannot yet be monitored with supercomputing powers. And it’s critical to track them since they impact everything at the system level. Data is never enough. You need accurate and abundant data to make sense of things. Right now, the data we have is sparse and incomplete, and involves a lot of work to fill in the gaps.

Types of digital twin systems

Different types of digital twins are classified according to their usage levels in the marketing and customer experience context:

• Descriptive twins: Live, editable versions of design and construction data of an object, system, or physical asset.
• Informative twins: Live and sensory (plus operational) data, strongly linked to operational guidelines. Offer real-time information about product performance and issues.
• Predictive twins: Leverage data to provide actionable insights for maintenance and other purposes.
• Comprehensive twins: Use data to simulate possible "what-if" scenarios and predict outcomes of changes or events.
• Autonomous twins: Act on behalf of the user, responding to global shifts and challenges. They can take action without human intervention.

The most relevant types are predictive, comprehensive, and autonomous twins since the goal is not just to describe a customer, but to forecast and simulate their next action.

Digital twins tell you how objects and people function under different environments, following the product lifecycle from design and development to disposal. They reduce the time to market by helping you optimize product design and decision-making.

For instance, you can research and develop marketable products, test multiple prototypes, and check product functionality. That’s not all. Once your product reaches its end of life, the digital model can also suggest product disposal options like recycling.

Synthetic personas and silicon sampling

A history section about the digital twin of the customer is incomplete without the mention of synthetic personas (synthetic users) and silicon sampling.

Synthetic personas, or silicon samples, are built using large language models like OpenAI’s ChatGPT to create digital representations of your actual customers. Data is added into the generative AI model, which is then prompted to generate and adopt the persona of the customer whose data is provided.

You can use these personas for several use cases, like testing new ads for your ecommerce site or evaluating product designs. Another interesting use is leveraging silicon samples to answer customer survey questions. Since you’re using an LLM, the output mostly sounds human and lists customers’ goals, interests, and preferences regarding your product.

The higher the training data quality, the better your survey responses. This synthetic data can fill the gaps created due to data scarcity and quality, and help you make customer-centric decisions.

How to Create a Digital Twin of a Customer (DToC): Step-by-Step Process

Developing a digital twin of the customer is a tedious process. You need to be familiar with data analytics and machine learning techniques to build analytical models that can combine online and physical interactions and guide customer conversions.

So, ask yourself three questions before you start creating the digital twin of a customer (DToC):

• Why do you want to build a digital twin of the customer? (personalization, churn prevention, journey optimization?)
• What data sources will you use, and do you have consent to use them?
• What is the result you want to achieve?

With these three answers, you can make the whole process a lot easier. A well-defined end goal and data set ensure that the digital twins meet the objectives set by your organization.

Step 1: Create buyer personas

The first step is to collect your existing customer data and use it to build buyer personas – they are semi-fictional representations of your ideal customers. As per ITSMA, 44% of marketers currently use buyer personas to inform their business activities.

Data is, of course, the most important part of persona creation.

• Demographics: Age, gender, location, education, family status, and income.
• Behavior: Website visits, pages visited, browsing habits, keywords, and channel preferences.
• Social analytics: Likes, comments, shares, hashtags, and mentions.
• Transactions: Order frequency, cart abandonments, purchase history, and payment preferences.
• Customer service: Support tickets, inquiries, customer feedback, and transcriptions of support calls.

Aggregate historical and current user data from different sources, then process, analyze, and segment it into 3–5 distinct persona groups that genuinely reflect different behavioral patterns, not just demographic differences. Involve your sales, customer service, and product development teams to build a cross-functional persona profile.

Select the customer persona for which you want to build a digital twin and enrich it with additional data sources.

Step 2: Refine with quality customer data

Once you have selected the customer persona, the next step is to supplement it with additional high-quality customer data. It can include both digital and physical interactions collected via analytics tools, location-based services, sensors, and smart cameras.

Market research, social media analytics, and Voice of the Customer (VoC) data is also great input.

But before this, ensure you have all the data you need to fully capture the entire customer experience. If you don't, find ways to change how you're currently gathering data. Learn about recent technological advancements in AI and data analytics, and understand how analytical models process data to offer insights into consumer behavior.

Step 3: Apply AI and machine learning

The third and probably most important step is to choose the right AI and machine learning systems to create the digital twin. These algorithms are needed to identify patterns in your customer dataset, like purchase triggers, churn signals, and preferences.

Train predictive models on historical outcomes and validate them against held-out data before deployment. For synthetic personas, fine-tune a base LLM on your customer data with proper privacy safeguards in place.

Step 4: Ensure data privacy and security

A big part of using first-party customer data, or any personal data for that matter, is making sure that your data usage meets all data privacy rules and regulations, like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

It helps mitigate privacy and compliance risks to protect potentially identifiable user data needed to create digital twins of customers. A way to get around this problem is by using synthetic data generated using AI algorithms.

Nonetheless, you have to maintain data transparency in all your activities and allow users to opt in or opt out of the digital twins programs. They need to know what it’s about and how it can benefit them – not you. In the end, the customer must have the final say in how their personal data is stored and used.

Step 5: Integrate into your workflow

After you have built actionable data models that make digital customer twins possible, start integrating them into your business workflow. Digital twins can help refine your products, services, and the overall customer experience. You can predict customer behavior to drive new monetization models, improve customer longevity, and build a loyal brand following.

Work closely with subject matter experts (SMEs) to adapt the logic and process of digital twin technology to different customer groups.

Once created, your digital twins need to stay up-to-date with new data, like recent transactions, social media activities, or customer support calls. Establish a routine for data refresh (at a minimum monthly) and integrate this information into your virtual model, which already includes product interests, communication preferences, personality, and sentiment analysis models.

Benefits of Using Digital Twins in Customer Experience (CX)

Customer experience (CX) is nothing more than the impression your customers have of your brand at all stages of their buyer’s journey. It can happen while they are interacting with your brand in any way, like clicking your ad for the first time, visiting your website, reading your blog posts, or contacting customer support.

Digital twins of customers give you the ability to improve the impression customers have of your brand. This is important because:

• 52% of prospects will shift to a competitor if they have a negative brand impression.
• Companies that focus on CX see an 80% increase in revenue.
• 73% of customers prioritize customer experience when purchasing from a brand.

DToCs takes a more proactive approach to customer experience. They help you adjust your marketing and messaging tactics in real time, vetoing activities that don’t work or might potentially lead to a negative customer experience. In fact, you can use them as synthetic research participants and get feedback on risky concepts or ad creatives.

Hence, brands can remedy negative situations before they occur, reinforce positive sentiments, and build a loyal customer base.

Plus, buyers aren’t restricted to one channel these days; they constantly move between apps, websites, and social media sites before making a decision. Your CX digital twins can track this cross-channel journey and provide a contextual omnichannel experience, so a user who saw a product on Instagram gets a relevant experience on the website.

They can additionally spot early-warning signals for churn (eg, declining engagement rate, reduced cart values), way before a customer signals their intention to leave.

Consequently, you can tap into the wants, needs, beliefs, expectations, and past experiences of prospects, which can help you create automated engagement strategies that keep up with evolving industry trends and customer preferences.

A word on customer journey optimization (CJO)

Digital twins of customers can primarily be used in marketing for customer journey optimization (CJO). CJO is the process of mapping customer actions and interactions across multiple touchpoints to control or influence the end-to-end customer experience. You direct a prospective customer towards a conversion event, like downloading an ebook or signing up for a free trial, based on their behavioral data.

A digital twin of the customer makes it easy in the sense that you don’t have to force people into making a decision – they do it willingly.

Since digital twins thoroughly analyze consumers’ current and historical data, they can spot trends and patterns unique to each individual. In turn, you get insights that help you build personalized marketing campaigns suited to their interests and send out the right offers to the right people at the right time.

Customization such as this results in higher engagement and conversion rates.

Digital twins can spot the touchpoints where customers drop off in the buyer’s journey. It can be a confusing checkout page, newsletter sign-up forms with poor CTAs, or even a product page with insufficient details. They identify the problem and also suggest solutions – enabling you to test out different scenarios.

Furthermore, simulating a customer’s behavior enables marketers to streamline the customer journey along with other inbound processes. It presents users with the best course of action at each stage, helping them reach their goals and overcome any barriers.

How Delve AI Helps with DToCs

As we’ve mentioned, the first step to creating digital twins of customers is persona generation. Persona by Delve AI, an online persona generator, lets you build data-driven personas automatically for your business, competitors, and social media audiences.

We create interactive personas using a diverse set of data sources, including first-party data (CRM, web analytics, and surveys), second-party data (social analytics and competitor intelligence), and public data (VoC data from reviews, ratings, forums, online communities, and news). Combined using machine learning algorithms and AI, these sources provide a 360-degree overview of your customers.

Our persona tool collects, analyzes, and segments data to create three to five buyer personas for both B2B and B2C businesses. The process is entirely automated and takes only a few minutes.

Each persona card provides an in-depth summary of your audience segments, showing important metrics like user percentage, sessions, action rate, conversions, transactions, and revenue.

The Persona Details page gives you a complete description of a particular persona. Everything from consumer demographics to psychographics is taken into account; you learn about their lifestyles, goals, aspirations, challenges, communication channels, content types, preferences, hobbies, interests, personalities, and more.

Further, industry-specific insights give you structured keyword data related to the industry you belong to.

Samples of single-user journeys for ecommerce/B2C and organizational journeys for B2B businesses for each persona segment show you how users interact with your website. It’s a great feature that helps you identify drop-off points and refine user experiences.

Personas are automatically updated with fresh data every month, offering AI-driven recommendations that help you acquire and retain new audiences. To get more information, read our article on the elements of buyer personas.

Now, traditional personas aren’t something you can engage with like your average customers. You look at the information they present and then derive your conclusions. But with Delve AI’s Digital Twin of Customer software, you can chat with your customers (persona segments) online!

As in the example above, you can ask your virtual customers anything and everything related to your business, product, or marketing strategy. This technology might remind you of silicon samples; however, Delve AI’s digital twins can be used for much more than marketing surveys.

You can access these twins from our platform or via your team’s collaboration software, such as Slack, and talk to them in 15+ languages (eg, German, Spanish, Japanese). Since they’re built on customer data, your entire team can make decisions beneficial to your business and create content, products, and features that resonate with your target audience.

Example of Customer Digital Twins in Retail & Other Industries

Jacqueline Alderson, a biochemist who uses the digital twin technology to help football players avoid knee injuries, says that digital twins allow you to “test scenarios with no risk.” After all, it’s a virtual replica of the same person or object with the same set of conditions.

These models can also be leveraged to add or replace information that is missing or hard to acquire.

Formula 1 racers consistently use digital twins to test different configurations before a tournament. They attach multiple sensors to their race cars and study suspension, aerodynamics, and other factors that could increase their chances of winning.

Now, here’s how it can work in retail.

A retail store can use digital twins to enhance in-store shopping experiences by adding motion sensors and smart drawers. This enables them to analyze customer movement and purchase behavior, ultimately helping them:

• predict when shoppers will need new products
• optimize store layout by customer preferences
• build staffing models based on sales performance and support
• create an interactive, AI-powered customer journey map

Retailers can take their store online and provide a multichannel shopper experience, tracking customer journeys across all platforms. In fact, they can create a virtual customer in a virtual store using AI. This will allow them to study the impact of market changes on their retail business and fine-tune operations to save costs.

Financial institutions can detect fraud.

Banks and financial institutions can use customer digital twins to model a customer's financial behavior. Because the twin is continuously updated with real transaction data, it can flag anomalous patterns the moment they deviate from the norm and catch potential fraud faster than normal rule-based systems.

Besides security, financial DTOCs can:

• detect unusual transaction activity and trigger fraud alerts,
• simulate how customers will respond to a new investment product,
• deliver personalized financial advice based on life stage,
• model the impact of interest rate changes on individual portfolios.

Manufacturers can personalize the pre-purchase experience.

The manufacturing industry has long used digital twins on the production side. But now, the same logic is being applied to the customer. An apparel brand, for example, can let a shopper virtually try on clothing using their body measurements, while the twin runs personalized fit and style recommendations based on their size, proportions, and purchase history. This creates a better purchase experience.

Further, manufacturers can use DToCs to:

• let customers build products in a virtual environment before ordering,
• give personalized recommendations based on use case or purchase history,
• reduce product returns by helping customers make better-fit decisions upfront,
• gather pre-purchase behavioral data to improve future product design.

Telecommunications companies can reduce churn.

Telecom providers deal with massive volumes of customer interactions daily. It’s nearly impossible to offer a personalized experience at scale without AI. Digital twins change that. They can model individual usage patterns and allow providers to anticipate service issues.

Instead of waiting for complaints, they can:

• predict when network degradation will affect a specific user,
• recommend personalized data or call plans based on actual usage,
• identify customers most likely to switch providers,
• simulate the impact of a new pricing structure on specific segments,

Also, these companies can reduce the average handling time by equipping support agents with a full user behavioral profile before a call.

A way forward

A digital twin of a customer lies at the intersection of advanced analytics, AI, and machine learning, and offers data-driven insights for content creation, ad targeting, and customer journey optimization, and can potentially drive revenue and business growth. It’s a tool that marketers should incorporate in their long-term plans to capture a customer’s interests and behaviors, not just in the past but also in the present and future.

Relying on third-party data sources is no longer an option. Organizations must start investing in alternative solutions, like synthetic personas and customer digital twins, to maintain data privacy and improve the customer experience.

Although the current generation requires human oversight to interpret outputs and decide on actions, the next generation will be more autonomous: AI agents that execute personalized journeys in real time as context shifts.

Ready to get started? Try out Delve AI’s Digital Twin of Customer software today!