Digital Twin of a Customer: How It Can Help Marketers

Artificial intelligence (AI) and machine learning (ML) systems are evolving faster than ever. We have so many new emerging technologies and tools that enable us to do things we couldn’t before — case in point: OpenAI’s ChatGPT.

This change is not limited to the tech and IT sectors. It’s affecting everything in marketing, from predicting end-to-end customer interactions to customer journey optimization (CJO).

With digital twin technology, which uses advanced analytics and ML models, marketers can now create a digital twin of customers (DToCs) using historical and real-time customer data. Unlike static entities or customer profiles, they are dynamic avatars continuously updated with real-life user data.

They reflect an individual’s current state of mind rather than their past actions.

Such virtual avatars not only simulate customer behavior but also provide context and predictions for future marketing endeavors. With the rise of cookieless marketing and data privacy regulations, DToCs are a great way to understand prospects and implement data-driven marketing strategies.

In this article, let’s look at the origins of digital twin technologies, learn about the concept of the digital twin of customers, and ensure personalization at all touchpoints.

A short history of the digital twin technology

Have you 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.

Of course, they weren’t called digital twins at the time. NASA regularly employed them to replicate systems in space since the 1960s.

When the Apollo 13 crash was about to happen, NASA fed data from the damaged spacecraft into a virtual model onsite to simulate situations and chose the one with the best outcomes – a scenario where the three astronauts would survive. And they did survive.

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 conduct simulations, solve performance problems, and enhance customer experiences.

As such, they are primarily used in design, manufacturing, construction, operations, maintenance, architecture, healthcare, and engineering.

However, it is not that easy to create digital twins. This is mainly because of two reasons: 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 large amounts of data to make sense of things. Right now, the data we have is sparse and incomplete, involving a lot of guesswork to fill in the gaps.

How it works? Simulations vs digital twins

Simulations, as you know, represent a single process and don’t use current data. Digital twins are not simulations; they simulate multiple scenarios and processes, using current data. It’s not a one-way workflow either – digital twin systems receive and transmit information.

So, how do they work? The process can be broken down into four steps:

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

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

It doesn’t make sense to develop a digital twin for everything. You don’t need one for a simple drill machine. Their use is mostly limited to physically large objects and complex projects to streamline efficiency.

Take for example Google Maps, which is a product twin of the Earth.

Additionally, different types of digital twins are classified according to their usage levels:

• 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.

Digital twins tell you how objects 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.

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.

Example of digital twins in retail

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.

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: using AI and ML algorithms to interpret behavioral data and tweak product experiences.

Marketing teams can identify bottlenecks in the customer journey, retain customer trust, and improve business outcomes.

Although a nascent technology, 70% of C-suite tech executives at large enterprises are already investing in digital twins. This market is expected to reach €7 billion by 2025 in Europe, growing 30%-45% annually.

And why 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 it is implemented, allowing you to rule out detrimental additions.

With virtual customers in a virtual store, DToCs can suggest customized deals and make shopping a seamless and personalized affair, ensuring customer satisfaction at all levels.

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-round view of the customer. Doing so is expensive and time-consuming, hence out of reach for many small and medium-sized businesses.

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.

Synthetic personas and silicon sampling

An article about the digital twin of the customer is incomplete without the mention of synthetic personas 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 case is leveraging silicon samples to answer customer survey questions. Since you’re using an LLM model, the output mostly sounds human and tells you about the goals, interests, and preferences of your customers about your product.

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

Benefits of using digital twins: In Marketing and Customer Experience (CX)

Digital twins of customers can primarily be used in marketing for customer journey optimization (CJO).

It 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.

A word on customer experience

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.

A DToC approach helps 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. By remedying negative situations before they occur, brands can reinforce positive sentiments and build a loyal customer base.

Consequently, tapping into the wants, needs, beliefs, expectations, and past experiences of prospects empowers you to create automated engagement strategies that keep up with industry trends and changes in customer preferences.

Creating the digital twin of a customer: How does it work?

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 wish to create a digital twin of the customer?
• What data sources will you use?
• 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 ensures that the digital twins meet the objectives set up by your organization.

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 to create a target customer persona. 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.

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.

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.

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. Integrate this information into your virtual model, which already includes product interests, communication preferences, personality, and sentiment analysis models.

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 is an online persona generator that lets you build data-driven personas automatically for your business, competitors, and social media audience.

We create dynamic 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). When 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 (highlighted in green) and B2C businesses (highlighted in blue). 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.

Simply click on Persona Details and you’ll be taken to a page that gives you a complete description of that 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 give you an idea of how users interact with your website and content.

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

A way forward

A digital twin of a customer (DToC) is an emerging technology that marketers should incorporate into their long-term plans. They capture a customer’s motivations, interests, and behavior like never before – not just in the past but also in the present and future.

Lying at the intersection of advanced analytics, AI, and machine learning, DToCs give out data-driven customer insights helpful in content creation, ad targeting, and customer journey optimizations, driving revenue and business growth.

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