Traditionally, brands have embraced personas to better understand and empathize with their audience. However, with the advent of Generative AI, a new type of persona is entering the scene: synthetic customers.

With Gartner predicting that synthetic data will overshadow real data in AI models by 2030, the role of synthetic customers becomes crucial for maintaining a competitive edge. As the business world explores the emergence and potential of synthetic customers, we’re weighing up the advantages and disadvantages in the context of customer experience. 

What are synthetic customers? 

Synthetic customers are data-driven representations of customers crafted using AI and machine learning algorithms. They emulate customer behavior based on diverse data sources such as demographics, website interactions, transaction data, and social media activity. They offer brands a new way to understand and engage with their customers. 

Though they may bear resemblance to personas, there are key differences between the two. Personas are static, fictional characters crafted from market research to represent a segment of a target audience, while synthetic customers are dynamic and AI-generated, evolving in real-time as customers' needs and behaviors change. It is this adaptability that makes them particularly valuable.  

The advantages of synthetic customers

In the context of CX and product marketing, synthetic customers offer a range of advantages. Far from being technological novelties, these ‘digital avatars’ represent a fundamental shift in how businesses can understand, engage with, and cater to their customers. Synthetic customers offer:

• Enhanced personalisation and prediction: The real-time learning capabilities of synthetic customers enable companies to stay agile and responsive to evolving customer preferences. This makes for more personalized and predictive experiences.‍
• Risk-free product testing and prototyping: Synthetic customers provide a safe playground to test new products and services before they reach the market.‍
• Scalability: Businesses can swiftly create and deploy synthetic customers, facilitating rapid testing and iteration of ideas. ‍
• Adjustability: Synthetic customers can be fine-tuned to the behavior and characteristics of customers using real-world data to ensure more accurate results.
• ‍Speed: Synthetic customer insights can be obtained in near-real-time, enabling organizations to make swift data-driven decisions and stay agile in response to market changes. Commercial tools like Google's Vertex AI can be leveraged for analyzing customer data, while Gemini AI’s generative capabilities can be used for creating realistic customer behaviors and preferences, allowing synthetic customers to adapt in real-time to changing trends.
• ‍Cost-effectiveness: Creating synthetic customers can be more economical than traditional research methods as they use existing data.‍
• Applications across industries: Synthetic customers can be used across industries. For example: In retail, they can help brands understand shopping behaviors, preferences, and trends. In healthcare, they can assist in simulating patient interactions, allowing for the testing of new medical technologies and improving patient care processes. 

The challenges associated with synthetic customers

Despite their potential, it is essential to acknowledge the obstacles that come with synthetic customers. 

• Human behavior and emotion: Replicating the intricacies of real-world human behavior can be challenging for synthetic customers. They may struggle to simulate or grasp the full spectrum of human emotions, affecting the accuracy of the insights — a common concern with AI systems.‍
• Explainability and transparency: The complex algorithms used in creating synthetic customers can lack transparency, making it hard to explain decision-making processes. This can make it difficult to understand whether the results can be trusted.
• ‍Data challenges: There may be challenges with data quality, dataset size, and bias. Businesses may struggle to gather a comprehensive dataset that encompasses the full range of customer behaviors, and regular data ingestion is crucial for the ongoing relevance and adaptability of synthetic customers. Poor-quality or biased data can lead to skewed results and misrepresentations.
• ‍Integration with existing systems and processes: Integrating synthetic customer insights seamlessly with existing systems and workflows may pose a challenge. Making sure they are compatible with established tools from the start is essential for a smooth integration.

The impact on customer experience

With both pros and cons in mind, the concept of synthetic customers marks a significant evolution in how brands can approach customer experience. Synthetic customers will soon become more ubiquitous, helping CX teams, marketers, and designers test hypotheses quickly.

While challenges will persist, the opportunities are undeniable — from enhanced personalization and prediction capabilities to risk-free product testing, businesses can leverage synthetic customers for a competitive edge. 

If you’re deciding whether to adapt personas or synthetic customers, the ideal approach is not an either/or scenario, but a strategic blend of both. Personas offer a deep, qualitative understanding of a distinct customer segment, while synthetic customers provide dynamic, scalable and cost-effective solutions for predictive testing. Employing both methods allows businesses to gain a comprehensive understanding and make informed decisions across the entire customer journey. 

The key to success in this evolving landscape is to move with agility, transparency and a commitment to continuous improvement. Businesses that embrace these principles will reap the greatest rewards from integrating synthetic customers into their CX strategies.

Get in touch with us to learn more about Beyond’s AI solutions and how we can help build synthetic customers for your business. Our Generative AI practice helps brands employ cutting-edge AI technology to enhance customer experiences.