The world of business is rapidly changing, and customer expectations are evolving at an even faster pace. To stay ahead of the curve, businesses must understand their customer’s behaviors and preferences at a granular level. This has led to the development of two key concepts in the world of customer analytics: Digital Twin of the Customer and Customer 360. While these two concepts may seem similar, they are actually quite different in their approach to understanding and engaging with customers.
Digital Twin of the Customer: A New Paradigm in Customer Analytics
A Digital Twin of the Customer is a virtual representation of a customer that is created by aggregating and analyzing data from various sources, including both online and offline interactions. This digital twin provides a dynamic 360-degree view of the customer journey and allows businesses to understand better their customers’ behaviors, preferences, and needs. By analyzing this data in real-time or near-real-time, businesses can make more informed decisions about engaging with customers, predicting their future behavior, and providing more personalized and targeted marketing interventions.
Digital Twin is not just about historical data or providing a static view of a customer. It is a living, constantly evolving representation of the customer that provides a deep insight into their behavior patterns. It also allows businesses to predict future behavior and identify areas of potential opportunity. This makes it a powerful tool for businesses looking to improve customer engagement, increase customer loyalty, and drive growth.
Customer 360: A Holistic View of the Customer
Customer 360 is a more traditional approach to understanding customer behavior. It involves consolidating all available customer data into a single view, often including demographic, behavioral, transactional, and social data. The goal of Customer 360 is to provide a holistic view of the customer across all touchpoints, channels, and interactions.
While Customer 360 is a valuable tool for businesses, it has its limitations:
- It is often based on historical data, which may not accurately reflect the current state of the customer.
- It is typically a static view of the customer and may not consider real-time data or changes in customer behavior.
- It may not provide the predictive insights businesses need to drive growth and engagement.
How Digital Twin of the Customer is Different from Customer 360
In summary, while Customer 360 is a valuable tool for businesses, Digital Twin of the Customer is a more evolved and dynamic approach that provides a deep understanding of the customer’s behavior patterns and enables businesses to make informed decisions based on current data. It is a powerful tool for businesses looking to improve customer engagement, increase customer loyalty, and drive growth.
For instance, consider a retail company that aims to enhance customer engagement and loyalty by providing personalized recommendations. To achieve this, they can first develop a Customer 360 view by aggregating data from diverse sources such as online and offline interactions, purchase history, and browsing behavior. This data enables them to comprehensively understand the customers’ preferences, pain points, and other relevant insights.
Subsequently, the company can upgrade the Customer 360 view to a Digital Twin of the Customer by analyzing the data in real-time or near-real-time to identify patterns and trends in customer behavior, including purchase frequency, product preferences, and channel preferences. With predictive analysis, they can also anticipate future customer behavior, such as potential cross-sell and upsell opportunities.
Going Beyond the ‘Digital Twin’ Hype
Leveraging these insights, the company can provide real-time personalized recommendations to their customers online and offline. These may include targeted promotions, product recommendations, and personalized messaging. As a result, Digital Twin of the Customer empowers businesses to make informed decisions and increase customer engagement.
Authored by: Rajat Narang, Associate Vice President, NAVIK and AI/ML Solutions and Abhimanyu Saraf, Manager, NAVIK and AI/ML Solutions at Absolutdata
Related Absolutdata products and services: NAVIK AI Platform, NAVIK Marketing AI, NAVIK Digital Twin, Customer Analytics, Analytics, Data Science & AI
