Hyper-personalization in customer dialogue: personalized customer experiences with real-time data and AI

In order to remain competitive, companies must constantly adapt to new requirements. The supreme discipline of this optimization is the hyper-personalization of customer dialogue. This means that every customer interaction is individually tailored to the customer’s respective needs. With this approach, financial service providers can not only increase their direct sales, but also delight their customers. How can this become a reality? With real-time data and artificial intelligence.

Hyper-personalization: when classic personalization reaches its limits

Everyone is familiar with classic personalization. A good example is the newsletter, in which we are addressed personally by name. However, this no longer fulfills the customer expectation of an individual approach. That’s why hyperpersonalization increases the degree of individualisation. Hyper-personalization differs from classic personalization in that it provides and uses real-time data. While normal personalization focuses on general characteristics and behavior, hyper-personalization goes one step further: it is based on an in-depth analysis of real-time data in order to understand individual customer preferences and behavioral patterns. The hyper-personalization of customer dialogue enables financial service providers to make tailored offers and recommendations in real time that are precisely tailored to the needs and interests of the individual customer. The goal is a highly personalized customer experience that increases customer satisfaction.

Overall, this hyper-personalization helps to build strong customer relationships and thus achieve long-term business success. This is because hyper-personalized offers and recommendations make customers feel better understood and valued, which leads to strong customer loyalty, reduces the likelihood of customer churn, and even increases the possibility of customer recovery. In addition, hyper-personalization offers a competitive advantage as it further increases efficiency through the use of real-time data and automated processes.

Data, data, data: challenges on the path to hyper-personalized customer dialogue

Financial service providers face major challenges on the path to hyper-personalization in customer management – especially when it comes to data. In order to create a holistic profile of a customer, customer data from various sources and different areas of the company must be integrated and analyzed.

One challenge, for example, is ensuring data heterogeneity, as differently formatted data and additional information such as data protection preferences come together. In addition, real-time customer interaction requires the service provider’s technical infrastructure to be excellent in order to cope with the volume of data from an ever-growing variety of channels. Furthermore, the processes relating to data protection, data security and privacy must be as transparent as possible in order not to lose the trust of customers.

However, data only forms the basis here. To turn data into action, a technological infrastructure must be established that can segment customers based on their characteristics and communicate with them through personalized messages and offers across a variety of channels. Furthermore, the introduction of AI within a company is not a sure-fire success. There is a lot to do in the area of change management to prepare employees for this collaboration. Last but not least, the company itself must also think in data-based terms and place the customer at the center.

The central role of artificial intelligence for a hyper-personalized customer approach

The use of artificial intelligence makes it possible to analyze large amounts of data, identify relevant patterns and draw conclusions that lead to predictions of individual user behavior. Deriving the appropriate actions from these findings and thus implementing hyper-personalized interactions such as offers or recommendations in real time is also a task that is only possible, to this extent, through the use of artificial intelligence. Ultimately, it is also the direct customer interaction through virtual assistants that is already being taken over by AI in some areas today.

Many financial service providers have already started to use artificial intelligence, particularly in the area of customer communications, for example to provide personalized financial recommendations. In addition, AI enables a more accurate assessment of creditworthiness and the detection of fraud.

Even if hyper-personalization is possible without AI, it is a decisive booster when it comes to scaling these processes and making them more efficient. With AI, it is not only easier to process large amounts of information and constantly gain new insights from it, but it also becomes easier to translate these insights into automatically generated customer interactions. This is where the currently much-discussed generative artificial intelligence can come into play. It is able to generate different types of content such as text, images, audio, or synthetic data. Therefore, it offers a wide range of options for automating individual customer dialogue.

However, the power of AI-supported automation also comes with responsibility. This is because the advantage of AI in automatically completing complex tasks can also become a problem of trust on the part of customers. To ensure that this outsourcing does not have a negative impact on the customer relationship, such automated processes must be designed to be as transparent and comprehensible as possible. This allows customers to understand what is happening with their data.

As has unfortunately already been observed in some cases, purely data-based decisions can lead to discriminatory results. And to prevent accusations of manipulation, the fine line between autonomy and influence must always be consciously balanced. Human expertise is therefore essential at this point in order to monitor the algorithm’s mode of operation and keep a constant eye on its exact direction.

What data is required for hyper-personalization?

In order to gain the most comprehensive understanding of customer needs, data must be collected from various sources and processed using advanced analytical methods and algorithms. Examples of such data, including demographic information, are as follows:

• Age and gender (used for segmentation).
• Transaction data such as purchase history to create product recommendations.
• Behavioral data such as click behavior, interests and preferences, (used to improve customer interaction).
• Customer feedback that contributes to the overall understanding of the customer relationship.

It is important that there is a high-level of data quality, meaning that the data collected is sufficiently accurate, complete, consistent, and relevant. It must be free of errors and inaccuracies, have no gaps or inconsistencies and stay up to date. The combination of these different types of data gives financial service providers a comprehensive picture of their customers and their needs, enabling them to manage customer interactions in a hyper-personalized way on an individual basis.

How hyperpersonalization and data protection influence each other

As hyper-personalization matures and the use of artificial intelligence increases, both the complexity and value of this business grow, along with the legal challenges it presents. In principle, the same rules apply outside of hyper-personalization. However, the difference lies in the fact that, on one hand, a large amount of personal data is required—specifically the type of data we consider to be particularly sensitive and deserving of protection. On the other hand, not only the processing but also the utilization of the knowledge gained from the data is largely automated. This means that as AI becomes increasingly autonomous, appropriate supervision will also be necessary to ensure adequate data security.

In addition to legal consequences, non-compliance with data protection regulations can lead to customers becoming less willing to use certain systems. In the worst case, trust in the brand can be shaken and a negative downward spiral of customer ratings can be set in motion. Successful hyper-personalization that leads to a long-term customer relationship is therefore based on trust and transparency. This can best be achieved by making the customer’s consent to data collection, the transparency of data processing and the secure storage of data a non-negotiable priority.

Future potential and possibilities of hyper-personalization in customer dialogue

The future of hyper-personalization in customer dialogue in the financial services sector is promising. Already today, every customer can be offered a hyper-personalized experience by addressing them individually. In the future, real-time interactions will become increasingly common. Artificial intelligence will constantly improve the accuracy of predictions and also drive virtual support in the interactive customer experience.

For example, financial service providers can use IoT devices such as smartwatches to collect additional data points about their customers and their behavior. Blockchain technology will further increase the security and transparency of transactions and customer interactions can be made more immersive through augmented or virtual reality. Hyper-personalization will become an increasingly valuable means of adapting communications to customer needs, especially in the future.