AI meets CX

development, practical example

and implementation tips

AI meets CX

development, practical example

and implementation tips

The rapid development of artificial intelligence (AI) is having a significant impact on the topic of CX in a wide range of industries. Integrating AI into customer experience strategy can be a game changer for companies looking to intensify their customer centricity efforts while making operational processes more efficient and effective.

In this article, we take a look at the development of AI in the field of CX, highlight various use cases and present a practical example of the application of AI in the area of churn prediction. Finally, we show concrete steps that companies can take to directly implement the use of AI in the area of customer experience.

What direction is AI development taking in the CX area and what use cases are there?

Continuously improving customer satisfaction is of central importance for every company. This is also confirmed by a recent KPMG survey. The results show that the Customer Experience Excellence (CEE) value for 2023 has fallen from 7.65 to 7.31 compared to the previous year. On average, German companies have deteriorated in all six factors influencing the CEE value, including empathy, personalization, integrity, expectations, time and effort, and problem-solving skills.

Personalization is a key factor in positive customer experiences. With technologies such as artificial intelligence and augmented reality, companies can respond more individually to customer needs and take the customer experience to a higher level.

Among other things, AI-driven chatbots can provide instructions and suggestions to call center agents during customer service phone calls, enabling them to respond to the customer on the phone in the best possible and most personalized way in real time.

Use cases:

In our view, AI-supported solutions can be divided into four categories:

Text Analytics: Automated analysis and interpretation of written customer feedback using AI to derive recommendations for action for the company.

Clustering: Combining customer feedback and other data to identify structures and patterns, e.g. customer segmentation based on survey and behavioral data.

LLM-based bots: Using AI-driven chatbots to enable personalized interactions with customers, e.g. persona chatbots or knowledge bases.

Predictive: Quantitative predictions for sales forecasting and individual recommendations, including the important churn prediction to identify customers at risk of termination and to reactivate them if necessary.

Case study: Churn prediction model for a SaaS provider in Germany

A SaaS provider in Germany has successfully implemented an AI-based churn prediction model with our support and solutions. An AI model was developed to predict customer churn and identify critical moments in the customer journey. By analyzing product usage behavior, customer master data and conversations with the call center, customers are classified as “at risk”. “At risk” are customers who are likely to cancel or leave.

There are three critical touchpoints or points in time along the company’s customer journey:

time of contract conclusion
100 days after first software use (free trial)
onclusion of a contract for a license that is subject to a fee or cancellation

However, only about 15% of customers are reached by queried feedback at the three touchpoints. No survey data is available for 85% of users. This is where AI comes into play: we search for “digital twins” based on product usage data, among other things, and can thus identify “at-risk” customers within the rest of the customer base, who can then be proactively addressed by customer service. A central component of this is the development of a central data stack platform for accessing all relevant data, such as operational data, customer care data, customer master data, CX data and insights data.

How can a company start to use AI effectively for CX?

To work in a targeted manner, you need a clear strategy for how you want to work with insights and CX data in the future. To do this, we work with our customers to develop a data strategy for the company in a workshop. The following components are examined:

What are the stakeholders’ requirements?
Where are changes in processes, content, tools necessary?
What role should AI play?
Are there any lighthouse projects?

Furthermore, it is important to rely on the right tools. The tool strategy answers the following questions:

Where is the insights data stored and how does it get there automatically?
Which AI models should be used? OpenAI, Meta, Google, open source or in-house developments?
How should data be visualized and results communicated?
Which tools are already in use in the company?

Finally, the organization and qualifications of the employees are important. The following points need to be clarified:

Who takes on what responsibility – for data, tools, data products?
Are there new roles, positions that are needed?
What training needs arise and how are they met?
• Where is internal and external support needed?

The workshop lasts 1 to 2 days and can be carried out both on-site and remotely.

Our tip: Start with small pilot projects to gain experience and then scale up gradually to maximize the benefits of AI across the entire CX.

We at SKOPOS support companies in developing customized strategies to successfully use data.

Experience the future of CX

The integration of AI into the CX area offers companies the opportunity to better understand their customers, create personalized experiences and ultimately increase customer satisfaction. In a rapidly changing business world, the combination of human skills and AI has the potential to open up new horizons for CX design.

With proven strategies and data-driven approaches, we support you with a clear roadmap that actively shapes the future of CX. Get in touch!