CTS Eventim is a leading international provider of ticketing services and live entertainment, headquartered in Germany. The company operates a network of ticketing portals, as well as various event venues, and is renowned for its technological innovation and wide-reaching distribution capabilities.

With a strong presence in numerous countries, CTS Eventim facilitates millions of ticket sales annually, making it a key player in the global entertainment industry with a market capitalization of approximately € 5 billion.

Exploring AI’s impact on media and entertainment reveals a transformative shift in content creation, distribution and consumption. AI integration into the industry offers innovative solutions to traditional challenges such as understanding audience behaviors and forecasting trends as well as creates new approaches for creativity and engagement through personalized user experience.

The integration requires a solid data infrastructure on one hand, however offers the opportunity to generate a competitive advantage on another hand.

The transformation of a company, especially a large one, to become a data- & AI-driven corporation, is highly complex and subject to various constraints such as existing data infrastructure, data quality or employee skill set. An analysis of the status quo of the technical side through thorough assessment of current systems and interviews unveiled information on the data/AI readiness.

Numerous stakeholder interviews provided insights into potential use cases based on the business needs and pain points of the stakeholders. The entire process was accompanied by the establishment of standards, business processes and documentation.

The company’s Data/ AI maturity was assessed from a technical perspective and matched with respective data product strategies for the current as well as the future states. From a business perspective, 50+ use cases and counting have been identified across various divisions, countries and all maturity states. These use cases have been prioritized according to a system which is based upon both business as well as technical criteria.

A data product strategy and vision have been derived as well as a roadmap for the use case implementation along the development of the data infrastructure and the establishment of a data-driven culture and literacy. Next steps entail the start of the development of the new generation of the data infrastructure, use case implementations and further iterations on prior steps.

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Streaming platforms face significant challenges in reducing churn due to high market competition, subscription fatigue, and diverse consumer preferences.

Churn reduction for streaming platforms is difficult due to intense competition and varying consumer preferences, requiring continuous innovation and personalized experiences to retain subscribers. Possible strategies for churn reduction of streaming platforms include personalized content recommendations, enhancing user experience, utilizing data analytics to identify at-risk subscribers, and engaging users with targeted marketing campaigns and promotions.

Thorough stakeholder discussions highlighted the need for a next-best-action model to predict the best action for each client to minimize churn. This would on one hand unload the marketing department by automating the campaign management for churn reduction while on the other hand be potentially more effective as the model would predict the next best action on a personalized level with a segment of 1.

A Deep Reinforcement Learning model was trained from scratch and deployed to the cloud. First A/B Testings showed a reduction of churn by ~5%. On a secondary level, this contained an increased customer satisfaction as well as reduced costs as vouchers were not offered anymore to users without impact expectation.

Call center customer support in public authorities often becomes a bottleneck due to high call volumes, limited staffing, and lengthy response times, leading to frustration for citizens seeking assistance.

Voicebots or Chatbots offer an effective solution by providing instant, 24/7 responses to common inquiries, freeing up human agents to handle more complex issues, thereby improving efficiency and user satisfaction.

Creating a satisfying voicebot for public authorities is particularly challenging when dealing with multiple languages, as it requires advanced natural language processing capabilities to accurately understand and respond to diverse linguistic nuances, dialects, and accents while ensuring consistent and clear communication across all supported languages.

Through communication with stakeholders, the pain points and needs for respective authorities were derived. Using cloud services and open source frameworks, voicebots and chatbots were implemented. Processes, standards and templates were developed for additional future bots to enable and speed up future development processes.

Implementing voicebots and chatbots in public authorities can lead to significant efficiency gains, showing up to 30% reduction in customer service costs and a 40−60% decrease in response times for handling routine inquiries. Additionally, these automated systems can handle up to 80% of standard queries, freeing up human agents to focus on more complex tasks and improving overall service delivery.