The key challenge of every marketplace is to balance supply & demand. Machine Learning can be a true gamechanger for marketplaces, with recommendation and dynamic pricing engines as the most important use cases.

Find out more about the Wunderflats case here.

Software as a Service (Saas)

In scaling a (subscription focused) software company the following challenge always exist: How do I grow my customer base and the value of every individual customer? Supporting your sales and customer service teams with use cases like lead scoring, customer lifetime value and churn prediction enables you to scale without doubling the number or FTE.

Find out more about the SendCloud case here.

Banking & fintech

Customer lifetime value, automated risk assessment, fraud detection, churn. The number of relevant use cases in this sector is limitless. Enjins helps scale-ups and mid corps in this space to setup a scalable ML infrastructure in which multiple of those use cases are realized.

Find out more about the Triodos Bank case here.

Medtech & Regtech

In regulated markets AI is a key technology for digitization. Use cases typically automate repetitive processes, like medical diagnosis or claim handling. Having explainable models and a human in the loop to ensure models and predictions are validated is key for every use case in this area.

Find out more about the Yource case here.

Supply chain

The challenge of many family businesses is to stay relevant between the large players in the market. Data and Machine Learning could help many family businesses to become more efficient and act better on the latest developments in the market. Data could also help family businesses to become even more personal and could fuel innovation. We love to help family businesses to stay relevant in a competitive market.

Find out more about the Berkman case here.