Cómo Mutua Tinerfeña aumentó la renovación de pólizas con Shimoku

Mutua Tinerfeña es una compañía de seguros que ofrece una amplia gama de pólizas de seguro para particulares y empresas, que incluyen seguro de automóvil, seguro de hogar, seguro de salud, seguro de vida, seguro de responsabilidad civil, entre otros.


La solución

Aumentar la retención de clientes con inteligencia artificial

En Shimoku, desarrollamos una solución de Inteligencia Artificial que ayudó a Mutua Tinerfeña Seguros a aumentar la retención mediante la identificación de políticas con una mayor probabilidad de no renovación y de clientes asociados. Esta solución incluía una capa de explainability (factores y barreras) y conocimientos que proporcionaron a los equipos un contexto más amplio para comprender los factores que influyen en la no renovación.


Once the AI model generated a prioritized list of policies and customers with their renewal probability, these results were integrated into a dynamic discount prediction system designed to increase the probability of purchase. Each customer received a personalized discount for each policy with the aim of maximizing renewal.

At the core of our solution lies an actionability layer that bridges the gap between data insights and tangible business outcomes. At Shimoku, we firmly believe that the true power of AI lies not just in understanding data but in leveraging it to drive meaningful actions within a company's operations.


With traditional analytics solutions, businesses often find themselves drowning in a sea of data, struggling to derive actionable insights that can drive real change. Our approach flips this paradigm on its head by not only providing valuable insights but also empowering businesses to swiftly translate these insights into concrete actions that drive monetization and efficiency.

Our solution harnesses the power of Generative AI technology to automate the offer creation process. Gone are the days of generic, one-size-fits-all promotions. Our AI solution analyzes vast amounts of customer data and model outcomes to craft automatically hyper-personalized offers tailored to each individual which are then seamlessly delivered to them via email.

At Mutua Tinerfeña, the actionability layer facilitated the automatic generation of tailored policy offers aimed at customers with a higher likelihood of non-renewal. This streamlined approach optimized conversion rates and improved commercial efficiency.

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