Nowadays, as people expect fine-tuned recommendations and real-time offers based on purchases from the likes of Amazon and Netflix, they also anticipate the same from their chosen financial institution. Hence, it is impossible to ignore the potential of personalized and relevant content. This is why financial institutions of all sizes today prefer advanced analytics to customize all components of the marketing mix, primarily to offer dynamic product pricing.
Recent researches have shown that predictive models are used by financial institutions to optimize product prices for every user in real-time. Take life insurance, which is viewed as a static, one-dimensional product. With very little incentive other than providing death benefits, life insurance companies have to put in tremendous efforts to drive sales growth. But what if life insurers can get access to near real-time data about policyholder’s lifestyle and fitness via telematic devices? With that, life insurers can continuously reassess a person’s risk profile and accordingly adjust the cost of coverage. When this data combines with patterns and correlations in customer data sets, insurers can now offer premium adjustments and discounts for a healthy lifestyle. In this way, health insurers can encourage a higher percentage of prospects to convert. Our SIA platform (Softweb Intelligence and Analytics) is not only limited to showing insurers dynamic product pricing, but its predictive models can also showcase the organization as a truly customer-centric company as it can notify policyholders to take medications and schedule checkups, thus improving customer loyalty.
In financial ecosystems, machine learning technologies bring together consumer insights from diverse data sets, including third-party insights from credit bureaus, social media channels, and others. The goal is to develop an ability to offer contextual and personalized product pricing as well as advice based on aggregated data.