Online Reciprocal Communities

The success of low price/low limit health insurance platforms on WeChat has fulfilled some of the hopes around P2P insurance in China. However, despite answering the customer acquisition question, their success has created user engagement and renewal problems that far outweigh their marketing success.

As Shuidibao, Trust Mutual and Crowd Health have amassed tens of millions of users, they are beset by a user attrition rate of up to 75% and this has been amplified by a user education gap that comes with embracing a purely digital B2C distribution approach for critical illness insurance.

Specifically, as long term life/health insurance requires comprehensive information regarding a customer’s marital status, income, pre-existing conditions and personal preferences regarding coverage, etc, several startups are trying to optimise the user journey of these online reciprocal communities.

One of these is Beijing based Bihu Technologies, which has adopted a two-pronged approach to customer acquisition.

The first is an AI-based user journey that educates users about critical illness insurance with a specific focus on coverage limits and hospital networks associated with respective policies.

The second is a departure from traditional WeChat based startups that acquire new members by posting directly to users within WeChat. Instead, Bihubao has adopted a B2B approach by working directly with some leading internet platforms such as Sunning.com and JD.com. Bihubao also works with offline retail outlets that have sizeable customer data sets and cosmetics platform ‘New Oxygen’. The motivation for internet partners is to leverage Bihubao’s expertise and an accepted reluctance for end users to buy life/health insurance in a self-directed manner.

Ultimately, by creating a new education orientated user journey that is focused on informing rather than pushing insurance and establishing partnerships with some of China’s biggest internet portals, Bihubao has been able to overcome many of the obstacles stymieing a promising model, and allowed it to pre-populate its new member signup forms in addition to accessing additional information about users.