For years, insurers believed they had to choose between traditional static forms or conversational flows to collect customer information. But in reality, the most effective approach lies in blending the two, merging the efficiency of forms with the engagement of conversation.

‍

Why it matters

‍

Insurance profitability depends on accurately differentiating between favorable and unfavorable risks. To do this, insurers need reliable data from customers.

• Sales teams want to simplify the journey and ask fewer questions to boost conversion.
  
  
• Risk teams push for more detail to improve underwriting accuracy and reduce losses.
  
  

Even one additional question can dramatically improve cost projections, reduce uncertainty, and either increase profit or lower customer premiums.

‍

The options

‍

Insurers today rely on three main methods for digital data collection:

‍

• Static forms: familiar and efficient, but rigid and often overwhelming.
  
  
• Dynamic forms: more flexible, showing questions step by step, but slower for simple use cases.
  
  
• Conversational flows: designed like a dialogue, enabling personalization and engagement, but requiring stronger validation and sometimes AI support for free-form responses.
  
  

Which works better?

‍

There’s no clear winner. Studies in healthcare data collection show conversational flows can increase engagement and satisfaction, though they may take longer to complete. What matters most is design quality; poorly designed flows or forms fail regardless of format.

‍

The winning formula

‍

The future is hybrid. A conversational flow that feels natural and intuitive, combined with embedded form-like elements for speed and accuracy, creates the best experience.

Insurers can achieve this in two main ways:

‍

1. Leverage existing channels like WhatsApp Business, enabling familiar, native communication with customers.
   
2. Adopt custom-made platforms like Bernoly Conversational Platform, specifically built for insurance journeys, blending conversational flexibility with form-based precision

‍

‍