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🧪 '''A/B testing''' is a controlled experimentation method used across the insurance and [[Definition:Insurtech | insurtech]] landscape to compare two variants of a digital experience, [[Definition:Underwriting | underwriting]] workflow, or customer communication to determine which performs better against a defined metric. In practice, an insurer or [[Definition:Managing general agent (MGA) | MGA]] might test two versions of an online [[Definition:Quote | quote]] page, two different [[Definition:Premium | premium]] presentation formats, or two claim notification email designs by randomly splitting traffic or policyholders between a control group (A) and a treatment group (B) and measuring outcomes such as conversion rate, bind ratio, or customer satisfaction.

🔬 Execution typically relies on digital platforms that randomly assign users to one of two experiences while capturing granular behavioral data. For example, an [[Definition:Insurtech | insurtech]] carrier launching a new [[Definition:Product | product]] might test whether showing a coverage comparison chart on the quoting screen increases bind rates compared to a simpler price-only display. Statistical significance is tracked in real time, and once the data reaches a confidence threshold — commonly 95 percent — the winning variant is deployed to all users. More mature organizations layer A/B testing into their [[Definition:Pricing model | pricing models]] and [[Definition:Underwriting guidelines | underwriting guidelines]], testing appetite adjustments or [[Definition:Risk selection | risk selection]] criteria on subsets of submissions before rolling changes across the full book.

📈 In an industry where small improvements in conversion or [[Definition:Loss ratio (L/R) | loss ratio]] compound into millions of dollars of impact, disciplined experimentation separates high-performing carriers from those relying on intuition. A/B testing gives [[Definition:Distribution channel | distribution]] and product teams empirical evidence to justify design decisions, reduces the risk of costly full-scale rollouts that backfire, and creates a culture of continuous optimization. As more insurance transactions move to digital self-service channels, the ability to test, learn, and iterate quickly has become a meaningful competitive advantage.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Insurtech]]
* [[Definition:Conversion rate]]
* [[Definition:Digital distribution]]
* [[Definition:Pricing model]]
* [[Definition:Underwriting guidelines]]
* [[Definition:Customer experience]]
{{Div col end}}

Definition:A/B testing - Revision history

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