Micro-segmentation in commercial insurance: An opportunity for growth

In today’s competitive insurance environment, launching niche products into attractive micro-segments, where loss probability is low and the appetite for coverage is high, can provide significant opportunities for growth. 

Segmentation is standard practice in commercial insurance, but micro-segmentation, where insurers target potential policyholders not just by industry but by more detailed characteristics such as demographic information, is less common.

In commercial insurance, segmentation does not usually progress much beyond industry and market type, making it difficult to address the differences in loss ratios, consumer expectations and servicing needs which exist within broad buckets of risk. Insurers often analyse a book using only two or three segments, meaning the selection and pricing strategy conceived is too crude to differentiate relative to their competitors.  

The business case for micro-segmentation

Micro-segmentation involves analysing volumes of rich internal and external data about a population to identify clusters of risk with nearly identical attributes and loss frequencies.

Once attractive micro-segments have been identified, significant improvements can be made across the entire value chain:

  • Appetite can be defined to exclude non-desirable sub-segments and target profitable niches

  • Product development and distribution can be better aligned to customer needs

  • Expenses are reduced as the ratio of risks submitted to risks bound is optimised

For commercial insurers looking to tilt their book towards profitability, identifying, attracting and retaining the right customers using a micro-segmentation approach provides a significant opportunity.

Key challenges for insurers

Vast quantities of granular data, both first and third party, are needed to achieve the statistical significance required to micro-segment effectively.

Despite the growing wealth of data available via IoT, the unstructured web and various third party suppliers, many decision makers lack the analytic tools required to acquire and process it.

For many commercial insurers who are operating with legacy systems, reconciling and analysing their own internal data is a significant challenge in itself. At best, most commercial insurers only catch a glimpse into the potential risks and rewards of a population.

They may understand the difference in loss probability between an office building and retail block, but they do not see the staggering difference in loss ratios between offices located next to restaurants and offices located next to sports complexes, as it is not captured in their internal claims data. They may understand the coverage needs of traditional SME business owners, but fail to understand the coverage needs of new SME businesses who only employ gig-economy workers.

Moving forwards with micro-segmentation

The nature of commercial risk is becoming increasingly diverse. Insurers relying solely on their experience to understand current and future policyholders will inevitably end up with products are that are ill-fitted to growing niche industries, under-optimised pricing and unprofitable micro-segments slipping onto the books unnoticed.

Leading insurers are recognising that existing underwriting and actuarial processes can be augmented with external data and predictive analytics to identify desirable micro-segments.

Figure 1 shows a random example of four distinct micro-segments identified by the Cytora Risk Engine within the SME retail commercial property segment. We show each micro-segment (X axis) against the annual technical price per annum (Y axis) with corresponding demographic rating factors (data labels). Cytora found that bars and nightclubs located in high-income, urban areas suffered a significantly greater number of losses than the other business types in the segment with the same demographic rating factors. This is reflected in the technical price.

Micro-segments within the SME Retail Commercial Property Segment

Figure 1:  Micro-segments within the SME retail commercial property segment based on the analysis internal data from an insurer and external data from the open web, loss data and data from other third party sources. We show each micro-segment (X axis) against the annual technical price (Y axis) with corresponding demographic rating factors (data labels).

Figure 1:  Micro-segments within the SME retail commercial property segment based on the analysis internal data from an insurer and external data from the open web, loss data and data from other third party sources. We show each micro-segment (X axis) against the annual technical price (Y axis) with corresponding demographic rating factors (data labels).

Using a micro-segmentation approach, insurers can identify high-risk sub-segments such as the urban bar/nightclub niche and calculate accurate technical prices, ensuring premiums are sufficient to cover future losses and remain profitable.

Micro-segmentation makes it possible to identify which attributes describe high profitability and low risk so that insurers can focus on serving the most attractive segments in the market and align sales and distribution to yield higher margins and retention rates.  

To learn more about how micro-segmentation powered by internal and external data can help insurers to identify the most desirable micro-segments of the population and access population-wide insights, please contact us for more information.