Personalization in Insurance – Thematic Research

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Personalization has spread beyond personal lines to both life and commercial insurance. All personal lines – motor, household, travel, and pet – have products with some personalization angle. It has taken longer in commercial insurance, but the scale of savings possible through fleet telematics and more flexible working brought on by the gig economy has driven progress. Meanwhile, the COVID-19 pandemic has also accelerated this trend. Global investment into the analytics theme within insurance saw record years in 2018 and 2019, before falling in 2020 due to the pandemic. However, Q1 2021 saw sharp growth compared to the same quarter of the previous year, meaning it looks like a global investment into the sector is set to return to pre-COVID levels. Investment into insurance analytics means there will be more companies creating better insights from data, which can be utilized to make policies fit individuals’ and businesses’ needs.

What are the market dynamics of personalization in insurance?

Personalization has become a key theme within the insurance industry. This has been driven by insurers, reinsurers, and brokers holding more and more data on their policyholders and becoming more adept at learning how to store, analyze, and use it. Digitalization has been a key driving force behind this, with tailored policies generally bought and managed online.

Learning a consumer’s behavior and creating products for their needs ultimately benefits both provider and policyholder. Personalization is at the forefront of insurance innovation, as it is changing the way insurance is packaged and sold. The biggest obstacle for the trend is consumer concerns over handing over personal data to insurers. The key technology theme driving the personalization trend in analytics. Insurers collect and store vast amounts of data, but it is analytics expertise and programing that allows them to create valuable insights on customers from this data. Consequently, this theme has seen considerable investment in recent years and has been the key enabler behind the rise of personalization in insurance. The value and volume of investment deals in the analytics theme in insurance dropped in 2020 after reaching record highs in both categories in 2019. This was obviously heavily influenced by COVID-19. There were further signs of the market recovering in Q1 2021 when the value of deals increased significantly.

The past three years have seen investments in analytics in insurance all around the globe. The US, the UK, China, India, and Germany are key insurtech hotspots, although significant deals were completed in over 50 countries across the world. The US saw 1,843 analytics deals completed from the start of 2018 to H1 2021, which is considerably higher than the UK (573), China (439), India (325), and Germany (133). The biggest investments during this period were received by Ping An Medical and Healthcare in China in Q1 2018 ($1.15 bn), Bright Health in the US in Q4 2019 ($635 m), Root Insurance in the US in Q3 2019 ($350 m), Beijing Internet Technology in China in Q3 2020 ($320 m), and Bright Health in Q4 2018 ($200 m). This shows that health and motor (Root) are leading the way in terms of generating insights into customers in the insurance space. Personalization is more widespread in health and motor policies, with consumers more accustomed to the tradeoff between giving access to personal data for cheaper premiums than with other lines, which makes them strong investment prospects.

What are the value chain segments within the insurance?

The insurance value chain is segmented into product development, marketing and distribution, underwriting and risk profiling, claims management, and customer service.

  • Product development is a key part of the personalization process. This trend has seen existing products added to and enhanced with data.
  • Marketing and distribution area of the insurance value chain is essential to success. Those operating within this space are responsible for ensuring that consumers are aware of the products on offer and for selling to them via a variety of distribution channels.
  • Underwriting and risk profiling is core to any insurance product on the market, making this central to the insurance value chain.
  • The majority of personalization within insurance happens before the claims management process, centering around customer acquisition and underwriting.
  • One area where personalization undoubtedly improves insurance policies is customer service. The idea of personalization is to improve the relationship between the insurer and the customer.

Which are the key players in personalization in insurance?

The key players in personalization in insurance include Admiral, Alphabet, Amazon, Apple, Aviva, Direct Line, Gen Re, Lemonade, RSA, By Miles, dacadoo, DeadHappy, Oscar, Root, YuLife, Zego, and Trov.

Personalization in insurance, by key players

Personalization in insurance, by key players

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Market report scope

Key Players Admiral, Alphabet, Amazon, Apple, Aviva, Direct Line, Gen Re, Lemonade, RSA, By Miles, dacadoo, DeadHappy, Oscar, Root, YuLife, Zego, and Trov.


Key Value Chain segments Product development, Marketing and distribution, Underwriting and risk profiling, Claims management and Customer service


This report is a thematic analysis of the Personalization in Insurance. It provides:

  • Overall, the total value of investment into analytics declined in 2020 due to the COVID-19 pandemic but looks set for growth in 2021.
  • Over half of UK consumers who do not currently have usage-based motor insurance policies would consider them if the financial savings reached a certain level.

Reasons to Buy

  • Benchmark yourself against the rest of the market.
  • Ensure you remain competitive as new startups and innovations enter the market.
  • Understand the impact personalization is having across a range of product lines.

Ping An
By Miles
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Direct Line

Table of Contents

Table of Contents

Executive Summary


Thematic briefing


Industry analysis

Value chain


Sector scorecard

Further reading

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