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A New Era in Insurance Telematics: Data-Driven Customer Engagement and Loyalty

From costly devices to rich data platforms, gamification elements, and loyalty programs, telematics has come a long way.

Join us as we look at the future of insurance telematics and demonstrate what insurers can do today to grow their motor segment.

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Key Details

Topic: Telematics, mobility data analytics, customer engagement, and loyalty

Who is the webinar for: CIO, Head of P&C, Head of motor, Head of underwriting, Head of sales and marketing, claims managers

The new age of telematics insurance

As more and more customers move online, the volume of available data increases exponentially as customers are always within reach. However, many insurers still struggle to monetize the data, boost customer engagement, and increase loyalty.

With mobile technology, data platforms, and connected ecosystems, telematics is disrupting insurance as we know it.

Together with Adacta, we will take a closer look at the evolution and the future of insurance telematics and demonstrate what insurers can do today to drive innovation in their motor insurance.

LEARNINGS

Trends and future of insurance telematics and ways insurers can utilize the data to grow their motor segment

How insurers can use the latest data-driven solutions to move from relatively simple UBI-based products to customer engagement and loyalty

How to collect and utilize data to calculate accident risk and create competitive, risk-adjusted insurance premiums with
Motion-S and AdInsure.

Frequently asked questions

What is telematics?

Telematics has been here for about 20 years. Nevertheless, it took a hit during the Covid-19 pandemic, when personal mobility has changed, and customer’s demand for products like pay-per-use, UBI, and telematics-based renewals became more evident. As the technology improved from the black-box to the smartphone sensors, there are almost no more obstacles for telematics to become prevalent in motor insurance.

What are the benefits of data-driven insurance telematics?

Tailored quoting, more accurate risk assessment, enriched claims contextualization, improved fraud detection, increased retention rates, additional touchpoints to create a continuous communication channel with customers, and more sales opportunities, such as cross- and upsell of other life and non-life insurance products, could all be advantages for insurance companies.

Customers do not need to install any hardware devices in their vehicles because the modern solution is available by simply downloading a mobile app. Customers can log journeys and keep track of all the excursions they’ve taken in the past, as well as how safe, eco-efficient, and car-wearing they’ve been. It gives customers a sense of transparency over its policy, and that is what sets your insurance company apart from your competition.

How can data-driven telematics reduce motor insurance fraud?

Car insurance fraud is a constant problem for insurers leading to higher insurance premiums due to higher cost/claim ratios. Telematics and data analytics is perfect means to overcome wrong customer information or even manipulated car crashes. For example, the correctness of customer information, place of living as one crucial factor for calculating insurance premiums can easily be matched with mobility patterns and verified.

Can claim context enable faster claim settlement?

Contextualization helps to track back what happened before the accident – a significant advantage in processing claims, as most persons involved in a crash can hardly remember all details. Sometimes, the data can help claim holders clarify the accident’s circumstances and fasten the claims settlement. On the other hand, the data analytics approach empowers insurers to understand better how claims come into being, allowing them to take measures to predict and prevent accidents.

Claim Context Enables Smart Pricing

What do you know about claims? The approximate location, when it happened, and a few details that your client provided you. This information might be enough for essential claim management, but there is an unused potential to be unlocked by adding context to claims.

It’s a hot topic in most InsurTech newsletters: the insurance industry needs to change from the traditional annual renewable car insurance policy to advanced usage-based insurance (UBI) products. Most stories focus on enabling tariffs pricing in near-to-real-time and creating more flexible insurance products, but insurance telematics offers much more.

Looking at the total worth of motor insurance claims paid out in Europe, Germany ranked number one with nearly 25 billion euros in 2019. Meaning each day, on average, German motor insurance companies settle claims at an amount of more than 68 million euros. It’s without a doubt that claim/cost ratios are the primary factor. To increase the competitiveness of a tariff, insurance companies aim to improve their client portfolio by increasing the proportion of safe drivers. For that, modeling claim frequency and severity through driving risk and assessing the impact on insurance tariffs are needed.

Revealing accident’s reality

The last few seconds before an accident occurs are most crucial for detecting the causes that led to it. Many people can hardly remember every detail. Moreover, the complicated way of submitting claim details, which in most cases is still a manual and non-digitized process, can lead to missing information. In another aspect, a minority of people might communicate misleading information in bad faith to conduct fraud.

Don’t ask for context: measure it.

Was a driver’s vision impaired because of the dazzling sun, was he/she ignoring give-way or even a stop sign, or was he/she driving much faster than the average traffic flow conditions? Our analytics comprises 19 base risk factors from six different categories, taking into consideration the driving environment. This so-called contextualization is crucial for calculating reliable and objective risk scores. But more than that, it helps to track back what happened before the accident – a significant advantage in processing claims. Our approach empowers insurers to understand better how claims come into being – allowing them to take measures to predict, but more than that, prevent accidents.

How to contextualize claims

A trip can be explained through a sequence of time-stamped locations. Longitude, longitude, speed, and heading are, in principle, enough to see where and when a driver has been traveling the roads.
However, to get meaningful insights about mobility data, the trip data must be put into its particular spatial-temporal context. Adding factors as road conditions, sun angle, speed limits, traffic density, road quality and type, road and lane configuration, or weather conditions to each location allows inferring the driving environment.

What is the influence of claims on insurance pricing, and how can it be improved by contextualizing claims?

Jane has almost twenty years of driving history. She appears to be a safe and sound driver, as her insurance record reveals only two minor responsible accidents. Nevertheless, the hassle of getting the claim submitted and repairs done, was not a pleasant experience. Her claims did only have limited costs associated and settled by her motor insurance compared to other drivers of the insurance’s customer portfolio. Let’s have a look at Jane’s last accident: she was driving early in the morning to her workplace when suddenly a herd of wild boars crossed the street. Unfortunately, she hit one of them, but it was not killed and escaped. The damage on the front of the car was, however, severe. According to the insurance’s procedure, the claim was filed a few hours later, indicating the approximate location of the accident, the approximate time, a description of the damage, and what had happened. The car was repaired for 2500 euros, and the claim was settled.

Process innovation

How would the process look like if Jane’s insurance had put at disposal a digital insurance solution? Jane’s insurance developed a mobile app to offer an easy-to-use solution to manage policies, request additional coverages, and file claims. When the wild boar hit the car, Jane could immediately file a claim by simply following a click-by-click process:

  1. The severity of the accident: Material damage
  2. Kind of accident: Animal involved in the accident
  3. Accident site via the automatic location of the mobile phone
  4. Pictures of the damage
  5. Description of the accident

As a result, a digitally empowered process with only five simple steps, not requiring a lot of effort, and done immediately after the accident has happened.

The impact on the claim manager’s process

Thanks to data augmentation, the insurance claim manager, David, does not only receive Jane’s accident information but a contextualized trip of the last few seconds before the accident was detected by Jane’s mobile phone.

  1. Road information: one-lane rural road with a speed limit of 90 km/h, but possible speed due to road curvature only 72 km/h; road roughness index of 6.2 indicating an older pavement with some damages
  2. Road topology: ascending road with a slope of 4.56%; curvature of the road at the accident location of a low radius of 83 meters
  3. Road signs: animals crossing warning 300 m before the hit point
  4. Traffic information: average speed of 78 km/h; jam factor of 2, indicating there is a low probability of a traffic jam happening
  5. Weather conditions: Sunny, 19 °C, 52% humidity, no wind, high visibility, improbable dazzling sun situation

The accident’s context made David conclude that Jane actually traveled too fast in the curve, with a challenging topology for a safe braking maneuver. She respected the speed limit, but she had little to no time to react when the wild boar crossed the road.

The next step: using the data lake to improve actuarial models

Alex is in charge of setting up the actuarial models. While investigating the past claims during a tariff update process, he detected that Jane’s accident was not a rare coincidence on that type of road. With the help of contextualization and data analytics, he was able to derive the following causality. In rural roads, and in particular, within an animal crossing warning area, the probability of having an accident with a crossing animal is ten times higher than in any other section of a rural road. Also, he realized that customers driving on roads with median roughness higher than 8.0 are making use of road assistance 35% more often than the average customer. Thus, he decided to incorporate into the tariff structure one additional question that will serve as a proxy variable for the product pricing: (i) Which are the five most frequent destinations where you travel by car?. Thanks to this question, the insurer can infer, a priori, the probability of a new customer being exposed to animal crossing areas and bad road conditions.

Key takeaway: Adjusting tariffs and pricing dynamically due to context holds the opportunity to lower cost/claim ratios. Adding more variables about when and where people drive helps dynamize pricing and better cluster client portfolios.

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The chicken and egg problem in insurance telematics

Is it impossible to have telematics tariffs without reaching the milestone of having relevant trip and claims data for a large reference group?

How do you start your telematics-based insurance product without pre-investing time and resources on this milestone? There is a way to launch data-enabled products without creating a new tariff from scratch or heavily adapting your existing Generalized Linear Models (GLM). Just by smartly using road accident statistics.

The challenge

Insurance telematics is nothing new – it has been around for more than two decades. First patented by the major U.S. auto insurance company Progressive Auto Insurance in the late 90s, many insurers have followed and tried to innovate with telematics-based insurance products. Many of them failed to make use of the suddenly available data. There are several reasons why multiple insurance telematics programs failed, and not always because of the much-cited missing user acceptance. The technology’s potential was often misunderstood, and the integration of telematics-based insights into the product structure was complex or completely absent. The impact of behavioral and contextual metrics on pricing has been neglected.  

Usage-Based Insurance (UBI) as a use case of big data

It all started with quite simple Pay-As-You-Drive (PAYD) products, meaning telematics was, and is, only used to quantify distance or time traveled. While it’s comparably straightforward to implement, these insurance products do not take advantage of all data available, e.g., driving context and style, but consider high mileage solely as high exposure to claims. They do not help classify good and bad drivers based on their actual behavior or when and where they drive.

The next evolutionary step happened with the introduction of Pay-How-You-Drive (PHYD) models, analyzing driving behavior in terms of acceleration, braking, speeding, and cornering. Premiums are adjusted using simple scores, enabling separation between “good” and “bad” drivers. However, those scores have no proven link to any actual risk (i.e., claims or accident frequency and severity), preventing quantifying discounts or increases in premium based on existing tariffs safely. The most recent product evolution popped up with Manage-How-You-Drive (MHYD), combining proactive customer engagement through driver-facing feedback with driving style and dynamic rate adjustment.

History does not stop here as the insurance industry tries to innovate constantly. In recent years, the adoption rates of telematics-based insurance products have grown continuously, and it’s forecasted to grow even more at a CAGR of 25.1% from 2020 to 2027 

The correlation of telematics data and claims

In the past, the insurance industry has spent a lot of energy on deploying telematics-based products without integrating telematics-based insights into the tariff. Why that? To integrate telematics data into pricing, actuaries would need to correlate, for a given group of representative customers, telematics-based metrics to claims characteristics. Both claims and telematics data need to be available for that group.

This would allow an actuarial team to integrate telematics-based variable into their Generalized Linear Models (GLM) with concrete explanatory variables from which frequency (in terms of telematics-based events) and severity (in terms of claim cost) are duly modeled, being able to use each of those variables for different risks coverages and premium calculation. However, not all the insurers having deployed telematics could reach that point, because of many reasons: (i) volumes in terms of telematics-based customers have not been huge,  (ii) telematics initiatives mainly focused on marketing-oriented “digital” products,  giving upfront discounts, (iii) the level of insights and metrics on the telematics data might be limited to third-party scores on acceleration, braking, and cornering, which for sure will not have a high explanatory power once correlating to claims. Integrating telematics not only in the frontend but also in pricing and using it for price modification of existing products is the real challenge. 

But, what other data instead of claims could be used to measure risk from drivers objectively? Road accident statistics are undoubtedly the best proxy for claims and give insights into accident causes. With publicly available data from, for example, the European Commission or the National Highway Traffic Safety Administration, one can derive models and factors.

Pricing schema UBI

A matter of frequency and severity

By going beyond driving dynamics and computing risk metrics based on contextual and behavioral data, we can cross-correlate to road accident statistics and build models for both frequency and severity. The key is on explaining with telematics-based metrics concrete risk factors for which road accident statistics are available for many road contexts (urban, rural, or freeway driving). Thanks to a patented approach, telematics-based metrics can finally be integrated into pricing as a simple multiplicative factor, with a proven link to risk. We used a reference set to build the pricing tool based on an extensive and representative reference set with more than 400 drivers tracked at least for one month each, counting over 6 million kilometers traveled in Europe.

The power of a single multiplicative factor

Translating a driver’s measured risk exposure into a single multiplicative factor is what our tool does. It can modulate up and down the calculated premium of an existing insurance product. To state it really explicitly: there is no need to create a new tariff, but our approach offers the unique opportunity to improve existing tariffs with one single added factor in a fully risk-oriented approach.  The insurer can adjust telematics’ influence on its pricing, meaning setting the range of discounts and price increases. Testing and creating different sets of multipliers can be done in the pricing simulator tool in a safe environment. The insurer can smoothly introduce telematics into its existing tariffs and give data-driven factors step by step a more significant influence as trust is being built over time. 

The tool’s power lies in its versatility for a wide range of telematics insurance products, ranging from simple PAYD, Pay-Where-You-Drive (PWYD), to the most advanced PHYD with advanced risk metrics based on context and behavior. It is also possible to model using telematics metrics, tariff multipliers for specific coverages like windshield or animal crossing protections. 

For a given coverage to be modeled, several telematics metrics will be selected, and multipliers will be calculated by computing the number of potential accidents explained by a given user and its position within a global reference population. 

To give some examples, a PWYD product could be modeled using the following risk factors (and their associated telematics metrics): 

  • Driving in animal crossing areas
  • Driving under dazzling sun conditions
  • Being exposed to a low-quality road surface
  • Driving with rain, sleet, or snow
  • Being exposed to slippery roads
  • Driving in challenging road layouts

Also, a simple windshield protection coverage could be modeled using: 

  • Driving over speed or too fast for conditions
  • Being exposed to a low-quality road surface
  • Driving significantly in freeways

Opportunities

With this new technology, several opportunities arise:

  1. Launching new telematics products safer, giving a new set of tools to underwriting and actuarial to build new and better explanatory variables.
  2. Upgrading your existing ABC telematics value proposition into something impacting your business.
  3. Starting to develop a real telematics data-lake, with meaningful insights, to prepare the field for connected and autonomous vehicle insurance.

The future of insurance in the connected car era

The integration of telematics-based pricing tools opens the door for building insurance products beyond traditional car insurance, with innovative features for situational or on-demand insurance coverage. Think about the endless opportunities of cross-selling and upselling non-motor insurance products when and where they are needed. With vehicles becoming more and more connected, the necessity of building a data lake and preparing your tariffication models for the era of autonomous vehicles is crucial. Investing in integrating telematics data into pricing models will empower you to compete with automotive players entering the insurance market.

Key takeaway: Integrating telematics data into insurance pricing has always been a hassle. Adding just one single and risk-oriented multiplicative factor to a base insurance premium is a game-changer. More than that, introducing telematics-based products to the market without the need to have simultaneous knowledge of claims and telematics data for your reference groups, but instead relying on objective accident-based risk scores lets insurers design safely competitive products.

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Insurance Pricing and Telematics:
A Historical Contradiction?

This post goes out to everyone in the insurance sector but especially to those who have tried to integrate telematics in their insurance pricing models. 

Why are most insurance companies hesitant to create telematics products? We will make a strong hypothesis now: it’s because nobody really knows how to translate telematics data into their pricing models. If you don’t agree to that directly, give us a chance to convince you and put forward a solution to this problem by the end of the blog post.

The rising of PAYD tariff popularity

With the global Covid-19 pandemic, many customers were evaluating to switch to Pay-As-You-Drive (PAYD) insurance tariffs simply because they did not want to pay for insurance while not using the car. Their vehicle was mostly stationed at its parking lot due to worldwide lockdowns and home office working schemes’ uptake. Recent numbers, however, show that mileage or in the general distance has already almost reached the pre-pandemic level again. Is that the sudden death of the PAYD model’s popularity? We argue no if you do not consider the basic distance-based PAYD tariffs only but also innovative ones, built on a monthly base tariff, to cover risks of the non-moving vehicle and a variable tariff based on the number of days that that vehicle moves. Even if the world will get back to normal at some (hopefully soon) point in time, partially remote work is sure to stay as it turned out to work pretty fine.  

Simple ABC analytics and basic PHYD as the next evolutionary step

However, PAYD is the simplest, although most common telematics-based motor insurance product. More and more insurance companies have tried to innovate and create low-risk customer portfolios with Pay-How-You-Drive products in the past few years. Many companies based their tariffs calculation on simple trackers or mobile apps, collecting trip data, and calculating risk scores based on acceleration, braking, and corning (sometimes speeding, too), commonly known as ABC analytics. The resulting insurance products were disappointing for both consumers and the insurer, especially the actuarial and underwriting experts. What happened with the brilliant idea of including driving behavior and the consequent accident risk into an insurance product resultantly? Consumers who opted-in got, in most cases, only a small discount upfront or a reduction for the following year. The positive reinforcement mechanism of being granted a discount faded over the next period, and most of the time, no long-lasting effect on driving style could have been observed. On the insurer’s side, telematics was mostly seen as a significant integration effort as it was challenging to incorporate into their pricing models. More than that, standard telematics-based technologies do not offer any correlation to accident risk or claims – the primary element for pricing. 

Traditional insurance vs. a data-driven approach

The solution consists of, first, better driving behavior analytics and resulting scoring. Second and most importantly, a translation of risky driving behavior and events to actual claim costs and the impact on insurance tariffs. But let us dive first into how traditional insurance tariffs are calculated. The price you pay to insure your car for a certain coverage depends on the car’s model (car model price and horsepower), e, your driving experience, and some demographic information. Advanced PAYD / PHYD models’ primary goal would be to offer good drivers rewards, limit claim costs due to reduction of risky drivers in the portfolio, or compensate possible losses with adjusted premiums. All that while respecting the insurer’s decision about the impact of telematics on the tariff, in terms of maximum discounts or increases.  

Driving behavior under the microscope

How would that work: it’s as simple as your trial period on a new job. Either you perform, or you don’t perform. Insurance companies have to collect trips over a significant period for an individual driver, enough to derive usual mobility patterns and driving characteristics to extract specific contributory risk factors. Such risk factors could be, for instance, aggressive driving, disobeying priorities, being exposed to low visibility or slippery road conditions, being involved in harsh maneuvers, or exceeding speed limits. These risk factors need to be seen in their context, meaning where the event is observed – rural, urban, or freeways. Each risk factor contributes in each environment differently to the accident risk and eventually claim costs.

The resulting driver profile consists of distance traveled on different road types, the observed risk factors per road type, and the estimated distance traveled per year. Matching these results with recent road accident statistics enables insurers to weigh risk exposure according to severity. This constitutes a major advantage compared to traditional telematics: by using road accident statistics from well-known risk factors, insurers can without any ifs or buts deploy telematics as a plug-in to existing products without having to integrate risk factors individually into their Generalized Linear Models (GLM), eliminating likewise the dependency between claims and telematics data.

So far, so good, but still rather complex to integrate with pricing. Thinking about the practical application of these advanced scoring methodologies, wouldn’t it be great to have a dynamic pricing tool to play around with various insurance product presets like PAYD, PHYD, and even more advanced Pay-Where-You-Drive? Stay tuned to one of our upcoming insurance article blog posts, and we will show you how the future of dynamic pricing looks like in reality.

Be prepared for the connected car era

In the early telematics initiatives, the insurers took the lead in collecting driving data through expensive devices. Most of those initiatives failed. 

The number of global smartphone users has continuously been growing ever since the first smartphones hit the market.  With smartphones in almost every driver’s pocket, there are new opportunities to collect mobility data and create accurate profiles at a low cost. Combining risk factors with road accident statistics to translate them into concrete exposure and severity metrics allows integrating telematics hassle-free into insurance pricing. 

We don’t need to have a fortune-teller ball to forecast that in the (near future) connected vehicle technologies will replace the smartphone in insurance telematics. Car manufacturers are rapidly deploying their connected car platforms and APIs, providing even more accurate driver profiles. Suppose insurers do not seriously take into account data-driven pricing. In that case, car manufacturers will be better positioned to act as risk-takers by bundling insurance into a single monthly mobility budget for the users.

Key Takeaway: Translating telematics data and risk scores into concrete insurance products requires accurate driving behavior profiling and matching it with context and accident statistics. It’s a complex task, but with an entirely data-driven approach, the competition’s main differentiating factor is to offer your best drivers the lowest insurance tariff.  

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Telematics?
What the heck, no!

Don’t judge a book by its cover: it’s not all about creating another usage-based insurance. Telematics offers a variety of opportunities to speed-up customer onboarding, increase competitiveness, and optimize your customer portfolio. 



You read it on every corporate blog, in every specific journal. It’s a hot topic in most InsurTech newsletters: the insurance industry needs to change from the traditional annual renewable car insurance policy to advanced usage-based insurance (UBI) products. Most stories focus on enabling tariffs pricing in near-to-real-time and creating more flexible insurance products. 

Although insurance telematics has been around for quite a while, it is still underrated as “a cock that won’t fight.” Why that? The public has not yet widely accepted UBI. In many European countries and other western countries, the adoption rates are meager due to lacking public acceptance of being “tracked” only to earn a slight discount for the following year. Most of the usage-based insurance products stay in their niche and are neither for customers nor for the insurance industry itself the most appealing way to go. Side note: This might change now with the experience from the COVID-19 pandemic situation and the resulting mobility behavior changes, as more and more people question why they should pay for car insurance if they don’t drive.

If we consider telematics usage for dynamic pricing only, this might partially justify some bias in insurance telematics’ perception. But that’s only the tip of the iceberg when it comes to all the benefits the connected insurance concept based on mobility data can put forward. 

 

Improving customer onboarding processes & eliminating sign-up abandonment

Jane wants to sign up for car insurance. She visits the insurance tariff calculator or a tariff comparison engine, fills out endless forms, and in the end gets an offer with a far too expensive premium – and visits the website of some competitors. It does not need to be like this. With today’s advances in AI and bots for customer support, the whole process could be simplified to just a few clicks. For sure, that does not eliminate the tariffs’ problem. Imagine now that Jane Doe would be offered, before process abandonment, a much better premium if she proves that she is a safe driver during an initial period. Just by downloading a mobile app. Who would not take the opportunity to receive a much better offer, with no drawbacks?

Enhancing customer loyalty & creating a constant communication channel

When is John usually in contact with his insurance? Usually, only then when he receives his car insurance invoice, or if he needs to file a claim. We all agree that these are not the best moments in life and putting a customer relationship at risk. Customer retention is a crucial point, and constant and positive communication can improve it. With mobility data at hand, additional features on top can be easily created: trip logbooks, for example, allow your customers to keep track of all trips they did in the past and how good they have been driving in terms of risk, eco-efficiency, and car wear. Provide your customers with a level of transparency that differentiates you from your competitors. Collecting benefits along the whole contract lifetime thanks to the customer’s mobility data increases their loyalty. We all have a little child inside us, so gamification and incentives are still a fantastic way to design an attractive insurance product. 

Serving the customer with personalized offers when and where they are needed

Jane is a mother of three, owns a dog, and is regularly making vacation trips as she is passionate about skiing. How do we know it? Through situational analytics taking into account daily mobility patterns, e.g., daily trips to school, to kindergarten, to the dog park, and some outliers, as such the trip to the ski resort. With this data at hand, Jane Doe could receive personalized and suitable offers for children’s accident insurance, pet health insurance, or travel insurance. Telematics data and mobility profiling can significantly boost upsell and cross-sell of traditional or smart insurance products.

Building up a low-risk driver portfolio

If John opts into telematics-powered insurance and receives benefits for his responsible driving, the magic of positive risk selection happens: he will most probably automatically drive safer. Creating a transparent feedback channel for John Doe with simple to understand yet deep insights into his driving behavior, and enabling coaching, will make him become an even better driver eventually. 

To conclude: insurance telematics does not automatically implicate an integration of driving data into pricing models or creating a UBI, but helps insurers to strategically improve their market position by building up their own data lake, enhancing customer communication, and differentiating from their competition with a client-centric, data-powered approach.

Key takeaway: Don’t judge too early on the tip of the iceberg, otherwise, you could end up like the Titanic. 

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A New Era in Insurance Telematics: Data-Driven Customer Engagement and Loyalty

Join Adacta and Motion-S as we look at the future of insurance telematics and demonstrate what insurers can do today to grow their motor segment.

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