Dynamic Pricing In Action: The Good, The Bad and The Beautiful

Capture investment opportunities created by megatrends

Dynamic Pricing In Action: The Good, The Bad and The Beautiful

5 August 2021 Technology & Digitalization 0

In “Is Pricing Innovation In Insurance a Zero Sum Game?”, consumers were more amenable to propositions based on evolving coverage types with dynamic pricing. Markets are pushing carriers to adopt pricing innovations. For many, the result is a zero-sum game, with improvements coming at the cost of reputational risks and increased regulatory oversight. But, those that master the fine balance between profitable pricing models and loyal customers, rule the roost. In this post, examples of pricing innovation in action, are presented.

Pricing In Embedded Insurance

AXS, a digital marketing platform, maximizes value of ticket buying experience for 350+ partners. To stimulate growth through scientific pricing, they integrated XCover’s distribution platform. This helped bypass traditional insurer systems with fixed-rate pricing and limitations in bundling products, such as basic travel medical and event cancellation insurance.

With XCover, AXS customers book events knowing that plan changes won’t burn a hole in their pockets. Tailored insurance offerings has driven growth rates of 200% in few months. Data scientists used historical sales data to bolster revenue per quote. Instead of flat rates with fixed premium, they reviewed price bands for past purchases and tested a hypothesis on tiered pricing using cognitive bias (anchoring). Customers readily bought, paying premium that was relative to ticket prices, more so in higher price bands.

Utilizing XCover, AXS tapped a source of additional revenue, with increased margins at higher price points. The modus operandi has been to establish floor prices and adjust in real time by adding a target premium to loss ratios.

Prices changing to reflect supply and demand factors is fundamental to market dynamics. However, in insurance markets traditionally, prices change infrequently. Price optimization is applied to classes of consumers instead of individual consumers. Two individuals of the same risk class and hazards would have paid same amounts.

Personalized Pricing – Auto Insurance

State regulators have been scrutinizing use of personalized pricing in private auto insurance customers, assessing whether algorithms treat customers differently. One analysis report showed that a significant factor correlated with policyholders’ ultimate price shift was how much they were already paying.

The analysis found the carrier pegged its highest percentage increases to policyholders already paying high rates. Drivers with premiums higher than a threshold faced rate increases of up to 20%. Apparently, the algorithm charged big spenders higher rates.

Some have objected to such models as part of price determination, arguing that it involves factors other than risk being incorporated. This has not been generally accepted for various reasons, primary being that inclusion of such business considerations is explicitly permitted and codified in regulatory frameworks. Insurers develop and file rating plans consistent with state rating laws, considering risk in determination of rate and price. Besides, the accuracy of rates have increased over time through continuous improvements in rating and pricing models.

Dynamic Pricing & Long-Term Results

Warta S.A. Insurance, among the oldest carriers in Poland, has been using Earnix’s pricing platform for 6-7 years. Since then, they have transformed into one of the most significant players in Polish insurance market, with 29% growth during 2015-18. A key strategic focus has been in improving pricing capabilities, with real-time, ML based pricing and advanced automation to deploy carefully calculated pricing decisions. Having built the largest pricing team in the Polish market, strict data and model governance have been as much the mandate as analytical agility and speed-to-market.

Newer models increase the rigor associated with pricing decisions, reducing reliance on judgment and inherent biases. These advances benefit policyholders, insurers and regulators. Randomness in pricing is reduced and rate stability enhanced. The long run impact is more price competitive markets, with insurers being able to satisfy the demand of customers with ever increasing digitalization and product innovations.

Cover Image

You get 3 free articles on Daily Fintech. After that you will need to become a member for just US$143 a year (= $0.39 per day) and get all our fresh content and our archives and participate in our forum.