What wins in Fintech: Distribution or data?

Coming out of two major fintech conferences this month (Insuretech Connect and Money 2020), it’s clear that fintech is evolving – but it’s not clear which evolutionary approach will dominate. Startup innovation apparently coalesces around a choice: either building toward either a distribution advantage or a data advantage in the insurance industry.

In 2020, I wrote about the unique characteristics of a successful fintech company, or the “3 Ds”:- distribution, data and delivery. I argued that successful startups had at least one of the three, and especially one of the first two: distribution or data. The best had more than one. Some even had a trifecta of all three.

But which of the Ds is most important? Which will lead to more consistent multi-billion dollar startup results?

Let’s start with a few considerations. I promise I will provide an answer at the end of the article.

Is the customer difficult to reach?

Some customer bases are easy to reach via easily accessible channels such as social media or online television. Over 85% of millennials make purchases online, and influencers, reviews and social recommendations are a big driver of decisions. Others are easy to reach via easily built existing channels – think the broker channel for car or car insurance.

Some target groups are more difficult to reach. Older people may not be on social media. Medium-sized companies face more esoteric risks such as climate insurance. You get the idea.

As a broad simplification: when the customer base is easier to reach (and willing to switch), a long-term distribution advantage matters less. When the customer is hard to reach, a distribution advantage is key.

Is the product specialized or is it a commodity?

Certain products have well understood parameters and dimensions. They are easily comparable between companies. Car insurance and bank accounts are clear examples. Of course, these also tend to be easier to distribute (e.g. online or via established channels).

A distribution advantage in commodity products is more difficult to achieve. The playing field can be leveled in online procurement (e.g. bank accounts) or brokerage channels (e.g. car insurance). That’s why the brand matters a lot. No surprise, to gain attention Geico spends $2 billion on marketing each year.

In more commoditized products, a data advantage can be used to build a border. For example, companies like Root promised to underwrite based on differentiated data (driving behaviour). But unless the new data creates a massive underwriting advantage across commodity categories, distribution remains important. This makes it possible for specialized players to better price the customers they seek, and gain market share.

More specialized products will allow providers to exercise greater pricing power. No surprise specialty insurance lines have much lower loss rates and higher profitability.

There are of course several nuances here. Is there a willingness to experiment with new products? What are the switching costs like (eg switching bank accounts and credit cards is challenging due to automatic payments creating stickiness)? How important is brand loyalty?

Is the market changing?

In a changing world, new risks and new needs develop. Some are on the horizon today, especially cyber and climate.

In insurance, new risks lead to new questions: how will losses manifest? How big will they be? Who will be affected? What behavior today will change losses in the future?

Unfortunately, these are massive black holes with no clear answers.

If the product was available at reasonable prices, customers would often clamor for it to alleviate this uncertainty. But if they are priced incorrectly, they can create major challenges for the insurance company. That’s why data in uncertain situations matters more.

That is one of the reasons why parametric climate is on the rise. As Nick Cavanaugh, CEO of Sensible Weather explains it: “The availability and reliability of remotely sensed data – increasingly derived from satellites – combined with high-resolution computational models and scalable computing architectures have made many parametric products possible for the first time. Purely data-driven risk products can now provide accurate coverage while dramatically increasing costs and operational efficiency.” Parametric simplifies and controls the risk equation (eg Descartes in the enterprise space and Sensible Weather in travel). But ultimately, these companies are built on a data advantage .

Profit margin for product

Some products have low margins. For example, the average loss rate in car insurance is between 60-70% (and in some cases over 100%). For ACA health care plans, it is mandated to be 80%. Other categories such as extended warranty insurance are far more lucrative, with 50-60% profit margins, including losses and also administration costs!

When the margin is lower, so is the margin of safety. As a result, data matters more in underwriting to ensure that low-margin profits can exist.

Conversely, when the margins are high there is room for error. There must be data, but through distribution, with a large enough margin for error, the data set can be built over time.

The role of regulation

Some products are more or less regulated. For example, in home insurance, there are limits on how much an insurance company can increase rates year on year. If you’re in a region with changing weather patterns (eg, California fires or Florida floods)—or have mispriced your policy for some reason—it makes it much more difficult and expensive to fix the mistake. In ACA plans, there is a minimum loss ratio of 80%. If you don’t hit it, you will be penalized.

Without delving into the pros and cons of the ordinance (generally speaking, I am to consumer protection), the more limits to regulation there are on pricing and price change, the more data matters.

Embedded financial services

Embedded financial services – by selling a financial product as part of a wider offering – have a built-in distribution advantage. This is the core value proposition. So by nature the distribution advantage of the original product or company is most important.

Embedded fintech also has a twist. It may enhance or enhance the original product. Spot insurance includes health insurance as part of the lift pass. In the event of an injury, the care experience becomes smoother and more integrated (and free of charge).

And if the built-in insurance offering helps improve sales conversion, the parent company can make money in different ways (regardless of the profitability of the insurance product). For lending, this is one of the most important incentives sellers have to implement buy-now-pay-later.

So which ‘D’ means the most?

The unsatisfactory answer, of course, is that it depends.

In my role as a venture capitalist, I gravitate towards companies with uniqueness distribution benefits, but where a data moat can be built over time through experience and scale. This is, for example, one of the benefits of embedded financial services, as well as new risk categories with great potential for dislocations (and the creation of multi-billion dollar businesses). These include new (e.g. cyber) or changing (e.g. climate) areas of risk.

However, your answer to the same question will depend on your strategy and business model.

Where do you land?