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 unclear which evolutionary approach will dominate. Innovation in startups seems to bifurcate around a choice: to build either towards a distribution advantage or towards a data advantage in the insurance sector.

In 2020, I wrote about the unique attributes of a successful fintech business, or the “3 Ds”: – distribution, data and delivery. I’ve argued that successful startups have at least one of three, and in particular one of the first two: distribution or data. The best had more than one. Some even had a trifecta of the three.

But which of the D’s is the most important? What will lead to more consistent multi-billion dollar startup results?

Let’s start with some considerations. I promise, I will answer at the end of the article.

Is the customer hard to reach?

Some audiences are easy to reach through easily accessible channels like social media or online TV. Over 85% of millennials shop online, and influencers, reviews, and social endorsements are a huge factor in decisions. Others are easy to reach through easily built existing channels – think of the broker channel for car or automobile insurance.

Some audiences are harder to reach. Older people may not be on social media. Midsize companies face more esoteric risks like corporate weather insurance. You had the idea.

As a general simplification: when the customer base is easier to reach (and willing to change), a long-term distribution benefit matters less. When the customer is hard to reach, a distribution advantage is essential.

Is the product specialized or is it a commodity?

Some products have well-known parameters and dimensions. They are easily comparable between companies. Car insurance and bank accounts are clear examples. These of course also tend to be easier to distribute (e.g. online or through established channels).

A distribution advantage in commoditized products is more difficult to obtain. The rules of the game can be leveled in online acquisition (eg bank accounts) or brokerage channels (eg car insurance). This is why the brand matters a lot. No surprise, to make itself known, Geico spends 2 billion dollars in marketing each year.

In more commoditized products, a data advantage can be used to create an advantage. For example, companies like Root promised to underwrite based on differentiated data (driving behavior). But unless new data creates a massive underwriting advantage in commoditized categories, distribution still ultimately matters. This allows specialized players to better price the customers they seek and gain market share.

More specialized products will allow suppliers to exercise greater pricing power. Unsurprisingly, specialty lines of insurance have much lower loss ratios and higher profitability.

There are of course several nuances here. Is there a desire to experiment with new products? What are the switching costs like (e.g. switching bank account and credit card is difficult due to self-pay creating inflexibility)? How important is brand loyalty?

Is the market changing?

In a changing world, new risks and new needs evolve. Some are looming on the horizon today, notably cyber and the climate.

In insurance, new risks bring new questions: how will claims manifest themselves? How big will they be? Who will be affected? What behaviors today will displace losses in the future?

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

If the product were available at affordable prices, customers would often demand it to alleviate this uncertainty. But if priced incorrectly, they could create huge challenges for the insurer. This is why data in uncertain situations is more important.

This is one of the reasons parametric weather is on the rise. As Nick Cavanaugh, CEO of Sensible Weather, explains: “The availability and fidelity of remote sensing data – increasingly from satellites – combined with highly resolved computational models and scalable data processing architectures have made many parametric products realizable for the first time. Purely data-driven risk products can now provide accurate hedges while dramatically increasing cost and operational efficiency.” Parametric simplifies and controls the risk equation (e.g. Descartes in the corporate space and Sensible Weather in travel.) But ultimately, these companies are built on a data advantage.

Profit margin for the product

Some products have low margins. For example, average loss rates in car insurance are between 60 and 70% (and in some cases more than 100%). For ACA healthcare plans, it is mandatory to be 80%. Other categories like Extended Warranty Insurance are much more lucrative, with profit margins of 50-60% including loss and management fees!

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

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

The role of regulation

Some products are more or less regulated. For example, in the case of home insurance, there are restrictions on how much an insurer can raise prices from year to year. If you’re in an area with changing weather conditions (e.g. California fires or Florida floods) – or if you’ve misjudged your policy for some reason – it’s much more difficult and expensive to correct the problem. ‘mistake. In ACA plans, there is a minimum loss rate of 80%. If you don’t hit it, you are penalized.

Without diving into the pros and cons of regulation (generally speaking, I’m for consumer protection), the more limits there are to price regulation and price modification, the more important the data.

Integrated financial services

Embedded financial services – by selling a financial product as part of a larger offering – have an embedded distribution advantage. This is the core value proposition. Therefore, by nature, the distribution advantage of the original product or company is the most important.

Embedded fintech also has a twist. It can improve or enhance the original product. Spot insurance includes health insurance as part of a lift ticket. In the event of an injury, the care experience is more fluid and integrated (and free).

And if the integrated insurance offer helps improve sales conversion, the parent company can monetize in different ways (regardless of the profitability of the insurance product). For loans, this is one of the main incentives merchants have to implement buy-it-now-pay-later.

So which “D” matters the most?

The unsatisfactory answer is of course that it depends.

In my role as a venture capitalist, I revolve around companies with Distribution advantages, but where a Data moats can be built over time through experience and scale. This is one of the benefits of integrated financial services for example, as well as emerging risk classes with great potential for dislocations (and the creation of multi-billion dollar companies). These include new risk areas (eg cyber) or those that are evolving (eg climate).

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

Where are you landing?

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