u/Julien_Coordable

I keep seeing €17 per failed delivery cited everywhere: carrier decks, industry reports, consultant benchmarks. Nobody shows how they got there and if it's actually verified on the field.

I tried to build the model myself from first principles: driver time, fuel, route disruption on subsequent stops, customer support contact rate. Came out at €15.30 for urban B2C redelivery in Europe, which is close enough to the €17 to feel plausible.

But I'm building this theoretically. Curious whether anyone actually measures this operationally: does anyone track it? Per event, estimate it from aggregate data, or just use an industry benchmark and move on?

French home insurers use the PPRI (government flood risk maps) to classify properties. Get it wrong and you're either overcharging customers or underpricing risk. In high-risk zones, coverage can be refused altogether.

The classification comes from geocoding: convert the address to coordinates, query the PPRI database, get the risk zone. The system trusts whatever coordinates it gets.

We ran 300 addresses through BAN (France's official open-source geocoder) and Google Maps, focusing on addresses where the two disagreed by at least 50 metres. Then queried the PPRI API for both coordinate pairs.

14.5% of those addresses ended up in a different flood risk classification depending on which geocoder was used.

Three examples from the dataset:

Loire-Atlantique: 1,294m gap between BAN and Google. BAN puts it in a known flood zone, Google puts it outside.

https://preview.redd.it/ycw3odq5ybyg1.png?width=2440&format=png&auto=webp&s=4814373c93b293c9ab3c996081038e8e6e188df9

Vendée: 1,135m gap, opposite direction. BAN misses the flood zone, Google catches it.

https://preview.redd.it/olazzxo6ybyg1.png?width=2440&format=png&auto=webp&s=b4ec43a152a7dc9b04de50ad4e3c7807937c9bb1

Moselle: Three addresses on the same street, same ~1,880m divergence, all in the same direction. BAN in flood zone, Google outside, every time. That's the pattern that matters at scale. A single address with a bad geocode is noise. Three addresses with the same systematic divergence on a whole street suggests a structured data quality problem that recurs across every address in that zone.

https://preview.redd.it/6zb7ull7ybyg1.png?width=2440&format=png&auto=webp&s=da0d3f4055fa4bb011b35fe5bd5a8d959e626f8b

The useful signal: BAN returns a confidence score per address. Every reclassification in our sample came from an address scoring below 0.71. That's your audit filter.

Full write-up with methodology and maps: https://coordable.co/blog/geocoding-ppri-insurance-impact-2026/

Curious how others approach this. Do you run any kind of geocoding audit on your portfolio, or is address quality not something that typically shows up in underwriting workflows?