veryWatch has collated the sale prices and histories of over half a million watch references, across 400 platforms, dating back to 1989. And the insights contained in this vast data repository have been unlocked through artificial intelligence.

The platform is capable of answering questions that might seem simple, but which, up to now, have been frustratingly difficult to answer. Questions like, where and when was a specific reference sold? What was its price? The platform can mine its data to generate an almost infinite array of statistics, such as the best-selling watches in a given year, the average price of a model over a specific time period, or the current market value of a particular timepiece – all based on actual sales, rather than on list prices or estimates.

Watch brands have long been reluctant to publish the prices of their models (many still are), which is why, when you try to find out, you’ll often end up on a pre-owned watch site. And that has effectively pushed the market towards greater transparency. EveryWatch, which was set up by venture builder Nacre Capital, sits at the crossroads of these trends. We caught up with its co-founder, Giovanni Prigigallo.

Giovanni Prigigallo, co-founder of EveryWatch

Europa Star: Who’s behind the launch of EveryWatch?

Giovanni Prigigallo: We’re part of Nacre Capital, a US venture builder specialising in new technologies. Its chairman is Howard Morgan, founder of First Round Capital and a pioneer in investing in tech companies such as Uber, LinkedIn and Square. After talking to people in his network, he had a hunch that there were opportunities in applying new tech to the world of watchmaking. One problem was the difficulty in tracking all the auctions and sales on the secondary market. You had to jump from one site to another, which was very time-consuming for people wanting to buy and collect watches. Other sectors already had meta-aggregators. EveryWatch solves this problem for watches.

What is your background, and how did you end up in charge of this start-up?

My background is in biomedical engineering, but I’ve been fascinated by watches ever since I was a child. As I grew up, I started buying and selling watches, which ended up making me more money than my job! I met Howard Morgan through my uncle. We started talking about biotechnology and then naturally moved on to watches. He told me about his idea for a platform, and that led to the creation of EveryWatch. I was in charge of building the entire platform, which took over two years. Several watch experts are part of the project, and former Piaget director Chabi Nouri is chairman of the board.

What is EveryWatch’s business model?

It’s a freemium model: some data is freely accessible, and some is subscription-based. Our aim is to form partnerships with all the producers of the data we compile, i.e. dealers, auction houses and other watch sales platforms. We generate traffic and sales, adding value for them as well as for our end users.

What kind of users do you target?

We focus primarily on watch collectors and enthusiasts, but our data can be useful to professionals too. For example, if a retailer or a journalist wants to know what the ten best-selling models currently are, along with their prices and sales volumes, they can find this information on our platform.

How many sources are aggregated on EveryWatch?

Over 400 at the moment, but we have an exhaustive list of over 20,000 potential sources! Today, we track sales from 150 marketplaces and dealers, plus 250 auction houses. More than 1,000 brands are listed on the platform, and we aggregate models sold for as little as $300. We aim to be the one-stop destination for all information on the watch market.

How can you ensure that your data is reliable?

It starts with having a catalogue of all the watches ever made, and their specifications, which can act as a reference. We use a mix of artificial intelligence and manual processing to validate the data, with built-in control procedures. All the statistics are based on actual sales data, i.e. the price that someone actually paid. And we rely on ’median’ effects to correct for potential exceptions: the more different sources we have, the more reliable the platform will be in terms of the statistics we can derive. We also use volume data to understand the liquidity and trading frequency of watches, while relating it to price. In addition, we closely monitor all the sources, which are individually selected. If a company closes its doors, we will stop recording its data.

What observations have you made after a few months in soft launch mode?

It’s always fascinating to see how users behave, because often it’s the opposite of what you expect! For example, we didn’t necessarily predict how interested they would be in the ’My collection’ function, which allows them to track the value of their collection, with the security of complete anonymity. We are also going to develop a mobile app. We have a lot of work ahead of us, but our main objective now is to establish our credibility and our brand.