It is fair for Thomas Hoe to describe his career path as “super not -linear”. After all, his career track is turned from award-winning video player (before it really was one thing), to computer science students, for chef in Michelin-star restaurants, to finance Ph.D. Before, he launched Amazon Stores’ first European economic science team.
But there is a theme for this two-year roller coaster: Occupation. When something catches Hoe’s attention, it catches all his attention. It was II 2019 when Britain’s threatening department from the European Union caught Amazon’s attention that a business recruiter claim the then assistant height after the Econometrics Society’s annual conference in Seattle. This meeting eventually led to joining Amazon as the first dedicated economist in his European stores retail arm.
“I was fascinated because Amazon crossed a lot of boxes for me: Big Data, Economics, Optimization, Problems that require deep science work, and the chance of working closely with leaders,” says Hoe.
Brexit represents a challenge for Amazon’s European retail business, so it was a baptism of fire for hoe. “I modeled all the investments we had to make to reduce Brexit’s influence.” His work played a key role in the enable Amazon to continue to meet the wide range of its customers, while seamlessly sent many millions of orders across the new Brexit limit. Not a bad start.
A size did not do everything
Brexit helped strengthen the need for local financial expertise in Europe, says Hoe. The dealer’s economic science team is largely US-based, so its processes and decision-making systems are typically built and optimized for the United States originally, then adapted and rolled across the rest of the world.
Europe is very fragmented and there is different – and more varied – competition.
“They work very well, certainly, but a size simple can’t fit everything,” says Hoe. “Europe is very fragmented and there is different – and more varied – competition.” His now well -daisy team, Economic Decision Science (EDS), addresses unique Eurocentric challenges using the latest techniques at the intersection of microeconomics, statistics and machine learning.
Back in 2021, with several quick gains – and a few misses – under his belt and his first few economists who were hired, Hoe was looking for ways to influence. “We felt like an economic science start in Amazon. We were the weird ones in the corner, ”says Hoe. The European business teams were not always sure what to do from Hoe’s EDS team, nor how and when to integrate economists into their problem solving.
To demonstrate where its value was, the team began to solve the problem that no one asked about. By 2020, for example, their analysis found that the customer’s demand was much higher among third-party sellers who provided free stores. Based on this research, Amazon shared insights from this work directly with salespeople to help them optimize their Amazon offers and be more successful in the store.
Economic decision science in action
Now, four years later, the EDS team is in demand. “What I like best about my job is the enormous breadth of opportunities and the challenge of trying to identify the projects that want the great sieve impact on the company and our customers,” says Hoe.
At the beginning of 2023, for example, Amazon’s US fulfillment network is re -emerged in eight largely self -sufficient regional networks. In Europe, the network had historically transformed in the other direction, originally built individually across the five largest European stores – UK, Germany, France, Italy and Spain – and later sewn together into a single European fulfillment network, allowing goods to flow countries. Hoes Team has worked in ways to optimize this unique European network.
A recent focus has tried to cut back on long change of fulfillment when followed by an equally followed versions of the product much closer to the customer. Think of a USB stick that may be shed from France to meet a British order when another but similar option sits in Amazon’s British warehouses.
With European countries that are more densely populated than the United States, customers often live closer to physical spaces, so our value can different. We need financial science to help understand where and when customers appreciate faster delivers the most.
The EDS team developed a model that incorporated data from Amazon’s USA-based Supply Chain Optimization Technologies team to explore what would happen if Amazon optimized its European ordering systems so items are located closer to customers shipments. Their findings? Customer choices may remain, but costs and shipping times cut. This is good news for both Amazon’s carbon print and its customers, as the savings allow the company to invest in making even more products locally Avaibles. Hoes Team continues to test its model and hopes to roll it out this year.
Other differences between Europe and the US are the way customers deal with online purchases and the alternatives available from physical stores. This makes understanding of local customer -critical preferences.
“Part of Amazon’s value proposal is the enormous convenience we offered customers by saving them a trip to the physical store and offering a number of quick delivery options. With European countries that are more densely populated than the United States, customers often live closer to physical spaces, so our value proposals can differentiate, ”says Hoe. “We need financial science to help understand where and when customers appreciate faster delivers the most, to make sure we deliver it.”
Hoe and his team are currently building a model to help them understand the customer’s expectations of online delivery speeds and the level of convenience Amazon must offer customers in Europe. “Amazon is constantly investing in faster delivers, and we want to ensure that these investments are pleased as many customers as possible,” says Hoe.
Europe’s financial insight mugs out
While the EDS team was created for European influence, Hoe is eager that the fruits of its projects are starting to flow back to the United States and beyond. Consider its creation of a machine learning algorithm trained to highlight the best deals for customers across Amazon’s enormous warehouse.
First, Hoe and his colleagues needed data on what customers thought of different combinations of products and prices. So they surpassed a large cut of Amazon customers and asked them a total of six million hypothetical price questions. The team fed this large part of customer feedback to a machine learning model that learned how people perceive the value of a number of Amazon products and prices. But here is the special sauce: The model trained at customer preferences could then be applied to millions of live products across Amazon’s European warehouse looking for several cases of particularly attractive pricing. It is equivalent to having a customer’s voice that tells you where all the best deals are.
“By directly incorporating customer perceptions into our algorithms, we constantly found that we can show a compelling selection of product that includes customer engagement,” says Hoe.
Some of the products identified by the model could then be highlighted in customer searches, on the Amazon website or in marketing campaigns to help even more customers find the best deals on Amazon. Following several iterations of successful prototype in Europe, technology has recently been tried in the United States. “I love that we have gone innovation to go the other way now,” says Hoe.
Now that Hoes Eds team is established and its capacity in demand, HOE considers it a success that Amazon’s European team has a clearer understanding of how economic science can help them tackle the unique business challenges they face in Europe.
“Even at Amazon, where we have some of the most advanced systems in the world, economic science is still in its infant when used on this scale,” says Hoe. “We were excited for that way in front.”