Farfetch
Circuit mix
Where Farfetch shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Python & R data science80%3 events
- Open-source data infrastructure20%1 event
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
| Conference | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|---|---|---|---|---|
| PyData | ||||||||||
| Berlin Buzzwords |
Activity by year
Three measures on one year axis; each panel has its own scale. 2027, 2028 is still mostly ahead, so its bars are faded and incomplete.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
Distinct speakers
People from this company on a program that year, each counted once.
Sponsorships by tier
Sponsorship rows per year (one per event and listing), stacked by normalized tier.
- Gold
Where they've grown or pulled back
DownNothing recorded in 2026; sponsored 1 conference in 2019.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. Tiers use the best tier each year; swatches show the path, oldest first. A conference counts as pulled out only when a later event published a complete sponsor list, since most events don’t say whether their list is complete.
Once or on and off 1
No clear direction, or a gap we can’t confirm because later sponsor lists are incomplete.
- PyDataonce, 2019, Gold
Not on the company map: lookalikes are computed only for companies present at four or more conferences.
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 1 this company sponsored).
| Company | Shared events | Share |
|---|---|---|
| Aetna | 1 | 100% |
| Amenity Analytics | 1 | 100% |
| BCG | 1 | 100% |
| Bloomberg | 1 | 100% |
| Blue Fire AI | 1 | 100% |
| BuzzFeed | 1 | 100% |
| Civis Analytics | 1 | 100% |
| Datadog | 1 | 100% |
| FactSet | 1 | 100% |
| IBM | 1 | 100% |
| Manning | 1 | 100% |
| Microsoft | 1 | 100% |
| Morgan Stanley | 1 | 100% |
| The New York Times | 1 | 100% |
| NVIDIA | 1 | 100% |
Sponsorships 1
| Event | Date | Place | Tier as listed | Normalized tier |
|---|---|---|---|---|
| PyData New York City 2019 | Nov 4–6, 2019 | New York, United States | Gold | Gold |
Speakers 5
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Davide Sarra | PyData Cambridge 2019 | talk | Practical methods to optimise model stability: case study using customer-lifetime value at Farfetch | — |
| Kishan Manani | PyData Cambridge 2019 | talk | Practical methods to optimise model stability: case study using customer-lifetime value at Farfetch | — |
| George Cushen | PyData London 2019 | talk | Knowledge graphs --enter--> the Hype Cycle | — |
| João Martins | PyData London 2019 | tutorial | AB-testing by clusters | — |
| Pedro Balage | Berlin Buzzwords 2019 | talk | Why data-driven methods will shape the future of relevance search | Slides · Video |