Our Data and Methodology
Our research is driven by an annual survey as well as the collection of a range of indicators measuring ecosystem dynamics. We guide founders, investors, and ecosystem builders in their search for startup destinations as well as in making strategic choices.
A particular interest lies in the exploration of success factors for cities that aim to grow their startup community by attracting international founders, investors, and collaborators.
Founder Opinions
Cities Tracked
Connections Mapped
Accelerators
The exploration of businesses´ competitive resources relies on the ability of the ecosystem to attract a differentiated set of resources and to enable sustainable synergies between them. The canonical indexes are still not able to capture the magnitude (*) of entrepreneurial ecosystems in which companies grow and develop their business. Renewed cross-sectoral and spatial relations are constantly redefining this magnitude. In this regard, single metrics, related for example to innovation and business performances, but also infrastructure availability, are not able to outline a consistent picture about the most favourable conditions triggering entrepreneurial development and featuring virtuous entrepreneurial ecosystems.
Focusing on startups, our approach combines different data sources and investigates entrepreneurial ecosystems at two different levels: the accelerators and the cities. These are our unit of analysis, whereas our unit of observation is multilevel.
* Magnitude refers to the environment’s attractiveness for new players, such as foreigners in a city, new creative talents in a co-working space, new members in a social association etc.
1. The structure of our dataset
To define and describe the structure of our dataset, we propose five composite variables:
- Connectivity
- Community
- Perception & Trust
- Performance, and
- Impact.
Theme | Variable | Description | Source of Data |
Perception & Outlook | City Popularity | % of founders naming the city as a possible startup location for a hypothetical startup. Variable can be broken down by year, origin and the field of the startup | Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | Popularity Score | Formula is N – (r-1) / N (r = rank) see here |
Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Popularity, Growth of Popularity | Percentage point increase in the % of the vote YoY | Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Popularity, Growth of Popularity | Percentage point increase, rolling 3 year average, e.g. avg. % founder vote 2017-19 vs. 2020-22 | Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Popularity among High-Tech startups | % of founders naming the city as a possible startup location among founders of startups in the category “High-tech” | Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Popularity by startup sector |
% of founders naming the city as a possible startup location among founders of startups in the categories: Consumer & Platforms – Tech/Hardware SaaS & Enterprise Software – Health & Biotech Fintech – Big Data eCommerce – Other |
Representative annual survey of startup founders in Europe since 2015 Data is accumulated over years to calculate the sector popularity. |
Perception & Outlook | City Popularity, sectors in which city ranks high | List of sectors in which the city has higher share of vote than the median of cities in the sector |
Representative annual survey of startup founders in Europe since 2015 Data is accumulated over years to calculate the sector popularity. |
Perception & Outlook | City Popularity by Origin Region |
% of founders naming the city as a possible startup location based on founder origin from:
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Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Popularity by Current Region |
% of founders naming the city as a possible startup location based on founder current location from:
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Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Ratings |
% of founders voting for a city and giving a high or very high rating (>6 out of 10) in the categories:
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Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Future Outlook | % of founders based in a city rating its future outlook positive or very positive (>6 out of 10). | Representative annual survey of startup founders in Europe since 2015 |
Perception & Outlook | City Visibility, Twitter | Sum of followers of tweets mentioning the city in connections with startups | Primary data collection from twitter (since 2018) |
Perception & Outlook | City Visibility, Linkedin | Sum of followers of posts mentioning the city in connections with startups | Primary data collection from Linkedin (since 2023) |
Perception & Outlook | City Visibility, News | Number of mentions of the city in startup news per year | Tracking of all articles on dedicated European tech blogs (full text articles since 2019). |
Connectivity | City Reach | Number of diverse countries reached based on founder votes | Representative annual survey of startup founders in Europe since 2015 |
Connectivity | Level of foreign born founders | Estimation of the % of foreign-born founders per city based on comparison of origin country and current location country of founders answering the survey | Representative annual survey of startup founders in Europe since 2015 |
Connectivity | Marketshare of international investments | % of total international early-stage deals available in Europe from 2015-2018 captured by city | Directional data of investments between 100.000 € and 5mn € from 2015-2018 based on Pitchbook investment database. |
Connectivity | International Conferences | Number of international followers of large tech conferences in Europe | Primary data collection of facebook followers’ origins of the 37 leading tech conferences in Europe. |
Connectivity | International Accelerators | Number of international participants in tech accelerators per city by year of acceleration | Primary data collection of all participants of >100 leading tech accelerator programs in Europe since 2015 |
Connectivity | Establishment of 2nd branches of unicorns | Number of 2nd branches established by unicorns per city showing the attractiveness of the city for internationally successful tech companies | Primary data collection of offices of offices of unicorns |
Startup Support | Number of accelerated startups per city | Number of startups participating in accelerator programs located in the city | Manual tracking by DEEP research team of all startups participating in a list of >120 top accelerator programs |
Startup Support | Number of accelerated startups per vertical |
Number of startups per vertical in the following categories: -Purpose -Fintech -Health -IoT -BigData -SaaS |
Manual tracking by DEEP research team of all startups participating in a list of >120 top accelerator programs |
Startup Support | Avg. number of employees of startups accelerated in city | Average of sum of the employees today based on year of acceleration, e.g. startups accelerated in 2020 in Berlin today have an average of 9 employees | Manual tracking by DEEP research team of all startups participating in a list of >120 top accelerator programs |
Startup Support | Avg. sum of funding of startups accelerated in city | Average of sum of funding raised today based on year of acceleration, e.g. startups accelerated in 2020 in Berlin today have raised 2mn € on average | Manual tracking by DEEP research team of all startups participating in a list of >120 top accelerator programs |
Startup Support | Share of follow-on funding success | % share of all startups participating in accelerators in a city that have total funds raised of more than 100,000 USD per acceleration year | Manual tracking by DEEP research team of all startups participating in a list of >120 top accelerator programs |
Startup Support | Number of Opportunities | Number of opportunities for international startups listed on Startup Heatmap per city per year |
Manual tracking of opportunities by DEEP Research team on weekly basis as well as partner submissions |
Diversity | Share of Female Founders | Share of female founders per year based on annual sample of >10-20,000 founders | Identification of a sample of 10-20,000 founders as a basis for the Heatmap Survey |
Diversity | Avg. funding for female-led startups | Avg. funding of female-led startups in accelerators based on accelerated year | Manual tracking by DEEP research team of all startups participating in a list of >120 top accelerator programs |
Diversity | Share of founders with Tech Skills | Share of founders with skills in programming listed on Linkedin profiles per city, based on sample >10-20,000 founders per year | Identification of a sample of 10-20,000 founders as a basis for the Heatmap Survey, only for about 2-5,000 per year we can identify skillsets |
Community | Meetups | Number of startup related meetup events per city | Primary data collection of tech related meetups, their title, date, description and street level location in European cities since 2020 |
Community | Meetup Group Members Growth | Growth of rolling average of 3 years of meetup group members per city, e.g. 2017-19 vs. 20-22 | Meetup.com |
Community | Availability Developers | Number of developers registered on the leading developer platform in Europe (stackoverflow) per city | Primary data collection of registered developers per city |
Community | Availability Developers, Growth | Growth of rolling average of 3 years of developers per city, e.g. 2017-19 vs. 20-22 | Dealroom.co |
Community | Meetup Participant Growth | Growth of active participants of meetups since 2014 | Secondary data provided by meetup.com |
Investments | Sum of funds raised | Sum of VC funding raised per year per city since 2014 | Secondary data provided by dealroom for 100 cities |
Investments | Sum of funds, 3 yr average | Total sum of VC investments over the past 3 complete years divided by 3 | Dealroom.co |
Investments | Number of seed deals | Number of investment rounds between 450k – 2.5mn USD | Dealroom.co |
Investments | Sum of funds, Growth of 3yr average | Growth of rolling average of 3 years of VC investments per city, e.g. 2017-19 vs. 20-22 | Dealroom.co |
Investments | Sum of exits realized | Sum of realized exits per year per city since 2014 | Secondary data provided by dealroom for 100 cities |
Investments | Number of deals per vertical | Last 3 years vs. last 12 months | Crunchbase |
Costs | Salary Level Developers | Median salary of senior software developer per city | Secondary data provided by Teleport |
Tech Trends |
Relative importance of tech trends
(Sheet “VerticalFocus PercofMedian”)
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Number of mentions of a topic in relation to a city in relation to the median number of mentions of the topic across all cities
Calculation is done across meetups, twitter (discontinued) and news data. Final score is the average of the three numbers Topics: -3D Printing – AgTech -AI & Data – CleanTech -Crowdfund – Cybersecurity -Drone -Female -FinTech – Gaming -Health & BioTech -High-Tech -Infrastructure – IoT -Logistics – Mobile -Mobility – SaaS -Sustainability -VR |
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Job Creation | Jobs Openings | Number of jobs created per quarter, per year and per sector | |
GeoData | Population | Number of people living in the metropolitan area | Eurostat |
Universities | Number of university founders | Number of founders identified on Crunchbase having studied at a university in that city per year | ETER |
Talent | Number of STEM Students | Number of students studying Science, Technology, Engineering or Mathematics as share of total students per city based on ETER database | ETER |
Talent | Share of STEM Students | Percentage of students studying Science, Technology, Engineering or Mathematics as share of total students per city based on ETER database | ETER |
Universities | Spin-off rate | Number of avg. seed deals of last 3 years divided by number of STEM Students | Crunchbase / ETER |
Investments | Investment Purchasing Power | Investments divided by median cost of software developer | Dealroom.co / Teleport |
Investments | Number of Kickstarter campaigns | Number of kickstarter campaigns launched per city per year based on a kickstarter.com | Kickstarter.com |
Innovation Culture | Total sum of EU Funding per city | Sum of Horizon Research Funding won by organizations headquartered in the city updated on quarterly basis | European Commission |
The five different composite variables are investigated by looking at a multitude of units of observation. We have privileged the startup giving us information about intangible resources spread out from the entrepreneurial ecosystem where the startups are embedded.
2. Multilevel dataset description
Our dataset is based on primary and secondary data collected by a tracking system and an annual survey. These elementary variables, at different levels, are at the core of the composite variables explored by the touched upon metrics.
The tracking system
Primary and secondary data is collected since 2015. At the core of our tracking system there is a composite data team that collects the qualitative data by monitoring social media, forums, blogs, and websites. The data collection results from double-checked processes implemented by different data analysts at the same time. Data is updated at least quarterly.
For Accelerators:
Tracking system maps more than 155 European accelerators since 2015.
Elementary variables are: Total funds raised by Alumni; follow-on funding, defined as the number of startups having participated in an accelerator who have closed more than one funding round and having reached a total equity funding amount higher than 100.000 USD; number of employees of startups having participated since 2015.
For Cities:
Tracking system maps startups, startup founders and CEOs, events, and related topics.
Elementary variables are: number of tweets mentioning at the same time the name of the city and the word “startup” (and variations thereof); number of startup related meetups per city; sum of VC (Venture Capital) funding; sum of realized exits; median salary of a senior software developer; directional data of investments between 100.000 EUR and 5.000.000 EUR from 2015-2018; sum of offices of leading startup companies in the city.
The SHM Annual Survey
The main goal of the survey is to measure the intangible resources of an environment, the so called “atmosphere” (Marshall, 1919). The survey focuses on understanding founder mobility and the perception of the quality of startup communities in the eyes of founders as a proxy for their future development potential. The survey is mainly based on categorical variables covering the recognition of startup places in Europe, their rating in the main categories (access to capital, access to talent, ease of doing business, industry connections and quality of the ecosystem) as well as the qualitative assessment of the brand image via associative questions (“What do you think of when you hear “Berlin startup scene”?). Further questions cover the actual mobility and transnational connectivity of startup founders in Europe, asking for the movement history as well as connections their startup has built, including opening international branches, attracting foreign investors, hiring international talent or also just frequent business trips.
These questions are paired with impartial variables measuring the number and topical focus of startup community events, investment activities or visibility of a startup hub in startup media.
The survey’s units of observation are: the tech founders, ecosystem experts, startup team members, employees or persons interested to join a startup, investors, startup community builders or service providers. It has run annually since 2015.
3. Our Indexes
We have implemented two indexes for investigating some of the composite variables touched upon.
The Specialization index:
For both accelerators and cities, the words cloud is created by applying a content analysis to the SHM dataset.
For accelerators, the analysis is based on self-descriptions provided by startups and a list of buzzwords maintained by the Startup Heatmap team.
For cities, the analysis is based on a text database of more than 1.5 million tweets continuously collected on startup cities and the count of mentions of buzzwords grouped in categories (e.g. “fintech” includes blockchain, crypto, payment, insurtech, etc.).
The Trust Score index:
The Trust Score index at both the accelerator and city level shows the relative position of a city or accelerator in the ranking. The term “trust” indicates both a high level of “brand awareness” among founders as well as a positive attitude towards the selected destination, as survey participants are asked to provide a “recommendation”. Data is collected by SHM Annual survey.
The index is defined as:
where r is the rank. Since r depends on the SHM Annual survey representativeness of the sample, ranking is a weighted value. The weight is based on the share of population represented in the survey at the regional level (here, we don’t refer to NUTS 2. We refer to the touched upon European partition).
N is the total number of accelerators or cities.
4. Our Graphs
Santini, E. (2020). Startup Heatmap Europe Data and Methodology. Available on https://www.startupheatmap.eu/methodology.