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Analyse is the home of summary statistics on our platform. Analyse offers the ability to conveniently understand the total employment, amongst many other metrics, of a list of companies.
You can read more here.
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Using multiple keywords will generally yield better results.
If you would like to search for a phrase please enclose the words in inverted commas e.g. "artificial intelligence".
Find out more here.
Keyword filtering will be performed over all website text.
For Custom Industrial Classifications (CICs) we use the same process we use for our RTICs but these are not released to every customer registered on the platform. These are custom built using our Taxonomy process but this is delivered to a specific set of users. We encourage all of our clients to open-source their CICs to all of our users as we are strong advocates for working in the open however we understand our clients may want to protect their classifications.
Find out more here
? For more information on how we define female founders, as well as how we recommend using this data, please read this article Founder gender Reset
If a company has reported employee count for three years or more we fit an exponential curve to those years and use this to calculate an annual employee growth rate. We do the same for turnover.
Read more on company growth percentages here.
Our proprietary Innovation Score is generated using ML trained on available R&D spend data. The model predicts whether a company is Innovative or Not Innovative, based on evidence of innovativeness in the company's webtext. The final score reflects our confidence in this prediction (3 being most confident).
It is more appropriate to filter by innovative companies once a list is built, rather than using innovative companies to seek to build lists of purely innovative companies.
Find out more
Our scale-up definition is the OECD's definition of a scale-up.
Our company sizes are based on the UK's legislative definition on company sizes. You can read more about the categorisation here.
The SICs filter is composed of Standard Industrial Classification (SIC) codes of economic activities (2007). These are used to classify businesses by the type of economic activity in which they are engaged.
The SIC sections filter is composed of sections where similar SIC codes are grouped together.
The Category filter consists of company categories as defined on Companies House. Find out more
The locations filter is composed of various geographical locations including cities, local authorities, regions, constituencies, local enterprise partnerships, ultimate parent company nations and postcodes.
You can read about our location filters here
Only return companies whose registered addresses are located in an active location filter. The analysis results will include both registered and operating locations for these companies.
Only return companies whose operating addresses are located in an active location filter. The analysis results will include both registered and operating locations for these companies.
The analysis results will be calculated based on registered addresses only. Operating locations will be excluded from the results.
Financial filters exclude companies with unknown values. Min and maxes require a known value, making them inapplicable to companies with unknown values.
? We have identified likely anomalous company financial accounts, using a model trained on confirmed anomalous financial accounts. In the companies filter you can choose to exclude these companies, or inspect them using 'Only outliers'. View outliers Reset
Definitions of the investment rounds are detailed on our knowledge base.