Artificial intelligence streamlining the conversion of users to paying customers (Q10117): Difference between revisions
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(Removed claim: summary (P836): The project aims at research and development of technology to improve the efficiency of investment in customer acquisition, based on the most recent knowledge of science and research in optimising genetic algorithms in combination with physical learning using deep neurolearning networks. The project will result in a system that, over a long period of time before the customer switching to the customer, will be able to predict the likelihood of...) |
(Created claim: summary (P836): The aim of the project is to research and develop technology to increase the efficiency of investment in customer acquisition, which will be based on the latest knowledge of science and research in the field of optimisation by genetic algorithms combined with machine learning using deep neural networks (deep learning). The result of the project will be a system that will be able to predict the probability that over time it will become a paying c...) |
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The aim of the project is to research and develop technology to increase the efficiency of investment in customer acquisition, which will be based on the latest knowledge of science and research in the field of optimisation by genetic algorithms combined with machine learning using deep neural networks (deep learning). The result of the project will be a system that will be able to predict the probability that over time it will become a paying customer. a. (English) | |||||||||||||||
| Property / summary: The aim of the project is to research and develop technology to increase the efficiency of investment in customer acquisition, which will be based on the latest knowledge of science and research in the field of optimisation by genetic algorithms combined with machine learning using deep neural networks (deep learning). The result of the project will be a system that will be able to predict the probability that over time it will become a paying customer. a. (English) / rank | |||||||||||||||
Normal rank | |||||||||||||||
| Property / summary: The aim of the project is to research and develop technology to increase the efficiency of investment in customer acquisition, which will be based on the latest knowledge of science and research in the field of optimisation by genetic algorithms combined with machine learning using deep neural networks (deep learning). The result of the project will be a system that will be able to predict the probability that over time it will become a paying customer. a. (English) / qualifier | |||||||||||||||
point in time: 22 October 2020
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Revision as of 14:58, 22 October 2020
Project in Czech Republic financed by DG Regio
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Artificial intelligence streamlining the conversion of users to paying customers |
Project in Czech Republic financed by DG Regio |
Statements
8,853,085.8 Czech koruna
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18,642,000.0 Czech koruna
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47.49 percent
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1 January 2016
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29 September 2019
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Webnode CZ s.r.o.
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49°11'25.62"N, 16°35'15.97"E
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60300
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Cílem projektu je výzkum a vývoj technologie pro zvyšování efektivity investic do získávání zákazníků, která bude vycházet z nejnovějších poznatků vědy a výzkumu v oblasti optimalizace genetickými algoritmy v kombinaci se strojovým učením pomocí hlubokých neuronových sítí (deep learning). Výsledkem projektu bude systém, který v době dlouho před konverzí uživatele na platícího zákazníka bude schopen predikovat pravděpodobnost, s jakou se časem stane platícím zákazníkem. a. (Czech)
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The aim of the project is to research and develop technology to increase the efficiency of investment in customer acquisition, which will be based on the latest knowledge of science and research in the field of optimisation by genetic algorithms combined with machine learning using deep neural networks (deep learning). The result of the project will be a system that will be able to predict the probability that over time it will become a paying customer. a. (English)
22 October 2020
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Identifiers
CZ.01.1.02/0.0/0.0/15_018/0004788
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