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Foresight Methods

Foresight methods are usually classified using two criteria:

  • method of analysis
  • data sources

In terms of methods of analysis, foresight techniques can be broken down into quantitative, qualitative, and mixed ones.

Quantitative methods (big data mining, benchmarking, bibliometrics, patent analysis, modelling) are based on analysing simple phenomena with the help of mathematical models.

Qualitative methods (brainstorming, expert panels, genius forecast, in-depth interviews, goal trees, scenarios, science fiction, weak signals, and wild cards) can be used to analyse complex phenomena by formalising subjective expert knowledge.

Methods combining both these approaches are also applied in foresight studies, such as Delphi, critical technologies, surveys, technology roadmaps, STEEPV analysis, and stakeholder analysis.

Another criterion for structuring foresight techniques is the nature of data sources.

Heuristic methods rely on participants’ creative potential, expertise, and ability to generate new knowledge by interacting with each other.

Analytical methods are based on documented data, evidence, and statistics.

Any foresight project is preceded by a decision on which methods to use, and what data sources employ, while options for the future are assessed based on expert evaluation.


Quantitative techniques

Big data mining



Patent analysis


Cross-impact analysis


Qualitative methods


Expert interviews

Expert panels

Weak signals

Environment/horizon scanning

Wild card events


Goal trees

SWOT analysis

Situational analysis

Competitive Intelligence

Foresight workshops

Integrated methods


Critical technologies

Technology roadmaps

STEEPV analysis

Stakeholder analysis