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Telcofy Methodology Overview

Telcofy’s analytics follow the Eurostat MultiMNO methodology. The summaries below use business-friendly names, with the original EU terminology in brackets so analysts can cross-check the detailed specifications in the EU Compliance library and Use Case catalogue.

Activity Stays (Staypoint Detection)​

  • What we do — detect locations where devices linger for meaningful periods (for example, longer than 10 minutes) by analysing cleaned event streams.
  • Why it matters — reveals how busy a site is and how long visitors remain, powering retail footfall studies, venue performance reviews, and destination benchmarking.
  • Tech notes — Implementation details, including event ingestion, windowing, and cache handling, are described in the Staypoint Detection guide and the processing modules in Methods & Data Objects.

Population Snapshots (Present Population Estimation)​

  • What we do — estimate how many unique devices are present in every grid tile at defined timestamps using Bayesian weighting against cell-coverage probabilities.
  • Why it matters — provides crowd counts for city operations, event management, safety monitoring, and infrastructure planning.
  • Tech notes — See Module 13 in Methods & Data Objects for tolerance windows, iteration thresholds, and quality outputs referenced by NSIs.

Movement Segments (Continuous Time Segmentation)​

  • What we do — label each stretch of time as STAY, MOVE, ABROAD, or UNKNOWN, generating a high-resolution activity timeline.
  • Why it matters — unlocks commuting routines, trip sequences, peak travel windows, and visit sequencing.
  • Tech notes — The Staypoint Detection guide details key inputs (semantic-cleaned events, intersection groups), segmentation parameters (minimum stay duration, maximum gaps, domain filters, MCC rules), and state assignment logic that produces labelled segments with continuity markers.

Usual Places (M-Usual Environment Indicators)​

  • What we do — roll up movement segments over weeks and months to determine people’s habitual locations (home, work/school, and other frequented spots).
  • Why it matters — exposes catchment areas, workplace concentrations, secondary-home patterns, and tourist routines for planning and marketing teams.
  • Tech notes — Mid-term aggregation settings, confidence measures, and recommended outputs are covered in Module 14 of Methods & Data Objects and the relevant use cases in Vol. II.

Home Base Identification (M-Home Location Indicators)​

  • What we do — pinpoint each device’s likely home using long-term permanence scores, with confidence metrics and change alerts.
  • Why it matters — offers accurate residential baselines for real-estate analysis, public-service targeting, audience segmentation, and churn detection.
  • Tech notes — Module 15 in Methods & Data Objects outlines the scoring thresholds, quality metrics, and metadata fields shared with NSIs.

Relocation Tracking (Internal Migration)​

  • What we do — compare historical home bases to flag moves between districts or cities, distinguishing temporary from sustained relocations.
  • Why it matters — informs housing strategy, infrastructure investment, workforce planning, and regional development initiatives.
  • Tech notes — Migration computation, confidence flags, and reporting templates are defined in Module 16/17 of Methods & Data Objects and the migration section of Use Cases.

Putting it all together​

  1. Activity Stays and Movement Segments produce the raw behavioural timeline.
  2. Population Snapshots and Usual Places aggregate those timelines into daily and monthly intelligence.
  3. Home Base Identification and Relocation Tracking deliver long-term insights, enabling products such as OD matrices and real-time dashboards showcased in the Telcofy Product Suite.

All modules honour privacy constraints, separate local versus foreign SIMs, and support market-share extrapolation where feasible, ensuring results are trustworthy for commercial and technical teams alike.