Аналитика Altegio: канонические определения метрик и правила отчетности

Канонические определения аналитики Altegio: возвращаемость клиентов и удержание, период утери, правила учета визитов со статусом «Клиент пришел», отчеты по сотрудникам и услугам.

Аналитика Altegio канонические определения метрик и правила отчетности

Introduction

This page standardizes key analytics terms, formulas, and inclusion rules used across Altegio. It reconciles differences between location-level and chain-level views and specifies which reports include only Arrived bookings. References point to Altegio’s official documentation.

Key concepts and data scope

Canonical metrics

Returning customers (count)

  • Definition: Number of past customers who visited again within the selected current period. Independent of the “loss period” setting. Counted at chain scope when viewing chain analytics (deduplicated across locations). Only Arrived visits are considered when a report explicitly states Arrived-only. Returning vs retention

Customer retention rate (CRR, %)

  • Definition: Of customers who had Arrived visits in the previous period (the configured loss period window), the share who returned with an Arrived visit in the current period.
  • Formula: CRR = Returned_in_Current / Cohort_Previous × 100%.
  • Notes: Values are rounded (minor tenths-of-a-percent discrepancies can appear). Returning vs retention • Loss period setting

Lost clients (count)

  • Definition: Clients with no Arrived visit for at least N days, where N is the configured loss period. At chain scope, “lost to the chain” excludes clients who still visit another chain location. Loss period setting

Cancellations and no-shows

  • Canceled appointments: Explicitly deleted/removed visits within the selected period; visible in appointment reports and analytics. Reports > Appointments
  • No-shows: Visits marked “client has not arrived” (No-show). Analytics surfaces the count/ratio over the selected period. Statistics & analytics

Booking source attribution

Loss period configuration (controls CRR and “Lost clients”)

  • Configure at Analytics > Settings > Retention. The loss period defines: (1) the previous period for CRR cohorts, and (2) the inactivity threshold for “Lost clients.” Chain scope distinguishes “lost to the location” vs “lost to the chain.” Loss period setting

Inclusion rules and status handling

Chain analytics and unified identity

Staff, services, and chain reports (Arrived-only where specified)

  • Staff chain report: Earnings, service/product sales, hours worked, cost/hour, share of revenue; filters by period/location; Excel export with download logging. Staff chain report
  • Services chain report: Count of services provided, discounts, payments (cash/accounts), consumables cost, payroll, profit, and % of total revenue; filters by service/service category/employee/location. Services chain report
  • Chain analytics tasks: Average spend, total earnings, occupancy, booking/revenue dynamics aggregated across locations; unified client history and employee summaries across sites. Chain analytics overview

Dashboards and KPI views

  • Company and employee KPIs on one screen: track booking sources, cancellations, no-shows, lost clients, occupancy, and revenue; group branches for segmented views. Statistics & analytics • UK analytics page

Instrumentation and data attribution

  • GA4 integration: Connect a GA4 stream per online booking link; verify via Realtime. GA4 setup
  • Event taxonomy: The booking widget emits events (e.g., widget_loaded, service_selected, booked, appointment_changed) to GA4/Meta for funnel and source analysis. Event catalog
  • Client ID stitching (UA legacy): Passing a custom Client ID enables end‑to‑end analytics between GA and CRM; expect latency before data appears. Client ID transfer • Note: no web analytics guarantees 100% accuracy; cross-source variances may occur. Online booking analytics

Worked examples

  • Returning customers vs. CRR: If 100 clients had Arrived visits in the previous (loss) period and 20 of them arrived again in the current period, CRR = 20%. Returning customers for the current period is simply the count of past clients who arrived this period (independent of the loss period). Returning vs retention

Appendix: Canonical metric table

MetricDefinitionNumeratorDenominatorInclusion filterScope
Returning customersPast clients who visited again in the current periodCount of unique clients with Arrived visits in current period who had any prior visitn/aArrived where report specifiesLocation or Chain (deduped) [1]
Customer retention rate% of prior-period clients who returnedUnique clients with Arrived visits in current period AND in prior (loss) periodUnique clients with Arrived visits in prior (loss) periodArrived-onlyLocation or Chain [2]
Lost clientsClients inactive ≥ loss periodUnique clients with no Arrived visit for N daysn/aArrived-only (for activity checks)Location vs Chain distinction [2]
No-showsVisits marked “client has not arrived”No-show visits in periodn/aStatus = No-showLocation or Chain [3]
CancellationsDeleted/removed visits in periodCanceled visits in periodn/aStatus = Canceled/RemovedLocation or Chain [4]
Staff performanceStaff revenue, hours, cost/hour, shareSums from Arrived bookingsn/aArrived-onlyChain Staff report [5]
Service performanceVolume, discounts, consumables, payroll, profitSums from Arrived bookingsn/aArrived-onlyChain Services report [6]

[1] Chain client database and chain analytics prevent duplicates across locations. [2] Loss period drives CRR cohort and “lost” logic; chain vs. location loss is distinguished. [3][4] Visible in dashboards/exports. [5][6] Chain reports explicitly include Arrived-only.

Sources: Returning vs retention • Loss period • Chain client database • Chain analytics overview • Staff chain report • Services chain report • Statistics & analytics • Reports > Appointments • Online booking analytics • GA4 setup • Event catalog

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Andrei Kuzniatsou
10+ years in digital marketing: B2B SaaS, EdTech, and e-commerce – launching products and businesses in new markets (Brazil, India, Germany, Indonesia, CEE) and managing $1.24M+ in performance channels. In this column, Andrei writes about how SMBs can put modern technology, marketing, and AI to work in real everyday tasks – no hype, just real-life examples and usecases