“Web Analyst — turning page views and clicks into clear business wins.”

A Web Analyst in IT measures and interprets user interactions on websites to help organizations optimize performance, increase conversions, and improve user experience. They collect data from tracking tools, analyze behavioral trends, and make actionable recommendations to drive business outcomes.

Barrier to Entry: ⭐⭐

Key Responsibilities of a Web Analyst

  1. Implement & Maintain Tracking - Set up and troubleshoot analytics tags (snippets of JavaScript code that send user event data to tracking platforms) via tools like Google Tag Manager.

  2. Dashboard & Report Creation - Build interactive dashboards (visual assemblies of charts and tables) in platforms such as Google Data Studio or Power BI.

  3. KPI Definition & Monitoring - Define KPIs (Key Performance Indicators - metrics like conversion rate, bounce rate, average session duration) and track them over time.

  4. Funnel & Path Analysis - Map user conversion funnels (sequences of steps leading to a goal) to identify drop‑off points and optimize flows.

  5. A/B Test Evaluation - Analyze A/B tests (controlled experiments comparing two versions) to determine the best‑performing variant with statistical rigor.

  6. Segmentation & Cohort Analysis - Group users by behavior or attributes (cohorts) to understand retention, engagement, and lifetime value.

  7. Data Quality Assurance - Audit data collection for completeness and accuracy, resolve issues like duplicate events or missing pageviews.

  8. Cross‑Functional Collaboration - Work with UX designers, developers, marketers, and product managers to translate insights into improvements.

  9. Presentation & Documentation - Translate complex analyses into clear slide decks or written reports for stakeholders and leadership.

Key Skills Required

Web Analytics Tools: Google Analytics (web traffic analysis), Adobe Analytics (enterprise‑level tracking), Google Tag Manager (tag management).

Data Visualization: Dashboard building in Google Data Studio, Power BI, or Tableau; choosing the right chart type.

Data Analysis & SQL: Writing SQL (Structured Query Language) queries to join, filter, and aggregate event data from web analytics databases.

Experimentation & Testing: Designing and interpreting A/B tests; understanding statistical significance (likelihood that results are not due to chance).

Funnel & Cohort Analysis: Mapping user journeys and cohort retention analysis (tracking groups over time).

Digital Marketing Metrics: CTR (Click‑Through Rate), CPC (Cost Per Click), ROI (Return on Investment), LTV (Lifetime Value).

Tagging & Implementation: JavaScript basics (for custom tags), debugging tag containers, ensuring accurate event tracking.

Data Quality Management: Identifying and fixing tracking gaps, deduplicating events, and timestamp alignment.

Communication & Storytelling: Crafting data narratives, stakeholder presentations, and writing clear executive summaries.

Collaboration & Influence: Working with UX, development, and marketing teams to prioritize and implement data‑driven changes.

What about pros and cons?

“From Click Detective to Executive Strategist — Your Web Analytics Journey”

Inside a Web Analyst’s Daily Routine

8:00 AM – Dashboard Health Check

  • Review key metrics in Google Analytics (web traffic tool) for anomalies in sessions, bounce rate, or conversions.

9:00 AM – Daily Stand‑Up

  • 10‑minute sync to confirm any urgent tagging issues or data requests for ongoing campaigns.

9:15 AM – Tag Implementation Review

  • Audit Google Tag Manager (tag container) to ensure events like button clicks and form submissions are firing correctly.

10:00 AM – Funnel Analysis Deep‑Dive

  • Analyze conversion funnel steps to pinpoint drop‑off points using cohort and path reports.

Noon – Lunch & Learn

  • Discuss a recent A/B test outcome or new tracking feature with the marketing team.

1:00 PM – Dashboard Enhancement

  • Add new segments or charts to a Data Studio dashboard based on stakeholder feedback.

2:30 PM – A/B Test Results Review

  • Check the statistical significance of test variants and draft recommendations for rollout.

3:30 PM – Cross‑Functional Sync

  • Meet with UX and development to plan tag updates or implement heatmap tools.

4:30 PM – Ad‑Hoc Reporting

  • Build custom reports for campaign performance, using SQL or platform APIs (Application Programming Interfaces—methods to request data programmatically).

5:30 PM – Wrap‑Up & Tomorrow’s Plan

  • Document any tracking gaps, update the analytics backlog, and set top priorities for the next day.