No strategy without web analytics: hard numbers replace guesswork, as the basis for every decision, and all the more for every AI decision.
Five blocks that build on each other. Which of them are needed depends on how mature the existing setup is.
Every project starts with the status quo: systems, interfaces, tracking setup and data flows. User behaviour online is measurable. The analysis shows what is actually being captured today and what opportunities the existing data holds.
Outcome: a picture of systems, interfaces and data flows as they are.
Which data points does the company need for its decisions? We clarify that together in a workshop, before anything is implemented. The requirements produce the tracking concept. From that comes data-backed planning of measures.
Outcome: a tracking concept with the data points decisions rest on.
Web analytics is a continuous process, not a project with an end date. Tracking providers keep changing, and every frontend change can affect measurement. A long-term data strategy defines what is measured, who uses the data and how its quality is kept intact.
Outcome: it is settled what gets measured, who uses it and how quality holds.
From concept to a running setup: we set up the tracking, connect the systems and make sure data collection is GDPR-compliant, consent management included. The full approach in six steps:
Outcome: a running setup, GDPR-compliant, with consent management.
Data only shows its value in the evaluation: dashboards for a quick overview, reporting per stakeholder, custom analyses with recommended actions. That is how numbers turn into decisions: for marketing, sales and management.
Outcome: dashboards and reports that decisions follow from.
Articles on measurability, infrastructure and the question of which data AI actually needs.
To the insights →The current setup put to the test: fix errors, surface potential, assess data quality.
The concept takes shape in a workshop: which data points does decision-making need? Where are the gaps?
Implementing the tracking including consent management. GDPR-compliant collection is the basis.
Automatically prepared data, one view per stakeholder: from the team dashboard to the management report.
Deeper evaluations with recommended actions, one-off or recurring.
Ongoing checks keep data quality intact. Data lost once cannot be collected retroactively.
Data instead of gut feeling: every recommendation we make is backed by numbers, every measure set up so its success is measurable.
Your contact
Managing Director & Digital Business Strategist
Managing Director and digital business strategist, with netzstrategen since 2016. Her expertise: market research, strategy definition, audiences and customer journeys. Previously head of project management and new media at daily newspaper publishers. At home in B2B manufacturing and media & publishing.
How reliable is your data?
The diagnostic call shows where measurement and data quality stand: 30 minutes, free of charge.