One Voice Needs a Data Foundation: How ZEISS Manages and Measures the Impact of Its Communications and Content

One Voice Needs a Data Foundation: How ZEISS Manages and Measures the Impact of Its Communications and Content

A guest post from the Data & Insights Experts interest group of the CommTech Working Group

Nearly 48,000 employees in about 50 countries, organized into a global matrix with numerous communications and marketing units. How does a company like this speak with one voice? And how can we determine which topics resonate and which content is actually effective? In our September session, Nicole Fröger, who is responsible for global content strategy and the end-to-end content value chain at ZEISS, and Hans van Bree, who oversees data analytics, provided in-depth insights into the structure of their management system.

From Channel Logic to Topic Management

At ZEISS, “One Voice” is what is known in the textbooks as integrated or topic-driven communication. Internally, the term is simply easier to understand. At its core is a shift in working methods toward interdisciplinary collaboration: away from a channel-oriented approach and toward joint topic management with clear messages. The strategic topics are derived from the communications and content strategy and are managed by an editorial board.

This requires clear roles and responsibilities. For example, Topic Leads are responsible for strategic topics, Subject Matter Experts provide in-depth expertise, and Channel Managers ensure content is delivered in a way that’s appropriate for each channel. Everyone works through the end-to-end process: Plan, Create, Publish, Promote, and Analyze.

The Shared Content Collaboration Platform as the Single Source of Truth

From a technological standpoint, a content collaboration platform covers the entire content value chain—from planning, through creation and approval, to publication and analysis. Others might call it a newsroom. But it is much more than that—it is the single source of truth within the ZEISS communications and content ecosystem. In the target vision—which is continuously refined and adapted based on feedback and experience—topic planning, content planning, campaign management, and metadata all converge there. Nicole Fröger made the sequence unmistakably clear: Technology helps us scale globally, but this requires a shared communications and content strategy as well as clarity in collaboration. This is a shared journey and a process of change that lays the foundation for process- and data-driven work and, ultimately, for the scalable use of artificial intelligence.

The Data & Insights Hub: From Data to Decisions

Hans van Bree opened his segment with a quote from Peter Drucker that sums up the entire session well:

The purpose of information is not knowledge. It is the ability to take the right action.

Peter Drucker

This is precisely the goal of the Data & Insights Hub, which ZEISS is developing as a central analytics platform. It is designed to consolidate topic, campaign, and channel performance data, provide content intelligence on formats and assets, and track audience response and reputation. The starting point is an existing Power BI dashboard. The crucial next step is a new data flow that combines metadata and content text data from the Collaboration Platform with performance data from social listening, reputation measurement, and website analytics.

The KPI framework follows the logic of input, output, outcome, and outflow. ZEISS has not yet been tracking outflow on a regular basis. There is currently no link to specific topics or communication activities. The focus is therefore on input, output, and outcome. Measuring topics reveals where the line is drawn: correlations are evident, but causality between an individual measure and its contribution to reputation has not yet been established.

Metadata determines the quality of insights

There is a significant practical difference from the way things were done before. Today, analyzing topics and campaigns involves collecting mailing lists, manually extracting data from various tools, consolidating it in Excel, and transferring it to PowerPoint. This process is time-consuming, prone to errors, and relies on individual analysts.

Going forward, teams will maintain a standardized set of metadata in the Content Collaboration Platform, including strategic topic, focus topic, campaign, and format. Once this metadata is linked to performance data, a single filter will suffice for a 360-degree analysis. This applies not only to individual topics and campaigns, but also to format series or all “always-on” activities across topics. The outstanding technical issue is reliable matching using identifiers such as Campaign ID, URL, or Post ID.

Potential AI-supported use cases include performance forecasting, topic planning, and automated management reports, with a “human-in-the-loop” approach. The goal is a closed-loop learning system in which insights are systematically fed back into strategy, planning, creation, and distribution.

Four Lessons Learned

• Think beyond technology and tools. Technology and tools are not solutions to a problem; they are enablers. Only through clear collaboration (operating model, governance) with a focus on people (adoption, training) can technology be effectively integrated as a value driver and the potential of AI and automation be harnessed.
• Data quality is key. A standardized metadata set, linked to performance data from social media, the web, and reputation metrics, replaces the manual process involving Excel and PowerPoint. A 360-degree analysis per topic or campaign thus becomes a simple filter query.
• Get people on board. Onboarding, training, Q&A sessions, and a shared playbook support the communications and content community. Tech & Data