All Client cases
CLIENT CASE Cefic

Understand website
behaviour

Distil insights and recommendations from a complex dashboard setup.

About
Cefic

Cefic, the European Chemical Industry Council, is the forum of the chemical industry in Europe. It plays a crucial role in reporting and communicating on industry trends and new regulatory initiatives at the European level.

To reach its diverse stakeholders, including Cefic members, the general public, and decision-makers, Cefic utilises various digital channels such as multiple websites, a news app, newsletters, and a podcast.

Different perspectives in a unified dashboard

To understand the content consumption of different groups they created a unifying dashboard. Considering the wide range of different data-sources, the need for audience segmentation and often news-driven traffic spikes, distilling insights from the dashboard proved difficult.

USE CASE

Content insights

Cefic contacted us to make sense of their data and help them formulate action points based on the performance of different content types and channels.

Key points

Data quality

We made sure that the data could be trusted and flagged inconsistencies. Pockets of untrustworthy data often mean a whole report is shelved and potentially valuable insights are not considered. In analysing the report, Stitchd ensures to perform logical checks on the data to pinpoint issues.

Segmentation

In our conversations with Cefic, it became clear the variety of their audiences was a key consideration. Who is consuming what channel? Which audience is over- or underrepresented in which type or category of content? We adapted the dashboard to take into account existing segmentation of Cefic's audiences and provide detailed cross-tables from which we could formulate insights and recommendations.

Lasting change

We gave direct recommendations on a strategic level according to a grow, keep, kill framework. Additionally, we provided Cefic with a plan of action to resolve their tracking, and dashboard setup to resolve lasting data quality issues and make future analysis less labor-intensive.

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