Cross-channel marketing across many channels is now standard in e-commerce. However, there is often a lack of evaluation of important touchpoints with users. How much does which channel contribute to my marketing success? Which campaigns are the biggest drivers and where can budgets be shifted?
Blinkist plays dedicated content on various channels. This includes platforms such as Google Ads, YouTube, Instagram, Microsoft News and other content marketing offerings. As a result, Blinkist has extremely extensive marketing data available, which must be consolidated and processed in order to be able to carry out meaningful analyses of their impact. Data management was becoming increasingly too technically demanding for previous solutions, which was primarily due to an outdated tech stack. Together with taod, blinkist is able to modernize the tech stack in connection with the development of a customer data analytics platform.
How can data from different touchpoints be brought together in a meaningful way?
What is necessary to be able to interpret the behavior of customers?
Which technical components are essential for customer journey analytics?
In a data use case workshop, taod develops a proof of concept together with the Blinkist finance team. More than half of the budget is invested in marketing or represents a necessary investment for effective marketing. Ultimately, investments must therefore be clearly allocated so that it is clear where exactly which money is being spent. Existing discrepancies between self-generated reporting and actual billing must be resolved. This use case should become the basis for further optimization of analysis activities.
During the workshop, it quickly becomes clear that the Blinkist data stack needs to be fundamentally renewed. Existing data pipelines, which have to be completely rebuilt, are identified as being highly susceptible to errors. The change of the connector tool from Data Virtuality via, as an experiment, Airbyte to Fivetran in order to relieve internal resources in particular, and the transformation tool from Matillion to dbt will provide enormous flexibility in the future and allow possible adjustments quickly and easily.
As a transformation tool, dbt reduces the effort required for thorough testing by up to 80 percent. In addition to the entire revision of the data transformation and the change of connector tools, taod is designing new logics to meet the specific analysis requirements. The data vault method is used. It ensures the fast and correct integration of data into a data warehouse. It is possible to react flexibly to major changes in content.
The existing Amazon Redshift data warehouse is being replaced by the Snowflake cloud data warehouse, which will ensure high scalability as required in the future. The development of the data stack is first led by taod and then supported, thus ensuring a high level of enablement of Blinkist. Data Vault also represents a new modelling technique for Blinkist's data team, which is trained through 1:1 coaching over the course of the project. Technology and enablement also reduce bug fixing time.
After four months, the Blinkist team is managing the new Modern Data Stack autonomously. taod takes on an advisory role from this point on and supports the project at regular intervals through an FAQ format. The optimized tech stack ensures smooth and error-free processing of complex data. Thanks to modern modelling technologies and efficient ETL processes, potential sources of error are significantly reduced and data quality is sustainably improved.
The project focuses on modernizing Blinkist's technical infrastructure. While Blinkist was initially able to access a small database that was managed in-house and with solid BI knowledge, the company recorded a rapidly growing volume of data. The lack of optimization in data management led to significant errors in data evaluation, which are primarily due to technical reasons. The existing modules are reviewed and scrutinized accordingly to ensure efficient and reliable data analysis. Broken data pipelines and unnoticed errors in data processing should be fixed. This is done in close cooperation with the Blinkist data team, which is also enabled by taod in order to be able to work independently with the tech stack. With a modernized tech stack, Blinkist is able to process and analyze extremely complex, heterogeneous data from marketing, CRM and sales.
Blinkist wanted to holistically evaluate marketing data from many touchpoints and build a modern customer data analytics platform for this purpose. The aim was to reliably analyze cross-channel marketing campaigns, better allocate budgets and modernize the outdated tech stack.
Prior to the project, Blinkist had to consolidate very extensive and heterogeneous data from channels such as Google Ads, YouTube, Instagram, Microsoft News and other content marketing offerings. The existing tech stack was no longer up to date, the data pipelines were prone to errors, and discrepancies between internal reporting and actual billing made reliable analyses difficult.
Together with Blinkist, taod developed a new data architecture using Fivetran, Snowflake and dbt Labs. To this end, the previous connector tool and transformation setup were replaced, new logics were designed for the analysis requirements and the data modeling was rebuilt based on the Data Vault method.
The introduction began with a use case workshop and a concrete proof of concept with the Blinkist finance team. The data pipelines were then rebuilt, the previous Amazon Redshift data warehouse was replaced by Snowflake, the transformation tool was converted to dbt and enabled the Blinkist data team to operate the new Modern Data Stack itself through 1:1 coaching.
Blinkist today processes and analyses complex and heterogeneous data from marketing, CRM and sales using a modernized tech stack. With dbt, the effort required for thorough testing can be reduced by up to 80 percent; errors are detected in less than 60 minutes instead of after several days.
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What components does a professional tech stack for analytics consist of?
