Our quick-start guide will help you master the language of Adobe’s Customer Journey Analytics and get the most out of your reporting tools.
In the ever-evolving world of data analytics, staying ahead of the curve is crucial for unlocking the full potential of your reporting tools. As we transition from Adobe Analytics to Adobe's Customer Journey Analytics (CJA), these changes affect how we communicate and comprehend our data. Understanding these new changes is not merely about semantics; it's about harnessing the power of a new era of analytics within the CJA framework. In this blog, we will explore the terminology changes that differentiate Adobe Analytics from Customer Journey Analytics. Whether you're an experienced Adobe analyst or new to CJA, this quick guide will provide you with a head start in mastering the language of Adobe's analytics innovation.
“Classifications” versus “Lookup Datasets”
Adobe Analytics enables analysts to link a variable value with associated metadata through 'Classifications.' In CJA, this concept evolves with 'lookup datasets,' expanding CJA's capabilities to support intricate data models and retrieve values or keys from other datasets. Lookup datasets offer an enriched approach to data management, enabling the linking of datasets using common keys or identifiers. This connection enhances data comprehensibility and usability, making it more actionable for analysts.
“Customer Attributes” versus “Profile Datasets”
'Customer Attributes' in Adobe Analytics allow uploading enterprise data from a CRM database. Similarly, CJA's 'Profile Datasets' enable CRM data import and take it a step further by integrating CRM data with Event datasets, provided there's a common identifier. Previously, using Customer Attributes for detailed CRM data was a more complex process. With CJA, creating CRM datasets and establishing connections becomes seamless, providing quicker access to comprehensive insights.
“Virtual Report Suite (VRS)” versus “Data View”
In Adobe Analytics, we use 'Virtual Report Suites (VRS)' to segment data and control access to different segments. In CJA, we utilize 'Data Views' to create containers for various data connections, specifying dimensions, metrics, and schemas. The significant distinction lies in data handling. Adobe Analytics directly feeds data into VRS, while CJA's Data Views empower us to select specific connections to pull from. Data Views grant analysts greater control, allowing them to customize reports and workspaces, and even decide which dimensions and metrics shape their analyses, providing richer insights and empowering analysts to craft compelling data stories.
“Segments” versus “Filters”
In the analytics realm, 'Segments' in Adobe Analytics identify visitor subsets based on characteristics or website interactions. In CJA, this term evolves to 'Filters.' While these terms seem similar, the distinction becomes evident with CJA's ability to integrate diverse datasets beyond Adobe Analytics. CJA's expanded capabilities broaden data sources, necessitating adaptation to filter data views based on commonalities within these datasets. These commonalities may encompass attributes, interactions, exits, entries, and custom variables.
Conclusion
As we transition from Adobe Analytics to CJA, our understanding of analytics terminology evolves. This shift offers a fresh perspective on data management, customization, and interpretation. Adobe’s Customer Journey Analytics presents more than a transition; it's an opportunity to elevate data stories and uncover deeper insights within a dynamic framework.
Analytics is an ever-evolving field. To continue your exploration of this landscape and harness Adobe's analytics tools' full potential, explore our additional articles offering valuable tips and insights for designing captivating Adobe Workspace projects. You can also reach out to our team of certified Adobe Analytics Experts, ready to assist you in creating remarkable visual experiences tailored to your specific needs.