Customer Journey Analytics: Pitfalls and How to Avoid Them Part I

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Customer Journey Analytics: Pitfalls and How to Avoid Them Part I

Introduction

Customer Journey Analytics (CJA) is a solution that offers insights into customer behavior. It enables you to analyze data and uncover valuable information to inform business decisions. However, like any data solution, there are potential pitfalls related to interpretation and design intention that you should be mindful of.

This blog post aims to identify potential issues to consider when using CJA and provide practical tips and strategies to avoid them. By understanding these pitfalls and following best practices, you can ensure that the insights derived from CJA are accurate, reliable, and valuable to your business.

Whether you are new to CJA or have experience with it, it is important to emphasize the significance of acquiring a comprehensive understanding of the fundamental concepts and techniques associated with CJA. This blog post aims to provide you with the necessary knowledge and strategies, as well as explore the intricacies and nuances of the solution. This will enable you to fully utilize and optimize your implementation's potential. By enhancing your understanding of the key ideas, you will be equipped to navigate the complexities and challenges that may arise, ensuring that you can effectively leverage the capabilities of CJA to achieve your desired outcomes.

Evaluation of the Person Container applies over the entire date range time frame

When evaluating a Person container in CJA, it is important to note that the evaluation criteria apply to the entire specified reporting date range. This means that if a Person meets the criteria at least once for the Person container during the specified date range, they will be included in the data results for the entire duration of the specified date range, regardless of when the activity occurs. A session container in the Data View is defined by the session settings and includes all events related to that session. An event container in CJA represents a specific row of data that meets certain criteria on an individual event level.

When working with the Person container, it is important to be precise and rigorous. Using a broad Person filter approach can sometimes result in unexpected outcomes. Therefore, it is crucial to carefully consider the specified reporting date range and ensure that the Person filter criteria are well-defined, precise, and specific. This will help prevent any confusion or misinterpretation.

By following these recommended guidelines and best practices with filters, you can significantly enhance the effectiveness of your Person container evaluations. This will help prevent any potential issues that may arise from ambiguity or oversight in the filter criteria. Consider using sequential logic and exclusion logic rules to narrow down expansive Person container logic.

CJA Profile-based data only appears in CJA when mapped to triggered person events

CJA's profile dataset provides a comprehensive view of individual attributes that apply to all events associated with a person. However, it's important to note that profile-based data is only visible for mapped triggered person events within the specified time frame in CJA. This means that if your profile data is not linked to a triggered person event, it won't be visible in CJA. It is recommended that you carefully review the mapping of your profile data with triggered person events to ensure a complete and accurate view of your data. By doing this, you will gain a better understanding of your audience's interests and preferences as they relate to their associated activity events. This will allow you to make more informed decisions based on the insights derived from the comprehensive dataset.

Even with the application of Data View Filter Logic, the Data View Session Logic remains at the Connection level

When applying filter logic to a Data View in CJA, it's important to understand the relationship between the filter logic and the session logic at the Connection level. The session logic at the Connection level is based on the overall Connection-based criteria and determines the expiration of sessions. However, when filter logic is applied to a Data View, it is applied according to the Connection level session logic, without re-evaluating based on any sub-filtering criteria set for the Data View filter, even if the Data View spans multiple datasets within the Connection.

This distinction is crucial to consider when using filter logic in CJA, as it impacts the accuracy and effectiveness of the analysis. It's essential to carefully evaluate the overarching connection level session logic and ensure that it aligns with the overall goals and objectives of the Data View. By doing so, you can ensure that the filter logic is in line with the desired outcomes and provides meaningful insights into customer behavior.

To illustrate this concept, let's consider an example. Imagine you have a Data View that spans multiple datasets within a Connection. In this scenario, you apply filter logic to focus on specific customer segments. The overarching filter logic is applied based on the Connection level session logic, which determines the expiration of sessions.

It is important to understand that when you combine multiple datasets, the resulting dataset may have a larger number of events per session compared to analyzing a single dataset. Consequently, the filter logic will not re-evaluate based on any sub-filtering criteria set within the Data View. Therefore, it is crucial to carefully review the entire logic of the Connection level session and ensure that it accurately reflects the desired criteria for session expiration in your analysis.

Understanding the relationship between filter logic and session logic at the Connection level enables you to make informed decisions when configuring Data Views and conducting analysis in CJA. It is crucial to consider the overall session logic at the Connection level and align it with the objectives of the Data View to ensure accurate and meaningful analysis.

Person and Session are determined at the Data View Level

CJA's person and session parameters at the Data View level are specifically designed to collect and integrate data from various unioned event-level data sets. This comprehensive and all-encompassing approach allows for a more accurate and precise analysis by providing a holistic and comprehensive view of the relevant activity data. In other words, the person and session parameters can draw from multiple sources, not just a single data set, thereby offering a more complete and nuanced understanding of the data. This approach also enables a more sophisticated analysis of person and session level logic, as it incorporates a wider range of data and variables, resulting in a more detailed and in-depth analysis. However, it is important to carefully consider the implications of combining different data sets and the potential impact on person and session boundaries in analysis work. Taking into account these considerations will ensure a thorough and comprehensive analysis of the data.

Selecting the right binding schema level such as Binding Dimension in CJA for Array Does Not Need Binding Metric

When considering the binding dimension in CJA, it is important to understand the significance of the applied schema level. The schema level determines how the binding dimension behaves when dealing with object arrays and non-object array top-level event data scenarios.

In the case of object arrays, the binding dimension is determined by the selected schema level. Choosing a schema level that contains object arrays means that the binding dimension will treat each individual object within the array as a separate entity. This allows for a more detailed analysis of the data, enabling you to track and analyze specific attributes or properties associated with each object.

In a non-object array scenario, the binding dimension needs a binding metric to function correctly. CJA automatically identifies the connection between the selected dimension and the binding dimension. If the binding dimension is within an object array while the selected dimension is at a higher level, a binding metric is necessary. A binding metric serves as a declaration of linkage for a binding dimension, binding itself exclusively to events where the binding metric is present.

When selecting the appropriate schema level for the binding dimension, it is important to consider the nature of your data and the specific requirements of your analysis. If your data contains object arrays and you need to analyze specific attributes within those arrays, it is recommended to select a schema level that includes the object arrays.

Conclusion

In conclusion, understanding and navigating the potential pitfalls of CJA is crucial for obtaining accurate and valuable insights. By being mindful of issues related to evaluation criteria, CJA profile-based data, Data View filters, and binding dimension schema-path logic, you can ensure that your analysis is reliable and aligned with your business goals.

This blog post has provided practical tips and strategies to help you avoid these pitfalls and make the most of CJA. However, there is still more to explore. In the upcoming second part of this blog post series, we will delve deeper into additional examples and complexities of CJA. We will continue to provide guidance and insights to help you leverage the full potential of CJA and make informed decisions based on comprehensive data analysis.

Check out Part II of "Customer Journey Analytics: Pitfalls and How to Avoid Them" for further examples and a deeper understanding of this powerful solution.