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

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

Introduction

This article is a continuation of my original blog post on Customer Journey Analytics (CJA) pitfalls and how to avoid them. If you haven't already, I recommend reading Part I to gain a comprehensive understanding of the challenges and solutions discussed in this series.

In this second part, I will delve deeper into additional pitfalls that organizations may encounter when utilizing CJA. We will explore topics such as addressing the Person ID inflation issue, using derived fields at the Connections level, the implications of using a non-default attribution model with the People & Sessions metric, the usage of duplicate component dimensions, and the importance of regularly updating and maintaining data sources and Connections. By understanding and mitigating these pitfalls, we can ensure the accuracy, reliability, and effectiveness of our data analysis in CJA.

Let's proceed to explore these additional pitfalls and discover how to navigate them successfully.

Addressing Person ID Inflation Issue in CJA

In CJA, there is a strict limit of one million events per Person ID across all data sets. This limit is in place to manage the large volume of data and prevent system overload. Once this limit is reached for a specific Person ID, any associated events linked to that Person ID thereafter will be dropped.

In addition to the static event limit, customers using CJA may encounter issues related to Person ID inflation. These issues can arise when testing or incorrectly implementing something with the production dataset and corresponding Connection configuration. To avoid these problems, it is crucial to follow the guidelines and best practices provided by the CJA architecture team regarding identity design and handling.

If an implementation tracks a placeholder Person ID value such as 'No Visitor ID' sourced from Adobe Analytics, Data Prep may be required for transformation layer processing. This ensures that whenever the 'No Visitor ID' value is present, it is changed to null. Additionally, it is important to note that incorrect design with Cross-channel analysis (CCA) stitching can also cause Person ID inflation issues. This occurs when the transient ID tracks against a placeholder value, such as 'No Visitor ID', even though these broad catch-all values should never be used in conjunction with CCA. Incorrect stitching can lead to serious data integrity problems caused by the Person ID issue. To prevent these problems, it is essential to thoroughly double-check all CCA stitching logic configurations and design methods for accuracy.

By understanding the event limit per Person ID, recognizing the potential issues with Person ID inflation, and taking necessary precautions to mitigate them, customers can ensure the reliability and integrity of their data in CJA.

Derived Fields are based at the Connections level

Derived fields play a crucial role in generating new data components that may not be available in the source data. By defining these fields at the Data View configuration level, users can unlock the potential for enhanced analysis and gain deeper insights into customer behavior.

One important point to note is that derived fields are applied at the Connection level. This means that any changes made to a derived field in one Data View will automatically be reflected in all associated Data Views within the same Connection.

It is worth noting that the impact of derived fields extends beyond individual Data Views. Since derived fields are defined at the Data View configuration level, any modifications made to a derived field will be propagated to all associated data views within the same Connection.

Using a non-default attribution model with the People & Sessions metric

Although it is technically possible to apply column level setting overrides in CJA to use non-default attribution models for the People and Sessions metrics, it is strongly advised against doing so. This is because the People and Sessions metrics, unlike point in time event-based metrics, do not have a specific set point in time and are continuous measures. As a result, accurately defining overrides for them is not viable and leads to unexpected results.

In conclusion, while it may be tempting to apply column level settings overrides for the People and Sessions metrics in CJA, it is crucial to consider the challenges posed by their continuous nature. By understanding and respecting the integrity of these metrics, businesses can ensure the accuracy and reliability of their data analysis, leading to more effective decision-making and improved marketing outcomes.

Using an excessive number of duplicate component dimensions

CJA provides a significant level of component flexibility with its Data View feature. One notable aspect of this flexibility is the ability to have duplicate schema path components within the Data View. This means that you can have multiple components with the same schema path, which can prove to be quite advantageous in certain scenarios.

However, as an administrator, it is important to carefully consider the potential impact on the user experience when using this feature. Without proper governance and maintenance, the navigation and selection process for users can become challenging. It can be difficult for users to identify and choose the correct component dimension when faced with multiple components sharing the same schema path. Clear and distinct naming conventions for dimensions or metrics that are based on the same schema path could also be helpful for better discernment.

In such situations, it might be more efficient and user-friendly to make optimal use of the attribution metric override settings instead of creating multiple variations of the same component setting. By leveraging the attribution metric override settings, administrators can modify the attribution metrics without the need for duplicating the component settings. This streamlined approach enhances the user experience and ensures accuracy in data analysis.

Therefore, while the flexibility to have duplicate schema path components in the Data View is undeniably valuable, it is crucial for administrators to exercise caution and consider the potential impact on user navigation and selection. By utilizing the attribution metric override settings, administrators can strike a balance between flexibility and usability, ultimately enhancing the overall effectiveness of the CJA platform.

Failing to regularly update and maintain data sources and Connections

To enhance the accuracy and effectiveness of your analysis in CJA, it is crucial to prioritize regular updates and ongoing maintenance of your data sources and Connections. This involves reviewing and validating the data sources to ensure that they are reliable and up-to-date. By doing so, you can mitigate the risks associated with outdated or incorrect data, which can result in flawed analysis and inaccurate insights. Moreover, this approach can help you avoid the need for potentially time-consuming backfill efforts.

Additionally, it is important to properly map the schemas of your data sources to ensure that the data is organized and structured in a way that is compatible with CJA. This step is essential for accurate analysis and interpretation of the data. By investing time in schema mapping, you can ensure that the data is correctly interpreted and analyzed within CJA, leading to more reliable and actionable insights.

Furthermore, it is recommended to regularly review and update your data sources to reflect the latest information. This includes making necessary adjustments and modifications to account for any changes or updates in the data. By keeping your data sources up to date, you can maintain the integrity of your analysis and ensure that your insights are based on the most current information available.

By proactively ensuring that your data sources are always up to date, properly schema mapped, and accurately reflecting the latest information, you can establish a solid foundation for reliable and relevant analysis in CJA. This, in turn, will lead to more informed business decisions and improved outcomes, as you will have a comprehensive and accurate understanding of your customer journey.

Conclusion

In order to fully optimize your utilization of CJA, it is important to have a comprehensive understanding of the potential pitfalls or "gotchas" that may arise while using this solution. By being aware of these potential stumbling blocks and developing effective techniques to overcome them, you can ensure the accuracy and reliability of the data and analysis generated by CJA. This, in turn, will provide a more precise and comprehensive reflection of your customers' behaviors, thereby enabling you to make well-informed decisions regarding your marketing strategies and ultimately enhancing the overall customer experience.

Furthermore, by proactively addressing and mitigating these considerations, you will not only ensure the reliability and integrity of your data analysis, but also pave the way for more informed decision-making and improved marketing outcomes. As a result, CJA will become an even more powerful tool in your analytics toolkit, providing you with valuable insights into your customers' journey and empowering you to optimize your marketing strategies for maximum effectiveness and success.