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Unleashing the Power of Filters and Sequential Filtering in Customer Journey Analytics

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Employee

6/13/23

Introduction

One of the powerful data organizing methods in Customer Journey Analytics (CJA) is called Filters. This feature allows companies to segment data based on captured dimensional and metric criteria against person, session, and event-level boundary parameters. By using filters, companies can gain deeper contextual insights into customer behavior.

This blog post explores the fundamental role of user-defined filters in CJA. By utilizing filters, you can effectively sift through vast amounts of data to identify relevant information and eliminate noise. Additionally, this post delves into the benefits of layering sequential filtering in CJA. By filtering data into smaller, more manageable logical chunks framed by CJA containers such as person, session, and event, you can more accurately identify trends and patterns. Overall, understanding the importance of filters and the benefits of sequential filtering can greatly enhance the effectiveness of CJA and lead to more successful outcomes.

Filters Overview

Filters are a powerful feature of CJA that allows organizations to narrow down their data set based on specific data criteria. Filters help businesses to focus on the data that is most relevant to their analysis. Examples of specific criteria include:

  • Person attributes (customer tier, account type, device, country, purchasers)
  • Session-associated attributes (campaigns, first-time and returning sessions)
  • Event-level properties (content page, event type, event action, platform indicator)

The Filter Builder uses a container boundary logical basis that defines a Person as the outermost and all-encompassing container. The Person container contains information that is specific to the individual across CJA sessions and events within the specific time frame. A Session container, nested within the Person container, allows you to set rules to break down the person's data based on bounded sessions. This is based on the session logic configured for the Data View against the overarching Connection. An Event container, also nested within the Person container, lets you break down individual person information based on individual tracked events. With each container, you can hierarchically report on a person's full behavior, interactions broken down by sessions, or narrow down to granular events.

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CJA offers a range of filter operators to establish the basic logic within the container(s). These operators enable businesses to create complex filters that segment data in various ways. For instance, a business can use a filter to display only customers who purchased a specific product in a particular month. This approach can be especially helpful when analyzing customer behavior over a given period and identifying trends in product preferences. By filtering data in this way, businesses can gain a detailed understanding of their customer base and tailor their marketing efforts accordingly.

Filters can be used to analyze various aspects of customer behavior, such as location, device type, or campaign source. Proper use of filters is particularly valuable in CJA for businesses that want to understand how specific groups of customers are interacting with their digital content and across platforms.

Sequential Filtering

CJA sequential filtering is an advanced segmentation technique that allows businesses to define filters based on a sequence of defined behavioral actions. This technique enables businesses to create a more detailed and tuned analysis of customer behavior. To use sequential logic, users must create a series of filters that are applied in a specific order.

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For example, suppose a user wants to analyze their customers' path to purchase within the context of specific platform activities. They can use sequential filtering to create a series of filters that segment customers based on their platform behaviors. The first filter might segment customers who visited both the website and called the call center within a session. The second filter might segment website and called the call center session customers who viewed a product. The third filter might segment website and called the call center session customers who viewed a product and then completed a purchase. This approach helps businesses understand the steps customers take before making a purchase and identify areas for optimization to improve the customer experience.

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Sequential filtering is an incredibly powerful tool for businesses seeking a deeper understanding of how their customers interact with their platforms. By using this technique, businesses can identify the most effective customer journey paths and optimize each platform to improve customer engagement.

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CJA allows businesses to see how customers move through their platforms, where they encounter difficulties or obstacles, and where they tend to drop off or abandon their journey. By identifying these pain points and bottlenecks, businesses can make targeted improvements and optimizations that can have a significant impact on customer engagement.

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Analyzing how customers interact with different elements of a platform can also help businesses gain a better understanding of what features and functions are most important to their customers. This information can inform product development, marketing, and broader business strategy.

Overall, sequential filtering is an incredibly powerful technique that can provide businesses with a wealth of valuable insights and opportunities for optimization. By using these techniques in CJA, businesses can gain a deeper understanding of their customers, improve their platforms, and ultimately drive greater success and profitability.

Conclusion

Adobe Customer Journey Analytics (CJA) offers businesses a comprehensive set of filters and sequential filter capabilities. These can be used to gain deeper insights into customer behavior, enabling businesses to analyze customer behavior more effectively and make informed decisions to enhance the overall customer experience. With multiple datasets in CJA, it is important to be mindful of dataset overlap and logical relationships. Filters can serve as a critical frame of reference to help manage this complexity.

If you're interested in learning more about filters and sequential filtering in CJA, check out the following Adobe resources:

By exploring these resources, you can better understand how and when to apply filters and sequential filtering in CJA to gain insights into your customers' behavior. You can then leverage the full power of CJA to analyze the customer journey.