adobe customer journey analytics lookup

Frequently asked questions

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Adobe Customer Journey Analytics is the next-generation analytics product. This article provides answers to frequently asked questions about Customer Journey Analytics. For more information, review Customer Journey Analytics feature support .

1. Prerequisites prerequisites

2. stitching data stitching.

Example scenario : You join two datasets in a Customer Journey Analytics connection by using CRMid as the Person ID. One is a Web Event dataset with CRMid in all records. The other dataset is a CRM profile data set. 40% of the CRM data set has CRMid present in the Web event data set. The other 60% are not present in the Web event dataset - do these records appear in reporting in Analysis Workspace?

Answer : Profile rows that have no events tied to them are stored in Customer Journey Analytics. However, you cannot view them in Analysis Workspace until an event tied to that ID appears.

3. Getting data into Customer Journey Analytics ingest

• Regarding past dates/timestamps: Event data up to ten years old.
• Regarding future dates/timestamps: Event data (predictive) up to one month in the future.

4. Latency considerations latency

• Live data or events: Processed and ingested within 90 minutes, once data is available in Adobe Experience Platform. (Batch size > 50 million rows: longer than 90 mins.)
• Small backfills: within seven days
• Large backfills: within 30 days

Adobe recently changed how it processes data in Customer Journey Analytics:

• Any event data with a timestamp less than 24 hours old is streamed in.
• Any event data with a timestamp more than 24 hours old (even if it's in the same batch as newer data) is considered backfill and is ingested at a lower priority.

5. Set rolling window for Connection data retention data-retention

The Enable rolling data window setting lets you define Customer Journey Analytics data retention as a rolling window in months (three months, six months, and so on). It is set at a connection level, not at a dataset level. Data retention is based on event dataset timestamps and applies to event datasets only. No data retention setting exists for profile or lookup datasets since there are no applicable timestamps.

The main benefit is that you store or report only on data that is applicable and useful and delete older data that is no longer useful. It helps you stay under your contract limits and reduces the risk of overage cost.

6. Implications of deleting data components deletion

For data deletion, you should be concerned about six types of components: sandbox, schema, dataset, connection, data view, and Workspace project. Here are some possible scenarios around deleting any of these components:

An error message indicates that:

• Any data views created for the deleted connection will no longer work.
• Similarly, any Workspace projects that depend on data views in the deleted connection stops working.

7. Considerations when merging report suites in Customer Journey Analytics merge-reportsuite

If you plan to ingest Adobe Analytics data through the Adobe Analytics source connector , consider these ramifications when merging two or more Adobe Analytics report suites.

8. Adobe Analytics components

9. estimate connection size estimate-size.

See Estimate and manage usage .

10. Regarding usage overages overage

Usage limits are regularly monitored and enforced by Adobe. “Rows of data” means the daily average rows of data available for analysis within Customer Journey Analytics.

For example, let’s say your contract entitles you to one million rows of data. Suppose that on day 1 of using Customer Journey Analytics, you upload two million rows of data. On day 2, you delete 1 million rows and keep your usage at that committed maximum (that is, one million rows of data) for the remainder of your License Term. Depending on your contractual terms, you may still incur prorated over-usage fees for day 1, since you exceeded your “rows of data” license entitlement.

11. Diagnose data discrepancies discrepancies

Sometimes, you may notice that the total number of events ingested by your connection is different from the number of rows in the dataset in Adobe Experience Platform. In this example, the dataset “B2B Impression” has 7650 rows, but the dataset contains 3830 rows in Adobe Experience Platform. There are several reasons why discrepancies can happen, and the following steps can be taken to diagnose:

Break down this dimension by Platform Dataset ID and you notice two datasets with the same size but different Platform Dataset IDs . Each dataset has 3825 records. That means Customer Journey Analytics ignored five records due to missing person IDs or missing timestamps:

In addition, if you check in Adobe Experience Platform, there is no dataset with Id “5f21c12b732044194bffc1d0”, hence someone deleted this particular dataset from Adobe Experience Platform when the initial connection was created. Later, it got added to Customer Journey Analytics again, but a different Platform Dataset ID was generated by Adobe Experience Platform.

Read more about the implications of dataset and connection deletion in Customer Journey Analytics and Adobe Experience Platform.

12. Regional data collection

The Adobe Experience Cloud uses Regional Data Collection (RDC) so that interactions between your visitors and Adobe and non-Adbobe solutions occur as close to your visitors as possible. Once data is collected regionally at a Data Collection Center (DCC, also known as Edge site, part of the Platform Edge Network), it is forwarded over a secure connection to the relevant solutions based on the configuration of your datastream and/or event forwarding.

The regional data collection process uses the following steps:

• DNS automatically resolves the collection hostname to the IP address of the Data Collection Center nearest to the visitor.
• The visitor sends the data to that location.
• The data is immediately forwarded over a secure connection to the solutions defined by the datastream or event forwarding configuration.

Using regional data collection provides several benefits:

• Performance : With RDC, your visitors connect to the closest DCC. This optimization provides the fastest response time, resulting in more accurate tracking and faster loading times.
• Redundancy : If there is a disruption in communication between the DCC and your DPC, Adobe’s RDC infrastructure saves data locally, then forwards it to the DPC when communications are restored.

RDC currently includes the following locations (subject to change):

When the data hits the regional data center, the datastream configuration determines how data is routed further.

Customer Journey Analytics requires datasets from Adobe Experience Platform, so your datastream / event forwarding configuration requires the Adobe Experience Platform service to route the data from the regional data center to the data center where your Adobe Experience Platform instance is located. Customer Journey Analytics and its supporting services and infrastructure are deployed at that same Adobe Experience Platform instance.

See Data collection overview for more information about the process of data collection beyond the Adobe Experience Platform Edge Network and its regional data centers.

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ADOBE CUSTOMER JOURNEY ANALYTICS

Your customer data has a story to tell. We give you more context, so you get more story.

Adobe Customer Journey Analytics gives you more: real-time analysis of both online and offline data, privacy-ready profiles, and access for teams across your organization. Now, everyone has the insights they need to create more connected customer journeys.

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The streaming data collection of Customer Journey Analytics follows customers in real time as they move from online to in-store to customer call centers and more. Then it unifies the data into a single customer profile and a comprehensive customer journey. The fully correlated data gives you unlimited breakdowns of any data elements so you can uncover deeper insights — all without writing SQL.

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Give marketers, analysts, and content creators tools to apply AI to their everyday tasks. Customer Journey Analytics applies AI to improve segmentation and attribution, help you identify causal relationships between customer interactions, and improve anomaly detection so you can quickly take action on outlier events that could impact your business. It also helps you weigh metrics and evaluate action plans so you can grow or streamline your business.

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Adobe is leading the charge in real-time customer analytics.

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