Monday, July 27, 2020

Comparison Between Adobe Analytics and Customer Journey Analytics

Adobe Analytics has long been the undisputed leader in the world of Web Analytics and is still a marquee product for analyzing web and mobile app data. It is the bread and butter for consultants and data analysts worldwide who work on enterprise level data. However, just like any enterprise level product, it does come with its share of challenges. I wrote some posts last year outlining some of these challenges. This post lists a challenge we face while uploading classification data in Adobe Analytics and this article talks about the implication of uploading historical data (see point #3).

So, is there a solution that can make these challenges go away? YES there is and the solution to these challenges is Customer Journey Analytics. Customer Journey Analytics or CJA is an enterprise wide analytics product that is built on Adobe Experience Platform. CJA allows us to join different data sources (online & offline) to give a complete view of our customers in real-time across channels. Please note that CJA is considered an add-on to Adobe Analytics, also available for Non-Platform (AEP) customers and works natively with Adobe Experience Platform.

In this article, I'll compare Adobe Analytics with CJA based on a set of standard capabilities which are common between the two solutions and highlight some of the differences. The writeup is long but I've combined all the content in a single matrix at the end so feel free to scroll down to view it in one tabular view.

Adobe Analytics

In this section, I've listed the various capabilities of Adobe Analytics and added a high level writeup explaining each of these separately. I've done the same for Customer Journey Analytics.

1. Data Capture

o Primarily takes place based on the AppMeasurement library (client-side-web), Mobile SDK (mobile app), Data insertion API and Bulk Data Insertion API (server-side).

2. Data Usage

o Data is stored in Report Suites usually setup to receive data globally or individually based on the requirement.

o Virtual Report Suites (VRS) can be created to “split” data based on web/mobile, region or Business group and can be setup based on custom session timeouts, expiration and time zones.

3. Reporting and Analysis

o Data is visualized in Analysis Workspace or the legacy UI.

o Workspace panel includes Freeform, Cohort, Fallout etc. options available to visualize data.

o Calculated metrics can be created, and marketing channels can be used for further analysis.

o Robust data export capabilities (PDF, CSV etc. formats) as well as access to raw data feeds.

o Ability to setup alerts in case of anomalies.

4. Identity

o Primarily based on cookies for client-side web tagging.

o Based on ECID for mobile app (tied to each installed instance of the app).

o Customer IDs converted to ECID for server-side implementations in general.

o Device graph data can be accessed via the People metric or leveraged via Cross-Device Analytics.

5. Segmentation

o Segmentation built into Analysis Workspace

o Visitor, Visit and Hit segment containers available.

o Sequential segmentation and exclusion capabilities available to users.

6. Data Limitations

o Limited to 200 eVars/props and 1000 events.

o UI limited to 500K unique rows of data per month (Low Traffic).

7. Data Classifications

o Classifications subject to the same restrictions as the UI in terms of only classifying the top 500K rows.

8. Historical Data Ingestion

o Historical data sent in but out of order hits can affect the sequence of events and attribution of eVars and marketing channels.

9. User Permissions

o User permissions are granted via the Admin Console at a more granular level for report suites etc. at a product profile level.

10. Data Latency

o Data can take up to 2 hours to be fully available in Adobe Analytics.

Customer Journey Analytics

In this section, I've put CJA through the same set of capabilities as I did for Adobe Analytics. Please note that there are some features that CJA lacks compared to Adobe Analytics which the product team is working on to add support for. Those are explained in more detailed here.

1.    Data Capture

o   Data needs to be conformed to Adobe Experience Platform’s XDM schema to bring in any type of data.

o   Web SDK is used for real-time data streaming and streaming API will be available for sending data server-side.

2.   Data Usage

o   Data is stored in datasets created within Adobe Experience Platform and added to CJA as Connections.

o Data Views are similar to VRS which also allow us to define data based on the type of datasets being analyzed as well as setting custom session timeouts, expiration and defining separate time zones.

3.   Reporting and Analysis

o   Data in CJA is visualized in Analysis Workspace.

o   Workspace panel includes Freeform, Cohort, Fallout etc. options available to visualize data.

o   Calculated metrics can be created for further analysis, but marketing channel support is not available yet, but support is planned.

o   No current ability to export data in CJA (Workspace) but support is planned. However, Query Service and Data Access API provides the ability to export data.

o   No current ability to setup alerts but support is planned.

4.   Identity

o   Tied directly to the Namespace defined within Adobe Experience Platform.

o   ID can be based on anything be it cookies, CRM id, Loyalty ID or Phone number.

o   Custom namespaces can be defined.

o   Data in the device graph is NOT available yet but support is planned.

5.   Segmentation

o   Filters built into Analysis Workspace.

o   Person, Session and Event segment containers available.

o   Leverages the same standard segmentation UI/features as Adobe Analytics.

6.   Data Limitations

o   Unlimited metrics and dimensions and data in eVars/props is available in XDM format within CJA.

o   Unlimited number of rows and unique values.

7.   Data Classifications

o   Lookup Datasets created in Platform are not subject to any volume restrictions in terms of volume but there is a 1 GB limit which isn't "enforced".

8. Historical Data Ingestion

o Any missing historical data can be uploaded into Adobe Experience Platform and then leveraged in CJA including support for out of order hits for a person.

9.   User Permissions

o   Only product admins (not all users) can now perform granular tasks such as deleting, updating and sharing Workspace dashboards with other users.

10. Data Latency

o   Data isn’t available in near real-time can be take up to 2 hours, but real-time support is being looked into.

Here's the matrix which consolidates the capabilities compared above in a tabular format. Please note that I took a stab at also calling out which solution is (currently) better for a particular capability by adding a checkmark. If there's no checkmark, then it means that the two solutions are on par with each other or support is planned to add that feature to CJA by the product team.

Hope this article provided you with some more information and context to figure out some similarities and differences between Adobe Analytics and Customer Journey Analytics. The key points to consider would be to see if you analyze large amount of dimensional data (exceeding 500K unique rows per month), often analyze customer data across multiple channels, need to add missing historical "hit level" data after the fact or connect offline data with online with the aim to get a single view of the customer, then you should seriously consider CJA.

Are you in the process of considering this tool or have any further questions? Feel free to post them here.

Sunday, October 20, 2019

Implications of Classifications in The Adobe Analytics UI on Analysis

As Analysts working in Adobe Analytics or any other system, we want to analyze data without any restrictions or caveats around volume. Analysts who work with Adobe Analytics must've heard about the dreaded "Low Traffic" bucket which shows up when a report in the UI has more than 500,000 unique values per month (eVar/prop etc.). Here's how it shows up in the report.

A customer recently asked whether they could upload over a Million rows of classification data in Adobe Analytics tied to a primary user ID stored in an eVar and leverage the UI for analysis and segmentation. We discussed the implications of uploading this amount of classification data into Adobe Analytics and even considered Audience Manager. I recently wrote about the impact of classifications on segments for a different use case. In this post, I will cover some of the pros and cons of uploading over a million rows of classification data into Adobe Analytics and onboarding records into AAM for deeper analysis.

Pros and Cons of Classification Data (Over 5ooK Rows)

Adobe Analytics UI

These pros and cons are listed for the Adobe Analytics UI and does not apply to Analytics data feeds or Data Warehouse.

Pros

Uploaded classification data is retroactive
There's no extra cost in uploading classification data
Recommended tool for deeper data analysis and segmentation of classified dimensions (below 500K rows)

Cons

Classification data over 500K rows might take days or weeks to catch up and may delay analysis
Classification data in the UI AND segments tied to it will be subjected to the 500K unique limit
Classification data is also subjected to hash collision where some IDs may show 2 or more Unique visitors in the UI as explained here.
Values over the “Low Traffic” bucket do not get classified. Data Warehouse does not have this limitation so if data can be exported out, we won’t run into the limit.

Audience Manager UI

These pros and cons are listed to highlight the advantages and constraints of uploading large amount of data in AAM and the constraints are called out for deeper analysis in the AAM UI. I recently wrote about the various ways to import data into AAM.

Pros

Onboarded data is retroactive IF data is segmented in AAM and an ID sync is in place
Large amount of data can be handled without any volume constraints
Data is uploaded to AAM quicker (24-48 hours) than how long Analytics classification over 500K rows will take (assuming there are no errors encountered during onboarding)

Cons

Limited ability for reporting and segmentation in the AAM UI compared to Analytics. The UI will show overall uniques based on segmentation but will not allow you to slice and dice the data like Analytics can. An example is lack of sequential segmentation which you get in Adobe Analyics
Onboarded data/segments will only apply to users who visit the site AFTER the data has been onboarded when AAM segments are shared with Analytics and analysis is done there. I cover this in a post I wrote about last year.
Each row of data will be charged as a separate server call
Data is mostly available in the form of Unique Visitors and not as Page Views or Visits

As you can see, there's no clear winner when it comes to classifying large amount of data for analysis in either UI. So to summarize, the Adobe Analytics UI is better suited for deeper analysis and segmentation (data under 500K rows per report) whereas Audience Manager is better suited to handle large amount of data with segmentation capabilities suited more for activation of audiences (Cookies and Mobile device IDs).

Potential Options and Alternatives

This section will cover some of the ways to get around the "Low Traffic" issue and large number of classification data in Adobe Analytics:

Leverage Adobe Data Warehouse: It's not perfect but you can leverage Adobe Data Warehouse to export data and run segmentation as you won’t run into the same limits as the UI.
Reduce the Cardinality: You can split dimensions with high cardinality into separate variables.
Increase the uniques exceeded limit "within reason": You can contact Customer care to increase the uniques limit from 500K to up to a Million. This will work for anything between 500K-1 Million but not for anything over that.
New- Customer Journey Analytics: CJA is a newly added offering which leverages AI/ML models and much larger datasets from Adobe Experience Platform. This offering will remove all limitations around uniques exceeded and will be added to Workspace to allow for easy analysis of big datasets.

Again, there's no perfect solution to tackle this issue currently but the new feature of Customer Journey Analysis aims to fix this issue which is exciting. I hope this post was helpful to give you an understanding on some of the implications of uploading large amount of data into Analytics but all is not lost and there are some potential ways to avoid this.

Product Management Insight