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Customer Journey Analytics Guide


This technical documentation guide provides self help assistance for Customer Journey Analytics. Customer Journey Analytics allows you to bring your customer data from any channel you choose (both online and offline) into Adobe Experience Platform, and then analyze this data just as you would your existing digital data using Analysis Workspace today.

Customer Journey Analytics lets you control how you connect your online and offline data in Analysis Workspace on any common customer ID, finally allowing you to do attribution, filters, flow, fallout, etc. across your customer data.

What’s new?

Get a glimpse of the newest enhancements in the Customer Journey Analytics product and documentation! For a comprehensive list of features, improvements, and fixes, check out the detailed Release Notes . Stay up-to-date with the latest changes in our documentation by visiting the documentation updates page .

Forecasting is an Analysis Workspace feature to forecast a standard or calculated metric with any supported time granularity (hourly, daily, weekly, monthly and yearly). Forecasting is available for time-series related data only.

A new view type that shows the percentage of users that return after their initial engagement within the desired date range. The horizontal axis represents the number of days since a user’s initial engagement. The vertical axis represents the percentage of users who engage again.

* Guided analysis is part of Adobe Product Analytics, which is a paid add-on to Customer Journey Analytics.

Trendline overlays are now available in the Usage view, which helps depict a clearer pattern in data. The types of trendlines available are linear, logarithmic and moving average.

When using the Key Metric Summary visualization, the Comparison date range can now automatically update, depending on whether the Comparison date range option you choose is relative to the primary date range or fixed.

Start with the basics

Start by reading the material in the links below to familiarize yourself with Customer Journey Analytics capabilities and functionalities.

customer journey analytics api

Beyond online data Learn how Customer Journey Analytics compares to Adobe Analytics, what features are shared and how you can use your Analytics data.

customer journey analytics api

Ingest and use data Learn about the options you have to ingest data into Experience Platform and use it for analysis and reporting in Customer Journey Analytics.

customer journey analytics api

Guided Analysis Learn how to use workflows to gain data and insights about your customer's product experience. Product Analytics through guided analysis…

customer journey analytics api

Analysis Workspace Use Analysis Workspace to perform basic and advanced analysis, like attribution, flow and fallout diagrams, dimension breakdowns.

Explore the documentation

Understand how Customer Journey Analytics compares to Adobe Analytics and how to get your data in the solution and then prepare, view, analyze and democratize that data and resulting analysis and reports.

Additional resources

customer journey analytics api

Customer Journey Analytics API – cjapy

Hello world,

Today is the day of an expected article related to the Customer Journey Analytics API.

Customer Journey Analytics is the future of analysis with the Adobe tool stack, it has the same main interface than Adobe Analytics but with more powerful features. There are many things that would push you to have Customer Journey Analytics but also some elements that may put you to stay with Adobe Analytics for the time being.

I put an article on that topic here: Customer Journey Analytics (2021 status) ?

Customer Journey Analytics at scale

I believe that if you are still reading this, you are either willing to switch from Adobe Analytics to Customer Journey Analytics, using Customer Journey Analytics already, or willing to just start with CJA.

Customer Journey Analytics (CJA) has many cool features that you can use, but for large organizations, having the capability to apply these features at scale is very important.

This is where I am usually coming, trying to make the API capabilities as easy as possible to master.

Most of the capability in CJA are the same than the Adobe Analytics API 2.0. Therefore I could have copied most of the code that I used for the aanalytics2 python module .

Obviously, that was not my intention, since the release of the aanalytics2 module I improved on my python skills, I also had many discussions with colleagues or pairs on Adobe Analytics API use-cases, so I had many ideas on what to improve (but no time).

Also, creating a replica of the aanalytics2 module would have been too easy and not fun at all. I decided to start again from scratch and bring you even more cool features so you can take advantage of the API to its full extend.

New Features on cjapy

cjapy is the name of the python module I developed and loaded into the pypi library.

There are few key differences on how this library works versus the aanalytics2 module and I will try to summarize it here. Don’t worry too much as this is nothing completely different, it has the same “ look & feel ” that made its predecessor famous, but brings you even more capabilities.

Single Class for reporting

With aanalytics2, you would have needed to get a Login Company before starting the Analytics class. With cjapy , once you have loaded the configuration file, you can directly call the CJA class in order to instantiate the connection.

Overall, you can have a look on how easy to get started with CJA by looking at the documentation focusing on get you running easily:

Logging capability included natively

It took some time for aanalytics2 to have such feature but in cjapy, you directly have a Logging capability. It is taking its root directly from the logging module in python.

It should really help you debugging the issues or just to know what is going on when you run the different methods on the cloud.

Your object myLogging would look like this:

The level key is the most important one I believe as most of the information are logged on the DEBUG level.

Possible values are:

Generating reporting request

After those nice elements, we are now we are getting to the juicy part of the cjapy module .

If you know the Adobe Analytics API and the official documentation, the process to retrieve report is first to generate a report request. The process is quite the same with Customer Journey Analytics. In order to do that, you will need to go to Adobe Analytics, or Customer Journey Analytics.

Enable the debugger, in the help section. (I have it already enabled)

customer journey analytics api

Then retrieve the JSON from the appropriate:

I didn’t find that particular setup easy and convenient to realize. The need to log in to CJA when you want to use the API is not optimal. You can have a request JSON ready to fire but when you need to change the date, the dimension, or the metrics. It is quite a hassle to handle that in the file or dictionary.

So I am very happy to present you the RequestCreator class.

Instantiating this class will provide you with an object that will have methods to generate a valid report request JSON.

You can generate that object by doing:

Once you have this object, you can add stuffs to it:

As you can see, there are different methods that you can use to create the appropriate dictionary. You can find a full documentation of that functionality here:

Important: You will need a global filter with a dateRange for any request you are using (as well as a DataViewId, a dimension and a metric). It basically requires to add a global filter with a dateRange specified such as: “2020-01-01T00:00:00.000/2020-02-01T00:00:00.000”

The format of the time period passed is important. To make your life easier, I created several time period directly available as attriute of the object, you can access them doing the following:

That should give you the following predefined dates in a dictionary:

  • thisMonth : full month date
  • untilToday : start of the month till yesterday midnight
  • todayIncluded : start of the month, today included
  • last30daysTillToday
  • last30daysTodayIncluded
  • last7daysTillToday
  • last7daysTodayIncluded

I hope you will find this class helpful and I am looking forward to make it better, by adding support to more use-cases.

Also , last but not least, you can directly load an existing (JSON) project definition to instanciate the class.

Workspace class response

A major change from the aanalytics2 report is the result returned from the getReport method. It is a dictionary and after some times working with it, I was not satisfied with that result. It misses some interaction capabilities.

I did (do) not have time to work on it as I was pretty busy with the (massive) aepp module but I decided to start the cjapy module directly where I want to head for the aanalytics2 module.

Therefore, now the result return from the cjapy.getReport method is a Workspace class. It provides you with more elements now, directly from attributes or methods.

You have now contextual information about the request directly in the following attributes:

  • dataframe -> the result itself is a dataframe
  • dataRequest -> a RequestCreator instance
  • globalFilters and metricsFilters : list of all filters used
  • settings: globale setting set for your report
  • pageRequested: the number of request generated to get all data
  • summaryData: the summary data when available
  • reportType: the type of report requested
  • rowNumbers: number of results
  • columns: columns returned

The same way than for the aanalytics2 module, it has the capability to return all available data in the CJA. When you request your report, you can use the “inf” value, that is set by default, to return all data.

There are some available methods available from the object return by the getReport method:

to_csv() and to_json() are the easy one that wrap the dataframe capability.

However, the very cool new capability that the returned object create for you is the ability to have a breakdown directly from the report.

Yes, you heard me right! You can breakdown your result directly from the result via python. For that, you need to pass the index of the value in your return dataframe that you want to break down and the dimension you want to breakdown that value with.

I find this new capability really interesting, I hope you will give me feedback to improve it even more.

You can have a complete view of the possibility and value return on the documentation:

Multi Dimensional report

Another major method available on the Customer Journey Analytics API wrapper in python is the ability to realize multilevel breakdown.

I had this idea for years and never knew how to handle it in my library. Many considerations I had in mind for its implementation. Then Jared R Smith developed it for R (cool –> R package for Adobe Analytics API 2.0 ) and showed me that I was overthinking it. So here it is: the getMultidimensionalReport method is taking some limited arguments but still returns a Workspace object.

This is still beta so be careful with it and let me know if you encounter any bugs, I would not recommend to use it for more than 1 level breakdown (so one primary dimension and a children dimension). If you have use case for more than 1 level breakdown, feel free to let me know (and also help me to develop the recursing part 🙂 )

Here is the documentation for the getMultidimensionalReport method .

Projects support

For the one of you that have been following the aanalytics2 python package, you may be familiar with my projects methods and my Project class that is able to analyze the workspace projects that you have.

Recently, Adobe Analytics API 2.0 and CJA API revealed these endpoints officially so I was able to port these methods over to the cjapy module.

It means that from a single Project (or for all projects, with the getAllProjectDetails method) you can now these different information:

  • Number of dimensions used
  • Which dimensions used
  • Number of segments filters used
  • Which Segments used
  • Data Views Id and names
  • Number of Freeform
  • Type of elements used

This is going to give you a lot of insights on how people are using your implementation, what do they use and with the combination of the audit Logs, how often do they use it.

Obviously, I also migrated the findComponentUsage method that helps you to find where a specific dimension, filter or calculated metric is being used.

You can have find all documentation here: Project class in cjapy

I hope you liked the preview I made for this new module, you will be able to find videos explaining these usages soon.

You can find the complete list of the methods available on the main CJA class here:

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Email analytics provide valuable insights that allow you to identify what’s working for your business and refine campaigns that can be made even better. The data provided can sometimes be difficult to decipher, but you’ll always receive digestible, easy-to-read insights with our digital marketing reporting software.

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When should I get analytics for email campaigns?

Our analytics reporting tools will start gathering data as soon as your email campaign is published and your audience begins engaging with it. Mailchimp’s reporting tools will also analyze data received from clicks, opens, and purchases. However, you may not be able to see the results of your campaign right away since some emails can take time to send and be delivered.

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Automated reporting involves automatically collecting and generating specific information that is made accessible to a particular individual on a set schedule. With report automation, a user can also create reports outside of the scheduled times to monitor the operations of a business throughout the day. Being able to access accurate and up-to-date data as it's needed is essential for any business to run efficiently.

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Predictive analytics uses data mining techniques, machine learning, and statistical modeling to find patterns in data and identify risks and opportunities. This area of data science is much like using a search engine, but the queries are more involved, and the resulting data is expected to be highly accurate in predicting the future.

How does predictive analytics work?

The predictive analytics process starts with a problem and goes through a series of steps to achieve the desired result.

The problem can be anything from detecting fraud to ensuring shelves are stocked for the holiday season. Relevant data sets or databases are collected for examination and then processed for analysis. The data scientist then applies the relevant tool to find the desired data and then validates the results for deployment to stakeholders via a report.

What is business intelligence?

A business intelligence system takes raw data, curates it, and transforms it into valuable insights that can inform business decisions.

In addition to strategic information, companies can also use BI tools to measure productivity and efficiency, assess different systems or processes, and audit specific business-related activities. Business intelligence resources can also include customer data. It can help companies define their target market, assess sales and marketing performance, and gain insights about customer interest and activities based on browsing history and online actions.

How does business intelligence work?

A business intelligence system handles specific data-related tasks to help companies gain insights into their business users. There are 4 key ways these tools do this.

  • Raw data mining. Business intelligence tools dig through data sources for information that could be useful for business analytics. This function is essential because businesses have vast amounts of information, but it is only valuable if well-organized. Business intelligence tools can start this curation process.
  • Automated reporting. Business intelligence systems usually come with centralized dashboards that analysts can use to create reports and visualize data so that decision-makers without a statistics or analytics background can understand the information.
  • Performance analysis. Business intelligence tools can collect information in real time to help measure financial or operational performance. Users can enter key performance indicators for specific metrics and get insights into how the company is doing with these benchmarks.
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Calculated metrics overview

  • Calculated Metrics


Calculated and Advanced Calculated (or Derived) Metrics are custom metrics that you can create from existing metrics. Our Calculated Metrics tools offer a highly flexible way of building, managing and curating metrics. They allow you as marketers, product managers and analysts to ask questions of the data without having to change your implementation.

  • Create filtered metrics that are derived at report run time, without having to change the implementation. These can be viewed historically because they are based on filters.
  • (Advanced Calculated Metrics only) Filter on metrics. For example, you can create a metric for “New persons”, with a count of people for whom this is the first session.
  • (Advanced Calculated Metrics only) Incorporate statistical functions to help you better describe your data. For example, you can count the number of items in a report or add in the number of standard deviations for each item.

Calculated metrics versus advanced calculated metrics

Here is a comparison of Calculated Metrics and Advanced Calculated Metrics capabilities:

  • Create calculated and advanced calculated metrics using advanced allocation models.
  • Add filters inline to metric formulas.
  • Compare filters in the same report. For example, compare local persons vs. international persons.
  • Use statistical functions.
  • Provide detailed metric descriptions (show what it does, where to use it, where NOT to use it).
  • Copy definitions into new metrics.
  • Provide an inline metric preview.
  • Set metric polarity, which indicates whether it’s good or bad if a given custom event (metric) goes up.
  • Tag metrics.
  • Share metrics with others.
  • Approve and curate metrics.
  • Organize (tag) your metrics so people can find them.
  • Delete metrics.
  • Rename metrics.

Calculated Metrics templates in Customer Journey Analytics


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Feedback Surveys arrow_right

  • Feedback survey

Exit Surveys arrow_right

  • Exit Surveys Overview
  • Customizing Exit Surveys
  • Exit Surveys Opt-in

Import Transactions arrow_right

Design arrow_right.

  • Survey design
  • Add language
  • Copy CX survey
  • Import a new survey
  • Social network toolbar
  • Back/exit button
  • Validation messages

Question Types arrow_right

  • CX Survey settings
  • Display question numbers
  • Save and continue setting
  • Survey timeout settings
  • Media library
  • Notifications
  • Notification group
  • Survey finish options
  • Thank you email settings
  • Email Invitation Settings

Distribute arrow_right

  • API and FTP integration
  • Email survey
  • Export Survey URL
  • Survey token
  • Distribution history
  • CX Scheduled invites
  • Support for SMS Providers
  • Anonymous tokens
  • Transactional tokens

Intercept 2.0

Getting started arrow_right, type of intercepts arrow_right, intercept settings arrow_right, analytics arrow_right, suitecx 2.0.

  • SuiteCX 2.0 - Overview
  • Journey AI - Interviews


  • Ticket Timeline Graph: A visual representation of the number of new and resolved tickets over a given period of time.
  • Ticket Priority Resolved Ratio Graph: This graph displays the ratio of resolved tickets based on their priority in both percentage and count.
  • Tickets by Survey Graph: This graph provides a visual representation of the feedback received from customers regarding the ticket resolution process in both percentage and count.
  • Top Ticket Solver Graph: This graph showcases the top assignee names in terms of resolved tickets.
  • Root Cause Frequency: This graph displays the frequency of the root causes of tickets in terms of both percentage and count.
  • Action Frequency: This graph shows the frequency of actions taken to resolve tickets in terms of both percentage and count.
  • Priority Trend Graph: This graph represents the trend of ticket priority over a given period of time.
  • Support Staff Analysis Graph: This graph displays an analysis of the performance of support staff in terms of ticket resolution. Basically for this term, the user needs to select the feedback very first, from the filter option.

customer journey analytics api

  • Choose the specific feedback and segment from the filtering option, based on the requirement.
  • Select the desired date range or assignee to narrow down the report.
  • Utilize the available graphs, such as the Ticket Timeline, Ticket Priority Resolved Ratio, Tickets by Survey, Top Ticket Solver, Root Cause Frequency, Action Frequency, Priority Trend, and Support Stuff Analysis, to understand the performance of the team and the tickets.
  • Analyze the data presented in the graphs to identify any trends or patterns.
  • Use the information to make informed decisions and take action to improve the ticket-handling process.

To provide users with meaningful and actionable insights, our analytics dashboard offers the ability to apply filters. By filtering the data, users can focus on the last 7 days and specifically analyze feedbacks, ensuring clarity and relevance. Additionally, filtering allows users to access support staff analysis results without any ambiguity, as feedback and segments can be precisely specified. Empower yourself with a customized view, harness the full potential of our analytics dashboard, and unlock the true value of your data-driven decision-making.

This feature is available with the following license :

Customer Experience

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Still have questions.

Microsoft Azure Blog

Category: AI + Machine Learning • 11 min read

From code to production: New ways Azure helps you build transformational AI experiences   chevron_right

By Jessica Hawk Corporate Vice President, Data, AI, and Digital Applications, Product Marketing 

What was once a distant promise is now manifesting—and not only through the type of apps that are possible, but how you can build them. With Azure, we’re meeting you where you are today—and paving the way to where you’re going. So let’s jump right into some of what you’ll learn over the next few days. Welcome to Build 2024!

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By Omar Khan General Manager, Azure Product Marketing

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By Misha Bilenko Corporate Vice President, Microsoft GenAI

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Introducing GPT-4o: OpenAI’s new flagship multimodal model now in preview on Azure   chevron_right

By Eric Boyd Corporate Vice President, Azure AI Platform, Microsoft

Microsoft is thrilled to announce the launch of GPT-4o, OpenAI’s new flagship model on Azure AI. This groundbreaking multimodal model integrates text, vision, and audio capabilities, setting a new standard for generative and conversational AI experiences.

AI + Machine Learning , Announcements , Azure AI , Azure Cosmos DB , Azure Kubernetes Service (AKS) , Azure Migrate , Azure Web PubSub , Compute , Industry trends

Published May 6, 2024 • 5 min read

Harnessing the power of intelligent apps through modernization   chevron_right

By Mike Hulme GM, Azure Digital Applications Marketing

81% of organizations believe AI will give them a competitive edge. Applications are where AI comes to life. Intelligent applications, powered by AI and machine learning (ML) algorithms are pivotal to enhancing performance and stimulating growth. Thus, innovating with intelligent apps is crucial for businesses looking to gain competitive advantage and accelerate growth in this era of AI.

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What’s new in Azure Data, AI, and Digital Applications: Harness the power of intelligent apps    chevron_right

Sharing insights on technology transformation along with important updates and resources about the data, AI, and digital application solutions that make Microsoft Azure the platform for the era of AI.

Hybrid + Multicloud , Thought leadership

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Cloud Cultures, Part 8: Recapturing the entrepreneurial spirit in the American Rust Belt   chevron_right

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Announcing Advanced Container Networking Services for your Azure Kubernetes Service clusters   chevron_right

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Microsoft’s Azure Container Networking team is excited to announce a new offering called Advanced Container Networking Services. It’s a suite of services built on top of existing networking solutions for Azure Kubernetes Services (AKS) to address complex challenges around observability, security, and compliance.

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AI + Machine Learning , Announcements , Azure Database for PostgreSQL , Azure Machine Learning , Azure OpenAI Service , Events , Migration

Published June 5, 2024 • 5 min read

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AI + Machine Learning , Azure AI , Azure AI Services , Azure OpenAI Service , Cloud Services , Partners

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By Silvio Bessa General Manager, SAP Business Unit

Learn more about the transformative synergy of the Microsoft Cloud and RISE with SAP for business.

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AI + Machine Learning , Announcements , Azure VMware Solution , Migration , Partners

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Microsoft and Broadcom to support license portability for VMware Cloud Foundation on Azure VMware Solution   chevron_right

By Brett Tanzer Vice President, Product Management

Microsoft and Broadcom are expanding our partnership with plans to support VMware Cloud Foundation subscriptions on Azure VMware Solution. Customers that own or purchase licenses for VMware Cloud Foundation will be able to use those licenses on Azure VMware Solution, as well as their own datacenters, giving them flexibility to meet changing business needs.

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Announcements , Azure Bastion , Security

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Enhance your security capabilities with Azure Bastion Premium   chevron_right

By Aaron Tsang Product Manager, Microsoft

Microsoft Azure Bastion, now in public preview, will provide advanced recording, monitoring, and auditing capabilities for customers handling highly sensitive workloads.

Abstract image

AI + Machine Learning , Azure AI , Azure AI Content Safety , Azure AI Search , Azure AI Studio , Azure Cosmos DB , Azure Kubernetes Service (AKS) , Azure OpenAI Service , Events

Published May 30, 2024 • 5 min read

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By Victoria Sykes Product Marketing Manager, Azure AI, Microsoft

From enhancing productivity and creativity to revolutionizing customer interactions with custom copilots, our customers demonstrate the transformative power of generative AI and truly, brought Build 2024 to life. So, how’d they do it? 

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AI + Machine Learning , Industry trends , Thought leadership

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In a recent study, Microsoft surveyed over 2,000 IT professionals across ten countries on their tech readiness for and adoption of AI as well as their concerns and challenges along the way.

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AI + Machine Learning , Announcements , Azure Maps , Integration

Azure Maps: Reimagining location services with cloud and AI innovation   chevron_right

By Nick Lee Corporate Vice President, Microsoft Maps and Local

Today, we’re announcing the unification of our enterprise maps offerings under Microsoft Azure Maps. This enables our customers to accelerate innovation by leveraging other Microsoft Azure cloud services while retaining many familiar features from Bing Maps for Enterprise.

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AI + Machine Learning , Announcements , Azure AI , Azure AI Studio , Azure OpenAI Service , Events

Published May 21, 2024 • 5 min read

At Microsoft Build 2024, we are excited to add new models to the Phi-3 family of small, open models developed by Microsoft.

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AI + Machine Learning , Announcements , Azure AI , Azure AI Content Safety , Azure AI Services , Azure AI Studio , Azure Cosmos DB , Azure Database for PostgreSQL , Azure Kubernetes Service (AKS) , Azure OpenAI Service , Azure SQL Database , Events

Published May 21, 2024 • 11 min read

A decorative image of two developers pointing towards a computer

Software as a service (SaaS) applications have become a boon for enterprises looking to maximize network agility while minimizing costs. They offer app developers on-demand scalability and faster time-to-benefit for new features and software updates. 

SaaS takes advantage of cloud computing infrastructure and economies of scale to provide clients a more streamlined approach to adopting, using and paying for software.

However, SaaS architectures can easily overwhelm DevOps teams with data aggregation, sorting and analysis tasks. Given the volume of SaaS apps on the market (more than 30,000 SaaS developers were operating in 2023) and the volume of data a single app can generate (with each enterprise businesses using roughly 470 SaaS apps), SaaS leaves businesses with loads of structured and unstructured data to parse.

That’s why today’s application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) technology to sift through big data, provide valuable business insights and deliver superior data observability.

What is application analytics?

Broadly speaking, application analytics refers to the process of collecting application data and performing real-time analysis of SaaS, mobile, desktop and web application performance and usage data.

App analytics include:

  • App usage analytics , which show app usage patterns (such as daily and monthly active users, most- and least-used features and geographical distribution of downloads).
  • App performance analytics, which show how apps are performing across the network (with metrics such as response times and failure rates) and identify the cause and location of app, server or network problems.
  • App cost and revenue analytics, which track app revenue—such as annual recurring revenue and customer lifetime value (the total profit a business can expect to make from a single customer for the duration the business relationship)—and expenditures such as customer acquisition cost (the costs associated with acquiring a new customer).

Using sophisticated data visualization tools, many of which are powered by AI, app analytics services empower businesses to better understand IT operations , helping teams make smarter decisions, faster.

AI in SaaS analytics

Most industries have had to reckon with AI proliferation and AI-driven business practices to some extent.

Roughly 42% of enterprise-scale organizations (more than 1,000 employees) have used AI for business purposes, with nearly 60% of enterprises already using AI to accelerate tech investment . And by 2026, more than 80% of companies will have deployed AI) ) AI-enabled apps in their IT environments (up from only 5% in 2023).

SaaS app development and management is no different.

SaaS offers businesses cloud-native app capabilities, but AI and ML turn the data generated by SaaS apps into actionable insights. Modern SaaS analytics solutions can seamlessly integrate with AI models to predict user behavior and automate data sorting and analysis; and ML algorithms enable SaaS apps to learn and improve over time.

Using comprehensive, AI-driven SaaS analytics, businesses can make data-driven decisions about feature enhancements, UI/UX improvements and marketing strategies to maximize user engagement and meet—or exceed—business goals. 

SaaS app analytics use cases

While effective for some organizations, traditional SaaS data analysis methods (such as relying solely on human data analysts to aggregate data points) sometimes fall short in handling the massive quantities of data SaaS apps produce. They may also struggle to fully leverage the predictive capabilities of app analytics.

The introduction of AI and ML technologies, however, can provide more nuanced observability and more effective decision automation. AI- and ML-generated SaaS analytics enhance:

1. Data insights and reporting

Application analytics help businesses monitor key performance indicators (KPIs)—such as error rates, response time, resource utilization, user retention and dependency rates, among other key metrics—to identify performance issues and bottlenecks and create a smoother user experience. AI and ML algorithms enhance these features by processing unique app data more efficiently.

AI technologies can also reveal and visualize data patterns to help with feature development.

If, for instance, a development team wants to understand which app features most significantly impact retention, it might use AI-driven natural language processing (NLP) to analyze unstructured data. NLP protocols will auto-categorize user-generated content (such as customer reviews and support tickets), summarize the data and offer insights into the features that keep customers returning to the app. AI can even use NLP to suggest new tests, algorithms, lines of code or entirely new app functions to increase retention.

With AI and ML algorithms, SaaS developers also get granular observability into app analytics. AI-powered analytics programs can create real-time, fully customizable dashboards that provide up-to-the-minute insights into KPIs. And most machine learning tools will automatically generate summaries of complex data, making it easier for executives and other decision-makers to understand reports without needing to review the raw data themselves.

2. Predictive analytics.

Predictive analytics forecast future events based on historical data; AI and ML models—such as regression analysis , neural networks and decision trees —enhance the accuracy of these predictions. An e-commerce app, for example, can predict which products will be popular during the holidays by analyzing historical purchase data from previous holiday seasons.

Most SaaS analytics tools—including Google Analytics, Microsoft Azure and IBM® Instana®—offer predictive analytics features that enable developers to anticipate both market and user behavior trends  and shift their business strategy accordingly. 

Predictive analytics are equally valuable for user insights.

AI and ML features enable SaaS analytics software to run complex analyses of user interactions within the app (click patterns, navigation paths, feature usage and session duration, among other metrics), which ultimately helps teams anticipate user behavior.

For instance, if a company wants to implement churn prediction protocols to identify at-risk users, they can use AI functions to analyze activity reduction and negative feedback patterns, two user engagement metrics that often precede churn. After the program identifies at-risk users, machine learning algorithms can suggest personalized interventions to re-engage them (a subscription service might offer discounted or exclusive content to users showing signs of disengagement).

Diving deeper into user behavior data also helps businesses proactively identify app usability issues. And during unexpected disruptions (such as those caused by a natural disaster), AI and SaaS analytics provide real-time data visibility that keeps businesses running—or even improving—in challenging times. 

3. Personalization and user experience optimization.

Machine learning technologies are often integral to providing a personalized customer experience in SaaS applications.

Using customer preferences (preferred themes, layouts and functions), historical trends and user interaction data, ML models in SaaS can dynamically tailor the content that users see based on real-time data. In other words, AI-powered SaaS apps can automatically implement adaptive interface design to keep users engaged with personalized recommendations and content experiences.

News apps, for instance, can highlight articles similar to the ones a user has previously read and liked. An online learning platform can recommend courses or onboarding steps based on a user’s learning history and preferences. And notification systems can send targeted messages to each user at the time they’re likeliest to engage, making the overall experience more relevant and enjoyable.

At the application level, AI can analyze user journey data to understand the typical navigation paths users take through the app and streamline navigation for the entire user base.

4. Conversion rate optimization and marketing.

AI analytics tools offer businesses the opportunity to optimize conversion rates, whether through form submissions, purchases, sign-ups or subscriptions.

AI-based analytics programs can automate funnel analyses (which identify where in the conversion funnel users drop off), A/B tests (where developers test multiple design elements, features or conversion paths to see which performs better) and call-to-action button optimization to increase conversions.

Data insights from AI and ML also help improve product marketing and increase overall app profitability, both vital components to maintaining SaaS applications.

Companies can use AI to automate tedious marketing tasks (such as lead generation and ad targeting), maximizing both advertising ROI and conversation rates. And with ML features, developers can track user activity to more accurately segment and sell products to the user base (with conversion incentives, for instance). 

5. Pricing optimization.

Managing IT infrastructure can be an expensive undertaking, especially for an enterprise running a large network of cloud-native applications. AI and ML features help minimize cloud expenditures (and cloud waste) by automating SaaS process responsibilities and streamlining workflows.

Using AI-generated predictive analytics and real-time financial observability tools , teams can anticipate resource usage fluctuations and allocate network resources accordingly. SaaS analytics also enable decision-makers to identify underutilized or problematic assets, preventing over- and under-spending and freeing up capital for app innovations and improvements.

Maximize the value of SaaS analytics data with IBM Instana Observability

AI-powered application analytics give developers an advantage in today’s fast-paced, hyper-dynamic SaaS landscape, and with IBM Instana, businesses can get an industry-leading, real-time, full-stack observability solution.

Instana is more than a traditional app performance management (APM) solution. It provides automated, democratized observability with AI, making it accessible to anyone across DevOps , SRE, platform engineering, ITOps and development. Instana gives companies the data that they want—with the context that they need—to take intelligent action and maximize the potential of SaaS app analytics.

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  • Español – América Latina
  • Português – Brasil
  • Tiếng Việt
  • Mobility services Documentation
  • Transportation and Logistics
  • Mobility services
  • Documentation

Google Maps Platform Mobility services are collections of Google Maps Platform services that you use to run transportation and logistics operations for your business.

The services offers the following capabilities via APIs and SDKs:

  • Addresses & Location Context The Address Capture capability is a Mobility toolkit that incorporates two primary APIs: Place Autocomplete API and Geocoding API. These APIs address a number of common problems with delivery addressing: undeliverable addresses, inaccurate address entry from consumers, and the need for adjustments to point-of-delivery locations.
  • Route Planning & Dispatch The Route Optimization capability in Mobility is a toolkit that incorporates Routes API, Routes Preferred API, Directions API, and Distance Matrix API. It offers enterprise-level features that solve the problem of finding the optimal order for drivers to complete their tasks.
  • Driver Routing & Navigation The Driver Routing and Navigation capability is a Mobility toolkit comprised of two SDKs: the Navigation SDK and the Driver SDK. It provides enterprise-level features that embed the Google Maps experience into your driver application.
  • Task Tracking The Shipment Tracking capability includes a JavaScript library for web and mobile solutions. With it, you can provide consumers with day-of tracking of their delivery state for an improved user experience and increased delivery success rate.
  • Fleet Analytics & Debugging The Fleet Performance capability includes a JavaScript library for web and mobile solutions. With it, you provide fleet operations and support teams with visibility into the state of your driver fleet, including real-time positions, ETAs, routes, and completed and upcoming tasks. It also provides insights into your fleet to better optimize performance.

Reference Solutions

The Mobility services can be used with a trip-based or task-based model of your operations. Refer to the reference solutions to understand these models, and pick the one suited for your business:

Mobility for On-demand Rides & Deliveries

Mobility for Last Mile Fleet

Mobility includes access to relevant Google Maps Platform products , as well as to the following components, which are specifically tailored to mobility use cases.

Fleet Engine is the Google backend service that enables orchestration across your drivers, consumers, and operations teams. You can integrate the Driver SDK, Navigation SDK, and JavaScript Journey Sharing Library into your applications to enrich relevant user experiences.

See your agreement for full details into the services included and terms for Mobility. For more information, contact sales .

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-06-07 UTC.

  • Adobe Journey Optimizer


Customer engagement at the right time, on any channel..

Adobe Journey Optimizer is a single application for managing scheduled cross-channel campaigns and trigger-based one-to-one engagement for millions of customers —and the entire journey is optimized with intelligent decisioning and insights.

Watch overview Take a tour

Adobe Journey Optimizer unlocks real-time personalization.

Built natively on the Adobe Experience Platform, Journey Optimizer lets you manage customer engagement across inbound and outbound channels using real-time insights and AI-driven decisioning so you can engage one customer — or millions — with relevant experiences anytime, anywhere.

  • Real-time profile & insights
  • Omnichannel orchestration
  • Personalized offer decisioning
  • Customer lifecycle engagement

customer journey analytics api

Real-time experiences. Real-time customer action.

Create the foundation for personalized journeys with a unified customer profile and real-time insights.

  • Real-time profiles built from both online and offline data.
  • Identity resolution, centralized audience management, and real-time messaging triggers.
  • Unified metrics framework across Journey Optimizer, Real-Time CDP, and Customer Journey Analytics for consistent measurement.

customer journey analytics api

Real-time journey orchestration.

Orchestrate and automate journeys based on real-time behavior, contextual changes, or business signals — all from a single application.

  • Omnichannel journey designer
  • Drag-and-drop message designer
  • Visual authoring for web push messages and web in-browser messages
  • Code-based authoring for nonvisual or server-side surfaces
  • Digital asset management
  • Guided channel setups for streamlined configuration

customer journey analytics api

Real value added to the customer journey using generative AI.

Personalize engagement at scale with centralized decisioning, experimentation, and intelligence so you always deliver the “next best offer.”

  • Central offer management helps enable reuse and govern eligibility.
  • Intelligent offer decisioning defines the best offer for the customer and allows you to place it in any channel.
  • AI decisioning optimizes journey pathways based on defined goals.

customer journey analytics api

Engage customers at every step of their journey.

Manage broad marketing campaigns and personalized, one-to-one interactions alike to stay top of mind no matter where your customers roam.

  • Audience-based marketing
  • One-to-one customer marketing
  • Business-driven segment updating
  • Immediate burst messaging

Boost efficiency with AI Assistant.

Streamline workflows and enhance productivity with this easy-to-use conversational AI Assistant.

  • Quickly access product information, troubleshoot issues , or learn new concepts.
  • Perform tasks faster, whether it’s exploring data, creating content, or understanding operational insights.

Boost efficiency with AI Assistant image

The latest innovations for Journey Optimizer.

  • Engage customers across additional channels with visual authoring for web push messages and web in-browser messages, as well as code-based authoring for nonvisual or server-side surfaces.
  • Measure impact consistently and accurately with a unified metrics framework across Journey Optimizer , Real-Time CDP, and Customer Journey Analytics.
  • Use AI decisioning to identify the optimal pathway for a customer to flow through their journey based on your defined goals.
  • Make next-best-offers available at any point in the journey, across multiple channels.
  • Speed up new channel configurations with guided channel setups for web and mobile apps.

Better define journeys and evolve how you deliver experiences.

customer journey analytics api

Simplify management

A single application for omnichannel campaigns and real-time engagement.

customer journey analytics api

Improve engagement

Personalized content based on profiles updated in real time.

customer journey analytics api

Increase conversion

Data-driven insights with instant views into journey progression.

customer journey analytics api

Speed time to market

Modern UX with journey templates and AI-powered workflows.

customer journey analytics api

Scale as you need

Cloud-native scalability and agility with API extensibility.

customer journey analytics api

The Total Economic Impact™ of Adobe Real-Time Customer Data Platform, Journey Optimizer, and Customer Journey Analytics.

Learn how this Adobe solution delivered a 431% return on investment.

Read report

customer journey analytics api


Journey optimizer + real-time cdp..

Pair Real-Time CDP with Journey Optimizer to build unified profiles and actionable audiences while creating, orchestrating, and delivering personalized customer experiences from a single platform.

customer journey analytics api

“We can feed profiles into Adobe Journey Optimizer to deliver more relevant emails and other communications across channels. We’re even looking to use location information through the mobile app to activate location-based communication. If a tourist is walking near a museum, it can trigger a push notification or email inviting them to visit.”

Francesco Paolo Schiavo, Director General at the Italian Ministry of Tourism

Related content.

Questions? We have answers.

Adobe Journey Optimizer is an agile, scalable application built on the Adobe Experience Platform for orchestrating and delivering personalized, connected customer journeys across any app, device, screen, or channel. It lets brands optimize and personalize experiences across the entire customer journey — whether it's a brand-initiated engagement like weekly promotional emails or personalized real-time interactions, such as providing customers with contextual information as they engage with products or services they've purchased from your business.

Brands can use a single application to put consumers at the heart of the customer journey, whether orchestrating individual campaigns or intelligent management of the entire customer journey.

Adobe Journey Optimizer — built on the Adobe Experience Platform — enables journey orchestration, customer segmentation, and content and delivery capabilities all within a single solution. Capabilities include intelligent personalization, dynamic customer journey designing, and real-time insights.

Adobe Journey Orchestration lets you tailor individual journeys for customers based on their prior preferences and behaviors.

customer journey analytics api

Watch overview

Take a tour, more power with generative ai., read article, journey mapping, customer experience (cx), customer journey orchestration, what is adobe journey optimizer, what is the difference between adobe journey optimizer and adobe journey orchestration, what is a user journey map, what is customer experience, what is customer journey orchestration.


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  6. Customer Journey Analytics


  1. Customer Journey Analytics overview

    Admin. Customer Journey Analytics is Adobe's next-generation Analytics solution that lets you use the power of Analysis Workspace with data from Adobe Experience Platform. It can break down, filter, query, and visualize years' worth of data, and is combined with Platform's ability to hold all kinds of data schemas and types.

  2. Customer Journey Analytics Getting Started

    The Customer Journey Analytics reporting API is in the same format, but uses a different endpoint. Port the reporting API usage from the Adobe Analytics reporting API to the Customer Journey Analytics reporting API. Step 6: Account for Data Feeds and Data Warehouse use cases

  3. Customer Journey Analytics overview

    The initial release of Customer Journey Analytics includes many of the features included in Adobe Analytics. For a complete list, see Customer Journey Analytics feature support. Key use cases. Customer Journey Analytics lets you: See the customer in a journey context: You can view and analyze data sequentially, spanning multiple channels. Data ...

  4. GitHub

    Customer Journey Analytics API Documentation. This is the source repository for the Customer Journey Analytics API documentation. See the Contributing page to learn how to make edits or improvements to this repository so they are reflected in the published documentation.

  5. Customer Journey Analytics Guide

    This technical documentation guide provides self help assistance for Customer Journey Analytics. Customer Journey Analytics allows you to bring your customer data from any channel you choose (both online and offline) into Adobe Experience Platform, and then analyze this data just as you would your existing digital data using Analysis Workspace today.

  6. Adobe Customer Journey Analytics

    Customer journey analytics provides a toolkit to business intelligence and data science teams that help them stitch and analyze cross-channel data. Its capabilities deliver context and clarity to the complex multichannel customer journey. This context, when paired with tools like SQL and Analysis Workspace, provide actionable insight into how ...

  7. Customer Journey Analytics

    The latest innovations for Customer Journey Analytics. Use next-generation AI to unlock more value from your customer data and deliver insights to new roles and use cases, including content analytics. Leverage AI Assistant to speed up analysis, surface hard-to-find insights, predict behavior with time-series forecasting, and ask questions in a ...

  8. Customer Journey Analytics API (cjapy): Introduction

    Hello,In this video, I will introduce you what is Customer Journey Analytics, why the API and how to setup the elements you will need for following these tut...

  9. Customer Journey Analytics API (cjapy): Overview

    Hello, On this video, I will show you quickly how to get started with the cjapy module for python. This module allows you reach the different endpoints avail...

  10. Customer Journey Analytics Guide

    User. Admin. This technical documentation guide provides self help assistance for Customer Journey Analytics. Customer Journey Analytics allows you to bring your customer data from any channel you choose (both online and offline) into Adobe Experience Platform. And then analyze this data just as you would your existing digital data using ...

  11. Customer Journey Analytics API

    If you know the Adobe Analytics API and the official documentation, the process to retrieve report is first to generate a report request. The process is quite the same with Customer Journey Analytics. In order to do that, you will need to go to Adobe Analytics, or Customer Journey Analytics. Enable the debugger, in the help section.

  12. Customer Journey Analytics landing page

    The Customer Journey Analytics landing page is comprised of the following subtabs: Projects and Learning. Projects are customized designs that combine data components, tables, and visualizations that you built or that someone else built and shared with you. Projects also refers to blank projects and blank mobile scorecards.

  13. Adobe Customer Journey Analytics

    Move to Person-Centric Insights with Customer Journey Analytics. Dig beyond the raw data of any given channel to gain a deeper understanding of your customers as people. Learn how maturing your analytics practice by connecting web, mobile, and in-person datasets under a common customer ID unlocks a complete view of the customer journey. Watch now

  14. Analytics and Reporting Tools to Track Marketing Campaigns

    Marketing API ; Transactional API ; Release notes ... you can centralize critical data across your entire tech stack-including Google Analytics customer behavioral data, Shopify sales reporting, and more. ... Customer Journey progress, postcard and survey tracking, and even data from third-party integrations like Google Analytics. What is ...

  15. Calculated metrics overview

    Calculated metrics overview. Calculated and Advanced Calculated (or Derived) Metrics are custom metrics that you can create from existing metrics. Our Calculated Metrics tools offer a highly flexible way of building, managing and curating metrics. They allow you as marketers, product managers and analysts to ask questions of the data without ...

  16. Analytics

    Uncover valuable metrics, track performance, and make informed decisions with ease, elevating your understanding and optimizing your support processes for exceptional results-These graphs and charts help the user to identify strengths and weaknesses, track progress, and make informed decisions to improve the ticket resolution process and ...

  17. Guided analysis overview

    Guided analysis enables users to self-serve high quality data and insights about the customer journey through guided workflows, built on the cross-channel data of Customer Journey Analytics. Cross-functional teams, from marketing to product, can connect in real time to use and understand these reports.

  18. Microsoft Azure Blog

    Customer enablement. Plan a clear path forward for your cloud journey with proven tools, guidance, and resources. Customer stories. See examples of innovation from successful companies of all sizes and from all industries. Azure innovation insights. Executive insights and guidance on AI innovation, intelligent data, cloud infrastructure, and ...

  19. Maximizing SaaS application analytics value with AI

    App cost and revenue analytics, which track app revenue—such as annual recurring revenue and customer lifetime value (the total profit a business can expect to make from a single customer for the duration the business relationship)—and expenditures such as customer acquisition cost (the costs associated with acquiring a new customer).

  20. Overview

    Addresses & Location Context The Address Capture capability is a Mobility toolkit that incorporates two primary APIs: Place Autocomplete API and Geocoding API. These APIs address a number of common problems with delivery addressing: undeliverable addresses, inaccurate address entry from consumers, and the need for adjustments to point-of ...

  21. Integrate Customer AI data with Adobe Customer Journey Analytics

    In Customer Journey Analytics, you can now proceed to create data views with the dimensions (such as score, score date, probability, and so on) and metrics that were brought in as part of the connection you established. Step 4: Report on CAI scores in Workspace. In Customer Journey Analytics Workspace, create a new project and pull in ...

  22. Adobe Journey Optimizer

    ADOBE JOURNEY OPTIMIZER Customer engagement at the right time, on any channel. Adobe Journey Optimizer is a single application for managing scheduled cross-channel campaigns and trigger-based one-to-one engagement for millions of customers —and the entire journey is optimized with intelligent decisioning and insights.