Adobe Analytics connector tools

  • Updated

The Adobe Analytics connector tools in Optimizely Opal lets you query and analyze comprehensive digital performance data directly from your Adobe report suites. This lets you run custom reports on dimensions like marketing channels and user geography, alongside critical metrics such as conversion rates and revenue. By bridging the gap between analytics and action, these tools let you derive actionable insights that create high-impact experimentation and content strategies.

First, an Opal administrator must connect Opal with Adobe Analytics using Optimizely Connect Platform (OCP). After installing the connector in OCP, individual Opal users and agent builders can log in to Adobe Analytics from Opal to access their individual information. Administrators only need to install the Adobe Analytics connection in OCP once.

Complete the following steps to enable the Adobe Analytics tools in Opal.

Connect Opal with Adobe Analytics

Install Adobe Analytics in OCP

Before users can call the Adobe Analytics connector tools, an administrator must add them in OCP.

In the OCP App Directory, complete the following:

  1. Click the Adobe Analytics connector tool.
  2. Click Install App.
  3. Log in to Adobe Analytics.

User-level auth for Adobe Analytics

After an administrator adds the Adobe Analytics connector tools, you can log in from Opal to access your information. The Adobe Analytics connector tools leverage user-level authorization to ensure individual Opal users can only access data they have permission to access within Adobe Analytics.

To authenticate, complete the following steps in Opal:

  1. Go to Tools > Connectors.
  2. Click Connect for the Adobe Scopes tools.
  3. Log in to Adobe Analytics. 

After you connect to Adobe Analytics, the Adobe Analytics connector tools become available in Opal Chat, agents, and workflows.

Adobe Analytics connector tools

After an administrator Installs Adobe Analytics in OCP and you log in to Adobe Analytics using User-level auth for Adobe Analytics, you can call the following tools in Opal. 

Click a tool's name to expand it and learn when to use the tool, required and optional parameters, and example prompts on how to call the tool. If you do not provide a required parameter, Opal prompts you for it.

Report suite and data discovery

Use these tools to identify available report suites (the data sources for your websites or apps) and discover the specific dimensions and metrics available within them.

adobe_analytics_list_report_suites – Discover all available Adobe Analytics report suites that the user has access to.
  • When to use
    • Identify which analytics properties (websites, apps, environments) are available for exploration.
    • Retrieve the Report Suite ID (RSID) needed for all other Adobe Analytics tool calls.
    • View the user-friendly names for each report suite to ensure you are querying the correct data source (for example, Production or  Development).
    • Browse a large list of report suites using pagination filters.
  • Parameters
    • (Optional) limit – Number of report suites to return (default: 50).
    • (Optional) page – Page number for pagination (0-based, default: 0).
  • Example prompts
    • Which Adobe Analytics report suites do I have access to?
    • List all available analytics properties for my organization.
    • Show me the names and IDs of our production report suites.
    • Find the RSID for our "Staging" website.
adobe_analytics_list_dimensions – Discover all available dimensions for a report suite in Adobe Analytics.
  • When to use
    • Discover which variables (for instance, Page, Marketing Channel, or Browser) are available for data breakdown in a specific report suite.
    • Retrieve the correct variable IDs needed for building reports in the run_report tool.
    • Browse specific dimension categories to see how technical data like device type or geography is tracked.
    • Check localized names and descriptions for dimensions to ensure clear reporting for international teams.
  • Parameters
    • reportSuiteId – The Adobe Analytics Report Suite ID to query.
    • (Optional) locale – The locale for dimension names and descriptions (for example, en_US).
  • Example prompts
    • What dimensions can I use to break down data in the mycompany.prod report suite?
    • Show me all the geographical dimensions available for our mobile app.
    • I need to find the ID for the "Marketing Channel" dimension.
    • What are the available traffic source dimensions in report suite acme.dev?
adobe_analytics_list_metrics – Discover all available metrics for a report suite in Adobe Analytics.
  • When to use
    • Identify the standard and custom metrics (e.g., Page Views, Visits, Orders) available for a specific report suite.
    • Obtain correct metric IDs to include in custom analytical queries.
    • Explore available engagement metrics like "Bounce Rate" or "Time Spent on Page" to build better performance reports.
    • Retrieve technical descriptions for metrics to understand exactly how they are counted in your environment.
  • Parameters
    • reportSuiteId – The Adobe Analytics Report Suite ID to query.
    • (Optional) locale – The locale for metric names and descriptions (for example, en_US).
  • Example prompts
    • What metrics are available in the mycompany.main report suite?
    • I need a list of all conversion-related metrics for our website.
    • Show me the engagement metrics available for report suite prod.site.123.
    • List all available metrics and their IDs for the mobile app report suite.

Custom metric management

Use these tools to create new formulas, browse existing custom metrics, and inspect how they are calculated.

adobe_analytics_create_calculated_metric – Create a new calculated metric with a custom formula.
  • When to use
    • Build custom business KPIs that combine multiple standard metrics, such as "Average Order Value" or "Conversion Rate."
    • Create complex formulas (addition, subtraction, multiplication, division) to measure specific performance data.
    • Define reusable metrics like "Cart Abandonment Rate" or "Revenue per Unique Visitor" for consistent reporting across an organization.
    • Set specific display formats (percentage, currency, time) for calculated data to improve report readability.
  • Parameters
    • name – The descriptive name of the calculated metric (e.g., "Revenue per Visit").
    • definition – A JSON string defining the mathematical formula using functions like divide, multiply, subtract, or add and referencing existing metrics.
    • (Optional) rsid – The Adobe Analytics Report Suite ID where the metric will be created.
    • (Optional) description – An optional explanation of what the metric measures and its business purpose.
    • (Optional) type – The display format for the metric: decimal, time, currency, or percent.
    • (Optional) precision – The number of decimal places to display in reports (e.g., 0, 1, or 2).
    • (Optional) polarity – Indicates if a higher value is positive (e.g., Revenue) or negative (e.g., Bounce Rate).
    • (Optional) tags – A comma-separated list of tags used to organize and search for metrics.
  • Example prompts
    • Create a calculated metric named "Revenue per Visit" for the acme.prod report suite that divides revenue by visits.
    • Build a "Cart Abandonment Rate" KPI in Adobe Analytics. The formula is (Carts - Orders) divided by Carts, and it should be formatted as a percentage.
    • I need a custom metric called "Mobile Conversion" that calculates the ratio of orders to visits specifically for mobile segments.
    • Create a new metric called "Net Margin." It should subtract total costs from revenue and then divide by revenue. Tag it with "Financial" and "KPI".
adobe_analytics_get_calculated_metric – Get details of a specific calculated metric by ID.
  • When to use
    • Retrieve the formula and definition of an existing custom KPI to verify its logic.
    • Inspect the metadata of a calculated metric, such as its owner, shared status, and applied tags.
    • Check the display settings (precision, polarity, and type) of a metric before including it in a professional report.
    • Troubleshoot why a metric might be returning unexpected results by reviewing its underlying components.
  • Parameters
    • calculatedMetricId – The unique identifier for the calculated metric (for example, "cm123456_abc").
    • (Optional) expansion – The metadata to include in the response, such as definition, tags, shares, or ownerFullName.
  • Example prompts
    • Show me the formula used for the calculated metric cm123456_abc.
    • I need to see the full details, including tags and shares, for the "Revenue per Visit" calculated metric.
    • What is the configuration for the conversion rate metric? Please include its definition.
    • Retrieve the owner and modification history for calculated metric ID cm987654_xyz.
adobe_analytics_list_calculated_metrics – Discover available calculated metrics in Adobe Analytics.
  • When to use
    • Search for existing calculated metrics by name to avoid creating duplicates.
    • Find the unique IDs of custom metrics for use in programmatic reporting with run_report.
    • Filter metrics by specific tags, such as "KPI" or "Revenue," to discover related business measurements.
    • Browse the underlying formulas of your calculated metrics to understand how data is being derived.
  • Parameters
    • (Optional) name – Filter to search for metrics by partial name (case-insensitive).
    • (Optional) tagNames – Filter to return only metrics that include specific comma-separated tags.
    • (Optional) expansion – Metadata to include, such as definition, tags, or shares.
    • (Optional) page – Page number for pagination (0-based, default: 0).
    • (Optional) limit – Maximum number of metrics to return per page (max: 1000).
  • Example prompts
    • Find all calculated metrics that have "conversion" in their name.
    • Show me the custom metrics tagged with "Q4 Strategy" and include their formulas.
    • List all available calculated metrics in our account.
    • I need the ID for the "Average Order Value" calculated metric.
adobe_analytics_list_calculated_metric_functions – Discover available functions for building calculated metric formulas.
  • When to use
    • Explore the full list of mathematical and statistical functions available for creating custom metrics.
    • Find specific operation syntax (for example, addition, subtraction, multiplication, or division) to build a formula.
    • Identify advanced functions for complex calculations like "Mean," "Median," or "Standard Deviation."
    • Retrieve localized names and descriptions for functions using a specific locale.
  • Parameters
    • (Optional) locale – Locale for function names and descriptions (for example, en_US or fr_FR). Default is en_US.
  • Example prompts
    • What functions can I use to build a calculated metric in Adobe Analytics?
    • Show me a list of all available mathematical functions for custom formulas.
    • What are the function names and descriptions in French (fr_FR)?
    • I need to find the specific syntax for the "divide" and "multiply" functions in Adobe Analytics.

Audience segmentation

Use these tools to build audience filters, browse existing segments, and validate the logic of your filters before they are applied to reports.

adobe_analytics_create_segment – Create a new segment (audience filter) in Adobe Analytics.
  • When to use
    • Filter report data to include only specific subsets of traffic, such as "High-Value Customers" or "Mobile App Users."
    • Create reusable audience definitions at the hit, visit, or visitor level for consistent cross-report analysis.
    • Build complex segment rules using nested containers and logical operators (AND/OR) to identify specific behavioral patterns.
    • Save specific traffic segments, such as "Returning Visitors who saw Product X," for further deep-dive analysis in Workspace.
  • Parameters
    • name – The unique name of the segment (for example, "California Mobile Users").
    • rsid – The Adobe Analytics Report Suite ID where the segment will be created.
    • definition – A JSON string representing the segment rules, rulesets, and logic containers.
    • (Optional) description – A detailed explanation of what the segment includes and its intended use cases.
    • (Optional)  owner – A JSON string containing the owner's information (defaults to current user).
  • Example prompts
    • Create a new segment in Adobe Analytics for users in California who are using mobile devices.
    • Build a "Purchasers" segment in report suite mycompany.prod that includes any visit where an order was placed.
    • I need a segment named "Non-Purchasing Return Visitors" that filters for visitors with more than 1 visit but 0 total orders.
    • Can you create a segment for organic search traffic? The description should be "Users who arrived via natural search engines."
adobe_analytics_get_segment – Get detailed information about a specific segment by ID.
  • When to use
    • Inspect segment rules and criteria to understand exactly how an audience like "High Value Customers" is defined.
    • Retrieve a segment's underlying JSON definition to use as a template for creating new, similar segments.
    • Verify metadata such as the owner, creation date, modification history, and applied tags.
    • Check segment compatibility and sharing settings before applying it to an organization-wide report.
  • Parameters
    • segmentId – The unique identifier for the segment (for example, "s300000123_5f6d7e8c").
    • (Optional) expansion – The metadata to include, such as definition, tags, shares, or modified.
  • Example prompts
    • What are the rules for the "Mobile Users" segment (ID: s300000123_abc)?
    • Show me the full definition and configuration for my "Recent Purchasers" segment.
    • Retrieve the specific criteria for segment ID s456_789, including any applied tags.
    • Check the ownership and sharing metadata for segment s789_012.
adobe_analytics_list_segments – Discover all available segments in Adobe Analytics.
  • When to use
    • Search for existing audience filters (for instance, "Mobile Only" or "Email Visitors") to reuse in new analysis.
    • Retrieve segment IDs to apply as filters in the run_report tool.
    • Find segments tagged with specific campaign or regional names to narrow down your audience list.
    • Verify if a specific audience already exists before creating a new one.
  • Parameters
    • (Optional) name – Partial match filter for searching segments by name.
    • (Optional) tagNames – Filter to return segments with specific comma-separated tags.
    • (Optional) expansion – Metadata to include, such as definition, tags, or shares.
    • (Optional) page – Page number for pagination (0-based, default: 0).
    • (Optional) limit – Maximum number of segments to return (max: 1000).
  • Example prompts
    • Do we have any existing segments for "Repeat Purchasers"?
    • Show me all segments tagged with "2025 Campaign".
    • Find all mobile-related segments and include their definitions.
    • I need the ID for the "Loyalty Member" segment.
adobe_analytics_validate_segment – Validate a segment definition before creating it.
  • When to use
    • Check for syntax errors or rule inconsistencies in complex JSON segment definitions before attempting to save them.
    • Ensure a new segment definition is compatible with a specific report suite's architecture.
    • Debug complex logic filters to ensure they follow Adobe's container and predicate rules.
    • Test a new audience definition as part of an automated workflow to ensure high-quality data.
  • Parameters
    • rsid – The Report Suite ID the segment is being validated against.
    • definition – The JSON segment definition string to be validated.
  • Example prompts
    • Is this segment definition valid for the web.prod report suite? INSERT_JSON
    • Please check my new "Holiday Purchasers" segment rule for any errors before I save it.
    • Validate this visitor-level filter for the mobileapp.prod suite.
    • I need to verify that this nested container logic is correctly formatted for Adobe Analytics.

Reports and analysis

adobe_analytics_run_report – Query Adobe Analytics data with dimensions and metrics to generate custom reports.
  • When to use
    • Analyze project-specific KPIs like total unique visitors or revenue for a defined date range.
    • Break down metrics by specific dimensions, such as "Top 5 Pages by Views" or "Revenue by Marketing Channel."
    • Trend data over time using the daterangeday dimension to see daily performance changes.
    • Apply complex filters to narrow down report data to specific urls, events, or categories.
  • Parameters
    • reportSuiteId – The ID of the report suite to query.
    • dateRange – The ISO 8601 date range for the report (for example, 2025-01-01T00:00:00.000/2025-01-31T23:59:59.999).
    • dimensions – A JSON array of dimension IDs to break down data (for example, ['variables/page']).
    • metrics – A JSON array of metric IDs to measure (for example, ['metrics/pageviews']).
    • (Optional) filters – JSON rules to filter data by dimension values or metric thresholds.
    • (Optional) settings – JSON for report configuration, such as limit and page.
  • Example prompts
    • What were the top 10 most viewed pages last week in the mycompany.prod suite?
    • Show me a daily trend of unique visitors for the month of February.
    • Compare the conversion rate of "Paid Search" vs "Organic Search" for the last 30 days.
    • How many orders did we get from users in New York yesterday?

Use cases

The following sections outline how you can leverage the Adobe Analytics connector tools within Opal to query and analyze your data.

Instant access to insights

You can ask natural language questions (for example, What were our page views yesterday? or Show me conversion rates for mobile users last week?) and receive immediate, data-driven answers directly within Opal Chat, without needing to go to separate dashboards or specialized analytics tools.

Personalized data exploration 

Opal Chat provides personalized insights by integrating with Adobe Analytics' segmentation capabilities. For example, a marketing manager could ask for performance metrics for a campaign or audience segment they manage.

Simplified ad-hoc analysis

Business users, who may not be analytics experts, can perform basic ad-hoc analysis by querying Opal Chat. This reduces reliance on data analysts for routine requests, freeing up analysts for more complex tasks.

Enhanced collaboration 

Insights shared with Opal Chat can be easily discussed and acted upon within team conversations, fostering a more data-driven collaborative environment.

Actionable recommendations

Combined with analytics and detection from Adobe Analytics, Opal Chat reports issues and suggests actions or areas for investigation.

If you use Opti ID, administrators can turn off generative AI in the Opti ID Admin Center. See Turn generative AI off across Optimizely applications.