Feature Request

In Analytics → Filters → Source dropdown, the default selection is set to Knowledge Base each time the page is refreshed or reopened. Any additional manually selected sources such as Widgets are not retained. Request to enhance the Analytics filter so that the last selected Source filter options persist across page refreshes and future sessions, instead of resetting to the default selection each time.

The request defines KPI framework that will be used to track article creation & status progression Created Articles Count measures article production activity across several creation methods, including Eddy AI, Blank, Template, and Import. For each creation type, the KPIs track both quantity and handling time, with results broken down per associate and timeframe. This provides insight into both productivity and efficiency of content creation methods. Article Status Count monitors the lifecycle state of articles. Statuses include Updated, Published, Unpublished, Archived, Deleted, and Set Reminders. All of these metrics are measured by quantity and analyzed per associate and timeframe, allowing visibility into how articles move through and exit the content lifecycle. Created Articles Count What this KPI should show: This KPI tracks how many articles are created and how long it takes to create them, depending on how they were started. How it’s broken down: By creation method (Eddy AI, Blank, Template, Import) By associate By time period Benefit: This tells you how much content is being created, who is creating it, how they are creating it, and how long it takes. It helps identify which creation methods are fastest, most used, or most time‑consuming. Eddy AI / Blank / Template / Import Quantity: How many articles were created using this method Handling Time: How long it took from start to creation Benefit: This helps compare whether articles created with AI, templates, manual blank pages, or imported content are quicker or slower, and which method is most effective for the team. Article Status Count What this KPI should show: This KPI tracks how many articles are in each status and who is managing them. How it’s broken down: By article status By associate By time period Benefit: This shows what is happening to articles after they are created—whether they are being published, updated, archived, or removed. Updated Tracks how many articles were modified. Why it matters: Shows ongoing maintenance and improvement of existing content. Published Tracks how many articles were made live. Why it matters: Shows how much content is reaching users. Unpublished Tracks how many articles were removed from public access. Why it matters: Highlights content that may be outdated, incorrect, or under review. Archived Tracks how many articles were retired but kept for reference. Why it matters: Indicates content lifecycle management and cleanup activity. Deleted Tracks how many articles were permanently removed. Why it matters: Shows content removal decisions and potential quality or relevance issues. Set Reminders Tracks how many reminder actions were set on articles. Why it matters: Shows proactive follow‑ups for future reviews or updates. Overall, this KPI structure supports data‑driven decision‑making, helping the business balance speed, quality, and governance across the entire article management lifecycle.

There appear to be inconsistencies in the link validation results in the following cases: Links that include punctuation immediately after them in the text (Dot at the end of the link) and the links with target="_blank" are reported as broken despite functioning correctly. Improving validation accuracy in these scenarios would help reduce false positives and minimize the need for manual verification.

This KPI framework provides a comprehensive view of lifecycle management, workflow efficiency, and reviewer performance. It enables leaders and operational teams to monitor both volume and efficiency, ensuring transparency, accountability, and continuous improvement across the content management process. Workflow Status Count (Live & Historical Data) What this KPI should show: This KPI tracks articles as they move through review and quality steps, including how many articles are in each step and how long they stay there. How it’s broken down: By workflow stage By associate or reviewer By time period Benefit: This tells you where articles are getting stuck, how fast reviews are happening, and whether review stages are efficient. In Country or SME Review (example) Quantity: How many articles are under expert or country review Handling Time: How long the review takes Benefit: Shows how quickly specialized reviewers validate content. For Revision by KB Team (example) Quantity: How many articles need fixes Handling Time: How long revisions take Why it matters: Highlights quality gaps and rework effort. Quality Check & Governance (example) Quantity: How many articles are in final quality approval Handling Time: Time spent on governance review Why it matters: Ensures content meets standards before publication and shows if final approvals slow things down. 2 Reviewer Performance What this KPI should show: This KPI measures how efficiently reviewers work across all articles. How it’s broken down: By reviewer By time period Benefit: This shows how many articles each reviewer handles and how long they take, helping compare workloads and performance fairly. All Articles Quantity: Number of articles reviewed Handling Time: Time spent reviewing Benefit: Helps identify high performers, bottlenecks, training needs, and workload imbalances. Overall, this KPI structure supports data‑driven decision‑making, helping the business balance speed, quality, and governance across the entire article management lifecycle.

Currently, Document360 provides analytics at an individual workspace level. However, for organizations managing multiple workspaces, there is no unified way to view analytics across all workspaces. Teams operating across several workspaces lack a consolidated view. This fragmentation makes it difficult to: Track overall documentation effectiveness Identify trends across products or business units Make data-driven decisions at an organizational level Introducing a cross-workspace analytics dashboard that aggregates data from all workspaces into a single, unified view will help.

Request to provide a consolidated export of country-level analytics data (including all countries with view/read metrics) as a single CSV file. Currently, users need to export data individually per country, which is time-consuming and manual. A unified export or API access for analytics would help streamline reporting and enable easier analysis such as country-based tiering. This enhancement would significantly improve efficiency for users working with global usage insights.

Within the analytics solution, two enhancements would significantly improve usability: Persistent Filter State Across Reports and Workspaces Currently, filters can reset when navigating between reports or workspaces. It would be very helpful if selected filters such as a date range or month were retained as users move between reports within a workspace, and also carried over when switching to a different workspace. This would eliminate the need to reapply the same filters repeatedly and allow for more consistent, efficient analysis across the platform. Cross-Workspace Overview Report A high-level overview report that surfaces key metrics across all workspaces from a single screen would be a valuable addition. Ideally, this report would include top-level data such as total queries and searches, positive and negative feedback counts, search success rates, and article views and reads. Users could then filter down by workspace directly from that screen, with links into deeper data such as categories, specific search terms, and individual feedback entries. This would make it much easier to get a holistic picture of performance without navigating workspace by workspace.

We request the ability to retain and access historical analytics for deleted articles within Document360. Currently, deleting an article removes its visibility from article-level analytics and CSV exports, which limits trend analysis and reporting accuracy. The expected behavior is to preserve analytics data for deleted articles within selected timeframes, even if the article no longer exists. This would help teams validate historical performance and maintain consistent reporting.

There is a discrepancy in data which is observed for readers in the analytics section. When viewing the data (views, reads) in the readers section it shows a different value than viewing the data for individual readers even though we select the same filter.

Sophisticated analytics API endpoints so we can pull and analyse read/view/region/feedback data for ourselves