| Table of Contents | ||
|---|---|---|
|
...
Introduction: How recommendations are personalized
In Orthogramic, recommendations are designed to empower users by providing actionable insights tailored to their specific business context. These recommendations are automatically generated based on data analysis, user interactions, and strategic alignment principles embedded in the platform. By leveraging advanced algorithms and real-time data, Orthogramic ensures that each recommendation is relevant, timely, and aligned with organizational goals.
One key aspect of our recommendation system is the personalization based on user type. Whether you are a Chief Operating Officer, Enterprise Architect, or Talent Acquisition specialist, the recommendations are adapted to fit your role's unique objectives and focus areas. This ensures that users receive insights that are not only aligned with the organization's goals but also directly relevant to their responsibilities. Learn more about how we tailor recommendations to user types in the section below.
The recommendation process integrates multiple inputs, such as business architecture data, ongoing initiatives, and performance metrics, to deliver suggestions that can guide decision-making, improve workflow efficiency, and ensure compliance with internal and external requirements.
How user type is considered in creating recommendations
Orthogramic’s recommendation engine adapts suggestions based on the user type, ensuring that each user receives insights aligned with their specific role within the organization. The platform recognizes that different user types—such as Chief Operating Officers, Enterprise Architects, Product Managers, and Talent Acquisition specialists—have distinct priorities, objectives, and workflows.
For example:
Chief Operating Officers (COOs) receive recommendations focused on operational efficiency, cross-departmental alignment, and strategic initiative tracking.
Enterprise Architects are provided with insights into technology gaps, architecture alignment, and future planning.
Product Managers get suggestions on aligning product development with strategic goals and optimizing resource allocation.
Talent Acquisition specialists are guided on workforce planning, recruitment strategies, and talent alignment with business capabilities.
By tailoring recommendations to the user's role, Orthogramic ensures that the suggestions are not only actionable but also directly relevant to the day-to-day decisions that users must make. This level of customization allows for more efficient workflows and better decision-making, as users are supported with insights that reflect their responsibilities and contribute to broader organizational success.
Recommendation metrics
Responsiveness Metrics
Time to First Response: How quickly the user responds to the initial recommendation or review and approval request.
Average Response Time: The average time taken by a user to complete recommendation requests and review and approval tasks.
Completion Rate: The percentage of Recommendations requests and review and approval tasks that the user fully completes (as opposed to partially responding or deferring).
Engagement Metrics
Frequency of Interaction: The number of times the user engages with Orthogramic per week or month across activities like recommendations, document editing, and alignment tasks.
Consistency of Interaction: The regularity of the user’s interactions, assessing whether they engage steadily over time or sporadically.
Task Initiation vs. Task Response: The ratio of tasks initiated by the user (e.g., uploading or editing a document) compared to tasks they are responding to (e.g., recommendations, approval or alignment tasks).
Effectiveness Metrics
Success Rate of Recommendation responses approved: How often recommendations responded to by the user are approved.
Accuracy of Responses: How frequently the user’s responses align with expected or required outcomes in recommendation, review, or alignment tasks.
Work Volume Metrics
Volume of Alignment Contributions: The number of alignment tasks (such as defining capabilities or value streams) the user performs.
Document Interaction Frequency: The number of documents the user uploads, edits, or contributes to over a given period.
Volume of Recommendation Responses: The total number of recommendation responses the user completes.
Quality and Relevance Metrics
Quality of Documents Added/Edited: A rating based on relevance, quality and quantity Organization goal for the associated business unit of documents the user uploads or edits.
Document Accuracy Rate: The percentage of user-added documents that do not require significant edits or corrections post-upload.