Smart, Dynamic and automated Matching of Field Agents with Assignments
Field teams often face inefficiencies due to manual task assignments that do not take into account individual skills, location, and availability, leading to suboptimal workforce deployment. Additionally, the complexity of assignments consisting of multiple tasks and the need for real-time location-based planning further complicates efficient resource allocation.
Why we’re better
shopeyeQ's Intelligent Assignment Recommender leverages advanced AI algorithms to automatically assign visits (assignments) to field team members based on their unique profiles. Each visit consists of multiple tasks, allowing for more comprehensive planning. By dynamically adjusting recommendations in real time using location data, the system adapts to changing conditions and priorities, ensuring that the right visits are assigned to the right individuals at the right time.
Empower Your Retail Success
Discover how our platform's advanced features—like dynamic clustering algorithms, real-time insights, and predictive analytics—empower you to drive smarter growth.
Data Products
Skills, locations, and availability data of team members gathered during the onboarding stage onto the shopeyeQ platform
Insights into previous assignments and outcomes to inform future recommendations
Comprehensive information about each visit, including constituent tasks, requirements, and priorities
Current positions of field agents for dynamic assignment optimisation
Map the customer journey
You provide us with your data, which we cluster in the background. We identify lookalikes for you and perform value-based segmentation.
1- Initial Data Collection
- Team Profiles: Users input skills, locations, and availability for each team member
- Historical Data Review: Assessment of past visit and task performance data to identify trends and inform future assignments
- Visit and Task Mapping: Definition of visits (assignments) and their constituent tasks
2- Data Processing (Case 1)
- Intelligent Assignment Algorithm: The system processes the collected data using machine learning to identify optimal visit assignments based on current team dynamics and task requirements
- Real-Time Location Analysis: Continuous monitoring of field agents‘ locations to optimise assignments and routes
- Visit Prioritisation: Ranking of visits based on urgency, importance, and geographical efficiency
3- Visit Assignment and Feedback
- Automated Visit Allocation: Visits (with their constituent tasks) are automatically assigned to field team members based on the AI’s recommendations
- Dynamic Reassignment: Real-time adjustments to assignments based on changing priorities or unexpected events
- Performance Tracking: Feedback on visit and task completion is collected to refine the assignment algorithms and improve future recommendations
Explore Similar Solutions
- Adaptable Workflow: The system allows for seamless integration with existing scheduling tools while providing enhanced functionalities for multi-task visit management
- Continuous Learning: The AI learns from user interactions and outcomes, continually improving its recommendations for optimal field team efficiency
- Granular Task Management: While assignments are made at the visit level, the system considers the requirements and complexities of individual tasks within each visit
- Geospatial Optimisation: Utilises real-time location data to minimise travel time and maximise the number of visits completed per day
Optimizing Retail Insights
This section presents a series of case studies that demonstrate the transformative impact of POS clustering on retail operations.
Explore Similar Use Cases
Turn Insights into Impact with shopeyeQ
