Agentic Workflow for Competitive Set Recalibration Recommendations

Replaces the costly, manual annual review—involving spreadsheets, market reports, and subjective debate—with a continuous agent that monitors performance drift. It benchmarks your property's RevPAR, ADR, and occupancy against a dynamic candidate pool, flagging when the official comp set no longer reflects true competitive pressure. This shifts analyst time from data gathering to interpreting strategic recommendations.

An irrelevant comp set leads to mispricing: you're either benchmarking against hotels that are no longer true competitors or missing new threats. This workflow ensures your dynamic pricing engine receives accurate, actionable market signals. By maintaining a relevant comp set, you protect rate integrity and can capture an estimated 1-3% uplift in RevPAR by avoiding underpricing or missed premium opportunities.

The workflow is built on a multi-agent architecture. A Data Ingestion Agent normalizes STR, OTA, and internal PMS data. A Clustering Agent periodically runs algorithms (e.g., k-means, DBSCAN) on attributes like location, amenities, and price tier. A Performance Analyst Agent calculates share indices and correlation metrics. Recommendations route through an Approval Workflow Agent integrated with tools like ServiceNow or Jira, requiring revenue leadership sign-off before syncing to the RMS/PMS.

Implementation starts with a pilot on 3-5 properties. Phase 1 builds the data pipeline from your data warehouse or BI tool (e.g., Snowflake, Power BI). Phase 2 deploys the clustering logic, initially in 'shadow mode' to compare its recommendations against the current manual set. Phase 3 integrates the approval workflow and the final sync to the revenue management system (e.g., IDeaS, Duetto). Success requires change management to transition revenue managers from owners of the process to overseers of a strategic system.

Governance is non-negotiable. Every recommendation is logged with a full audit trail: the data inputs, clustering parameters, performance metrics, and suggested change. Approval gates are mandatory, with escalation paths for contested recommendations. The system includes a 'sandbox' mode to simulate the impact of a comp set change on historical performance. This controlled, explainable approach builds operational trust and ensures strategic changes are deliberate, not automated.

For management companies and ownership groups, this workflow scales to a portfolio level. A central agent can monitor comp set relevance across all properties, identifying consistent underperformers and facilitating cross-property learning. It provides asset managers with a data-driven narrative on market positioning, supporting investment and repositioning decisions. The workflow transforms competitive analysis from a defensive, operational task into a proactive strategic capability.

This foundational agent orchestrates the continuous collection and normalization of raw market data from disparate sources, including STR reports, OTA scrapes, and local event feeds. It handles API authentication, data validation, and loading into a unified data lake, creating the clean, timestamped dataset required for reliable clustering and benchmarking. Without this automated pipeline, analysts waste hours manually compiling inconsistent spreadsheets.

The core analytical component applies unsupervised learning (e.g., k-means, DBSCAN) and rule-based logic to segment properties based on dynamic attributes: location, amenities, star rating, ADR tier, and guest sentiment. It continuously recalculates similarity scores, identifying properties that have drifted into or out of the logical competitive set. This moves the set from a static, annually reviewed list to a living model of the market.

This specialized agent calculates key performance gaps—RevPAR Index, Occupancy Index, ADR Index—against the current and proposed competitive sets. It surfaces whether underperformance is due to an outdated comp set (comparing to irrelevant hotels) or a genuine pricing/positioning issue. The business impact is direct: accurate benchmarking is the prerequisite for effective pricing decisions.

An LLM-powered agent synthesizes the clustering and benchmarking outputs to draft a clear business recommendation: 'Add Property A, Remove Property B.' It generates a narrative rationale citing specific performance gaps and attribute similarities, formatted for a revenue management committee. This transforms complex data into an actionable proposal, reducing preparation time from days to minutes.

This component manages the governance layer. It routes recommendations to designated revenue leaders and asset managers via Slack, email, or a dedicated dashboard, capturing approvals or rejections with comments. All data, logic, and decisions are logged to an immutable audit trail, which is critical for explaining strategy shifts to ownership and ensuring compliance with portfolio-wide standards.

The execution agent. Upon approval, this component programmatically updates the competitive set in the core Revenue Management System (RMS), Property Management System (PMS), and business intelligence platforms via their APIs. It confirms successful sync and triggers a recalibration of all pricing models based on the new set. This closes the loop, turning strategic decisions into immediate operational reality.