analyses your business data to provide actionable suggestions. Tourism operators can use these insights to identify growth opportunities, enhance efficiency, and optimise resource allocation, leading to better strategic planning.
Accessing this feature
Go to Dashboard > Insights > AI Recommendations
How to use it
Navigate to the AI Recommendations Section:
Once you have followed the access path, the AI Recommendations dashboard will load, presenting a summary of current and historical recommendations.
Understand Recommendation Categories:
Recommendations are automatically categorised to facilitate easier review and action. Common categories include:
Marketing & Promotion: Suggestions for campaign optimisation, audience targeting, and channel effectiveness.
Product & Service Development: Insights into new offerings, package enhancements, or underperforming services.
Operational Efficiency: Advice on streamlining internal processes, resource management, or cost reduction.
Customer Experience: Guidance on improving guest satisfaction, personalising interactions, or reducing churn.
Market Expansion: Identification of new geographical markets or guest segments to target.
Review Individual Recommendations:
Click on any recommendation to view its detailed analysis. Each recommendation includes:
The Recommendation: A clear, concise suggestion (e.g., "Optimise social media advertising spend by 15% on Platform X").
Supporting Data: The key metrics, trends, or data points that informed the recommendation (e.g., "Analysis of Q3 ad spend shows diminishing returns on Platform X compared to Platform Y for similar target demographics").
Expected Impact: A projection of the potential benefits if the recommendation is implemented (e.g., "Potential to increase conversion rates by 8% and reduce guest acquisition cost by 5%").
Priority Level: An indication of urgency or potential impact (e.g., High, Medium, Low).
Action and Track Recommendations:
Implement: If a recommendation is deemed suitable, proceed with its implementation within your organisation.
Mark as Complete: Once a recommendation has been actioned, update its status to "Complete" within the platform. This helps track progress and informs the AI model of successful interventions.
Dismiss: If a recommendation is not relevant or feasible for your current strategy, you can dismiss it. Providing a brief reason for dismissal helps the AI learn and refine future suggestions.
Snooze: For recommendations that are relevant but not immediately actionable, you can snooze them for a later review date.
Filter and Sort Recommendations:
Use the available filters to narrow down recommendations by category, priority, status (Active, Complete, Dismissed, Snoozed), or date range. Sorting options allow you to arrange recommendations by priority, recency, or category for efficient management.
Export Recommendations:
You can export a list of recommendations, including their details and status, into a CSV or PDF format for offline review, team discussions, or integration into external project management tools. This feature supports comprehensive reporting and collaborative strategic planning.
Tips
Establish a consistent schedule (e.g., weekly or fortnightly) to review new and pending AI Recommendations. Timely review ensures you capture opportunities and address challenges promptly.
Do not attempt to implement every recommendation simultaneously. Prioritise based on the recommendation's potential impact, alignment with your current strategic goals, and the resources required for implementation. Focus on high-impact, low-effort suggestions first.
Incorporate relevant AI Recommendations into your broader strategic planning sessions and departmental goal setting. This ensures that data-driven insights directly inform your organisation's direction.
While AI provides powerful insights, always cross-reference recommendations with your team's deep industry knowledge, local market understanding, and organisational context. Human oversight is crucial for nuanced decision-making.
For every recommendation you implement, establish clear metrics to measure its effectiveness. Track changes in conversion rates, guest satisfaction, operational costs, or other relevant KPIs to validate the AI's suggestions and understand their real-world impact.
Use the "Dismiss" or "Snooze" functions to provide feedback. Explaining why a recommendation was not actioned helps the AI model learn and improve its future suggestions, making it more relevant to your specific needs over time.
Regularly verify the accuracy and completeness of the data your organisation provides to the platform to ensure optimal and reliable insights. The quality of recommendations is directly tied to the quality of the data feeding the AI.
Encourage collaboration across teams to ensure a holistic approach to implementation. Many recommendations may span multiple departments (e.g., marketing recommendations affecting product development).
Need help?
For further assistance, contact us at [email protected]