Revenue / Increase Conversion Rate 20% daily consistently

[krisnaBukitVista]

Description

The objective of this project is to enhance the existing AI system, MOR, to automatically offer cross-selling opportunities. The enhancements will include updating event information, integrating hyperlinks for quicker manual follow-up, adding more models to offer on short periods of checking out, and incorporating guest sentiment analysis for validation.

Problem

• Outdated Event Information
• Inefficient Manual Follow-Up
• Limited Cross-Selling Models
• Lack of Sentiment-Based Validation

Solutions

task written on GAIA MOR @1.1 here Update the Event Information

• Label "Failed Inquiry" or "Cross-Selling": Clearly label each follow-up event to distinguish between failed inquiries and cross-selling opportunities.
• Provide User Last Messages and GAIA Answer: Include the last message sent by the user and the corresponding response from GAIA in the follow-up data. This helps provide context and continuity in communication.
• Comparison Between Current Inquiry and Cross-Sell Offer: Present a clear comparison between the original inquiry and the cross-sell offer. This highlights the benefits or alternatives being suggested.

• Sentiment Analysis Data: Incorporate sentiment analysis of the user's previous messages to tailor the follow-up approach based on the guest's mood and likelihood to convert.

  Add Hyperlink to Airbnb for Quicker Manual Follow-Up

• Making it easier for reservation team to do follow up such as providing special offer

Add More Models to Offer on Short Period of Checking Out

• Provide model that focuses on short period of check-out such as 2 days

Add Validation Based on Guest Sentiment Analysis

• Implement sentiment analysis tools to assess the tone and content of guest feedback and inquiries.
• Use sentiment data to validate follow-up messages, ensuring they are appropriately tailored to the guest's mood and likelihood to convert.

Measurement metrics

Conversion Rate: Track the percentage of follow-up inquiries that result in bookings.

SLA

The Service Level Agreement (SLA) for this project is to achieve a 20% conversion rate on follow-up inquiries within one month of implementing the enhancements.

Prioritization result

Relevancy

• Revenue Potential: Implementing this strategy can significantly boost our revenue by providing guests with compelling reasons to extend their stay.
• Strategic Location: Showcasing local events highlights the strategic location of our properties. This not only serves as an attraction for potential guests but also enhances our location rating as a secondary benefit.

Feasibility

• Event Information and Hyperlink Updates: We have successfully implemented event information and hyperlink updates on other GAIA channels.
• Validation Modification: The methods required for validation modifications are already in place. We can leverage sentiment analysis with only minor adjustments, ensuring that the implementation process is efficient and effective.
• Model Experimentation: We possess the necessary model framework. By modifying specific variables, such as the checkout date, we can tailor the models to meet the new requirements.

Impact

• Conversion Rate: With this project, we aim to achieve a consistent 20% conversion rate, driven by the added value and convenience of providing updated event information to our guests.
• Location Rating: By highlighting the vibrant events around our properties, we aim to increase our location rating by 5%. This improvement will make our properties more appealing to potential guests, enhancing our overall market competitiveness.

Team Member

@rifqinvnd

[katibpasha]

Will be evaluated by @katibpasha

[katibpasha]

Evaluation Result

The development stage has been finished on Aug 16th 2024.

Model Improvement

• Now, MOR also targets the guests who will check-out in the next 2 days and 7 days

• We also implemented the sentiment analysis to provides insights into customer feelings and experiences, guiding us in making informed improvements to our services by only send offer to positive sentiment guests

Slack Message UI Improvement

• Linked-up the guest's booking id to Airbnb conversation page, made users easier find the specific guest's conversation
• We successfully added guest last message section in event recommendation to provides the user’s last message and GAIA’s answer ensures clear communication and allows us to assess the AI's performance

Inquiry Conversion Rate

Based on this dataVista visualization, we successfully achieved the minimum target of inquiry conversion rate for 20%

Conclusion

We have met our minimum target for the inquiry conversion rate, achieving a 20% success rate as confirmed by the DataVista visualization.

cc @krisnaBukitVista @Vidiskiu

[Vidiskiu]

Overall Point: 6.1

Functional Complexity: 1.1

Enhancing the AI system to support cross-selling involves several functional improvements which are innovative but fairly complex to integrate cohesively.

Technical Complexity: 1.1

Technical complexity is high due to the integration of sentiment analysis and the need to update models for different scenarios, although existing frameworks and methodologies may ease the process.

UI/UX Complexity: 0.6

While UI/UX changes seem minor, the incorporation of hyperlinks and clear labeling demands thoughtful design to improve efficiency for manual follow-ups.

Data Manipulation: 0.9

Significant data manipulation is required to manage event information, sentiment analysis data, and model outputs to provide actionable insights.

Testing: 0.5

Rigorous testing is necessary to ensure the reliability of new features and their impact on conversion rates, warranting maximum score within this category.

Dependencies: 0.4

There are dependencies such as external sentiment analysis tools and data from Airbnb which add complexity, but these integrations are manageable given prior experience.

Risk and Uncertainty: 0.5

Any update to the revenue-generating components includes the risk that changes may not yield the expected conversion rate increase, justifying the highest score within this limit.

User Impact: 1

Successfully improving conversion rates and guest experiences with more personalized follow-ups has a direct and substantial impact on revenue and user satisfaction, deserving the highest allocation.