Effective Price Adjustment on BIGRR

[krisnaBukitVista]

Description

This project aims to streamline and enhance the pricing adjustment process on BIGRR, allowing efficient price modifications while minimizing manual effort. By automating critical components, we aim to free up Wahyu’s bandwidth to focus on strategic discussions.

The current pricing adjustment workflow is manual and time-intensive, requiring two distinct cycles:

• First Cycle: Utilizes default rules but lacks flexibility for sudden decisions.
• Second Cycle: Requires manual interventions to address urgent changes, which consumes significant effort.

Problem

The current pricing adjustment workflow faces several challenges:

• Non-Standardized Input: The input data for pricing adjustments lacks consistency, leading to inefficiencies and potential errors during processing.
• Fragmented Tools and Platforms: The process involves multiple platforms, including Datavista, spreadsheets, and collaborative notebooks, creating a disjointed workflow that increases complexity and time consumption.
• Dependency on Technical Expertise: The price adjustment process requires Wahyu, with his technical background, to manually execute changes. This dependency limits scalability and diverts Wahyu's focus from more strategic discussions.
• The Revenue Management team currently inputs and modifies bottom-line prices in a Coda document, which creates inefficiencies in their workflow.

Solution

• Automate the Second Cycle to enable immediate price changes without manual intervention.
• Integrate features into the BIGRR platform for better flexibility and user convenience.

Feature Placement

All features will be integrated into BIGRR under a new Pricing Page, which includes:

• NAB Generator
• Price Adjustment
• Exception List
• Property Pricing Settings

NAB Generator

• Purpose: Retrieve all NAB data (generic or specific).
• Filters: Dates, Property Name, Property Type, Unit Type
• Action Button: PULL NAB: Fetch data based on selected filters.

Price Adjustment

• Purpose: Display retrieved NAB data with an additional Status column (Staged/Pushed).
• Filters: Same as NAB Generator.
• Bulk Changes: Enable with predefined quick actions:
  • Increase all prices by 5% or 10%.
  • Decrease all prices by 5% or 10%.
  • Refresh current price (reset to 0).
• Visual Indicators: Use icons (up/down) to signify price changes compared to previous values.

Exception List

• Purpose: Table of properties excluded from daily price pushes.
• Columns: Dates, Property Name, Property Type, Unit Type, Expiration Status (Aligned with Dates/Never/Expired).

Property Pricing Settings

• Purpose: Configure property-specific pricing settings (e.g., minimum stay requirements).
• Managing bottom-line prices in BIGRR
• Example Settings:
  • Minimum nights required for booking.
  • Custom rules for property-specific pricing.

Visualization and Analytics

Integrate Datavista to evaluate the performance of manual pricing supervision.

• Key Metrics:
  • Compare booking performance on dates with manual adjustments versus automated rules.
  • Identify patterns correlating price decisions with booking trends.

Measurement metrics

• Manual Effort Reduction: Measure the time saved in manual interventions.
• Response Time: All price adjustments must be time efficient
• Independence: Ensure every member of the Revenue Management team can push prices without seeking technical support or escalating issues.

SLA

• Manual Effort Reduction: Grey can push the price without Wahyu's intervention
  Response Time: All price adjustments must be reflected within 30 minutes of action.

MSM Links

• Implementation

[Vidiskiu]

@krisnaBukitVista confirm design deadline to Nadia during AES meeting

[Vidiskiu]

@krisnaBukitVista @katibpasha Milestone deprioritized