Understanding Our Data

To trust the insights, it helps to understand the journey your data takes. We move data through three main stages: Raw, Cleaned, and Processed.

The Data Lifecycle

1. Raw Data: The “Untouched” Source

This is the data exactly as we receive it from your Property Management System (PMS).

Characteristics: It may contain duplicates, inconsistent formatting, and system-specific codes.
Example: A booking might have a status code “CHKOUT” and a country code “US”.
Why we keep it: We always keep a copy of the original data so we can trace back any issues or re-process it if our rules change.

2. Cleaned Data: Standardized & Error-Checked

In this stage, we apply a set of rules to make the data consistent and easy to use. We don’t change the meaning of the data, just its format.

Key Cleaning Steps:

Standardization: We convert all dates, phone numbers, and addresses to a standard format.
- Example: “01/02/2023” becomes “2023-02-01” (YYYY-MM-DD).
- Example: “+1 (555) 123-4567” becomes “+15551234567”.
Translation: We map system-specific codes to human-readable Guestpulse standards.
- Example: “CHKOUT” becomes “Checked Out”.
- Example: “CNCL” becomes “Cancelled”.
Deduplication: We remove exact duplicate records that might have been sent multiple times by the source system.

3. Processed Data: The “Golden Profile” & More

This is the final, most valuable stage. It’s not just one step; it’s a pipeline of smart processes that enrich your data.

Step 1: Metric Calculation

Before we even look at who the guest is, we calculate key numbers about their booking.

Reservation Metrics: We calculate the Booking Window (days booked in advance), Length of Stay, and Cancellation Window.
Room Metrics: We calculate exact Stay Nights and Room Revenue for each day of the stay.

Step 2: Guest Matching (De-duplication)

Now we identify who the guest is. We look for clues that tell us two records belong to the same person.

Matching Logic: We match based on Email, Phone Number, or Name + Date of Birth.
Merging Logic: We merge duplicates into a single “Golden Profile”, keeping the most complete and recent information.

Step 3: Loyalty Analysis

With the Golden Profile established, we can look at the guest’s entire history to understand their value.

Lifetime Spend: We sum up the revenue from all their past stays.
Returning Guest: We identify if this is their first visit or if they are a regular.
Properties Visited: We track which of your properties they have visited, helping you identify cross-brand loyalty.

Step 4: Value Replacement

Finally, we apply any specific business rules you’ve configured.

Example: Mapping an old, retired rate code to a new reporting category so your reports stay consistent over time.

This fully processed data is what powers all your reports, ensuring you have a deep, accurate view of your business.