Profile Merging and Golden CDP Profiles

In modern hospitality, guest data is everywhere — in booking engines, property management

systems (PMS), CRMs, Wi-Fi sign-ups, restaurant systems, and loyalty programs. Each

interaction creates a new fragment of identity. The same traveler can appear under multiple

.)”محمد“ ,”names, emails, phone numbers, or even scripts (e.g., “Mohammed”, “Мухаммед

This fragmentation challenges hotels to deliver personalized experiences, accurate reporting,

and compliant communication.

Our deduplication and golden profile merging process addresses this problem by using

intelligent normalization, weighted similarity scoring, and consent-aware selection to form one

reliable, unified guest identity — the Golden Profile.

The Challenge: Fragmented Guest Identities

1. Multiple Booking Contexts

Guests book through OTAs, direct websites, corporate channels, or phone reservations. Each

source may hold different or incomplete details:

OTAs often mask emails or phones.

Corporate systems use placeholders or aliases.

Manual check-ins introduce spelling or formatting variations.

2. Data Quality and Script Diversity

In hospitality, data entry is global. The same name can appear in Latin, Cyrillic, or Arabic

scripts, and transliteration differences (e.g., Mohamed, Muhammad, Mohamad) make simple

text comparison ineffective.

3. Communication Consent

Guests may provide marketing consent in one system but revoke it in another. Keeping track of

the most up-to-date consent per channel (email or phone) is critical for GDPR-compliant

communication.
 

Our Approach

Our deduplication engine unifies scattered records into a single, accurate guest representation

through three key stages:

1. Attribute-Level Normalization

Each field is cleaned and standardized before comparison.

This ensures “müller”, “Mueller”, and “Мюллер” all align to the same normalized form.

Name

• Lowercase, trimmed, and transliterated from non-Latin scripts (e.g., “Мухаммед” →

“mukhammed”).

• Germanic ligatures and umlauts replaced: ä→ae, ö→oe, ü→ue, ß→ss.
• Accents removed (é→e, ç→c).
• Multiple spaces collapsed; non-letters removed.
• Phonetic fallback: A code per token captures similarity across spelling and script variations.

Email

• Temporary OTA aliases (e.g., booking.com) are ignored.
• Lowercased and trimmed.
• Gmail tags (“+tag”) stripped, and common domain typos corrected (e.g., gmai.com →

gmail.com).

Phone

• Trimmed, normalized (“00” → “+”), and stripped of symbols.
• Comparison uses only the last five digits for robustness against formatting differences.

Birthday / Country

• Kept as standardized date and lowercase text.

2. Weighted Similarity Scoring

Each attribute contributes a weighted score toward an overall match confidence.

Only fields that meet their individual acceptance thresholds contribute.

This ensures:

Shared email and phone alone (20 + 25 = 45) don’t merge family members.

Typo-tolerant, cross-script matches (e.g., دمحم, Мухаммед, Mohammed) do merge correctly

when combined with other fields.

3. Consent-Aware Golden Profile Creation

When profiles are identified as duplicates, they are grouped into a cluster.

From this cluster, the system builds a unified Golden Profile.

Building a Cluster

All database profiles with basic similarity to a new or updated record are retrieved.

Pairwise matching is performed across this pool, counting transitive links — ensuring that

interconnected duplicates are merged while unrelated profiles remain separate.

Selecting the Best Identifiers

Only non-temporary emails and valid phone numbers are considered.

Consent records (email or phone) are evaluated:

1. Profiles with more consent = true entries are preferred.

2. If equal, the one with the most recent consent update wins.

3. If no consent difference exists, the most frequently used identifier across the cluster is

chosen.

The chosen identifier is stored together with its respective consent status.

Merging Demographic Attributes

For categorical fields, the most common value across the cluster is used:

Names → mode of first/last name; combined into full name.

Gender → most common non-unknown value.

Country, language, birthday, address → most frequent valid value.

Each merged Golden Profile represents the statistically most complete and trustworthy

view of that guest.

Why This Approach Works

1. Domain-Aware Precision

The algorithm balances technical rigor with hospitality-specific realities:

Handles OTA placeholders gracefully.

Understands cross-language and transliteration differences.

Prevents false merges for families or shared bookings.

2. Data Quality Amplification

By normalizing, weighting, and validating every attribute, the system upgrades raw, inconsistent

PMS data into structured, comparable, and analytics-ready guest identities.

3. Compliance by Design

Consent tracking is built into identifier selection, ensuring marketing and CRM integrations

always use channels with verified, up-to-date permission.

4. Scalable Intelligence

Each deduplication cycle reuses past cluster relationships.

As more data flows in, the Golden Profiles become richer, not redundant.

Outcome

The result is a single source of truth per guest:

• a Golden Profile that integrates every verified data point across systems, languages, and

channels.

• Hotels benefit from: Reliable, deduplicated guest data.
• Accurate lifetime value analytics.
• Personalized communication based on real identity.
• Automated GDPR-compliant contact management.