One customer, one profile—no matter how many channels they use
Graph-based entity resolution unifying fragmented identifiers across 12+ touchpoints into a single, consent-aware customer record.
The same customer appears as three records in three systems—and every downstream decision inherits that fragmentation.
The Challenge
A multi-channel retailer with 4M+ customer records across e-commerce, point-of-sale, loyalty, mobile app, and customer service systems found that 22% of records were duplicates or fragments of the same person. Marketing campaigns over-contacted some customers while missing others entirely. The loyalty program double-counted redemptions. Customer lifetime value models were unreliable because purchase history was split across identities. Previous deterministic dedup (exact email match) resolved only 40% of duplicates—the remaining 60% had name variations, address changes, or shared household attributes that required fuzzy matching at scale.
The Innovoco Solution
We built an identity resolution platform that maps every customer identifier—email, phone, address, loyalty ID, device—and uses probabilistic matching and relationship analysis to merge records that belong to the same person. The result is a single golden record with full provenance and confidence scoring for every merge decision.
Phase 1 — Identity graph construction
Ingested identifiers from all source systems and mapped the relationships between them. Shared attributes—same phone on two accounts, similar addresses, matching devices—create weighted connections. Smart grouping strategies limit comparisons to plausible matches, then score each candidate pair on name similarity, address proximity, and recency.
Phase 2 — Resolution + activation
Applied clustering algorithms to identify groups of records that belong to the same person. Produced golden records with merge provenance and exposed resolved profiles via API to the CDP, CRM, loyalty, and marketing platforms. Consent and preference records follow the golden identity—so opt-outs propagate across all fragments automatically.
Key implementations
Multi-signal probabilistic matching
Name similarity, address proximity, phonetic encoding for spelling variations, and device fingerprint overlap—combined into a composite confidence score per candidate pair.
Graph community detection
Clustering algorithms identify groups of identifiers that belong to the same person—handling transitive matches that pairwise dedup misses (A matches B, B matches C, so A-B-C are one person).
Merge provenance and explainability
Every golden record carries a full audit trail: which source systems contributed each attribute, the confidence score of each merge, and the algorithm version—critical for GDPR/CCPA compliance and customer service dispute resolution.
Consent propagation
Opt-out and preference signals follow the resolved identity—when one fragment opts out, the golden record propagates that across all channels. No more contacting a customer who already said no through a different touchpoint.
Real-time incremental resolution
New events (purchases, logins, support calls) are matched against the existing graph incrementally—not in nightly batch. Resolution latency under 200ms for activation use cases.
Technical Innovation
The identity model places each person at the center with their identifiers (email, phone, address, device) radiating outward. Shared identifiers create implicit links between person candidates. Smart grouping reduces the comparison space, then scoring combines name similarity, address proximity, and temporal signals. AI-powered clustering identifies matches that simple rules miss—particularly household-level resolution where family members share addresses and devices but are distinct people.
Impact
- Duplicate records reduced from 22% to under 3%—golden records cover 94-98% of the true customer base with high-confidence merges.
- Marketing spend efficiency improved 28%—campaigns reach actual unique customers, not fragments, eliminating redundant impressions and conflicting offers.
- Customer lifetime value models became reliable—purchase history unified across channels produced 35% more accurate CLV predictions for retention targeting.
- Consent compliance achieved across all channels—opt-outs propagate in real time, eliminating the risk of contacting customers through unresolved fragments.
The retailer sees one customer where they used to see three—marketing is more efficient, loyalty is more accurate, and compliance is automatic. The identity graph becomes the foundational layer that every downstream system trusts.
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