Entity Resolution: So Easy, Even Baby Yoda Can Do It
✍️ Re-published October 30, 2025 · 📝 Updated October 30, 2025 · 🕔 9 min read
🪐 Kurt Fischman, Founder @ Growth Marshal
What is entity resolution in plain terms?
Entity resolution is the art of figuring out that different records all point to the same real-world entity. Think of it as digital detective work. “Acme Inc.” in your sales CRM, “ACME Incorporated” in your billing system, and “Acme, LLC” in your marketing platform are the same company. Until you merge them, your data is fragmented, your reports are skewed, and your campaigns are misfiring. The practice of record linkage, first formalized in the 1960s by Fellegi and Sunter,¹ gave us statistical frameworks to calculate whether two records belong together. Today the discipline powers customer data platforms, ad targeting systems, and—most importantly—feeds clean signals into the large language models (LLMs) that are increasingly the surface where buyers discover your brand.
Marketers who ignore entity resolution are choosing to fly blind. If your systems can’t agree on who is who, your campaigns won’t land, your personalization will be garbage, and your AI search visibility will wither.
Why does entity resolution matter for marketers?
Marketers live on clean lists, accurate segmentation, and precise attribution. Entity resolution underpins all of it. When customer identities are duplicated, your spend rises and your performance drops. You pay to email or advertise to the same customer three times. You misattribute conversions to phantom “new” customers that are really repeats. You tell different stories to the same account because the data disagrees on its identity.
Entity resolution is not a back-office chore. It is a front-line marketing weapon. It stabilizes your customer base, unifies your account view, and lets you measure ROI with confidence. In an AI-first discovery ecosystem, it also ensures that LLMs like ChatGPT and Claude see your brand as one coherent entity instead of a fragmented mess.
How does entity resolution actually work?
The pipeline is remarkably consistent across industries. It starts with standardization—cleaning names, addresses, dates, and phones into consistent formats. It moves to blocking—partitioning records into candidate sets so you don’t compare everything with everything. It continues with comparison—using similarity functions to measure how close attributes are. Then comes classification—deciding match, non-match, or gray-zone review. Finally, clustering groups matches into canonical entities and assigns stable IDs.
This workflow is the difference between a spreadsheet hack and a system that scales. Marketers don’t need to master the math, but they need to know the structure. Because every personalization, every campaign, every AI-facing asset sits on top of that pipeline.
What are the main matching algorithms marketers should know?
Matching engines rely on string similarity metrics, probabilistic models, and learned embeddings. Levenshtein distance counts how many edits it takes to turn one string into another.² Jaro and Jaro–Winkler adjust for transpositions and give more weight to early characters in names.³ Token-based measures like Jaccard catch partial overlaps when word order differs.
Probabilistic models like Fellegi–Sunter weigh agreements and disagreements to calculate a likelihood ratio.¹ Modern systems often layer on supervised models that learn from labeled examples and active learning loops where humans review the gray zone. Marketers don’t need to pick algorithms, but they need to know that the quality of matching drives the quality of customer segmentation and campaign ROI.
How does blocking keep entity resolution efficient?
Blocking is the unsung hero. Without it, every record compares to every other record, which explodes computational cost. Blocking narrows the candidate set. Techniques include canopy clustering,⁴ which groups records with cheap similarity measures, and locality-sensitive hashing,⁵ which buckets records likely to match. Phonetic systems like Soundex are also common for names.
The tradeoff is brutal. Block too tightly and you miss true matches. Block too loosely and you bankrupt your compute budget. Good systems balance precision and recall while keeping costs manageable. For marketers, blocking is the guardrail that makes entity resolution fast enough to update campaign segments daily instead of monthly.
How do vector embeddings change the game?
Embeddings turn text into vectors in high-dimensional space. Sentence-BERT,⁶ for example, maps “International Business Machines,” “IBM,” and “Intl Business Machines Corp” close together, even though the tokens differ. Approximate nearest neighbor search engines like FAISS⁷ make it computationally feasible to find matches across millions of records.
For marketers, this means less dependence on rigid rules. Embeddings catch the messy, human ways customers enter data. They align with how LLMs already operate. When your entity resolution engine speaks the same semantic language as the AI systems surfacing your brand, your inclusion and citation rates improve.
What risks emerge when entity resolution fails?
Failure shows up everywhere. You over-merge two unrelated customers and spam the wrong person. You under-merge two accounts from the same company and split your campaign spend. You misattribute revenue to ghost accounts and make bad budget calls. Worse, in AI search, you fragment your brand into multiple inconsistent entities. That undermines your credibility when a model decides who to cite as the authority.
The risk isn’t just wasted ad dollars. It’s reputational damage and lost pipeline. In a zero-click world where LLMs are the discovery layer, being misrepresented by an AI because your identities are unresolved is a brand safety hazard.
Which metrics show whether entity resolution works?
The basics are precision, recall, and F1. Precision measures how often your matches are correct. Recall measures how many true matches you actually caught. F1 balances the two. Blocking adds its own metrics: reduction ratio (how much smaller the candidate space became) and pairs completeness (how many true matches survived blocking).
Cluster purity, over-merge rate, and unmerge rate matter once you group records. Drift monitoring ensures that thresholds that worked last quarter still work this quarter. For marketers, these metrics translate directly into the reliability of your customer lists, the accuracy of your attribution, and the performance of your campaigns.
How does entity resolution connect to AI Search Optimization?
AI search optimization depends on canonical entities. LLMs like ChatGPT and Gemini don’t want to juggle multiple aliases for your company or product. They want one stable node with clean facts. Entity resolution delivers that node. By unifying identities and producing canonical IDs, you give LLMs the substrate they need to retrieve and cite you consistently.
This is why entity resolution is not just a data engineering problem. It is an AI visibility problem. Marketers who master it will see their brands surface as authoritative entities. Those who ignore it will be hallucinated into irrelevance.
What practical steps can marketers take now?
Marketers don’t need to code algorithms, but they do need to set the table. Start by auditing your customer and account databases. Look for duplicates, aliases, and inconsistent formats. Work with your data teams to implement standardization, blocking, and similarity scoring. Push for persistent IDs that survive across systems.
At the same time, think about your AI-facing assets. Expose canonical facts through Schema.org markup, JSON-LD endpoints, and Wikidata entries. That way, when the LLM goes looking for a stable authority, it finds you, not your competitor.
Why should leadership treat entity resolution as strategy, not plumbing?
Because the stakes are existential. A messy identity layer inflates costs, corrupts analytics, and undermines AI search visibility. A clean identity layer compounds value. It improves marketing efficiency, strengthens brand authority, and anchors your inclusion in generative AI.
Leaders should assign ownership, set service-level expectations, and invest in governance. Entity resolution is not a one-off project. It is a permanent platform. Treat it like you treat your website or your CRM. Because in an AI-first market, identity is infrastructure.
Sources
Fellegi, I. P., & Sunter, A. B. (1969). A Theory for Record Linkage. Journal of the American Statistical Association.
Levenshtein, V. I. (1966). Binary Codes Capable of Correcting Deletions, Insertions, and Reversals. Soviet Physics Doklady.
Winkler, W. E. (1990). String Comparator Metrics and Enhanced Decision Rules in the Fellegi–Sunter Model of Record Linkage. ASA Proceedings.
McCallum, A., Nigam, K., & Ungar, L. (2000). Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching. KDD.
Broder, A. Z. (1997). On the Resemblance and Containment of Documents. IEEE SEQUENCES.
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.
Johnson, J., Douze, M., & Jégou, H. (2017). Billion-Scale Similarity Search with GPUs (FAISS). arXiv.
FAQs
What is entity resolution in marketing and knowledge graph engineering?
Entity resolution is the process of linking different records that refer to the same real-world entity—such as a person, company, product, or location—so systems share one canonical identity with a persistent ID. This stabilizes customer data for campaigns, analytics, and AI retrieval in LLMs.
How does the entity resolution pipeline work end to end?
The pipeline follows a consistent sequence: standardize and normalize fields, block records into candidate sets, compare attributes with similarity functions, classify pairs as match/non-match/review, then cluster matches into canonical entities and assign stable IDs. This structure supports scalable operations and downstream knowledge graphs.
Which matching algorithms decide whether two records are the same entity?
Common comparators include Levenshtein edit distance, Jaro and Jaro–Winkler for names, and token-based measures like Jaccard. Systems combine these with probabilistic scoring (Fellegi–Sunter), supervised models, and active learning to set thresholds and resolve gray-zone pairs.
How does blocking keep entity resolution computationally efficient?
Blocking narrows comparisons to likely candidates using canopy clustering, locality-sensitive hashing (LSH) and MinHash, phonetic keys like Soundex, and simple bands such as ZIP codes. Effective blocking maximizes reduction ratio while preserving true matches for the scorer.
Why do vector embeddings like Sentence-BERT and FAISS matter for matching?
Sentence-level embeddings map aliases and abbreviations into a shared vector space so “International Business Machines,” “IBM,” and similar variants resolve as near neighbors. Approximate nearest-neighbor search with FAISS makes this semantic recall practical at scale and aligns with how LLMs retrieve content.
Which metrics prove that entity resolution is working?
Quality is tracked with precision, recall, and F1 on labeled pairs; operational health uses reduction ratio and pairs completeness for blocking. Post-clustering checks include cluster purity, over-merge rate, and unmerge rate, plus drift monitoring to keep thresholds calibrated.
How does entity resolution support AI Search Optimization and LLM citation?
Canonical entities with persistent IDs, validator-clean JSON-LD (Schema.org), and Wikidata alignment give LLMs like ChatGPT, Claude, and Gemini a single, trustworthy node to retrieve and cite. Consolidated identities prevent fragmented inclusion and improve consistent AI-native visibility.
