[GTM glossary](/glossary.txt) Lookalike modeling Market & Targeting # Lookalike modeling In short Lookalike modeling is the use of statistical or machine learning methods to score a pool of companies or people by how closely they resemble a set of known best customers. The output is a ranked list of likely good fits. It is the technique behind lookalike audiences and lookalike searches. The focus here is the model: which traits it uses, how it is trained, and how you check that it works. ## How lookalike modeling works ### 1. Define the positive set Choose customers who represent success, for example accounts with strong retention and expansion. Optionally add a negative set of churned or poor-fit accounts. ### 2. Choose the features Features are the traits the model compares: industry, employee count, region, technologies used, hiring patterns, and similar fields. Clean, consistent data matters more than a complex algorithm. ### 3. Score the candidate pool Each candidate gets a similarity score, from a distance measure or a trained classifier. Higher scores mean a closer match to the positive set. ### 4. Validate Hold back some known customers, mix them into the pool, and check whether the model ranks them near the top. If it does not, revisit the features. --- ## Simple vs. advanced approaches * **Rule-based:** Filter on the traits your best customers share. Transparent and fast, but it misses patterns you did not think of. * **Distance-based:** Measure how far each candidate sits from the average best customer across many traits. * **Supervised models:** Train a classifier on wins versus losses. More powerful, but needs enough labeled outcomes to be reliable. --- ## Practical example Say a team has 150 retained customers and wants new targets. It builds a feature table, scores 50,000 candidate companies, and sends the top 2,000 to outbound. It then compares reply and win rates for the top-scored group against a random sample to confirm the model adds value. All figures here are illustrative. --- ## Common mistakes * **Tiny seed set:** With very few customers the model overfits to quirks of those accounts. * **Biased history:** If you only ever sold to one segment, the model will only find that segment. * **No ongoing check:** Retest as products and markets change. --- ## Lookalike modeling FAQs **What is lookalike modeling?** Lookalike modeling is a method that scores companies or people by how closely they resemble your best customers, using statistical or machine learning techniques. The result is a ranked list of prospects most likely to be a good fit. **How does lookalike modeling work?** You define a set of successful customers, choose the traits to compare, and score a candidate pool by similarity. You then validate by checking whether known customers rank highly, and refine the traits if they do not. **How many customers do you need for lookalike modeling?** There is no universal minimum, but very small seed sets overfit to the quirks of a few accounts. More customers, and a clear definition of success, produce more stable results. Validate the output against held-back customers. **Is lookalike modeling the same as an ICP?** No. An ICP is a written description of ideal customers, usually in a handful of traits. Lookalike modeling is a scoring method that can weigh many traits at once and find accounts that match patterns an ICP may not capture. Share term --- ## More related phrases * [Machine learning model](/glossary/machine-learning-model.txt) * [Companies like](/glossary/companies-like.txt) * [Customer look-back analysis](/glossary/customer-look-back-analysis.txt) ## Similar phrases * [Lookalike audiences](/glossary/lookalike-audiences.txt) Lookalike audiences are groups of people or companies that resemble a seed list of your best customers, built mainly by ad platforms to extend campaign reach to similar profiles. * [Lookalike search](/glossary/lookalike-search.txt) Lookalike search is a prospecting method where you start from one or more example companies and search a database for others with similar firmographic, technographic, or content traits. * [Ideal customer profile (ICP)](/glossary/ideal-customer-profile.txt) An ideal customer profile (ICP) describes the type of company that gets the most value from your product and brings you the most revenue. * [Fit score](/glossary/fit-score.txt) A fit score is a number that rates how closely a company or contact matches your target customer, using static traits such as industry, size, and tech stack. * [Lookalike companies](/glossary/lookalike-companies.txt) Lookalike companies are accounts that resemble your best customers across firmographic, technographic, and behavioral traits, found by comparing company data.