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What is an entity propensity model?
Bill Schmarzo defined an EPM in his March 11, 2025 article as “a predictive analytic profile that quantifies an entity’s likelihood of a specific outcome or behavior.” The entity might be an individual customer, a business, a piece of equipment or an athlete; the outcome must be specified, such as buying, failing, resolving a service issue or dropping out.
An EPM therefore answers a forecasting question: How likely is this entity to do or experience X within a stated time period? It does not, by itself, answer whether offering a discount, changing a treatment or assigning a technician will cause X to happen.
What an EPM is not
- Not entity resolution. Entity resolution is record linkage: determining which records refer to the same real-world person, business or establishment. The U.S. Census Bureau describes that challenge in its 2024 Federal CASIC Workshops archive.
- Not a causal or uplift model. A high purchase propensity does not prove that an advertisement caused a purchase, nor that the advertisement will create an incremental sale.
- Not a guarantee. A probability is useful only with a decision threshold, a time horizon and a plan for acting on the prediction.
How could EPMs create economic value?
The proposed value comes from changing a decision before the outcome occurs. A model might rank accounts for retention outreach, flag equipment for preventive maintenance, identify patients who need additional infection-control attention or route a complex support case to a more experienced technician. These are mechanisms described or suggested in the source, not independently validated deployments.
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Targeting and conversion
If an organization has a limited number of calls, offers or sales visits, a propensity ranking could focus those resources on entities with a higher predicted likelihood of responding. The business case must measure incremental conversions against the existing targeting method, not merely the response rate among people selected by the model.
Risk and loss prevention
Predictions can support earlier review of possible breakdowns, defaults or safety events. Economic value depends on avoided loss after accounting for investigation costs, false alarms, missed events and any harm caused by unnecessary intervention.
Capacity and operating efficiency
Forecasts can help schedule staff, prioritize maintenance or allocate inventory. Benefits are credible only when the new allocation improves a defined operational measure—such as downtime, first-contact resolution or service cost—relative to a realistic baseline.
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Reuse and updating
A model may be reused across related decisions and refreshed as new data arrives. Reuse can lower the cost per decision, but drift, new data-collection costs and integration work must be included rather than treated as free.
What data and techniques are used?
An EPM normally combines historical records describing the entity, context and outcome. Depending on the decision, inputs could include transactions, service history, sensor readings, clinical observations, game situations or engagement events. The model may use statistical or machine-learning methods, but the method is secondary to a clearly defined target and reliable labels.
Minimum design specification
- Entity: the person, account, asset or other unit being scored.
- Outcome: the event to predict, with an exact definition.
- Horizon: when the event must occur, such as 30 days or one season.
- Action: the intervention available after scoring.
- Threshold: the score at which the organization acts.
- Comparator: the current process or another model.
Accuracy measured in a laboratory or at an arbitrary threshold may not translate into value at the operating threshold. Calibration, coverage, latency and the cost of obtaining each input matter as much as a headline accuracy metric.
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How are propensity models used in baseball?
The source opens with Strat-O-Matic, a baseball simulation board game whose player cards describe tendencies. That is an analogy for an analytic profile, not evidence that the cards are machine-learning models.
The article proposes illustrative player-level scenarios: selecting a reliever for a particular matchup, estimating a batter’s weakness against a pitch type, predicting where a ball may be hit to position defenders, informing scouting and acquisition, managing workload, preventing injury and supporting return-to-play decisions. No named Major League Baseball team, dataset, deployed system or measured win, injury or financial result is supplied. These examples should therefore be read as possible applications, not case-study outcomes.
What evidence exists for economic impact?
No published EPM-specific dollar, percentage or controlled-return figure was identified for the claims in the title article. Several adjacent statistics are informative about analytics or algorithmic interventions, but none proves that EPMs generally produce a particular return.
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| Finding | What it measures | Limitation |
|---|---|---|
| 11% to 30% adoption | Erik Brynjolfsson and Kristina McElheran’s 2016 study reports that U.S. manufacturing plants’ adoption of data-driven decision-making nearly tripled between 2005 and 2010. | This is adoption of data-driven decision-making, not EPM adoption or EPM ROI. |
| 356% three-year ROI | A 2021 Forrester Consulting study commissioned by FICO, as reported by FICO, modeled a composite $10 billion financial-services organization using FICO Decision Modeler. | It is vendor-commissioned, product-specific modeled evidence, not an independent EPM-wide result. |
| High estimated social returns | A 2024 paper by Jens Ludwig, Sendhil Mullainathan and Ashesh Rambachan evaluates selected algorithmic interventions in regulation, criminal justice, medicine and education. | The paper does not study EPMs as a category, and its authors caution that high estimates do not automatically mean an intervention should be scaled. |
How to test whether an EPM pays off
A finance or operations team can turn the proposition into a measurable business case by following a defined comparison rather than extrapolating from a score.
- Specify the decision. Name the entity, outcome, horizon and action. “Improve retention” is insufficient; define which customers receive which intervention and when.
- Cost the baseline. Record current conversion, loss, labor, delay or downtime, including variation over time.
- Cost the model. Include data acquisition, preparation, software, engineering, validation, integration, monitoring, human review and security.
- Choose a credible comparator. A randomized holdout is strongest when feasible. Otherwise, use a defensible quasi-experimental design and disclose its assumptions.
- Measure incremental outcomes. Separate model selection from intervention effect. Compare outcomes for entities offered the intervention with comparable entities receiving the baseline process.
- Include errors and side effects. Price false positives, false negatives, appeals, customer attrition, missed opportunities and any remediation.
- Calculate net value. Report incremental benefit minus recurring and one-time costs, with the period, discounting assumptions and sensitivity to different thresholds.
- Monitor after launch. Track calibration, drift, data coverage, latency, group-level performance and whether staff follow or challenge the recommendation.
What risks can erase the economic benefit?
Algorithmic decision systems can create discrimination, unfair treatment, loss of autonomy or reduced access to markets. The European Parliament Research Service’s 2019 review addresses these risks broadly; it is not an EPM-specific finding.
- Data quality: Missing, stale or selectively collected data can make scores unreliable.
- Unequal error rates: A single threshold may produce different false-positive or false-negative burdens across groups.
- Feedback loops: Decisions based on an earlier score can change the data later used for training.
- Privacy and security: More individualized profiles increase the consequences of unauthorized access or inappropriate reuse.
- Governance: People need a documented reason for the score, a way to challenge it and authority to override it when circumstances differ.
The UK Government Digital Service and Department for Science, Innovation and Technology’s Digital and Data Benefits framework, published April 7, 2026, provides a useful context for quantifying benefits and costs; it is guidance, not evidence of EPM outcomes.
What this means for financially minded decision-makers
For a bank, insurer, employer or household-facing service, an EPM should be treated as a decision aid with a priced intervention—not as a revenue engine by itself. Before approving investment, ask:
- What specific action follows a high score?
- What does that action cost per entity?
- How much better is it than the current rule or human process?
- Who bears the cost of an error?
- Can the organization explain, audit and contest the decision?
- What evidence will justify expanding, changing or stopping the program?
Until those questions are answered with comparator-based results, “transforming the game” remains a plausible economic thesis rather than an established return.
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