Pre-Deployment Intelligence

Overview

Pre-Deployment Intelligence is a suite of five simulation APIs that answer “what if?” questions about proposed configuration changes. Instead of deploying a new policy, decisioning gate, or experiment and hoping for the best, you can preview its impact on your actual customer data first. Each API samples real data from your tenant’s interaction history, customer segments, and schema tables to produce quantitative impact estimates. No changes are written — these are read-only analytical queries.

All Pre-Deployment Intelligence endpoints require admin or editor role (except Budget Forecast, Power Calculator, and Segment Overlap, which also allow viewer). Responses are computed on-demand from live data, so execution time scales with sampleSize.

Policy Impact Preview

Endpoint: POST /api/v1/contact-policies/impact-preview Previews how a proposed contact policy (frequency cap) would affect your customer base. The API samples recent interaction history and calculates how many customers would hit the frequency cap, which offers would be suppressed, and which segments are most affected.

Request Body

{
  "policyType": "frequency_cap",
  "scope": "global",
  "scopeId": null,
  "config": {
    "maxFrequency": 3,
    "periodDays": 7,
    "outcomeType": "impression"
  },
  "sampleSize": 1000
}

Field	Type	Required	Default	Description
`policyType`	string	Yes	—	Type of policy being previewed
`scope`	enum	No	`"global"`	Scope of the policy: `global`, `offer`, `category`, or `channel`
`scopeId`	string	No	`null`	ID of the scoped entity (required when scope is not `global`)
`config.maxFrequency`	integer	Yes	—	Maximum interactions allowed in the period
`config.periodDays`	integer	Yes	—	Length of the frequency window in days (1—365)
`config.outcomeType`	string	Yes	—	Interaction type to count (e.g., `impression`, `click`, `convert`)
`sampleSize`	integer	No	1000	Number of customers to sample (100—10,000)

Response

{
  "totalCustomersAnalyzed": 1000,
  "customersAffected": 142,
  "affectedPercent": 14.2,
  "offersSuppressed": {
    "Premium Card": 89,
    "Cash Back Card": 53
  },
  "avgOffersBeforePolicy": 4.7,
  "avgOffersAfterPolicy": 3.2,
  "topAffectedSegments": [
    { "segment": "High Value", "affected": 67, "total": 312 },
    { "segment": "Early Adopter", "affected": 41, "total": 198 }
  ]
}

Field	Description
`totalCustomersAnalyzed`	Number of customers in the sample
`customersAffected`	Customers who would hit the frequency cap
`affectedPercent`	Percentage of sampled customers affected
`offersSuppressed`	Offers that would be suppressed, with suppression count per offer
`avgOffersBeforePolicy`	Average interactions per customer without the policy
`avgOffersAfterPolicy`	Average interactions per customer with the policy applied
`topAffectedSegments`	Segments most impacted, sorted by affected count

Qualification Reach Estimator

Endpoint: POST /api/v1/qualification-rules/reach-estimate Estimates how many customers qualify or are excluded by a proposed decisioning gate. Queries your actual schema tables to produce reach percentages and field distribution statistics.

Request Body

{
  "ruleType": "attribute_condition",
  "field": "credit_score",
  "operator": "gte",
  "value": 720,
  "schema": "customers",
  "sampleSize": 5000
}

Field	Type	Required	Default	Description
`ruleType`	string	Yes	—	Type of decisioning gate
`field`	string	Yes	—	Column name in the schema table
`operator`	enum	Yes	—	Comparison operator: `eq`, `neq`, `gt`, `gte`, `lt`, `lte`, `in`, `not_in`, `contains`, `not_contains`
`value`	string / number / boolean / array	Yes	—	Value to compare against
`schema`	string	Yes	—	Name of the data schema to query
`sampleSize`	integer	No	1000	Number of rows to sample (100—10,000)

Response

{
  "totalSampled": 5000,
  "qualifying": 3420,
  "excluded": 1580,
  "qualifyPercent": 68.4,
  "excludedBySegment": [
    { "segment": "Young Adults", "excluded": 892 },
    { "segment": "New Customers", "excluded": 445 }
  ],
  "fieldDistribution": {
    "min": 320.00,
    "max": 850.00,
    "avg": 698.42,
    "median": 712.00,
    "p25": 645.00,
    "p75": 755.00
  }
}

Field	Description
`totalSampled`	Total rows sampled from the schema table
`qualifying`	Rows that pass the proposed rule
`excluded`	Rows that would be excluded
`qualifyPercent`	Percentage of sampled rows that qualify
`excludedBySegment`	Breakdown of excluded customers by segment
`fieldDistribution`	Statistical summary of the field values (numeric fields only): min, max, avg, median, p25, p75

Use the fieldDistribution statistics to calibrate your threshold. For example, if the median credit score is 712, a rule requiring gte 720 excludes roughly half your customers. Adjusting to gte 680 (below p25 at 645) would include about 75%.

Budget Burn Projector

Endpoint: POST /api/v1/offers/budget-forecast Projects when a budget will be exhausted based on historical conversion rates. Uses interaction history to estimate daily conversion volume and calculate an exhaustion date with a confidence level.

Request Body

{
  "offerId": "offer_premium_cc",
  "totalBudget": 50000,
  "costPerConversion": 25,
  "lookbackDays": 30
}

Field	Type	Required	Default	Description
`offerId`	string	No	—	Specific offer to forecast (omit for tenant-wide forecast)
`totalBudget`	number	Yes	—	Total budget in currency units
`costPerConversion`	number	Yes	—	Cost per conversion (must be > 0)
`lookbackDays`	integer	No	30	Historical window for rate estimation (1—365)

Response

{
  "totalBudget": 50000,
  "costPerConversion": 25,
  "maxConversions": 2000,
  "historicalConversionRate": 0.0842,
  "estimatedDailyConversions": 12.3,
  "estimatedDaysUntilExhaustion": 163,
  "exhaustionDate": "2026-09-01",
  "confidenceLevel": "high",
  "recommendation": "Budget will last approximately 5.4 months at current conversion rates."
}

Field	Description
`totalBudget`	The budget provided in the request
`costPerConversion`	Cost per conversion provided
`maxConversions`	Maximum conversions the budget can support (`totalBudget / costPerConversion`)
`historicalConversionRate`	Observed conversion rate from the lookback period
`estimatedDailyConversions`	Projected daily conversions based on historical data
`estimatedDaysUntilExhaustion`	Days until budget is fully consumed (null if no conversion data)
`exhaustionDate`	Projected date of budget exhaustion
`confidenceLevel`	`high` (14+ days, 50+ conversions), `medium` (7+ days, 20+ conversions), `low`, or `insufficient_data`
`recommendation`	Human-readable summary with actionable guidance

Experiment Power Calculator

Endpoint: POST /api/v1/experiments/power-calculator Calculates the required sample size and estimated duration for an A/B experiment to reach statistical significance. Uses your tenant’s actual traffic volume to estimate how long the experiment will need to run.

Request Body

{
  "baselineConversionRate": 0.12,
  "minimumDetectableEffect": 0.02,
  "significanceLevel": 0.05,
  "power": 0.80,
  "trafficSplit": 0.5
}

Field	Type	Required	Default	Description
`baselineConversionRate`	number	Yes	—	Current conversion rate (0.001—1)
`minimumDetectableEffect`	number	Yes	—	Smallest effect size you want to detect (0.001—1)
`significanceLevel`	number	No	0.05	Alpha level for the test (0.01—0.20)
`power`	number	No	0.80	Statistical power — probability of detecting a true effect (0.5—0.99)
`trafficSplit`	number	No	0.5	Fraction of traffic to the treatment group (0.1—0.9)

Response

{
  "requiredSampleSize": 7842,
  "perVariant": 3921,
  "estimatedDuration": {
    "days": 16,
    "basedOnDailyTraffic": 490
  },
  "recommendation": "With 490 daily decisions, you need ~16 days to reach statistical significance.",
  "sensitivityTable": [
    { "effect": 0.01, "sampleSize": 31368, "days": 64 },
    { "effect": 0.02, "sampleSize": 7842, "days": 16 },
    { "effect": 0.05, "sampleSize": 1254, "days": 3 },
    { "effect": 0.10, "sampleSize": 314, "days": 1 }
  ]
}

Field	Description
`requiredSampleSize`	Total sample size needed across all variants
`perVariant`	Required sample size per variant
`estimatedDuration.days`	Estimated days to reach the required sample size
`estimatedDuration.basedOnDailyTraffic`	Daily decision volume used for the estimate (from the last 14 days)
`recommendation`	Human-readable summary
`sensitivityTable`	Sample size and duration at different effect sizes for comparison

The power calculator uses the standard two-proportion z-test formula: n = (Z_alpha + Z_beta)^2 * (p1(1-p1) + p2(1-p2)) / (p2-p1)^2. Traffic split adjustments account for uneven splits, which require larger total sample sizes.

Segment Overlap Analyzer

Endpoint: POST /api/v1/segments/overlap Analyzes overlap between two or more customer segments by querying their underlying PostgreSQL views and computing pairwise set intersections. Helps identify redundant segments and targeting conflicts.

Request Body

{
  "segmentIds": ["seg_high_value", "seg_early_adopter", "seg_premium"],
  "sampleSize": 5000
}

Field	Type	Required	Default	Description
`segmentIds`	string[]	Yes	—	Segment IDs to analyze (2—10 segments)
`sampleSize`	integer	No	5000	Maximum customers to sample per segment (100—50,000)

Response

{
  "segments": [
    { "id": "seg_high_value", "name": "High Value", "size": 4200 },
    { "id": "seg_early_adopter", "name": "Early Adopter", "size": 1850 },
    { "id": "seg_premium", "name": "Premium Tier", "size": 920 }
  ],
  "overlaps": [
    { "segments": ["seg_high_value", "seg_early_adopter"], "count": 712, "percent": 38 },
    { "segments": ["seg_high_value", "seg_premium"], "count": 890, "percent": 97 },
    { "segments": ["seg_early_adopter", "seg_premium"], "count": 445, "percent": 48 }
  ],
  "totalUniqueCustomers": 5163,
  "overlapWarnings": [
    "97% of Premium Tier customers are also in High Value — consider merging or adjusting targeting",
    "48% of Premium Tier customers are also in Early Adopter — consider merging or adjusting targeting",
    "38% of Early Adopter customers are also in High Value — consider merging or adjusting targeting"
  ]
}

Field	Description
`segments`	Each segment with its ID, name, and sampled size
`overlaps`	Pairwise overlap: segment pair, intersection count, and overlap percentage (relative to the smaller segment)
`totalUniqueCustomers`	Total unique customers across all segments
`overlapWarnings`	Warnings for segment pairs with 30%+ overlap, with actionable recommendations

Segment overlap queries execute against the segments’ underlying PostgreSQL views. All segments must have materialized views (created during segment refresh). If a segment view does not exist, the API returns a 400 error with a message to refresh the segment first.

Contact Policies

Configure frequency caps and suppression rules previewed by the Policy Impact API.

Decisioning Gates

Define eligibility rules estimated by the Reach Estimator.

Algorithms & Models

Scoring models and experiments powered by the Power Calculator.

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Reference

Pre-Deployment Intelligence

Overview

Policy Impact Preview

Request Body

Response

Qualification Reach Estimator

Request Body

Response

Budget Burn Projector

Request Body

Response

Experiment Power Calculator

Request Body

Response

Segment Overlap Analyzer

Request Body

Response

Contact Policies

Decisioning Gates

Algorithms & Models

​Overview

​Policy Impact Preview

​Request Body

​Response

​Qualification Reach Estimator

​Request Body

​Response

​Budget Burn Projector

​Request Body

​Response

​Experiment Power Calculator

​Request Body

​Response

​Segment Overlap Analyzer

​Request Body

​Response

​Related

Contact Policies

Decisioning Gates

Algorithms & Models

Overview

Policy Impact Preview

Request Body

Response

Qualification Reach Estimator

Request Body

Response

Budget Burn Projector

Request Body

Response

Experiment Power Calculator

Request Body

Response

Segment Overlap Analyzer

Request Body

Response

Related