Collection methodology

Trust the row, not the marketplace count.

A useful commerce feed preserves where and when a value was observed, explains how products were matched, and makes missingness visible. This is how PLOTT scopes and validates that contract before production.

Request representative rows Review ready sources

From requirement to validated feed

Source access is only the first step. The operating discipline below is what makes recurring observations usable for pricing, digital shelf, research, diligence, and AI products.

1. Freeze the question

Define sources, countries, locations, accounts or sessions, products or queries, fields, cadence, history, and the decision the data must support.

2. Prove source state

Confirm the right store, postal code, language, currency, seller, and delivery context. A valid page from the wrong location is a failed observation.

3. Preserve raw evidence

Retain the source URL, source identifier, observed time, collector version, and raw or replayable evidence needed to explain a normalized record.

4. Normalize without guessing

Map source values into typed fields, keep source-native values, use explicit nulls, and separate observed facts from derived classifications.

5. Match with reviewable rules

Prefer stable identifiers, then brand, model, variant, pack, and unit evidence. Return unmatched and ambiguous rows instead of forcing false joins.

6. Validate every run

Check coverage, duplicates, required fields, type and range rules, location persistence, schema drift, outliers, and change versus prior observations.

POC acceptance scorecard

Agree what “good data” means before scale.

A polished CSV can hide wrong locations, forced matches, missing products, and stale values. The proof of concept should expose those failure modes and quantify them against a frozen sample.

Representative rows include normal, missing, ambiguous, sponsored, out-of-stock, and promoted examples.

Requested location, store, account, language, and currency persist across the sample.

Product identity and pack normalization are manually reconciled against a truth set.

Coverage is reported as successful observations divided by the frozen sample frame—not only returned rows.

Every normalized row carries observation time, source identity, and quality or warning state.

Delivery format, retry rules, corrections, schema changes, and ownership are agreed before production.

Observed, derived, and unavailable

We distinguish source-observed values from derived fields such as canonical product IDs, normalized units, promotion types, or search-share calculations. A field the source does not expose remains null; it is not silently inferred into a fact.

A source can be added—after proof

PLOTT has 131 ready commerce sources and can scope another public website or app. New coverage is not presented as production-ready until the representative sample passes source, field, location, matching, and delivery checks.

Bring the hard source and the real acceptance criteria.

We will return representative rows, coverage diagnostics, and the proposed production contract before you commit to a recurring feed.

Scope a sample