Marketplace Data for Private-Equity Due Diligence

The investment decision: which commercial claims survive outside-in testing?

Private-equity teams use marketplace data to pressure-test a target’s growth, pricing power, distribution, availability, customer reception, and competitive position before committing capital. The goal is not to turn public listings into audited revenue. It is to create independent, repeatable evidence that can confirm a management claim, expose a diligence question, or define a post-close operating baseline.

Define the diligence question before collecting data

Public deal context: why ecommerce operating evidence matters

In July 2025, KKR and SupplyHouse announced a strategic partnership around a pure-play ecommerce distributor of HVAC, plumbing, and electrical products. The announcement is not proof that marketplace data was used in that transaction; it is an example of the kind of ecommerce business where assortment breadth, availability, pricing, and digital execution are central operating questions.

Keep the distinction explicit: public deal announcements establish context, while the diligence dataset must establish its own evidence. Never imply access to a target’s internal performance or a role in a transaction without authorization.

A defensible evidence record

{
  "target": "illustrative retailer",
  "metric": "in_stock_assortment_count",
  "marketplace": "example-market",
  "geography": "US",
  "period": "2026-05-01/2026-07-31",
  "methodology_version": "v1.2",
  "sample_frame": {
    "locations": 25,
    "cadence": "daily",
    "categories": [
      "A",
      "B"
    ]
  },
  "result": 18420,
  "coverage": 0.94,
  "caveats": [
    "public digital shelf only",
    "sales not directly observed"
  ]
}

Every chart in an investment-committee pack should trace back to a definition, sample frame, collection period, coverage figure, and source observations. This prevents a change in marketplace layout or location mix from masquerading as target performance.

A six-part outside-in workplan

1. Form hypotheses. Translate the thesis into falsifiable questions and thresholds.
2. Freeze the sample frame. Choose marketplaces, SKUs, competitors, locations, keywords, and cadence before reviewing results.
3. Resolve identity. Match exact variants and normalize packs; retain unmatched listings.
4. Collect longitudinally. Capture enough repetitions to separate one-off page state from a pattern.
5. Triangulate. Compare marketplace observations with management data, third-party evidence, and expert calls.
6. Document exceptions. Quantify missingness, blocked runs, source changes, and analytical judgment.

Relevant PLOTT entry points include marketplace coverage, pricing observations, availability observations, and the investor and private-equity use case.

Red flags worth investigating

• Growth concentrated in new duplicate listings rather than new products or locations.
• Frequent out-of-stocks on the highest-rated or most visible SKUs.
• Increasing promotion depth required to maintain search presence.
• Unauthorized sellers or wide price dispersion that may weaken channel control.
• Review growth accompanied by declining ratings or repeated product-quality themes.
• Expansion claims that disappear when results are controlled for geography and collection coverage.

Limitations and what a POC must prove

Public digital-shelf data does not directly reveal units sold, revenue, margin, returns, customer cohorts, or warehouse inventory. Review counts are an imperfect demand proxy; search results can be personalized; and location sampling can bias availability conclusions. Collection must also pass legal, contractual, privacy, and information-security review for the sources and jurisdictions in scope.

A POC should use the target SKU list where permissible, named competitors, representative locations, and a defined historical or forward window. Reconcile sample observations manually, test match quality, quantify coverage, and deliver a reproducible calculation workbook or query—not only slides. Request a diligence sample dataset with row-level evidence, a methodology memo, coverage diagnostics, and two or three thesis-linked analyses using the exact market scope under consideration.