Use case
Consumer & Retail transaction or investment diligence
Download the CSV, open it in Excel or Google Sheets, customise the rows, then upload the finished index to Data Room Builder. The columns are intentionally simple: Level 1, Level 2, Level 3 and Notes.
| Level 1 | Level 2 | Level 3 | Notes |
|---|---|---|---|
| 01 Brand | 01.01 IP and registrations | — | Trademarks, domains, disputes |
| 02 Sales | 02.01 Channel P&L | — | DTC/retail/marketplace split, promotions |
| 03 Stores | 03.01 Estate | — | Leases, turnover rents, break options |
| 04 Supply | 04.01 Sourcing | — | Factories, ethical audits, incoterms |
| 05 Inventory | 05.01 Stock health | — | Ageing, markdown history, returns rates |
| 06 Digital | 06.01 Ecommerce stack | — | Platform terms, data/CRM, subscriptions |
| 07 Regulatory | 07.01 Product compliance | — | Safety testing, labelling, recalls |
Implementation tips
- Start from this sector pack, then merge the standard M&A folders your process needs.
- Channel-level contribution (after promo and returns) is the number bidders rebuild first.
- Number folders to mirror the buyer request list so tracing stays one-to-one.
FAQs
What surprises buyers in consumer deals?
Markdown-driven margin erosion and store-lease breaks — both need clean history in the room.
How much customer data goes in the room?
Aggregated CRM/cohort analysis only; raw personal data stays out on privacy grounds.