Identifier map

A dated table of old identifier to new, applied on every upload before any measure, so a migration, a merge or a re-key joins history instead of breaking it. Old identifiers are never reused. Proposed pairs from pattern matching are confirmed by a person, never applied automatically.

DefinitionA dated table of old identifier to new, applied on every upload before any measure, so a migration, a merge or a re-key joins history instead of breaking it. Old identifiers are never reused. Proposed pairs from pattern matching are confirmed by a person, never applied automatically.

What drift does without it

Phantom churn, phantom new accounts, dormancy on half the book, concentration halved.

The threshold

New identifiers above a stated share of the base in one upload is a migration, and the run stops until the map exists.

How it is used

A dated table of old identifier to new, applied to every upload before any measure is computed, so that a system migration, a merger or a renumbering does not break history.

Example

After a system migration, account 40017 became C-88213 from 1 April. The map carries both, and the account's three-year history stays in one row.

Where it goes wrong

Handled by a one-off fix in a spreadsheet. The next upload brings the old identifiers back.

Articles that use this term

Data quality and reconciliation

Identifier drift: when a system re-keys an account and the history breaks

What happens to every measure when a CRM migration, a merge or a re-numbering changes an account's identifier: the account appears new, its history appears lost, dormancy fires, concentration drops, and share of wallet resets. The dated identifier map that prevents it, the three ways drift is detected on upload, the rule that old identifiers are never reused, and the worked recovery after a migration that re-keyed a third of the base.

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Sales intelligence from the data you already hold: five sources, one roll-up

What sales intelligence means when it is built from finance, CRM, activity, product and benchmark data a company already has: the five sources, how they join on the client, the roll-up that reconciles them, and the questions a salesperson can then ask that no dashboard anticipated.

16 Sept 20263 min read AI and self-service analytics

Can ChatGPT analyse sales data? What a general chatbot gets right, where it goes wrong, and how to check

A practical answer to whether a general-purpose AI chatbot can analyse a sales export. It can: given a clean file and a precise question, a chatbot with a code tool will compute correct totals, rankings and simple rates. It goes wrong in predictable places: silent assumptions about what columns mean, duplicated customers, joins across files, definitions that change between sessions, arithmetic done in prose rather than in code, and no check against the ledger. This page lists what works, the seven failure points, five checks anyone can run on an answer, and what a purpose-built tool does differently.

17 Sept 20265 min read