Build the Field-Mapping Doc: Concord Biotech's CRM Migration Prep

Marketing Academy · Field Work●Marketing Tools

All projects·From: Marketing Tool Migration & Data Portability

MiniBuild the Asset· 30 minutes

Concord Biotech

Objective: Build a real field-mapping document and phase timeline, the two artifacts the lesson says must exist on paper before a single record moves, for a company migrating CRMs.

You're supporting the marketing operations lead at Concord Biotech, the Ahmedabad-based, Nasdaq-adjacent NSE/BSE-listed pharmaceutical API manufacturer (~$1.19B IPO market cap), who is migrating from a legacy CRM to a modern one before a global partner-conference push. Regulatory and audit-trail requirements mean nothing can move without documentation.

Build a field-mapping table (old field, new field, transformation needed) for a realistic 12-field CRM schema, then lay out the 5-phase migration timeline from the lesson.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets

FreeBuild the field-mapping table with transformation rules and owners

Free, easy to share for sign-off before migration starts

Notion

FreemiumTrack the 5-phase timeline as a checklist the team can update live during migration

Free tier handles a single migration project tracker without needing a dedicated project-management tool

The process

1 step

Step 01 of 01

Field mapping before data moves

Phase 1 of the lesson's framework is field mapping: documenting every field in the old system, where it lands in the new one, and what transformation it needs, approved on paper before anything moves.

The legacy CRM has a free-text 'industry' field and a single 'contact_notes' blob. The new CRM uses a picklist for industry and separate structured fields for notes. What does the mapping doc need to capture that a simple 1-to-1 export would miss?

Google Sheets— Build the mapping table with columns: old_field, new_field, data_type_change, transformation_needed, owner.

Procedure

List all 12 legacy fields (contact info, deal stage, industry, notes, last-touch date, lifecycle stage, etc.)
For each, identify the destination field in the new CRM
Flag any field needing a real transformation (free-text industry -> picklist, blob notes -> structured fields)
Assign an owner to each transformation (marketing ops vs. the CRM vendor's migration team)
Build the 5-phase timeline (audit, mapping, sandbox test, parallel run, cutover) with real week-by-week durations

Sample output

Concord Biotech field-mapping doc (excerpt)

old_field: industry (free text) -> new_field: industry (picklist)
  transformation: map 40+ free-text variants to 12 standard picklist values
  owner: marketing ops

old_field: contact_notes (single blob) -> new_field: notes[] (structured, dated entries)
  transformation: parse blob by date-stamp pattern into separate note records
  owner: CRM vendor migration team

old_field: lifecycle_stage -> new_field: lifecycle_stage
  transformation: none, direct 1:1 map
  owner: either

TIMELINE
  Phase 0 Audit & cleanup: 3 weeks
  Phase 1 Field mapping: 2 weeks
  Phase 2 Sandbox test load: 1 week
  Phase 3 Parallel run: 3 weeks
  Phase 4 Cutover: 1 week

Healthy

Every field with a real transformation has a named owner and an explicit rule before migration starts.

Unhealthy

A 1:1 export/import with no mapping doc, which silently drops the industry picklist mapping and mangles the notes blob.

What this means

The mapping doc's real value is catching the fields that need transformation, not the ones that map 1:1. Those are the ones that break silently during a rushed migration.

So what do I do about it?

Symptom	Action	Effort
Free-text fields get exported with no transformation rule	Write the exact mapping (which free-text values become which picklist option) before the sandbox load	30 min

YouYou can do this yourself, no engineering access required.

Final deliverable

A 12-field mapping table with transformation rules and owners, plus a 5-phase timeline checklist ready for sign-off.

See a reference example

Sample output

Postman marketing ops, field-mapping doc (excerpt)

old_field: lead_source (free text) -> new_field: lead_source (picklist, 8 values)
  transformation: map 60+ variants down to 8 standard sources, owner: marketing ops
old_field: deal_stage -> new_field: deal_stage, transformation: none, owner: either

TIMELINE: Audit 2wk -> Mapping 2wk -> Sandbox 1wk -> Parallel run 4wk -> Cutover 1wk

Success criteria

You're done when you can:

All 12 fields are mapped with an explicit transformation rule or 'none'
Every transformation has a named owner
The 5-phase timeline has real week-by-week durations, not placeholder text