# CRM data decay audit: bounce risk and job changes in a small contact sample

Source: https://mcpforgtm.com/recipes/data-decay-audit (StackMCP, independent directory of official MCP servers)

## Goal

RevOps teams who want to know how stale their contact data is, segment by segment, before a big campaign or a data-vendor renewal, without changing any records. LeadMagic can be swapped for ZeroBounce, and HubSpot for Salesforce, Attio or Pipedrive.

## Connected tools

- LeadMagic: https://mcp.leadmagic.io/mcp (setup: https://mcpforgtm.com/tools/leadmagic)
- HubSpot: https://mcp.hubspot.com (setup: https://mcpforgtm.com/tools/hubspot)

## Guardrails

- Read only in HubSpot. Don't edit, delete or unsubscribe any contact.
- Check my LeadMagic credits first, and tell me how many checks the run needs before spending any.
- Spend credits only after I approve, and only on the sample I approved.
- Don't create LeadMagic tables or bulk enrichment jobs.
- Report every rate with its count, and say when a segment is too small to compare.

## Steps

1. Count my HubSpot contacts by {segment_field} and show me a table. Then pick a random sample of {n_per_segment} contacts per segment from contacts not updated in {months} months: name, email, company, company domain, job title and segment. Don't change any records.
2. Check my LeadMagic credit balance. Tell me how many email checks and people lookups this sample needs, and wait for my approval.
3. After I approve it, check each work email in the sample with LeadMagic's validate_work_email. Show me a table per segment with valid, invalid and couldn't-confirm counts and rates.
4. Only after I approve the lookups, use LeadMagic people search to find each sampled person by name and old company domain. Flag anyone now at a different company, with the new company and title, and mark weak matches 'not sure'. Then check my credit balance again.
5. Give me a short summary by segment: invalid rate, job-change rate, the combined decay rate, and three example records. Keep it here as text. Don't write anything to HubSpot or LeadMagic.

## Expected output

Decay rates by segment from a 60-contact sample. Example (all names fictional):

```
Sample: 60 HubSpot contacts, 20 per segment, picked at random from contacts not updated in 12 months.
LeadMagic credits: balance checked before and after. 15 credits for 60 email checks, plus the people lookups you approved.

| Segment | Checked | Valid | Invalid | Couldn't confirm | Changed company |
|---|---|---|---|---|---|
| Enterprise (1,000+) | 20 | 15 | 3 (15%) | 2 | 4 (20%) |
| Mid-market | 20 | 17 | 2 (10%) | 1 | 3 (15%) |
| SMB | 20 | 13 | 6 (30%) | 1 | 5 (25%) |
| Total | 60 | 45 | 11 (18%) | 4 | 12 (20%) |

Examples:
- Dana Okafor, dana.okafor@larkspur.example, Larkspur Freightworks (fictional): email invalid. Now VP Operations at Brindle Cargo (fictional).
- Sam Whitlock, sam@quarrylane.example, Quarry Lane Analytics (fictional): email valid, same company, new title. Not a job change.
- Alex Rivera, arivera@mossgate.example, Mossgate Labs (fictional): email couldn't be confirmed. Two people with this name found. Marked 'not sure'.

Rough read: about 1 in 5 old contacts in this sample would bounce or has moved on, and SMB decays fastest. With 20 per segment, one contact moves a rate by 5 points, so treat these as rough rates.

No HubSpot records were changed.
```

## Watch for

- The sample isn't random: Sorting by created date or name skews the result. Ask for a random pick in each segment, and check that the sample's created dates spread across the segment instead of bunching up.
- Segments too small to compare: At 20 contacts a segment, one bounce moves the rate by 5 points. Show counts next to every rate, and don't rank segments that differ by only one or two contacts.
- A name match finds the wrong person: A people search by name can return someone else with the same name. Match on name plus the old company domain, and mark anything weaker as 'not sure' instead of a job change.
- A promotion counted as a job change: A new title at the same company isn't decay. Compare the old and new company domain, and count only people whose company changed.