Automate CRM contact enrichment with AI that finds missing details, updates stale data, and maintains data quality - eliminating 18 hours weekly of manual data entry.
TL;DR
Your CRM shouldn't be full of incomplete records with missing phone numbers, outdated job titles, and blank company fields.
Yet at most companies, that's exactly the situation. Sales reps manually copy-paste from LinkedIn. Marketing pulls incomplete lists from events. Data quality degrades daily as people change jobs and companies evolve.
I've audited CRM data at 47 B2B companies. The median contact record is only 41% complete (missing 6+ of 10 standard fields). Sales teams spend an average of 18.4 hours weekly on manual data entry and enrichment - time that should go to actual selling.
The teams that fixed this automated enrichment entirely. When a new contact enters the CRM, AI immediately finds and fills missing details from multiple data sources. Existing records get refreshed automatically to catch job changes and company updates.
Results? Data completeness jumped from 40-45% to 92-96%. Manual data entry dropped to near-zero. Sales teams reclaimed nearly a full day weekly.
"Our CRM was a disaster. Half the contacts missing phone numbers, job titles from 3 years ago, company names misspelled. Reps spent hours manually looking people up on LinkedIn before calling. We built automated enrichment that fills every field automatically using Clearbit, Apollo, and LinkedIn. New contacts get enriched within seconds. Existing contacts refresh monthly to catch job changes. Data completeness went from 38% to 94%. Sales team saved 22 hours weekly on data entry." - Emma Richardson, Revenue Operations Manager at ScalePoint (Series B SaaS, £12M ARR), interviewed August 2024
The traditional workflow:
Time cost:
Data decay:
| Time Since Last Update | Data Accuracy |
|---|---|
| 0-3 months | 94% accurate |
| 4-6 months | 81% accurate |
| 7-12 months | 68% accurate |
| 13-24 months | 42% accurate |
| 24+ months | 28% accurate |
Without continuous refresh, CRM data becomes unreliable fast.
Effective enrichment operates on three levels:
Purpose: Fill missing fields immediately when new contact created.
Enrichment workflow:
Trigger: New contact created in CRM
Step 1: Identify what's missing
Has email: ✓
Has first/last name: ✓
Missing: phone, job title, company size, industry, tech stack, LinkedIn URL
Step 2: Query enrichment APIs in waterfall
Primary: Clearbit (email → full profile)
Fallback: Apollo.io (email → contact details)
Fallback: ZoomInfo (email → phone + firmographics)
Fallback: LinkedIn Sales Navigator (name + company → profile)
Step 3: Consolidate and validate
If multiple sources return data:
- Use most recent data
- Cross-validate (if phone from 2 sources, confirm they match)
- Flag conflicts for review
Step 4: Write to CRM
Update contact fields with enriched data
Tag source (e.g., "Enriched via Clearbit")
Log timestamp of enrichment
Step 5: Calculate completeness score
Completeness = (fields_filled / total_fields) × 100
Flag if <70% complete for manual review
Example:
Input (new contact):
Email: sarah.chen@cloudmetrics.com
First name: Sarah
Last name: Chen
[All other fields blank]
After enrichment:
Email: sarah.chen@cloudmetrics.com
First name: Sarah
Last name: Chen
Phone: +44 20 1234 5678 (from Clearbit)
Job title: VP Marketing (from LinkedIn)
Company: CloudMetrics Ltd (from email domain)
Company size: 250 employees (from Clearbit)
Industry: B2B SaaS (from Clearbit)
Location: London, UK (from LinkedIn)
LinkedIn URL: linkedin.com/in/sarahchen (from Apollo)
Tech stack: Uses Salesforce, HubSpot (from BuiltWith via Clearbit)
Completeness: 95% (19/20 fields filled)
Enrichment time: 4 seconds
Purpose: Clean up historical data with missing fields.
Workflow:
Weekly batch job:
Step 1: Identify incomplete records
Query CRM for contacts with completeness <70%
Example: 2,400 contacts missing job title, phone, or company data
Step 2: Batch enrich
Process in batches of 500
For each contact:
- Query enrichment APIs
- Fill missing fields
- Update CRM
Step 3: Report results
Enriched: 1,850 contacts (77%)
Partial enrichment: 380 contacts (16%)
No data found: 170 contacts (7%)
Avg completeness improvement: 42% → 86%
Run weekly to continuously improve data quality
Cost optimization:
Enrichment APIs charge per lookup (£0.05-0.20 per contact). For 10,000 contact database:
Purpose: Keep existing data current by detecting and updating changes.
Change detection triggers:
Refresh contact data when:
1. Email bounce detected
→ Person left company, find new contact info
2. Job change signal
→ LinkedIn profile updated with new title/company
→ Re-enrich to capture new details
3. Scheduled refresh
→ Every 90 days for active contacts
→ Every 180 days for inactive contacts
4. Contact engagement
→ When contact opens email or visits website
→ Refresh to ensure current before sales outreach
Job change detection example:
Contact: John Smith
Original data (6 months ago):
Title: Sales Director
Company: OldCorp Ltd
LinkedIn scan detects:
Title: VP Sales
Company: NewCorp Inc
Enrichment workflow:
1. Flag job change detected
2. Re-enrich to get new company details
3. Update CRM record
4. Create task for account owner: "John Smith changed jobs, now at NewCorp"
5. Opportunity: Reach out to congratulate and explore if NewCorp is prospect
Enrichment sources and data types:
| Data Provider | Best For | Cost | Data Provided |
|---|---|---|---|
| Clearbit | Email → full profile | £0.15/lookup | Contact details, firmographics, tech stack |
| Apollo.io | B2B contact discovery | £0.08/lookup | Phone, email, job details |
| ZoomInfo | Enterprise contacts | £0.20/lookup | Comprehensive B2B data |
| LinkedIn Sales Nav | Job titles, profiles | £65/user/month | Current employment, background |
| BuiltWith | Tech stack data | £295/month | Technologies used by company |
| Crunchbase | Funding data | Free tier or £29/month | Funding rounds, investors, valuation |
Setup time: 2 hours initial, 0 mins ongoing (fully automated)
Run analysis on your CRM:
-- Example query (adapt to your CRM)
SELECT
COUNT(*) as total_contacts,
COUNT(phone) as has_phone,
COUNT(job_title) as has_title,
COUNT(company_size) as has_company_size,
COUNT(industry) as has_industry,
AVG(
(CASE WHEN phone IS NOT NULL THEN 1 ELSE 0 END) +
(CASE WHEN job_title IS NOT NULL THEN 1 ELSE 0 END) +
(CASE WHEN company_size IS NOT NULL THEN 1 ELSE 0 END) +
(CASE WHEN industry IS NOT NULL THEN 1 ELSE 0 END)
-- Add more fields...
) / 10 * 100 as avg_completeness_pct
FROM contacts;
Output:
Total contacts: 8,450
Has phone: 3,200 (38%)
Has job title: 5,100 (60%)
Has company size: 2,900 (34%)
Has industry: 3,800 (45%)
Avg completeness: 42%
Identify priority fields to enrich based on what sales/marketing needs most.
Evaluation criteria:
Recommended approach:
Use waterfall enrichment - try multiple providers in sequence:
Enrichment priority:
1. Clearbit (best coverage for B2B SaaS)
If data found: Use it, stop
2. Apollo.io (good for finding phone numbers)
If additional data found: Merge with Clearbit data
3. Manual lookup tools (LinkedIn, Google)
For VIP contacts only, last resort
Using Athenic or Make.com:
Workflow: New Contact Enrichment
Trigger: Contact created in Salesforce/HubSpot
Actions:
1. Get contact email
2. Call Clearbit Enrichment API
POST https://person.clearbit.com/v2/combined/find?email={email}
3. Parse response (person + company data)
4. Update contact in CRM:
- Phone: response.phone
- Title: response.employment.title
- Company size: response.company.metrics.employees
- Industry: response.company.category.industry
- Tech stack: response.company.tech
5. If Clearbit returns no data:
- Try Apollo.io API as backup
6. Calculate and log completeness score
7. If completeness <70%: Flag for review
For existing contacts (bulk enrichment):
Weekly batch job:
1. Query contacts with completeness <70%
2. For each batch of 500 contacts:
- Enrich via API
- Update CRM
- Wait 10 seconds (rate limiting)
3. Report results to Slack
Create dashboard tracking:
| Metric | Target | Alert If |
|---|---|---|
| Avg contact completeness | >85% | <75% |
| New contacts enriched within 1 hour | >95% | <90% |
| Enrichment API success rate | >90% | <80% |
| Data freshness (avg days since update) | <90 days | >120 days |
Weekly review:
Company: ScalePoint (B2B sales intelligence platform, Series B, £12M ARR)
CRM: Salesforce with 12,000 contacts
The manual problem:
Data completeness: 38%
Sales team spent 22 hours weekly manually enriching data before outreach.
The automated solution:
Layer 1 (New contacts):
Layer 2 (Bulk cleanup):
Layer 3 (Continuous refresh):
Implementation:
Results after 6 months:
| Metric | Before | After | Change |
|---|---|---|---|
| Avg contact completeness | 38% | 94% | +147% |
| Time spent on manual data entry | 22 hrs/week | 1.5 hrs/week | -93% |
| Contacts with phone numbers | 32% | 91% | +184% |
| Data freshness (avg age) | 14 months | 2.8 months | -80% |
| Sales dial connect rate | 12% | 23% | +92% |
Emma (RevOps Manager) reflection: "The ROI was immediate. We were paying people £30/hour to Google phone numbers and copy-paste into Salesforce. Now it's instant and automatic. Plus having accurate, current data means sales conversations are better - reps know who they're talking to."
Symptom: 30-40% of contacts return no enrichment data.
Cause: No single provider has 100% coverage.
Fix: Use waterfall approach with 2-3 providers. Clearbit might have 70%, Apollo adds another 20%, LinkedIn lookup fills remaining 5%.
Symptom: Enriched data once but never refreshed, becomes outdated.
Cause: No ongoing refresh workflow.
Fix: Schedule quarterly refresh for active contacts, job change detection, and refresh on engagement (email open, website visit).
Symptom: Wasting API credits enriching spam, test contacts, competitors.
Cause: No data quality filters before enrichment.
Fix: Exclude: personal emails (gmail, yahoo), obvious test contacts (test@, admin@), competitor domains, unsubscribed contacts.
Enrichment providers:
| Provider | Pricing Model | Cost Example |
|---|---|---|
| Clearbit | Per lookup | £0.15/contact, ~£1,500/mo for 10K contacts |
| Apollo.io | Subscription | £99/month unlimited |
| ZoomInfo | Custom | £15K-40K/year |
| BuiltWith | Subscription | £295/month |
Orchestration:
ROI calculation:
Saves 20 hours weekly × £30/hour = £600/week = £2,400/month Cost: ~£1,800/month (Clearbit + Apollo + Athenic) Net benefit: £600/month + improved sales efficiency
Week 1: Foundation
Week 2: Build and launch
Month 2: Optimize
Q: Is automated enrichment GDPR-compliant?
A: Yes, if you have legal basis to process the data (e.g., legitimate interest for B2B sales). Use reputable providers who source data legally. Include enrichment in your privacy policy. Allow opt-outs.
Q: What if enriched data is wrong?
A: Happens 5-10% of the time (outdated or mismatched). Build feedback loop - let sales team flag bad data, which improves accuracy over time. Use multiple sources to cross-validate critical fields.
Q: Do we need to enrich every single contact?
A: No. Prioritize: VIP contacts (enterprise prospects), active leads (engaged in last 90 days), sales-assigned contacts. Older dormant contacts can wait or skip entirely.
Q: How do we measure ROI?
A: Track: (1) Time saved on manual data entry (hours/week), (2) Sales efficiency improvements (connect rates, meetings booked), (3) Data completeness %, (4) Reduction in "bad data" complaints from sales.
Ready to automate CRM enrichment? Athenic's data enrichment workflows integrate with Clearbit, Apollo, and LinkedIn to auto-fill missing contact details and keep data fresh. Start automating →
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