Data Accuracy and Limitations

Understanding Data Sources and Accuracy

Catchlight aggregates data from multiple third-party sources. No data is 100% accurate, but understanding confidence levels helps you use it appropriately.

Accuracy by Data Category

High Accuracy (85-95%+)

Profile & Identity Data (when Match Confidence is Excellent/Good):

Full name
Current address (if recently updated in public records)
Homeownership status
Property values (within 10-15% typically)

Why accurate: Based on public records, property databases, verified sources

Use confidently for: Geographic segmentation, property ownership filters, mailing campaigns

Moderate Accuracy (70-85%)

Professional Data:

Current employer (if LinkedIn is updated)
Job title
Industry
Approximate tenure

Why moderately accurate: Based on self-reported LinkedIn and professional databases; accuracy depends on how recently updated

Limitations:

Job changes may lag 1-6 months
Job titles vary (what one company calls "Director" another calls "VP")
Self-employed shown as outdated previous employer
Recent promotions may not reflect

Best practice: Verify current employment in conversation before citing specifics

Demographic Data:

Age (usually within 1-3 years)
Marital status (lags behind changes)
Presence of children
Children's ages (approximate ranges)

Why moderately accurate: Based on public records, consumer data models; life changes take time to reflect

Limitations:

Recent marriages/divorces may not be current
Adult children still at home hard to detect
Blended families complex
Custody arrangements not captured

Best practice: Use for segmentation; verify details in conversation

Lower Accuracy (50-70%)

Financial Indicators:

Income range (modeled estimates)
Investable asset range (modeled estimates)
Wealth segment (derived from estimates)
Projected client revenue (calculated from estimates)

Why less accurate: These are modeled predictions, not actual financial data

Accuracy variability:

More accurate for salaried employees at known companies
Less accurate for self-employed, variable income, private wealth
Can miss recent windfalls, inheritances, or losses
Doesn't capture debt levels

Potential error margins:

Income: ±30-50%
Assets: ±50% or more
Could be off by entire wealth segment category

Critical: NEVER cite specific estimated figures to clients

Best practice: Use only for internal prioritization and segmentation; verify in discovery conversations

Variable Accuracy (30-70%)

Interests & Lifestyle Data:

Hobbies and interests
Sport affinities
Household activities
Financial interests

Why variable: Based on inferred behavior, purchases, subscriptions; may be outdated or incorrectly attributed

Limitations:

One-time purchases misinterpreted as ongoing interests
Household data may reflect spouse's interests, not individual's
Aspirational purchases (bought golf clubs, never played)
Outdated (was interested years ago, not now)

Best practice: Use as conversation starters, not assumptions; ask open-ended questions to verify

Life Events:

Recent job changes (70-80% accurate if sourced from LinkedIn)
Home purchases (85-90% from property records)
Marriages (70-80% with lag)
New children (60-75%, delayed detection)
Other life events (highly variable)

Timing accuracy: Life events can have 3-12 month lag from occurrence to data detection

Best practice: Use to prompt outreach, but confirm timing and details in conversation

Low Coverage/Accuracy (Under 50%)

Social Media Profiles:

LinkedIn: 60-70% coverage (higher for professionals)
Facebook: 30-40% (many private profiles)
Twitter/X: 20-30%

Why low coverage: Privacy settings, people who don't use social media, inactive profiles

Best practice: When available, use; when not, don't assume absence means they don't exist on platform

Catchlight & Social Capital Scores:

Proprietary algorithms
Directional indicators, not precise measures
Can change over time

Best practice: Use for prioritization; don't share specific scores with clients

Common Data Limitations

Lag Time

Job changes: 1-6 months behind
Life events: 3-12 months behind
Financial estimates: Updated quarterly or annually
Property values: Annual updates

Impact: Recent changes may not be reflected yet

Mitigation: For high-priority prospects, verify via LinkedIn, Google, or ask directly

Household vs. Individual

Many data points are household-level, not individual:

Income (household, not personal)
Home value (household asset)
Children (household composition)
Some interests (household purchases)

Impact: Can't always distinguish between individual and spouse

Mitigation: Ask clarifying questions; don't assume

Privacy and Availability

Not everyone has public data:

Intentionally private individuals
Younger people with less public history
Recently relocated or changed names
Non-digital natives

Impact: Lack of data doesn't mean low value; some wealthy people are very private

Mitigation: Don't penalize prospects for data gaps; use other indicators

Model-Based Estimates

Financial indicators are statistical predictions, not facts:

Based on correlations (job + location + home value = estimated income)
Averages, not individuals
Can miss outliers (frugal millionaires, high spenders with low assets)

Impact: Estimates can be significantly wrong for any individual

Mitigation: Never rely solely on estimates; always verify through conversation

Verification Best Practices

When to Verify

Always verify before:

Making specific financial recommendations
Citing exact figures to client
Making important business decisions
Assuming critical facts

How to verify:

Discovery questions in initial conversation
Fact-finding forms during onboarding
Comparison with client-provided documents
LinkedIn for professional details
Direct questions when rapport allows

Verification Questions

Instead of: "I see you have $2.5M in investable assets..." Ask: "Tell me about your current financial situation. What are your approximate investable assets?"

Instead of: "You make about $200K per year..." Ask: "What's your approximate household income?"

Instead of: "You have two kids, ages 8 and 12..." Ask: "Tell me about your family. Do you have children?"

Let them share: Demonstrate you're informed, but verify through conversation

Appropriate vs. Inappropriate Uses

✅ Appropriate Uses

Segmentation:

"Filter for households with children ages 14-18" for college planning campaign
"Target age 63-65 for Medicare workshop"
"Focus on Affluent and HNW segments for comprehensive planning service"

Prioritization:

"Contact high Catchlight Score prospects first"
"Prioritize recent job changers for rollover outreach"
"Focus on pre-retirees with high projected revenue"

Personalization:

"I noticed you recently joined [Company]..."
"I see you're a fellow [School] alum..."
"Congratulations on your recent home purchase..."

Preparation:

Review profile before meeting to understand background
Check LinkedIn for recent updates
Note conversation starters from interests

❌ Inappropriate Uses

Never say to a client:

"I know you make $175,000 per year..." (Estimated, not factual)
"You have $1.2 million in assets..." (Modeled estimate)
"Your home is worth $450,000..." (Could be wrong)
"I see you're interested in golf..." (If you're not sure, ask instead)

Never assume:

Estimated figures are exact
All data is current
Data gaps mean the person lacks that characteristic
Household data reflects individual's situation

Never share:

Catchlight Scores with clients
Specific data sources
Detailed enrichment methodology
Data that might make client uncomfortable

Disclosing Data Sources

If a Client Asks: "How do you know that about me?"

Appropriate responses:

For public data: "That information is publicly available from [property records/LinkedIn/professional databases]. We use data research to better understand how we might help prospective clients."

For estimated data: "We use publicly available data and analytics to get a general sense of client profiles, but these are estimates. I'd love to learn the actual details from you."

For LinkedIn: "I reviewed your LinkedIn profile to learn about your background before our meeting. I hope that's okay—I like to be prepared."

General: "We work with a data provider that aggregates publicly available information to help us identify who might benefit from financial planning. Everything we have is from public sources, and we'd love to verify details with you."

Don't: Overshare technical details, mention "enrichment," or make it sound invasive

Red Flags and Data Quality Issues

Indicators of Low Data Quality

Suspect data if:

Match Confidence is "Needs Review" or low
Enrichment Status is "Failed" or "Partial"
Data Coverage under 40%
Professional info seems outdated
Multiple conflicting data points

Action: Verify heavily before using; consider manual research

Handling Obvious Errors

If you spot clear errors (wrong person, outdated employer, etc.):

Update your CRM with correct information
Flag for Catchlight support to correct
Don't use suspect fields for outreach
Verify other data more carefully

Setting Client Expectations

Transparency: If client engagement deepens, explain:

"We use data research to understand prospective client needs, but it's all high-level. I'm looking forward to learning the real details from you."
"Think of our initial research like a hypothesis—now we verify and refine through conversation."

Privacy: Assure them:

"All information we accessed was publicly available."
"We take privacy seriously and don't share or sell any information."
"We're happy to explain our data sources if you have questions."

Summary: Data Confidence Levels

Data Type	Accuracy	Use For	Verify Before
Identity & Address	High	Segmentation, mailing	Major decisions
Professional Info	Moderate	Conversation starters	Citing specifics
Demographics	Moderate	Campaign targeting	Assuming details
Financial Estimates	Low	Prioritization only	ANY recommendation
Interests	Variable	Ice breakers	Assuming passion
Life Events	Variable	Timely outreach	Confirming timing
Social Media	Variable	Research prep	Connecting/engaging