Why this matters: your CRM, marketing automation, and sales stacks only work as well as the information inside them. When contact details rot, segmentation breaks, and campaigns waste budget. Good Data Enrichment fixes that by filling gaps, correcting errors, and adding signals that make every outreach smarter. [1]
Quick reality check (numbers to care about)
- B2B contact data decays at roughly 2.1% per month — about 22–30% a year depending on the source. That means a list of 10,000 contacts could lose 2,200–3,000 useful records in 12 months.
- Dirty or incomplete data contributes to the multi-trillion dollar cost of poor data across economies; many estimates put the annual cost to US companies in the trillions. Fixing that is why businesses invest in Data Enrichment.
How to use this checklist
Treat this as a buyer’s guide when evaluating Data Enrichment Companies. For each item, ask vendors for a demo, a sample enrichment run on a 1–2k row extract, and SLA terms. Below are practical checkpoints and the questions to ask.
1) Data sources & freshness (must-ask)
Why: enrichment is only as good as the sources behind it.
Ask: Where do you get firmographic, technographic, intent, and contact data? How often do you refresh records? Can you show a data lineage?
Look for vendors who combine multiple verified sources (public records, direct integrations, proprietary crawlers, partner datasets) and publish refresh cadence — daily or weekly is ideal for contact data.
(Primary keyword included above again: Data Enrichment.) [I’ll count occurrences below.]
2) Accuracy guarantees & SLAs
Why: accuracy affects delivery rates, meeting attendance, and sales productivity. Industry benchmarks for high-quality B2B contact data target >95% accuracy for critical fields.
Ask: Do you guarantee accuracy? What’s your sample bounce rate for emails after enrichment? Ask for a written SLA and an onboarding test.
3) Depth of enrichment (what fields are added?)
Why: basic enrichment fixes names and titles; deep enrichment adds intent, technographics, revenue, employee ranges, and buying signals — which are essential for ABM and Revenue Ops.
Ask: Which enrichment attributes do you provide? Can you customize fields for our ICP (industry codes, product usage, custom firmographic tags)?
4) Matching logic & de-duplication quality
Why: merging external enrichment with messy internal profiles requires smart matching (fuzzy matching, deterministic + probabilistic matching). Poor matching creates duplicates and damaged scoring.
Ask: Show your matching algorithm approach, dedupe rates, and conflict resolution rules (which source wins if values differ).
5) Privacy, compliance & data licensing
Why: enriching contact data touches privacy laws (GDPR, CCPA) and vendor licensing. Non-compliance is risky.
Ask: Do you support consent management, lawful basis for processing, and do you provide data provenance for GDPR/CCPA requests? Get proof of compliance and licensing terms.
6) Integration & workflow support
Why: the value of Data Enrichment is realized when it flows into systems — CRM, MAP, CDP, analytics, or sales engagement tools.
Ask: Which integrations are native (Salesforce, HubSpot, Marketo, Snowflake)? Is enrichment available via API, batch jobs, or stream? Can you enrich records in place and push updates automatically?
7) Speed, scale & cost model
Why: some providers are great for small enrichment jobs but falter at scale or cost-effectiveness.
Ask: What are your throughput and pricing models (per record, subscription, tiered)? Can you run a pilot to estimate ROI and show cost per corrected record?
8) Support, onboarding & success metrics
Why: ongoing hygiene is a partnership. You want a provider that offers onboarding, validation, and quarterly business reviews.
Ask: What does onboarding look like? Will a Customer Success Manager help define enrichment rules and KPIs (bounce rate reduction, lead-to-opportunity conversion increase, cost-per-lead change)?
9) Transparency & sample output
Why: you should be able to verify improvements before committing.
Ask: Can you run a free or low-cost sample enrichment on a representative dataset and provide before/after metrics (email deliverability, missing field fill, duplicates removed)? Insist on exportable logs.
10) Industry specialization & vendor comparisons
Why: some Data Enrichment Companies specialise by industry (healthcare, finance, tech) and deliver better field coverage and accuracy for those verticals.
Ask: Do you have case studies or references in our industry? Can you show field coverage specifically relevant to our use cases (e.g., NPI numbers for healthcare, SIC/NAICS enrichment for B2B)?
11) Security & technical controls
Why: your raw customer data is sensitive. Encryption, access controls, and SOC/ISO certifications matter.
Ask: What security certifications do you maintain? How do you encrypt data at rest and in transit? Who has access to our data during processing?
12) Ability to support ML/AI use cases
Why: enriched features fuel models — better propensity scoring, lookalike audiences, and churn prediction.
Ask: Can you deliver feature sets formatted for ML teams (vectorized outputs, feature store support, sample pipelines)? Do you provide confidence scores for each field?
Quick vendor evaluation template (use during demos)
- Data sources listed? (Y/N)
- Refresh cadence (days)
- Accuracy guarantee (%)
- Native CRM/MAP integrations (list)
- Pilot available? (Y/N) + sample results delivery time
- Pricing model (per record / subscription)
- Compliance docs provided? (Y/N)
- Security certifications (SOC2/ISO27001)
Closing — picking a partner
Choosing a Data Enrichment provider is more than buying a dataset — it’s selecting a partner to protect and grow pipeline velocity. Start small with a pilot that proves value (deliverability lift, better lead scoring, fewer duplicates), measure outcomes, and then scale. Remember: with B2B contact data decaying by roughly 20–30% a year, investing in the right enrichment partner pays for itself quickly.
