A lead comes in from your website form. You have a name, an email, and a company name.
Your sales rep opens the CRM. Sees those three fields. That's it.
They have no idea if this is a 5-person startup or a 500-person company. No idea what industry. No idea if they can even afford what you sell.
That lead could have been a complete profile before anyone touched it.
TRANSFORMATION LAYER - Enrichment turns sparse data into actionable intelligence.
When data first enters your system, it's usually incomplete. A form captures what someone was willing to type. An API returns the bare minimum. A spreadsheet has whatever columns the original creator thought were important. But you need more than what you were given to make decisions.
Enrichment is the process of taking what you have and adding what you need. You have an email domain? Look up the company. You have a company name? Pull their industry, size, funding, tech stack. You have a phone number? Append location data. The record you started with becomes something you can actually act on.
The key isn't just adding more data. It's adding the right data at the right time. Enrichment happens automatically when records arrive, so by the time a human sees them, the context is already there.
Every minute your team spends researching a lead is a minute they could have spent talking to them. Enrichment does the research before anyone asks.
Enrichment solves a universal problem: how do you turn partial information into complete context without manual research for every record?
Start with an identifier (email, domain, phone, address). Query external sources that can expand it. Merge the results back into the original record. Apply the same expansion rules to every record that arrives.
Each lead starts with just name, email, and company. Click "Enrich" to pull in company size, industry, funding, and tech stack automatically.
sarah@acmecorp.com
mike@techstart.io
emma@retailmax.com
Pull data from specialized providers
Services like Clearbit, ZoomInfo, or Apollo specialize in company and contact data. You send them an email or domain, they return firmographics, technographics, social profiles. Instant and comprehensive, but costs per lookup.
Connect to data you already own
Your CRM has customer history. Your support system has ticket counts. Your billing system has payment patterns. Enrichment can pull from all of these. Same customer appears in a new context? You already know their story.
Let models fill gaps intelligently
When APIs don't have data and your database is empty, AI can infer context from what's available. Company website copy reveals industry. Job titles suggest company size. Email signatures contain phone numbers. Pattern matching at scale.
A website form captures three fields. Before the lead even hits the sales queue, enrichment has added company size, industry, funding stage, and tech stack. Your rep opens it and knows instantly: 200-person SaaS company, Series B, uses Salesforce. That's a qualified lead.
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You have access to 50 data points about every company, so you pull all 50. Now your records are bloated, your API bills are huge, and most of that data sits unused. Your sales team never looks at 'estimated annual IT spend' anyway.
Instead: Start with 3-5 fields your team actually uses. Add more only when someone asks for them.
The API is down. The company isn't in their database. The email domain is personal (gmail.com). Your workflow crashes, the lead sits in limbo, and nobody notices for three days.
Instead: Design for the unhappy path. Records that fail enrichment should still flow through with what they have, flagged for manual review.
Clearbit says it's a 500-person company. Your sales rep quotes enterprise pricing. Turns out they're a 50-person startup. The data was stale. You lost the deal.
Instead: Add timestamps to enriched fields. Show confidence levels. Make it easy to override when the data is wrong.
You've learned how to turn sparse records into complete profiles automatically. The natural next step is understanding how to make sense of those enriched records at scale.