Lead enrichment in Google Sheets: the complete guide

Lead enrichment in Google Sheets means taking a list of companies or people and filling in the missing fields, firmographics, contact details, technology signals, directly in the sheet, using web scraping plus AI extraction instead of a separate platform. You start with thin rows (a company name, maybe a domain) and end with rows complete enough to segment, score, and write to. ReplyLabs does this from a sidebar inside Sheets: it scrapes the source pages, extracts the fields you ask for with AI, verifies the emails, and only charges for rows that succeed. This guide explains what enrichment is, the difference between firmographic and contact enrichment, how waterfall logic works, how to run the whole pipeline in one sheet, and what it costs against a standalone tool.

What is lead enrichment?

Lead enrichment is the process of appending missing data to a lead record so a thin row becomes a usable one. A raw export from a form, a conference list, or a scraped directory usually carries a name and not much else. Enrichment fills the gaps: company size, industry, location, funding stage, job title, work email, phone, the tools the company runs. The point is to turn a list you cannot act on into one you can segment, prioritise, and personalise against.

There are two reasons this matters for outbound. First, you cannot target what you cannot see: without firmographics you cannot tell which rows match your ideal customer profile. Second, you cannot personalise on a blank field: an AI opener handed only a company name writes something true of any company, while one handed a funding stage and a recent hire writes something true of exactly one.

Firmographic vs contact enrichment

Enrichment splits into a few data types, and they answer different questions. Knowing which one you need keeps you from paying for fields you will not use.

• Firmographic enrichment describes the company: employee count, annual revenue, industry, headquarters location, ownership, and growth signals like funding rounds or hiring. Firmographics decide which accounts to target, because they are what your ICP is defined in. A typical filter reads "SaaS companies, 50 to 200 employees, North America, raised a Series A or B."
• Contact enrichment describes the person: job title, seniority, work email, direct phone, LinkedIn profile. Contact data decides who to reach inside an account you have already qualified.
• Technographic enrichment describes the stack: the CRM, analytics, or infrastructure a company runs. It is useful when your product complements or replaces a specific tool, for example "companies running HubSpot" or "sites on Shopify."

Most real workflows want firmographics first to cut the list down to fit, then contact data only for the rows that survive the cut. Spending on contact lookups for accounts that were never a fit is the most common way to waste an enrichment budget.

What is waterfall enrichment?

Waterfall enrichment is the practice of querying several data sources in sequence (or in parallel) for the same field and keeping the first or highest-confidence result. No single provider has complete coverage: one vendor typically finds a verified email for 60 to 75 percent of mid-market B2B contacts, so a single source leaves a quarter of your list blank. Stacking four to six sources pushes coverage past 90 percent because each provider fills a different slice of the gap.

There are two shapes. A sequential waterfall stops at the first source that returns a match, which keeps cost down because you only pay the next provider when the previous one came up empty. A parallel waterfall queries every source at once and picks the highest-confidence answer, which is faster but pays for every lookup. The trade-off is cost versus speed, and the right choice depends on how expensive your sources are.

The reason waterfall logic matters for cost is subtle: many standalone tools charge you even when a lookup fails, so a low-coverage source quietly drains credits on rows it never resolved. The honest version of a waterfall only bills for fields it actually filled.

Doing enrichment in Google Sheets with scrape plus AI

You do not need a dedicated data platform to run a credible waterfall. A spreadsheet add-on that can scrape pages and extract fields covers most of what enrichment needs, because a surprising amount of firmographic and contact data is sitting on the open web: company about-pages, careers pages, pricing pages, press releases, and team pages.

The mechanic is two steps chained together. First, web scraping in Google Sheets pulls the raw text of a company's site or a specific page into a column. Second, an AI extraction prompt reads that text and writes structured fields back, "from this page text, extract the industry, employee range, and headquarters city, one per column." Because the AI step reads real scraped text rather than guessing from a name, the output is grounded in something verifiable rather than invented.

This is the part standalone tools hide behind a credit meter: enrichment is mostly scrape the source, extract the fields, verify the result. When you run it in a sheet you can see every intermediate column, spot-check the scraped text, and re-run only the rows that came back thin.

The ICP Enrichment workflow

ReplyLabs ships an ICP Enrichment template that chains the three operations into one pipeline so you do not wire it up by hand. It runs in this order:

1. Scrape the company domain to pull the about, product, and careers text into context columns.
2. AI extract the firmographic fields you care about (industry, size band, location, funding signals) from that scraped text into clean, one-value-per-column outputs.
3. AI score each row against your ICP definition, so you get a "fit / not a fit" label or a numeric score you can sort on.
4. Verify the email addresses on the rows that passed, so you do not carry dead contacts into a send.

Each stage feeds the next inside the same sheet, with no export and re-import between tools. Because every step writes to its own column, you can filter after the scoring step and only spend the verify budget on rows worth contacting. The output is a list that is already segmented, scored, and deliverable, which is exactly the shape a cold email run from Google Sheets wants as its input.

This multi-step shape, Verify plus Scrape plus AI in one chain, is the thing a single-purpose enrichment API cannot do on its own. You would normally stitch it together across three vendors.

How to run your first enrichment pass

The flow below works for any list that has at least a company name or domain.

1. Open the sidebar with Extensions, ReplyLabs, Open sidebar, and select the rows you want to enrich.
2. Run the Scrape step on the domain column to pull source text into a context column.
3. In the AI tab, write an extraction prompt that references the scraped column by header, for example "From {{Scraped text}}, return the industry and employee range. If the text does not say, return Unknown."
4. Review the cost preview for your exact row count, then run. Output streams into new columns row by row.
5. Filter to the rows that match your ICP, then run Verify on the email column for just those rows.

Two habits keep the output clean. Tell the model what to do with missing inputs ("return Unknown if the page does not say") so it never invents a revenue figure. And sample twenty rows before running the whole list, because a prompt that works on one clean row often needs tightening once it meets the messy ones.

What lead enrichment costs in Sheets vs standalone tools

Standalone enrichment platforms price on a credit system, and the headline number rarely matches the real one. On a typical credit-based platform the effective cost per enriched lead runs from roughly $0.14 to $0.67 depending on plan, and several charge for failed lookups, which can quietly consume 20 to 30 percent of an allocation on incomplete data. There are usually platform seat fees on top, plus top-up markups when you run out mid-month.

ReplyLabs prices each operation transparently and charges only for rows that succeed:

• Scrape runs from $0.005 per URL (raw provider cost times the published markup), with the exact source shown before you run.
• AI extraction and scoring is priced at the provider's raw cost times 1.25 plus a $0.0025 base fee per succeeded row. A 1,000-row extraction on a small model lands in low single-digit dollars.
• Verify is $0.01 per email, again only on rows that return a verdict.

The two structural differences that matter: you see the price for your exact row count before anything runs, and failed or skipped rows cost nothing, so there is no silent credit drain. On Pro and Scale you can bring your own AI key, in which case the AI step runs at your provider's raw rate with no markup. To model a specific run, use the AI cost calculator.

For a like-for-like breakdown against the best-known credit-based platform, see how to enrich leads without Clay and the Clay alternative comparison.

When a standalone enrichment platform is the better fit

Honesty matters here, so it is worth saying where a dedicated platform wins. If you need direct-dial mobile numbers at high coverage, a deep technographic database across thousands of tools, or a 15-source parallel waterfall that resolves a list in minutes, a specialist data vendor will out-cover a scrape-and-extract approach. Those tools buy and license data you cannot scrape from the open web.

The scrape-plus-AI approach in Sheets wins when your enrichment is mostly firmographic and lives on company websites, when you want to see and audit every field, when you do not want a per-seat platform contract, and when the AI step (scoring, summarising, personalising) is as important to you as the raw data. For most outbound teams enriching company-level fields and writing personalised copy, that is the common case.

Common questions

What is the difference between lead enrichment and lead generation?

Lead generation finds new leads; lead enrichment fills in the missing data on leads you already have. Generation gives you a name; enrichment turns that name into a row you can segment and write to.

Can I enrich leads in Google Sheets without a separate tool?

Yes. A sidebar add-on that scrapes a company's pages and extracts fields with AI covers most firmographic enrichment without leaving the sheet. See lead enrichment in Google Sheets for the full pipeline, and web scraping in Google Sheets for the scrape step.

What does enriched lead data actually include?

Firmographics (company size, revenue, industry, location, growth signals), contact data (title, work email, phone), and technographics (the tools a company runs). Most workflows use firmographics to qualify accounts and contact data only for the ones that pass.

Do I pay for rows that fail to enrich?

In ReplyLabs, no. Every step charges only for rows that return a result, so failed or blank lookups cost nothing. Many credit-based platforms do charge for failed lookups, which is a common hidden cost.

How accurate is AI extraction for enrichment?

It is as accurate as the source you feed it. Because the AI reads scraped page text rather than guessing from a company name, the output is grounded in real content. Telling the model to return Unknown when the page does not state a field stops it from inventing data.