Google Maps Lead Lists: A Repeatable Workflow for Enrichment, Verification, and Delivery

A Google Maps lead list is only useful if it lets a campaign operator reach real decision-makers without wasting time on dead numbers or damaging deliverability. You are not producing an “export.” You are producing outreach-ready records with QA gates, documented coverage, and truthfully labeled verification statuses.

Your output must include a locked column schema, a coverage log, verification labels, observable tags, and a delivery packet that a non-technical assistant can use without a follow-up call.

What this job is / isn’t

Is: building outreach-ready records with QA gates and documented coverage (so the list is usable and defensible).
Isn’t: promising outcomes, doing marketing audits, teaching scraping hacks, or dumping raw data and calling it “leads.”

Introduction
ICP first: why narrow beats broad every time
Day 0 / Week 0 operational setup (before you pull volume)
Identifying high-profit market triggers
Extraction without risky behavior: conservative collection and consistency
The 8-step SOP (your core workflow)
Intake template (minimum fields)
Coverage planning: partition patterns you can repeat
Minimum viable schema (what you pull and what you ship)
Decision-maker confidence rubric (simple and enforceable)
Verification statuses (and what each allows)
Normalization + dedupe rules (so the dataset is import-ready)
Tagging: observable signals only
Delivery packet (exact bundle you produce every time)
Worked example (mini end-to-end run)
Tool stack roadmap (start minimal, upgrade at bottlenecks)
Unit economics and data decay
Template A: Home Services (student template)
Template B: Outreach Agencies (student template)
Pitfalls (beginner failure modes)
Risk management defaults (compliance is operational)
Before you deliver (final gate checklist)
Conclusion: start this week (action sequence)

ICP first: why narrow beats broad every time

ICP (Ideal Customer Profile) is your highest-leverage decision because it determines how much pulled volume becomes usable output. Broad extraction (“all plumbers in a city”) creates duplicates, irrelevant categories, missing websites, and unverifiable contacts—then you burn time and credits trying to rescue the dataset later.

Use the MQL vs SQL distinction to stay disciplined:

MQL (Marketing Qualified Lead): broad, interest-based, often incomplete; fine for awareness, weak for outbound execution.
SQL (Sales Qualified Lead): outreach-ready records that are matched to ICP + complete enough to contact + verified/labeled.

Your process must aim for SQL-quality records, not MQL volume.

Gate block (ICP)

Inputs: niche, territory, exclusions, required outreach channels, minimum quality rules.
Actions: write a one-sentence ICP and lock it.
Outputs: ICP spec used for every step.
Pass/Fail gate: if the ICP can’t be enforced as written, tighten it before you collect anything.

ICP examples with constraints (copy patterns)

“HVAC contractors in three ZIP clusters, <10 reviews, website required, emergency service keyword present.”
“Roofers in a 15 km radius, exclude franchises, rating ≥ 4.0, decision-maker email required.”
“Dental clinics in a city, multi-location only, LinkedIn company match required, verified email required.”
“Pest control companies, no website = exclude, phone-first outreach, tag ‘EmergencyService’ when visible.”
“Moving companies, recent negative review in 90 days, contact page required, exclude PO boxes.”

Day 0 / Week 0 operational setup (before you pull volume)

This is your operator boot sequence. If you skip it, you create inconsistency, scope creep, and risky outreach.

Day 0: deliverability and identity basics (high level)

You will use a professional domain for outreach (not a free mailbox).
You will set up basic authentication (SPF/DKIM/DMARC) so legitimate mail doesn’t look suspicious.
You will decide the sending posture: if you can’t verify emails reliably, you do not run email-first.

Week 0: standards you lock once

Normalization rules (names, phones, URLs, casing, suffix stripping).
Dedupe rules:
- Primary: Maps URL (This is the only immutable unique ID assigned by Google).
- Secondary: Business Name + Full Address.
- Tertiary: Domain.
Schema lock (your column list and allowed values don’t drift mid-run).
Proof Kit (your minimum operator proof pack):
- 50–100 spec-matched leads
- Coverage log
- QA report
- Short walkthrough showing your method end-to-end

Gate block (setup)

Inputs: schema, allowed values, dedupe hierarchy, QA thresholds, proof-kit checklist.
Actions: write these once and treat them as immutable for the run.
Outputs: “locked standards” you follow every week.
Pass/Fail gate: if standards aren’t written down, you don’t start collection.

Identifying high-profit market triggers

You will perform better when you recognize when a market operator must refill pipeline quickly. These trigger moments create demand for clean data because the alternative is chaos.

Common triggers:

Ad spend goes up while booked jobs stay flat.
Sales teams burn hours calling bad numbers and chasing dead inboxes.
The first week of a slow season hits and inbound drops suddenly.
A competitor expands (new trucks, new locations).
Reply rates fall because databases are overused.
A CRM is purchased and the pipeline is empty.

Gate block (triggers)

Inputs: observable symptoms + the outreach channel to be used (phone, email, LinkedIn, contact forms).
Actions: decide whether the dataset needs speed (phone-first) or precision (verified email + labels).
Outputs: the correct delivery template (A or B).
Pass/Fail gate: if the outreach channel isn’t defined, the dataset spec isn’t defined.

5-minute readiness score (0–6)

Score 0–2 for each:

Outreach capacity: none (0), inconsistent (1), daily process (2)
Spec clarity: vague (0), partial spec (1), locked spec (2)
Quality posture: refuses verification costs (0), negotiates (1), funds verification (2)

Extraction without risky behavior: conservative collection and consistency

You don’t need scraping tricks. You need consistent collection, logged coverage, and a stable routine.

Acceptable collection approaches

Manual collection for small pilots (slow, safe, controllable).
Recipient-provided exports (when they already have access and rights).
Reputable tools used conservatively and in line with platform terms and applicable law.

Consistency rules (non-negotiable)

Same keywords per geo unit (so results are comparable).
Same geo units (ZIP clusters, grid points, neighborhoods) across the run.
Same field capture routine every time (so QA is meaningful).
Always keep a coverage log (what you searched, where, when, how many results).

Stop rule (collection discipline)

If the next action is “pull more,” stop and check whether it increases U (usable) or only inflates V (raw pulled).

The 8-step SOP (your core workflow)

Your workflow is a pipeline that turns raw listings into SQL-quality records. Each step is a gate.

Intake → lock ICP + exclusions + required channels
Coverage plan → partition territory into reproducible searches
Collection → capture listing fields consistently
Website pass → collect contact paths + basic signals (surface only)
Enrichment chain → attempt decision-maker contact with strict stop rules.
- STOP RULE: Max 3 minutes per record. If no name/direct email is found,
  downgrade to “contact-path-only” and move to the next row.
Verification → assign strict statuses; never ship guesses as verified
Normalization + dedupe → produce clean, import-ready rows (schema locked)
Tagging + delivery packet → observable tags + full documentation

Intake template (minimum fields)

Inputs (required)

Primary keywords:
Territory definition:
Exclusions (franchises, no website, outside service area):
Required channels: phone / verified email / LinkedIn / contact path only
Required tags (from your tag dictionary):
Success metric: booked estimates/jobs or deliverability + replies + meetings

Outputs

A one-page intake spec you can paste into README and the Data Dictionary.

Pass/Fail gate

If exclusions aren’t written, they will be ignored later. Write them now.

Coverage planning: partition patterns you can repeat

Pick one method and stick to it for the run.

ZIP clustering: run searches per ZIP group; strong for service territories.
Grid radius: drop points across the map and run a radius from each point; strong for cities.
Neighborhood sets: use neighborhood modifiers; strong when citywide queries miss pockets.

Gate block (coverage)

Inputs: territory definition + chosen partition method + fixed keyword list.
Actions: execute the same routine per geo unit; log counts per unit.
Outputs: coverage log + pulled volume V.
Pass/Fail gate: if a geo unit wasn’t logged, it didn’t happen.

Minimum viable schema (what you pull and what you ship)

Required (baseline)

business_name
primary_category
address_full (plus city/region/postal_code if you split)
phone
website
maps_url
rating
review_count
source_date

Required if email outreach is in scope

email_1
email_status (verified / unknown / failed)
email_source (site / provider / public profile)
decision_maker_label (decision-maker / role / generic / contact-path-only)

Optional (only if stable)

contact_page_url
linkedin_company_url
tags (comma-separated)
notes (short, factual)

Pass/Fail gate (schema lock)

If you add columns mid-run, you break QA and imports. Lock the schema at the start.

Decision-maker confidence rubric (simple and enforceable)

You will not label a record “decision-maker” unless you have at least two matching signals.

Two-signal minimum examples

Name + role on a public page + same domain
Name on LinkedIn + company match + domain match
Staff page name + leadership title + consistent company identifiers

Downgrade ladder (required)

decision-maker → role email → generic inbox → contact path only

Never-ship rules

Never call a record “decision-maker” without the rubric.
Never call an email “verified” if it’s guessed/generated or unverified.

Verification statuses (and what each allows)

Use strict statuses so the dataset is safe to run in an outreach system.

verified: allowed for email outreach
unknown: Not allowed to be shipped as verified; treat as “use cautiously” or contact-path-only.
- Note: These are often valid mailboxes that don’t respond to ping-tests; advise the client to use them for social media matching or manual LinkedIn reach-outs only.
failed: do not use for email; keep only as a note if useful for QA learning

Pass/Fail gate (truthfulness)

If it isn’t verified, it must not be labeled verified. No exceptions.

Normalization + dedupe rules (so the dataset is import-ready)

Normalization prevents messy outreach and reduces false duplicates.

Gate block (normalization + dedupe)

Inputs: raw pulled records + locked schema + dedupe hierarchy.
Actions: standardize casing; strip non-printing characters; normalize phone formats; clean URLs; dedupe using the hierarchy.
Outputs: clean rows + dedupe metrics (counts + %).
Pass/Fail gate: if duplicates remain, fix dedupe before delivery.

Tagging: observable signals only

Tags must be based on what you can see. No invented intent. No “needs marketing.”

Starter tag dictionary:

LowReviews: review_count below your threshold
RecentNegative: a 1–2 star review within last 90 days (when review recency is available)
NoWebsite: website missing
WeakContactPath: website exists but no obvious contact path
EmergencyService: “24/7,” “emergency,” “same-day” visible on listing/site
MultiLocation: multiple locations visible
NoHours: hours missing
SparseProfile: thin listing (few photos, minimal details)
NewListing: very low reviews and recent first review (when visible)

Pass/Fail gate (tag integrity)

If a tag can’t be explained as observable in one sentence, remove it.

Delivery packet (exact bundle you produce every time)

Your delivery is not “a CSV.” Your delivery is a packet that makes the CSV usable, auditable, and repeatable.

Files (required)

CSV (the dataset)
README (1 page)
Data Dictionary (column definitions + allowed values)
Coverage Log (what you searched, where, when, counts)
QA Report (completeness, dedupe rate, verification breakdown, notes)

README must include

Scope (ICP + territory + exclusions)
How to use (filters, priority logic, what labels mean)
Tag meanings (observable definitions)
What failed QA (and how many rows were removed)
Next-run adjustments (what you will change in the next pull)
Accessibility Clause: Written for a non-technical assistant (No jargon; ensure the list is usable without a follow-up call).

Gate block (delivery packet)

Inputs: final CSV + dictionary + coverage log + QA report + README.
Actions: verify packet completeness and readability (no jargon).
Outputs: one delivery bundle.
Pass/Fail gate: if any required file is missing, delivery fails.

Worked example (mini end-to-end run)

This example uses fictional rows to demonstrate artifacts and discipline.

Filled intake (example)

Keyword: “plumber”
Territory: 3 ZIP clusters (A/B/C)
Exclusions: franchises, no website, outside service area
Required channels: phone + contact path; email optional
Tags: LowReviews, EmergencyService, NoHours, SparseProfile
Success metric: booked estimates

Coverage log snippet (example)

Cluster A / “plumber” / 2026-02-12 / 62 results
Cluster B / “plumber” / 2026-02-12 / 55 results
Cluster C / “plumber” / 2026-02-12 / 49 results
Total pulled (V): 166

10-row sample CSV (example)

business_name,primary_category,city,postal_code,phone,website,maps_url,rating,review_count,email_1,email_status,decision_maker_label,tags,source_date
Northside Plumbing Co,Plumber,Sampletown,10001,+1-555-0101,https://example.com,https://maps.example/1,4.6,8,,unknown,contact-path-only,"LowReviews,EmergencyService",2026-02-12
RapidDrain Pros,Plumber,Sampletown,10002,+1-555-0102,https://example.org,https://maps.example/2,4.2,31,service@example.org,verified,role,"EmergencyService",2026-02-12
Oak & Pipe Services,Plumber,Sampletown,10003,+1-555-0103,,https://maps.example/3,4.0,5,,unknown,contact-path-only,"NoWebsite,LowReviews",2026-02-12
BlueValve Plumbing,Plumber,Sampletown,10001,+1-555-0104,https://example.net,https://maps.example/4,4.8,112,info@example.net,unknown,generic,"SparseProfile",2026-02-12
CityFlow Plumbing,Plumber,Sampletown,10002,+1-555-0105,https://example.edu,https://maps.example/5,3.9,14,,unknown,contact-path-only,"NoHours",2026-02-12
PrimeRooter,Plumber,Sampletown,10003,+1-555-0106,https://example.io,https://maps.example/6,4.4,9,owner@example.io,verified,decision-maker,"LowReviews",2026-02-12
PipeCraft Team,Plumber,Sampletown,10001,+1-555-0107,https://example.co,https://maps.example/7,4.1,22,,failed,contact-path-only,"WeakContactPath",2026-02-12
DrainFix Local,Plumber,Sampletown,10002,+1-555-0108,https://example.biz,https://maps.example/8,4.7,6,,unknown,contact-path-only,"LowReviews,EmergencyService",2026-02-12
EastEnd Plumbing,Plumber,Sampletown,10003,+1-555-0109,https://example.info,https://maps.example/9,4.3,44,contact@example.info,verified,role,,2026-02-12
MetroPipe & Heat,Plumber,Sampletown,10001,+1-555-0110,https://example.app,https://maps.example/10,4.5,18,,unknown,contact-path-only,"SparseProfile",2026-02-12

QA metrics (example)

V pulled: 166
Dedupe removed: 14 (8.4%)
Excluded for no website (intake rule): 22 (13.3%)
Usable rows after QA (U): 130
Email status breakdown (when captured): verified 18, unknown 37, failed 5

Exclusions (and why)

No website: excluded because the intake required contact standards.
Outside territory: excluded because scope is territory-bound.
Franchise: excluded to match ICP and avoid multi-entity confusion.

Tool stack roadmap (start minimal, upgrade at bottlenecks)

You will start with one tool per category. You will upgrade only when a measured bottleneck appears.

Extraction: Apify OR Lead Scrape
Enrichment: Hunter.io OR Snov.io OR Apollo
Verification: MillionVerifier OR Form Guard
CRM/Delivery: HubSpot OR GoHighLevel OR Instantly

Gate block (tools)

Inputs: delivery template (A or B) + required channels + volume target.
Actions: pick one tool per category; document it in README.
Outputs: a minimal stack that supports the workflow.
Pass/Fail gate: if you have two tools for the same category without a measured bottleneck, remove one.

Unit economics and data decay

You will measure cost on usable output, not raw volume.

TC = (S + C + L) ÷ U

S = software overhead
C = credit cost
L = labor cost
U = usable, spec-matched leads after QA (not raw pulled volume)

Data decay example (V → U)

Stage	Count	%
V pulled	500	100%
After dedupe	440	88%
After exclusions	360	72%
Verified/spec-matched usable U	240	48%

Gate block (unit economics)

Inputs: S, C, L, and the QA report count for U.
Actions: compute TC; compare TC week-to-week against the same ICP and template.
Outputs: true cost per usable lead and a trend over time.
Pass/Fail gate: if U drops, you do not “pull more” first—you fix ICP, dedupe, or verification gates.

Template A: Home Services (student template)

When to choose: phone-first outreach, smaller batches, fast follow-up.

Gate block (Template A)

Inputs: territory + phone + Maps URL + observable tags.
Actions: produce a priority-ready call list sized to follow-up capacity.
Outputs: clean, deduped dataset optimized for calling.
Pass/Fail gate: if email is not verified, it does not enter the workflow as “verified.”

QA priorities: dedupe accuracy, territory correctness, observable tags, clean formatting.
Common mistakes: over-delivering volume, ignoring exclusions, mixing in unknown emails that create confusion.
Best next step: add a simple priority column (A/B/C) driven by tags + territory fit.

Template B: Outreach Agencies (student template)

When to choose: email-first or multichannel outreach at scale with strict deliverability.

Gate block (Template B)

Inputs: verified emails + strict status labels + consistent schema + suppression support.
Actions: enforce verification truthfulness and decision-maker rubric before delivery.
Outputs: CRM-ready dataset with auditable labels and documentation.
Pass/Fail gate: if “verified” coverage is too low for the intended channel, switch to phone-first or contact-path-only rather than mislabeling.

QA priorities: verification truthfulness, completeness %, dedupe %, schema stability.
Common mistakes: shipping unknown as verified, labeling decision-makers without the rubric, schema drift.
Best next step: tighten the verification gate until “verified” is truly verified.

Pitfalls (beginner failure modes)

Over-enrichment without stop rules (credits burn, little gain).
Invented tags instead of observable signals.
Schema drift mid-run (breaks imports and QA).
Weak dedupe (duplicates poison outreach and reporting).
Shipping unverified emails as verified (deliverability damage).
Ignoring suppression lists (repeat outreach to opt-outs).

Risk management defaults (compliance is operational)

This work touches personal data and outbound contact. You will treat compliance as risk management: it protects the campaign operator, the sender reputation, and the workflow itself.

Operational defaults:

Data minimization (collect only what’s needed for outreach)
Source logging (where each contact field came from)
Strict verification labeling (verified/unknown/failed)
Suppression support (remove opt-outs from future runs)
Retention window (don’t store contact data forever)
Conservative methods and respect for platform terms
No deceptive tagging or claims

Concrete stakes exist. The FTC states that each separate email in violation of CAN-SPAM can be subject to civil penalties of up to $53,088.
Under GDPR, sanctions can include fines of up to €20 million or 4% of annual worldwide turnover, whichever is higher.

Gate block (risk management)

Inputs: delivery template, outreach channel, and the fields you collected.
Actions: remove unnecessary personal fields, label verification truthfully, maintain suppression support, and document sources.
Outputs: a dataset that can be used without avoidable compliance risk.
Pass/Fail gate: if you cannot explain where a personal contact field came from (source logging), it does not ship.

Before you deliver (final gate checklist)

Required-field completeness meets your target (and you report the %).
Dedupe rate is reported and duplicates are removed.
Verification labels are correct (no guessed emails marked verified).
Decision-maker labels meet the two-signal rubric.
Tags are observable and defined.
Delivery packet is complete: CSV + README + Data Dictionary + Coverage Log + QA Report.

Conclusion: start this week (action sequence)

Pick one narrow niche and one territory. Build the intake spec and lock your schema. Run a coverage plan and produce a 50-lead sample. Apply QA gates, label verification strictly, and assemble the delivery packet. Package the sample as your proof kit, then repeat weekly with the same standards so your usable output (U) stays high and your process stays defensible.

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