System4 IPS — HubSpot Process Optimisation

Client Decisions — 22 April 2026

What's moving forward, what's not, and revised priorities

The client has reviewed this plan. Direction below applies across all sections — look for the status pills inline.

✓ Agreed — proceeding

Zapier/Make to parse paid source emails → auto-create contacts, companies, deals
Website form: postcode → auto-state, remove subject
Auto-associations (contact → company → deal) + HubSpot Data Enrichment
Vertical-specific approaches with research agent, email sequences, lead scoring by # locations
Schools summer deep-clean workflow

✗ Not proceeding (for now)

Extracting sq ft / property type / cleaning frequency — lives in franchisor's software, maybe API later
"Services interested in" field on the website form — captured on the call
AI message parsing at handoff — sales captures on the call
Deal properties: estimated locations, cleaning buyer model, multi-location opportunity, estimated project volume

◆ Already done

SNE number is now required on deal creation

+ New requests

Use existing video-sharing platform for site pre-qualification — train BDR on this
Auto-populate all contact/company fields on creation
Explore HubSpot Quotes before the data quality/enrichment/parsing phase

Revised priority order

Reduce data entry + minimise duplicates (paid source automation + auto-associations)
Website form improvements (1-2 click scheduling)
Explore HubSpot Quotes — before enrichment/parsing phase
Data quality, enrichment, parsing, vertical research agent

Client Q&A answered

HubSpot Quotes: Interested in exploring. Quotes are simple — work schedule + monthly cost. Some negotiation occurs.
Quote vs proposal: Same document — one send covers both.
Multi-location billing: Customer-dependent. Some want per-location invoices, some combined.
Referrals: Not tracked currently. Mostly come from existing customers.

Lead Journey Overview

🔮 = doesn't exist yet (proposed)

💡 Consider using Lead object for non-paid source leads — enables progress tracking + SLA measurement

Lead Sources

BuyerZone
Thumbtack
LeadBlast
Bark.com
Website form
Chatbot
Buyer's Intent
🔮 UpLead (or Apollo.io)
Referrals

▶

Lead Entry

Auto-create contact
Parse email/form
🔮 AI extract fields
🔮 Enrich company
🔮 Auto-associate

▶

Qualification

🔮 ICP matching
🔮 Multi-location flag
🔮 Urgency detection
Auto-create deal

▶

Sales

🔮 Speed to lead (instant ack + SLA)
Video walkthrough received (BDR pre-qual)
Quote via PandaDoc / 🔮 HubSpot Quotes
🔮 Follow-up sequence

▶

Close

Closed Won → Welcome
🔮 Provider auto-assigned
Closed Lost → Re-engage
✓ Closed Lost = Not now / Follow up later → prompt deal owner for follow-up date
✓ Task auto-created for deal owner on follow-up date
✓ SNE number (required)

▶

Retain & Expand

Satisfaction survey
Google review nudge
Cross-sell RS↔SS
🔮 Multi-location expand
🔮 Renewal tracking
🔮 Referral request

Lead Entry — Paid Sources

Current State

Manual entry from paid sources — each sends different fields via email. Zapier/Make parses emails to create deals, but content lands as raw text.

Source	Integration	Fields	Status
BuyerZone	Zapier (mail parse)	Size, Location type, Frequency	Exists
Thumbtack	Zapier (mail parse)	Frequency, Property type, Sq ft, Windows, Area, Dates	Exists
LeadBlast	Make (custom)	Full contact + company	Uroš managing
Bark.com	Zapier (mail parse)	Various	Exists
HomeAdvisor	Zapier (mail parse)	Various	Exists
Prospectr	Zapier (mail parse)	Various	Exists
Cohesive AI	Zapier (mail parse)	Various	Exists

Recommendations Agreed

Automate lead creation Agreed

Use Zapier/Make to parse lead emails → auto-create contacts, companies and deals in HubSpot. Eliminate manual data entry.

AI parsing Workflow

Extract core fields only

Parse name, email, phone, company, domain, address, lead source detail. Skip sq ft, property type and cleaning frequency — those live in the franchisor's software and may be pulled via API in the future.

Core contact fields

Lead Entry — Website

Client priority: 1-2 click scheduling. Keep forms short.

Change	Why	Effort	Status
Postcode/city mandatory → auto-populate US state	Fewer fields, better data. Zip→state mapping via JS or workflow	Low	Agreed
Remove subject form field	Adds friction, rarely used for routing	Low	Agreed
Add "Services interested in" multi-select	Sales prep + segmentation for nurture	Low	Captured on call

Handoff to Sales

Auto-Associations Agreed

When a contact is created:

Match/create company by domain
Associate contact → company
Trigger deal creation

Workflow Zapier/Make HubSpot setting

HubSpot Data Enrichment Agreed

Use HubSpot Breeze Intelligence:

Industry / vertical
Employee count
LinkedIn (contact + company)
Phone, job title, role
Is public company

Freemail addresses won't return company data

HS Data Enrichment

AI Message Parsing

Not proceeding

Sales captures urgency, requirements and summary on the discovery call. No need for automated parsing given the phone-first motion.

Revisit only if call volume makes this a bottleneck.

Sales to Ops

SNE Number ✓ Done

Status update from client: SNE number is now a required field when creating a deal. No further action needed.

Business Model Factors

Multi-Location Opportunity

One relationship can unlock many locations. See Vertical Deep-Dive for multipliers by industry.

Speed to Lead

Commercial cleaning leads go stale fast — prospects typically request 3-5 quotes. First responder wins 35-50% of the time.

Instant acknowledgement email/SMS on lead capture
Auto-assign to rep + SLA timer
Track time to first contact as KPI
Escalation workflow if no contact within 30 minutes

Site Survey — Video-First New direction

Client already has a video-sharing platform where customers send videos of their site — no physical rep visit needed.

BDR trained on video walkthrough pre-qualification
New deal stage: "Video Walkthrough Received"
Faster progression — no travel bottleneck
Physical visit only when video reveals complex scope

Churn Signals to Watch

Increased complaints in 30-day window
Scope reduction requests
Contact person change
Late payments

Referral Network

Property managers, RE agents, facility managers refer constantly. Likely highest-quality source with zero tracking.

🔮 Use association labels (e.g. "Referred by") between contacts/companies to track who referred whom. Layer properties on the referrer to track volume, close rate, revenue. Available on all tiers — no custom object needed.

Vertical Deep-Dive: Multi-Location Multipliers

Operationalising This Client loved this approach

Direction agreed: use a research agent to identify leads per vertical, enrich the data, enroll them in email sequences and score them by number of locations.

Schools first: bolster existing summer deep-clean workflow (or build if not in place) — this is a priority seasonal campaign.

Detection methods: AI / Research Agent HS Data Enrichment Smart Property Human Required Workflow / Automation

Click each vertical to expand details

🏫 Schools & Education

▼

Structure

Public schools → districts: 1 superintendent/facilities director controls all buildings
Single RI district = 5-25 buildings (elementary, middle, high)
Private/charter = individual but often part of networks (e.g. Achievement First)
Primary vs secondary = different cleaning needs (frequency, science lab hazmat)

Expansion Signals

Win one school → facilities director hands you the rest
Annual contract cycle tied to school year (Jul-Aug decision window)
Budgets are public record (school board minutes)

Detection & Action

Method	What it reveals	Type
Company Research Agent	District website → all schools + addresses	AI
HS Data Enrichment	Employee count, industry classification	HS Data Enrichment
Vertical sub-type property	"Public district" vs "Private" vs "Charter network"	Smart prop
Estimated locations	Building count from research	Smart prop
SDR / sales	Facilities director relationship, budget politics, incumbent	Human

Seasonal Priority

Existing workflow created by Monique → HubSpot workflow 387861284. Review, then rebuild so it triggers every year automatically (not a one-off).

Apr-May: "Planning summer deep cleans — here's what we recommend"
May-Jun: Testimonials from other school district clients
Jun-Jul: Availability + booking push + contract bidding cycle
Post-clean: Thank you + next-year reminder
Oct-Nov: Flu season antimicrobial follow-up

👶 Tuition / Study Centres / Childcare / Daycares

▼

Structure

Franchise/chain models common: Kumon, Mathnasium, Sylvan, KinderCare, Bright Horizons
One franchise owner → 3-8 locations in a region
Even independents often have 2-3 sites
Compliance is the hook — strict sanitation requirements (state licensing, health dept inspections)

Detection & Action

Method	What it reveals	Type
AI / Smart property / Human	Parent company, franchise flag	AI Smart prop Human
Business type property	"Franchise" vs "Independent" — depends on ownership structure, needs human judgement	Human
Sales call	"Do you have other centres?" — high-value question	Human

🏢 Property Management

▼

Structure

Property managers control cleaning decisions for all their tenants
1 PM firm with 20 commercial buildings = 20 potential contracts
May offer cleaning as bundled tenant amenity OR approved vendor list
Even if tenants choose their own, PM is the gatekeeper

Detection & Action

Method	What it reveals	Type
Company Research Agent	Website → property portfolio list	AI
HS Data Enrichment	Industry = property management	HS Data Enrichment
Decision-maker type	"Property manager" vs "Tenant" vs "Owner" — smart prop if email mentions it, otherwise human	Smart prop Human
Portfolio size	Number of managed properties — smart prop if email mentions it, otherwise human	Smart prop Human
Cleaning buyer model	"Landlord-provided" vs "Tenant-selected" vs "Approved vendor" — smart prop if email mentions it, otherwise human	Smart prop Human
Prospecting Agent	"We service Building A — can we quote Building B, C?"	AI
Relationship	Vendor selection politics, contract structure	Human

🏗️ Construction / General Contractors / Developers

▼

Structure

Post-construction cleaning needed for every new build, renovation, and fit-out
GCs don't clean once — they have a pipeline of projects
1 GC relationship = rolling stream of SS deals (not monthly contract, but recurring project work)
Developers: multi-unit builds = multiple handover cleans per project
Sub-contractors also need cleaning (flooring, painting) — secondary referral path

The Real Opportunity

GC doing 10-20 projects/year in RI/MA/CT = 10-20 SS deals from ONE relationship
They don't think about cleaning until 1-2 weeks before handover — be the default
Preferred vendor status = they add you to their project playbook automatically

Detection & Action

Method	What it reveals	Type
Building permit database	Active projects in RI/MA/CT, who's building, where	AI
Company Research Agent	GC website → project portfolio, areas served	AI
HS Data Enrichment	Industry = construction, employee count	HS Data Enrichment
Project volume (est.)	Projects/year in territory	Human
Prospecting Agent	After SS clean: "Any upcoming projects we can help with?"	AI
Project manager relationship	Handover timelines, getting on preferred list	Human

Seasonal

Construction slows in NE winter, ramps in spring
Post-construction cleaning follows 2-3 months later

🛍️ Retail Chains & Franchises

▼

Structure

1 franchisee = multiple locations (Subway owner with 6 stores, gym chain with 4)
Corporate retail = centralised facilities management (regional FM decides all)
Strip malls = 1 landlord, many tenants (same as property management)

Detection & Action

Method	What it reveals	Type
AI / Smart property / Human	Parent company, franchise flag	AI Smart prop Human
Sales discovery	Franchise owner vs corporate FM — who decides?	Human

🏥 Medical / Healthcare

▼

Structure

Private practices often have 2-5 offices (dentist x3, physio x4)
Medical groups: one admin controls all offices
Hospitals = usually large national contractors (less relevant for S4IPS)
Compliance is everything — OSHA, infection control, biohazard
Medical cleaning commands premium pricing

Detection & Action

Method	What it reveals	Type
State medical board databases	All licensed practices	AI
Practice website scrape	All office locations	AI
Compliance requirements	OSHA / Infection control / Standard	Smart prop
Sales / specialist	Compliance nuance, premium pricing justification	Human

🏭 Industrial / Manufacturing

▼

Structure

Fewer locations (1-3) but each is high-value — large sq ft, specialised needs
Multiple cleaning TYPES per facility: production floor, offices, break rooms, exterior
Compliance-driven: OSHA, EPA, FDA (food manufacturing)
Seasonal deep cleans during shutdown periods

Expansion

Manufacturing + office space = two separate cleaning scopes (RS + SS)
Warehousing/distribution in addition to manufacturing
Site survey is essential — scope varies wildly

💪 Gyms & Fitness

▼

Structure

Franchise-heavy: Planet Fitness, Orangetheory, CrossFit, F45
1 franchisee = 2-6 locations typically
Corporate gyms (inside office buildings) = sold through property manager
High-frequency cleaning (multiple times daily) = high contract value

Seasonal

January: New Year's resolution surge = more foot traffic = more cleaning
New gym openings (Google Alerts, local business filings)

Detection Method Summary

Fully automated

HS Data Enrichment

Industry, employees, parent company, franchise flag. Auto on new records via Breeze/Apollo.

Fully automated

Company Research Agent

Website scraping for portfolios, location lists. Trigger on deal creation.

Fully automated

Smart Properties (calculated)

Business type, estimated locations, compliance, decision-maker type. Set by workflows.

Possible to automate

Public Database Scraping

School districts, licensed daycares, medical practices, building permits. Periodic research tasks.

Always human

Smart Properties (manual)

Confirmed locations, cleaning buyer model, relationship strength. Discovery call required.

Always human

Human Discovery

"Do you have other locations?", budget politics, decision-maker dynamics, compliance nuance.

Properties & Data Strategy

Client direction — keep it simple

Client priority: auto-populate everything on contact/company creation. Don't add deal properties that don't materially move the needle.

Auto-Populated on Contact/Company Creation Priority

Landed automatically — no manual entry.

From paid source parsing (Zapier/Make)

First name / Last name
Email
Phone number
Company name
Company domain
Address / City / State / Postcode
Lead source detail

From HubSpot Data Enrichment

Industry / vertical
Number of employees
LinkedIn (contact + company)
Job title / Employment role
Is public company
Revenue range

Note: Freemail addresses (gmail, yahoo) won't return company data.

Company-Level Custom Properties — Minimal Set

Property	Type	Values / Notes	Source
`Vertical sub-type`	Dropdown	Public school district, Private school, Charter network, Daycare chain, Independent daycare, Property mgmt, Medical group, Solo practice, Retail chain, Gym franchise, General contractor, Developer, Fit-out contractor, Religious	AI Smart prop
`Parent company`	Text	Franchise parent or corporate entity (from enrichment)	HS Data Enrichment
`Number of locations (confirmed)`	Number	From discovery call — feeds lead scoring	Human

Deal-Level Custom Properties — Minimal Set

Property	Type	Values / Notes	Source
`Service location address`	Text	Specific site this deal covers (vs company HQ)	Human
`Service type`	Dropdown	Recurring janitorial, Post-construction, Deep clean, Floor care, Window, Antimicrobial, Porter, Other	Human

Not Adding — Per Client Feedback

Keep deal properties simple — don't add what doesn't move the needle.

Property	Why not
`Estimated locations in territory`	Not a deal property
`Cleaning buyer model`	Not a deal property
`Multi-location opportunity`	Not a deal property
`Estimated project volume (annual)`	Not a deal property
`Facility type`	Covered by vertical sub-type at company level
`Compliance requirements`	Captured on the call, not structured

Multi-location dynamic still matters commercially — handled via research agent enrichment and lead scoring, not deal properties.

AI Agents

Data Agent

Not set up

Flag: missing phone, missing vertical, freemail on company

Additional AI agent opportunities (prospecting, customer agent, research agent, ICP) documented separately.

Questions — Answered by Client

Process & Quoting

Negotiation pattern?
✓ Some negotiation happens, but quotes aren't wildly custom. Templated generation is feasible.

HubSpot Quotes vs PandaDoc?
✓ Client wants to explore HubSpot Quotes before the enrichment/parsing phase. Quotes are simple (work schedule + monthly cost).

Does a quote go before the proposal?
✓ They are the same document. One send covers both quote and proposal.

Business Model

Multi-location billing structure?
✓ Customer-dependent. Some want per-location invoices, some want all locations combined on one invoice.

Referral tracking today?
✓ Not tracked currently. Most referrals come from existing customers. Lightweight tracking proposed via association labels.

Revised Roadmap Updated 22 Apr 2026

Priority reflects client direction: reduce data entry first, then web form improvements, then explore HubSpot Quotes before the enrichment/parsing phase.

Phase 1 — Reduce Data Entry & Duplicative Work (Weeks 1-3)

Client priority #1

Automate paid source lead entry

BuyerZone, Thumbtack, Bark, etc. → Zapier/Make parses emails → auto-creates contact, company and deal in HubSpot. Core fields only (no sq ft / property type / frequency).

Auto-associations

Match/create company by domain, associate contact → company, trigger deal creation. Eliminates manual duplicate entry.

✓

SNE required on deal creation Done

Client has already made SNE a required field.

Phase 2 — Website Form Improvements (Weeks 2-3)

Client priority #2 — 1-2 click scheduling

Postcode → auto-populate state

Simple JS or workflow mapping zip → state. Reduces form fields.

Remove subject field

Reduces friction. Rarely used for routing.

Instant acknowledgement + SLA timer

Auto-email/SMS on capture, auto-assign rep, track time-to-first-contact as KPI.

Phase 3 — Explore HubSpot Quotes (Weeks 3-5)

Client priority #3 — before enrichment/parsing

Evaluate HubSpot Quotes vs PandaDoc

Quote = proposal (one document). Simple structure: work schedule + monthly cost. HubSpot Quotes ties to deals, tracks views, can trigger follow-up.

Template + line items

If adopting: build quote template, set up product/service library for line items.

Multi-location invoicing flexibility

Customer-dependent: some want per-location invoices, some combined. Build template variants.

Phase 4 — Data Quality, Enrichment & Research Agent (Weeks 5-9)

Enrich, prioritise, operationalise

Enable HubSpot Data Enrichment

On new records: industry, employee count, LinkedIn, phone, job title. Auto-populates on creation.

Data Agent setup

Flag missing data: phone, vertical, freemail on company record.

Research Agent — vertical lead identification

Identify leads per vertical, enrich, enrol in email sequences, score by number of locations. Start with schools.

Lead scoring — location-weighted

More locations = higher score. Re-enable 29+ SDR trigger once scoring model is rebuilt.

Phase 5 — Vertical Workflows & Expansion (Weeks 8-12)

Vertical-specific nurture and multi-location motion

Schools — summer deep-clean sequence Priority

Existing workflow by Monique → rebuild so it triggers every year. Open workflow. Expand: Apr-May planning, May-Jun testimonials, Jun-Jul booking push, post-clean thank you + next-year reminder.

Other vertical nurture tracks

Daycares, property management, retail, medical — each with vertical-specific seasonal triggers.

Multi-location expansion prospecting

Agent surfaces existing clients with untapped sites → outreach for additional locations.

Referral tracking — lightweight

Not tracked today. Use association labels (e.g. "Referred by") between contacts/companies. No custom object needed.

Contract tracking + renewal workflows

Contract start, end, renewal dates. 90-day renewal notifications. Churn signal monitoring.