Copy-paste formulas for self-storage lead enrichment
Paste a formula into row 2, test it on a few prospects, then drag down to run it across your list.
Summarize a prospect
A: name · B: website or notes
=GPT("Summarize this self-storage prospect in 2 sentences for a sales rep. Name: " & A2 & ". Source: " & B2 & ". Note what to verify.")
Fit score 0-100
A: prospect · B: ICP criteria
=GPT("Score this prospect 0-100 for fit as a self-storage lead. Prospect: " & A2 & ". Criteria: " & B2 & ". Return a number then a short reason.")
Extract key attribute
A: source text
=GPT("From this text, extract facility counts, unit mix, and markets served if present. Return short labels or 'unknown'. Text: " & A2)
Personalized opener
A: prospect summary · B: your offer
=GPT("Write a one-sentence, specific cold-email opener for this self-storage prospect. Prospect: " & A2 & ". Offer: " & B2 & ". No hype, under 30 words.")
Short answer
A Clay alternative for self-storage is simply doing the same research and enrichment work, scoring fit, summarizing prospects, extracting attributes, and drafting outreach, with AI formulas inside Google Sheets instead of a separate platform. With GPT for Sheets you keep your list of facility owners, REITs, and local operators in one tab and run prompts across every row.
Fastest path: Install GPT for Sheets → add prospect and source columns → paste a formula below → review a few rows → fill down.
This page is for self-storage sales and marketing teams who already keep lists in spreadsheets and want reviewable enrichment without standing up a new tool. AI output is a decision aid, not a guarantee; verify before you act. Clay is a trademark of its respective owner. This page is independent and unaffiliated, offers a factual comparison only, and does not link to or speak for any competitor. “Clay alternative” describes a use case: running enrichment and research with AI formulas inside Google Sheets.
Workflow
A practical sheet for this workflow usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Prospect / company name | Stable row anchor for each lead |
| B | Source text: website, listing, notes | Keeps AI grounded in inspectable evidence |
| C | ICP criteria for self-storage | Defines what the fit score measures |
| D | AI summary | Fast context for the rep |
| E | Fit score | Numeric basis for prioritization |
| F | Extracted attributes | Captures facility counts, unit mix, and markets served |
| G | Personalized opener | Turns research into outreach |
| H | QA flag | Catches weak or unsupported output |
Build your source columns
Start by gathering inspectable evidence for each prospect: a website, a public listing, CRM notes, or pasted text. Keep these raw source fields in their own columns so every AI result can be traced back to something you can check. For self-storage, useful signals often include facility counts, unit mix, and markets served, and the best place to capture them is from facility listings, operator sites, and review pages.
Run, review, then fill down
Test each formula on 10 representative prospects before filling down hundreds of rows. Inspect the highest- and lowest-scoring leads, tune your ICP criteria, and add a QA column that flags rows where the evidence is thin. Once the model behaves on the sample, fill down, then sort by fit score so reps work the strongest self-storage leads first.
Use cases
- Operator prospecting: qualify owners by facility count.
- Market mapping: group operators by metro served.
- Vendor sales: target facilities that fit your offer.
- Acquisition research: summarize a target’s footprint.
- QA: flag operators with missing facility data.
Best for / not best for
Best for: self-storage teams that keep prospect lists in Google Sheets and want a reviewable, repeatable enrichment workflow without a separate platform or per-credit enrichment bill.
Not best for: teams that need a fully managed data provider with guaranteed coverage and contractual data accuracy; AI formulas here research and summarize but do not guarantee contact data.
The strongest use case is turning a flat list of facility owners, REITs, and local operators into a prioritized, personalized worklist where each score and message has visible supporting text. Refresh the sheet on a cadence as your market changes.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- AI lead enrichment guide
- ICP fit scoring in Google Sheets
- Account research automation
- AI sales prospecting in Sheets
Safety, compliance, and data quality
AI output is a decision aid, not a guarantee, and this page makes no claim about contact-data accuracy. Keep source columns visible, require human review before outreach, use lawful data, and follow your team’s compliance rules. Clay is a trademark of its respective owner. This page is independent and unaffiliated, offers a factual comparison only, and does not link to or speak for any competitor. “Clay alternative” describes a use case: running enrichment and research with AI formulas inside Google Sheets.
Frequently Asked Questions
Is this really a Clay alternative for self-storage?
It covers the same core jobs, research, enrichment, scoring, and message drafting, using AI formulas inside Google Sheets. It is unaffiliated with Clay and does not guarantee contact data; it runs prompts across your rows. Clay is a trademark of its respective owner. This page is independent and unaffiliated, offers a factual comparison only, and does not link to or speak for any competitor. “Clay alternative” describes a use case: running enrichment and research with AI formulas inside Google Sheets.
Do I need any contact database to start?
No. You start with whatever list and source text you already have. GPT for Sheets summarizes, scores, and drafts from that evidence; you can paste in more sources as you gather them.
How do I prioritize multi-facility operators?
Use the extract formula to pull facility counts from listing or site text where present, then score and sort so reps reach the largest operators first.
Will the AI output be accurate?
Treat it as a decision aid grounded in the source text you provide, not a guaranteed fact. Keep a QA column and review a sample before your team acts on the results.
Start enriching self-storage leads in Google Sheets
If your self-storage list already lives in a spreadsheet, install GPT for Sheets and run research, scoring, and outreach drafting right where your data is.