Dizenly for Real Estate & PropTech
Real estate and proptech buyers think in markets, not verticals - the same pitch lands differently in two cities twenty miles apart. This playbook raises geography and company size, drops the technology and AI weighting, and keeps outreach grounded in the buyer’s specific market.
Who you're selling to: VP Operations, Director of Technology, Broker-Owner, Head of Portfolio Management. Tone: Hyper-local and relationship-first. A vendor that understands the specific market outperforms one with a bigger name but no local read.
Ideal Customer Profile
This playbook- Roles
- VP Operations, Director of Technology, Broker-Owner, Head of Portfolio Management, COO
- Company size
- 20–2,000 employees
- Maturity
- Actively managing or transacting a live property portfolio - not a pre-launch fund
- Geography
- US, UK - metro markets with active transaction volume
Buying signals
- New market or metro launch announced
- MLS or property-management-system integration need
- Funding raised for portfolio expansion
- Brokerage or portfolio headcount growing
Disqualifiers
- Pre-launch fund with no active portfolio
- Operating in a single market with no expansion plans
- Portfolio currently under active divestment
How the nine scoring factors get re-tuned
Default weights come straight from the platform's baseline model and total 100. The PropTech tuning keeps the same total and moves points toward whichever factors actually predict a good proptech prospect.
Role / decision authority
Default 20 ptsTuned 18 pts▼ -2Decisions are often shared between a broker-owner and an operations prospect, softening this slightly.
Industry match
Default 15 ptsTuned 14 pts▼ -1Still a strong signal, just marginally less decisive than market fit in this vertical.
Services-to-needs alignment
Default 15 ptsTuned 13 pts▼ -2Slightly less predictive once geography and company size already capture most of the fit signal.
Company size fit
Default 10 ptsTuned 12 pts▲ +2Portfolio size correlates reasonably well with budget and buying authority here.
Geography match
Default 10 ptsTuned 20 pts▲ +10The dominant signal - real estate is transacted market by market, and the wrong metro is a non-starter.
Technology relevance
Default 10 ptsTuned 6 pts▼ -4Digital maturity still varies widely across the industry, so the stack signal is less reliable.
Outsourcing probability
Default 10 ptsTuned 9 pts▼ -1A modest reduction - outsourcing propensity tracks market and portfolio size more than general willingness.
AI / digital transformation fit
Default 5 ptsTuned 3 pts▼ -2The sector is still catching up on basic digital tooling, let alone AI, so this is dialled back.
Contact info completeness
Default 5 ptsTuned 5 pts- no changeBroker and portfolio contacts are usually already well documented.
Default total: 100 pts · Tuned total: 100 pts
The tuned four-day sequence for PropTech
Same four-step structure the platform uses everywhere - reviewed profile, engagement, connection, AI opener - paced and worded for this vertical.
- Day 1
Review profile & market footprint
Confirm the specific metro or region before writing anything - local precision matters more than product detail here.
- Day 2
Engage with a market-expansion post
React to a new-market or portfolio-growth announcement.
- Day 3
Send the connection request
Reference the specific market by name.
- Day 4
Send the AI-drafted opener
Prospects with a market-specific outcome, not a generic platform pitch.
Sample prospects, re-ranked
Illustrative prospects showing how the proptech tuning moves a score against the same nine-factor model.
VP Operations at a 300-person brokerage launching in a third metro market
Default 75%Tuned 90%▲ +15 ptsDirector of Technology at a 40-person pre-launch fund with no active portfolio
Default 71%Tuned 55%▼ -16 ptsBroker-Owner at a 90-person firm integrating a new property-management system
Default 73%Tuned 87%▲ +14 ptsCOO at a 600-person firm operating in a single market with no expansion plans
Default 78%Tuned 66%▼ -12 pts
PLACEHOLDER data: these companies, roles and scores are invented for illustration and are not real prospects.
Questions about real estate & proptech
What this playbook weights differently, and why those factors predict better here than the defaults do.
Is this how you sell?
Bring your own list and we will run it against this playbook while you watch. Half an hour, and you will know whether the ranking matches your judgement.
Why is geography weighted above almost everything else?
Because real estate is decided market by market. Portfolio strategy, regulation and even vocabulary change between cities, and a prospect outside your markets is rarely worth the follow-up however well they fit otherwise.
Why is technology fit weighted down?
Because digital maturity varies enormously in this sector without predicting willingness to buy. Plenty of large, sophisticated portfolio owners run deliberately simple systems.
Does portfolio size work as a proxy for budget?
Better than headcount does, which is why company size is weighted up here. It is not perfect, but it tracks buying power closely enough to earn the points.
Start here
Bring one export. Leave with a ranked queue.
Tell us how your team sources and qualifies today, and we will show you the scored, ranked, owner-assigned queue that comes out the other side - and what it replaces.
Not ready to talk? Start the 14-day trial on your own list instead.
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