Dizenly for SaaS & Software
Software companies buy on technical fit, not brand. This playbook pushes weight onto role and technology relevance, drops the emphasis on company size and geography, and times outreach around funding and hiring signals rather than a calendar quarter.
Who you're selling to: CTO, VP Engineering, Head of Product, VP Sales. Tone: Technical and ROI-led. Buyers self-serve research before any call and expect a fast, low-friction evaluation.
Ideal Customer Profile
This playbook- Roles
- CTO, VP Engineering, Head of Product, VP Sales, Founder/CEO at seed-to-Series C companies
- Company size
- 20–500 employees
- Maturity
- Product-market fit through scale-up - has shipped a paid product and is hiring to grow it
- Geography
- North America, UK & Ireland, EU - English-speaking or English-first product
Buying signals
- Raised a funding round in the last 6 months
- Actively hiring engineers or a Head of Sales
- Recent G2 or Capterra review activity
- Tech stack includes a known CRM or product-analytics tool
Disqualifiers
- Pre-revenue with no paying customers
- Internal tools team only, no external product
- Acquired and being wound into a parent platform
How the nine scoring factors get re-tuned
Default weights come straight from the platform's baseline model and total 100. The SaaS tuning keeps the same total and moves points toward whichever factors actually predict a good saas prospect.
Role / decision authority
Default 20 ptsTuned 22 pts▲ +2Software buyers are almost always the technical leader themselves - there is rarely a procurement layer to route around.
Industry match
Default 15 ptsTuned 15 pts- no changeSoftware-to-software fit is already well captured by the default weight.
Services-to-needs alignment
Default 15 ptsTuned 17 pts▲ +2Evaluation is feature-by-feature, so alignment to their actual stack and roadmap counts for more.
Company size fit
Default 10 ptsTuned 6 pts▼ -4A ten-person startup and a five-hundred-person platform can have an identical need - size is a weak predictor here.
Geography match
Default 10 ptsTuned 4 pts▼ -6Software sells globally by default; a shared timezone rarely decides the deal.
Technology relevance
Default 10 ptsTuned 16 pts▲ +6The single strongest signal in this vertical - a matching stack means a faster, lower-risk evaluation.
Outsourcing probability
Default 10 ptsTuned 8 pts▼ -2Slightly less predictive once technology relevance is already carrying the technical-fit signal.
AI / digital transformation fit
Default 5 ptsTuned 8 pts▲ +3Software teams are the fastest adopters of new AI-enabled workflows, so this signal earns extra weight.
Contact info completeness
Default 5 ptsTuned 4 pts▼ -1Public engineering and product profiles are usually already complete, so this matters marginally less.
Default total: 100 pts · Tuned total: 100 pts
The tuned four-day sequence for SaaS
Same four-step structure the platform uses everywhere - reviewed profile, engagement, connection, AI opener - paced and worded for this vertical.
- Day 1
Review profile & recent product launches
Check their changelog and recent funding announcement for a specific hook.
- Day 2
Engage with a recent technical post
A thoughtful comment on their engineering or product blog beats a generic reaction.
- Day 3
Send the connection request
Reference the specific launch or hire, not a generic “great work”.
- Day 4
Send the AI-drafted opener
Prospects with a capability relevant to their current scaling stage, not a generic pitch.
Sample prospects, re-ranked
Illustrative prospects showing how the saas tuning moves a score against the same nine-factor model.
VP Engineering at a 140-person Series B analytics platform, hiring for three backend roles
Default 81%Tuned 92%▲ +11 ptsIT Director at a 900-person software reseller with no engineering team
Default 84%Tuned 66%▼ -18 ptsHead of Product at a 40-person seed-stage devtools startup, raised three months ago
Default 74%Tuned 88%▲ +14 ptsVP Sales at a 60-person SaaS company, no recent hiring or funding signal
Default 77%Tuned 71%▼ -6 pts
PLACEHOLDER data: these companies, roles and scores are invented for illustration and are not real prospects.
Questions about saas & software
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.
Does company size matter at all for SaaS prospects?
It matters less than almost anywhere else, which is why it is weighted down here. A ten-person startup and a thousand-person platform can both be excellent fits; what separates them is technical relevance and whether the person you reach can actually say yes. Size is a weak proxy for both.
Who is the real decision-maker in a software company?
Usually an engineering or product leader rather than whoever holds the most senior title on the org chart. The playbook weights decision authority on evidence of it - owning a stack, publishing about the problem - rather than on seniority alone.
We sell to both startups and enterprises. One playbook or two?
Two. That is a blended ICP, and a single profile covering both will score everyone in the middle. Run the SaaS playbook twice with different size and maturity criteria and keep the queues separate.
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.
- 14-day free trial
- No credit card
- A person replies within 24 hours