Which questions will we answer about prospecting data, and why do they matter?
Because you will waste hours sending emails to people who will never buy, here are the exact questions I’ll answer, and why each one matters in plain terms:
- What exactly should my qualification filters be and why do they cut lists by 60-70%? - Cuts noise so outreach lands on decision-makers. Will cleaning every data point actually increase results? - Saves time by showing what’s worth fixing. How do I set up data tracking, list fields, and discovery logging step by step? - So you know who's warm and why. Should I build my own stack or buy lists and automation? - Money, control, and speed tradeoffs. What changes in 2026 will affect how we collect and contact prospects? - Plan ahead so you’re not blindsided.
If you only take one thing from this: qualification filters reduce noise fast. You’ll see the math and the exact fields to use.
What exactly should my qualification filters be and why do they cut lists by 60-70%?
Short answer: choose three to five hard filters that remove non-buyers immediately. The rest can be soft signals you score later.
Example hard filters I use on day one (apply in your data provider, SQL, or spreadsheet):
- Job title - exact match or clear seniority (CEO, VP, Head, Director). If role is not decision-maker, drop it. Company size - employees between 50 and 1,000 (or your sweet spot). Outside that range is usually wrong fit. Industry - include only target SIC/NAICS codes or industry keywords. Recent tech or trigger event - funding in last 24 months, recent hire spike, or using a specific competitor product. Contact verification - only keep prospects with at least one verified email or phone.
Concrete numbers: start with 10,000 raw leads from a vendor. Apply filters like this and expect:
- - Title filter removes 40% (4,000) because many are non-decision titles -> 6,000 left. - Company size filter removes 35% of remaining (2,100) -> 3,900 left. - Industry filter removes 20% (780) -> 3,120 left. - Trigger/tech filter removes 30% (936) -> 2,184 left. - Verification step invalidates 20% (436) -> ~1,748 final prospects.
Net reduction: ~82% in this example. In practice you’ll see 60-70% more commonly because your starting lists and filters will vary. The point: focus on filters that remove non-buyers fast.
Operator strings and sample queries
Booleans I use for LinkedIn/Google scraping:
- LinkedIn-ish Google search: site:linkedin.com/in ("Head of Sales" OR "VP Sales" OR "Director of Sales") AND ("SaaS" OR "software") AND ("Series A" OR "Series B" OR "seed") Product fit tech trigger: "uses Salesforce" OR "implements HubSpot" OR "built on AWS" - use with company name and "case study" to find public mentions.
SQL example to apply hard filters in your prospect database:
SQL SELECT id, name, title, company, employees, revenue_estimate, verified_emailFROM prospects
WHERE (title ILIKE '%VP%' OR title ILIKE '%Head%' OR title ILIKE '%Director%' OR title ILIKE '%Chief%')
AND employees BETWEEN 50 AND 1000
AND industry IN ('Software','SaaS')
AND (last_funding_date >= '2021-01-01' OR tech_stack ILIKE '%Salesforce%')
AND verified_email = true;
Will cleaning every data point actually increase response rates or am I wasting time?
Short answer: don’t chase perfection. Fix the high-leverage problems and stop there.
What works and what doesn’t, with real numbers:
- Email verification: high value. Removing invalid emails typically increases deliverability and reduces bounce rate by 15-25%. If your starting bounce is 10%, cleaning can drop it to 2-3%. Job title normalization: high value. Standardizing titles increases hit rate with role-based outreach by 20-30%. Company revenue estimates: low immediate value. Often noisy. Use as secondary filter, not primary. Manual enrichment for every contact: low ROI. Enrich top 5-10% of list you plan to outreach manually; automate the rest.
Example: you have 5,000 contacts after basic filters. Spend money on email verification for all (cost about $0.005 - $0.02 per check depending on vendor) - that’s $25-$100. That reduces invalids by 20% (1,000), leaving 4,000 valid contacts. Manual enrichment on top 400 (10%) at $2/contact = $800. Result: a heavily qualified set of 400 high-probability prospects and 3,600 automated ones. That split performs far better than manually enriching all 4,000.
How do I actually set up prospecting data tracking, list organization fields, and discovery logging?
Set up a few standardized fields and a discovery log template. Don’t overcomplicate. This is what I put in every CRM or spreadsheet and why.
Core prospect list fields (must-have):
- prospect_id (unique) first_name, last_name title_normalized (VP Sales, Head Product, CFO) company_name, company_size, industry verified_email, email_source (vendor, enrichment) tech_triggers (comma-separated values) initial_score (0-100) discovery_status (uncontacted, contacted, interested, no-go) last_contacted_date, last_response_date next_action (call, email, nurture)
Discovery logging template - one line per meeting or discovery interaction. Store as notes linked to prospect_id.
FieldExample Entry prospect_id12345 date2025-11-04 whoJane Doe (SDR) summaryHas budgeting cycle Q1, current stack = HubSpot + custom ETL, main pain = lead routing, decision-maker = Head Sales next_stepsSend POC details and case study; follow up 7 days score_change+10 (moved to warm)Example Discovery log entry in plain text I hand new hires:
"Met with Sarah (Head Revenue). Budget approved Q1, main issue - duplicate leads and slow routing. Wants 30-day pilot, decision in 3 weeks. Score +15. Next action: send pilot brief and two product references. Follow up in 5 business days."
Why this works: it captures decision timing, budget, clear pain, and next action. That lets you calculate real pipeline velocity instead of guessing.
KPIs you should track from day one
- Deliverability (bounces %) - target < 5% after cleaning. Reply rate - cold email 2-8% is normal; if under 2% your targeting or pitch is wrong. Meeting rate - 0.5-2% of valid list converts to meetings from cold outreach. SQL rate - meetings to qualified opportunity conversion, aim 20-35%. Cost per meeting - vendor lists + sequence costs should be < your LTV of a closed deal divided by expected conversion.
Should I build my own prospecting stack or buy lists and automation?
Short answer: it depends on volume and speed. I’ve run 50+ campaigns; here’s the rule I use:
- Run your own stack if you plan to run sustained, iterative campaigns with in-house SDRs and want control. Upfront cost higher, long-term cost lower, faster learning loop. Buy lists and automated sequences if you need quick volume or are validating a market. Faster to start, but vendor quality varies and you’ll pay per record.
Cost comparison with real numbers (approx):
- Build stack: CRM $50/month/user, enrichment $200/month, verification $50/month, person (SDR) $4,000/month - breakeven at ~6-9 months depending on deal size. Buy vendor lists: $0.50 - $5 per contact; for 5,000 prospects expect $2,500 - $12,500 upfront. Add sequence tool $100-300/month.
Which mistakes I see most:
- Buying 50k random contacts and emailing everyone. Result: low reply, high spam complaints, domain reputation damage. Automating the whole discovery call qualifying step. If you remove human judgment too early you’ll waste demo bandwidth on dead leads. Not tracking source attribution. If you don’t know which vendor or list produced a meeting, you can’t optimize spend.
What data/privacy changes are coming in 2026 that will affect prospecting, and how do I adapt?
Expect three practical shifts and the adjustments you should make now:
- Stricter inbox heuristics and AI spam detection. What to do: authenticate domains (SPF/DKIM/DMARC), warm domains slowly, and keep cold sends under 300/day per sending domain. Privacy-first data policies and smaller third-party datasets. What to do: build first-party intent signals - website visitors, gated content downloads, webinar attendance - and invest in progressive profiling. More enforcement on cold calling/robocall rules. What to do: keep calling scripts short, record consent where required, and prioritize inbound lists.
Practical checklist for 2026 readiness:
Implement domain authentication and monitor reputation weekly. Cut outreach to verified contacts only - maintain a verification queue. Track source_id on every prospect and measure meetings per source monthly. Segment lists by intent signals and prioritize those with 1+ signals. Keep a "no-go" list of companies and titles to avoid repeat mistakes.Which tools and resources should I use right now?
Tools I rely on and why, plus quick settings I recommend:
- CRM - HubSpot or Pipedrive. Reason: quick setup for pipeline and custom fields. Required fields: title_normalized, tech_triggers, initial_score. Enrichment - Clearbit or Apollo. Use only for soft scoring; don’t replace qualification filters with enrichment. Email verification - NeverBounce or ZeroBounce. Run on every imported list before sending. Sequence tool - Salesloft or Outreach for high volume; Lemlist or Mailshake for lean teams. Limit sends per domain to keep deliverability healthy. Data vendor - ZoomInfo or Apollo for targeted buys, but always test 1,000 records before buying 10,000. Tracking - Post-click UTM tracking and use a simple spreadsheet to map source_id -> campaign -> meetings so you can calculate CPL.
Outreach template that works (short, specific, human)
Subject: Quick question about lead routing at [Company]
Hi [First],
We help revenue teams fix duplicate leads and get new leads routed in under 30 seconds. You mentioned lead volume growth on [Trigger - e.g., Series B, new hire], so I wondered - how are you routing leads today and what’s the biggest bottleneck?
No deck, just one quick question. If it makes sense I’ll send a 2-page pilot outline. If not, no harm.
- [Your name]
Follow-up cadence: 3 follow-ups over two weeks. Keep them one-sentence and always add new value or a social proof line.
Extra questions you probably have (and short answers)
- How many prospects per month should an SDR handle? - 1,000-1,500 contacts in active rotation for outbound sequences; 50-80 manual high-touch prospects. What’s a realistic timeline to see results? - 6-12 weeks from first send to measurable meeting flow if targeting and message are right. When do I stop buying lists from a vendor? - If meetings per 1,000 contacts < 2 after two iterations, stop and retarget another vendor or adjust filters.
Final practical rule: your list is only as valuable as your tracking. Put the filters in place first, automate cheap verification, enrich top 10%, and log every discovery. If you follow the steps above you’ll find fewer prospects but better ones - and that’s how you stop wasting afternoons on bad dibz.me outreach.
