Account list quality checklist: a quick scoring method

What goes wrong when your account list is low quality

Great copy can still fail if it goes to the wrong people, at the wrong time, or to addresses that never reach an inbox. When founders say “cold email doesn’t work,” the list is often the real problem.

Low-quality lists waste sends in quiet ways. Opens drop because addresses are invalid or your mail lands in spam. Replies drop because the accounts aren’t a real fit, even if they read the message. Deliverability drops because bounces, spam complaints, and fast unsubscribes teach providers that your emails aren’t wanted.

For founders, “quality” is simple: the list should include the right accounts, reachable contacts, a believable reason to care right now, and enough data to personalize and measure.

Four things matter most:

• Fit: Do these accounts match what you sell (size, industry, tech, geography)?
• Reachability: Are the emails valid and likely to land in an inbox?
• Urgency: Is there a trigger that makes the timing make sense (new hire, funding, tool change)?
• Data completeness: Do you have the basics (name, role, company) and one usable personalization hook?

You can usually improve a list in 30 to 60 minutes before sending. Pick one narrow segment, remove obvious bad fits, drop risky addresses (like generic inboxes if they’re not your target), and fill in missing fields for the prospects you care about most.

A realistic example: if you have 300 “marketing leaders” across every industry, cut it down to 80 SaaS companies with a Head of Growth and a clear sign they’re hiring. Even with the same email copy, results often shift quickly.

Deliverability is part list and part sending setup. Tools like LeadTrain can help by warming up mailboxes and surfacing bounce and unsubscribe signals early, but the fastest win is still tightening who you email.

Set your baseline: one campaign, one target, one bar

Before you score your list, decide what “success” means for this one campaign. If you want meetings, you’ll judge the list differently than if you’re trying to learn fast (like which ICP responds) or validate a new angle.

Write down one primary goal and one secondary goal. Keep it concrete: “book 5 calls,” “get 20 replies,” or “learn which persona responds.” This prevents you from padding the list with random accounts just to hit a send count.

Next, pick one persona and one use case. “Founder at a 10-50 person SaaS who just hired their first SDR” is far clearer than “B2B companies.” When the persona is fuzzy, the message gets generic, and even a clean list underperforms.

Set one minimum bar that decides send vs fix. A simple pass/fail rule works well:

• Minimum average score for the list (for example, 70/100)
• Minimum per-account score (for example, nothing under 50/100)

If you miss the bar, fix the data, tighten the target, or cut the list size. If you meet it, send and improve based on real results.

Finally, align on what “good enough” data means. You don’t need perfection, but you do need consistency so you can personalize and track outcomes.

For a founder’s first outbound campaign, a practical minimum is: company name, website, one decision-maker email, role/title, and one reason the account is on the list (hiring, tech stack, recent funding, or another clear fit signal). If you’re running sequences in LeadTrain, those fields also make setup and reporting cleaner.

Score 1: Fit (are these the right accounts?)

Fit is the fastest way to waste sends. If the accounts are wrong, great deliverability and strong copy still produce silence (or polite “not a fit” replies).

Start with firmographics you can verify quickly: company size, industry, geography, and business model. Only keep the traits that genuinely change whether your product works.

A simple rule: if you can’t explain why this account would buy in one sentence, it’s not a fit yet.

Quick fit checks

Ask a few direct questions:

• Do they match your ICP basics (size, industry, location, B2B/B2C)?
• Do they feel the problem often (weekly), not rarely (once a year)?
• Is your solution realistically allowed in their environment (compliance, procurement, security)?
• Can you name a role that would care and can influence budget?
• Can you describe a believable first win in 30 days?

Disqualifiers matter just as much. A reachable account can still be a bad match: too small to pay, too regulated to adopt, wrong region, or locked into constraints you can’t work around.

Fit score (0-5)

Keep definitions sharp so you can score fast:

• 0-1: Mostly random. ICP traits missing, unclear buyer, or no obvious use case.
• 2: Some alignment, but key traits are unknown or the problem seems optional.
• 3: Solid match on basics with a clear use case, but one major question remains.
• 4: Strong match. You can name the buyer, the pain, and a believable first win.
• 5: Ideal. They match your best customers, show clear signals, and you know why now.

On early sends, stick to fit scores of 3+.

Score 2: Reachability (will your email actually land?)

Reachability is about confidence: if you hit send, does the message arrive in the inbox of someone who can act on it?

Start with role relevance. If you’re selling to founders but emailing interns, your deliverability might be fine, but your practical reach is close to zero. Choose 1 to 2 primary titles and a short set of alternates (for example: Founder, CEO, Head of Sales), and downgrade contacts outside that band.

Then scan for email risk signals. Catch-all domains (where every address “accepts” mail) can hide bad data. Generic inboxes like info@, sales@, or support@ are filtered more and reply less. Be cautious with unknown domains, very new domains, and addresses that were guessed without any verification.

You can add multiple contacts per account, but keep it tight. Two to three people makes sense when decisions are shared (for example, Founder plus Head of Sales) or you need time zone coverage. Beyond that, you often create internal noise and raise complaint risk.

Reachability score (0-5)

• 5: Verified email, real person inbox, target title, established company domain, no risk flags.
• 4: Likely deliverable, minor risk (like catch-all), title is a strong match.
• 3: Mixed signals: title is fine, but verification is unknown or the domain looks weak.
• 2: High risk: generic inbox, multiple red flags, or the title is a stretch.
• 0-1: Unusable: invalid/bounce history, wrong domain, or clearly not a decision-maker.

If you send from new mailboxes, exclude 0-2 scores from your first wave. LeadTrain’s warm-up can help protect sender reputation while you start with higher-confidence contacts.

Score 3: Urgency (is there a reason to act now?)

Fit answers “should we talk?” Urgency answers “should we talk now?” Even perfect-fit accounts ignore you when there’s no near-term trigger.

What counts as real urgency

Look for signals that shift priorities or create deadlines: new hiring on the team you sell to, fresh funding, a product launch, a new market push, or a leadership change (new VP, Head of Sales, or RevOps owner). These often create short windows where teams are actively evaluating tools or changing processes.

Timing signals can be just as strong: annual renewals, contract end dates, end-of-quarter budget, seasonal peaks, or a known planning cycle.

A useful gut check: urgency sounds like “we must fix this” tied to a timeline. Curiosity sounds like “nice to know” and “maybe later.”

Urgency score (0-5)

• 0: No trigger, no timing clue. Delay the send.
• 1-2: Weak signal (old news, vague plans). Use only if your list is small.
• 3: One clear trigger in the last 30-90 days, or a known cycle coming soon.
• 4: Strong trigger plus timing (hiring plus launch, funding plus new team build).
• 5: Deadline-driven (renewal next month, active vendor switch, public “we’re hiring X now”).

If an account scores 0-1, don’t force it. Park it and revisit when new signals show up. If you’re organizing prospects in a system like LeadTrain, adding a simple “urgency” field makes it easier to send to higher-intent accounts first.

Score 4: Data completeness (can you personalize and track?)

Data completeness is the difference between “spray and pray” and a message that feels like it was meant for that person. It also prevents wasted sends and messy reporting.

Start with the minimum fields cold email needs to function:

• First and last name
• Role/title
• Company name
• Work email
• Company domain

If any of these are missing, personalization breaks, targeting gets fuzzy, or deliverability risk rises.

Once the basics are there, a few extras make outreach sharper and results easier to measure: tech stack/tools, location or time zone, a recent trigger, a LinkedIn profile field (as a reference), and company size or revenue band.

Also watch for problems that hide inside “complete” sheets: duplicates that cause double-sends, outdated roles (job changes), and rebrands where company name and domain no longer match.

Completeness score (0-5)

• 5: All minimum fields plus two or more useful extras, no obvious duplicates or mismatched domains.
• 4: All minimum fields plus one useful extra, minor cleanup needed.
• 3: Minimum fields present but weak for personalization (generic titles, missing most extras).
• 2: Missing one minimum field or high doubt about role/company accuracy.
• 0-1: Missing multiple minimum fields or clear signs the data is stale.

For a first campaign, a strict but effective rule is to send only to 4-5 scores. You can always enrich and expand later.

Build a simple scoring model (step by step)

A scoring model keeps you honest. Instead of debating “good leads,” you give every account the same quick test, then decide what to do next.

Step 1: Create a 4-row scorecard

Give each account a 0-3 score per row (0 = bad, 3 = great):

• Fit (0-3): matches your ICP basics and has a believable use case.
• Reachability (0-3): a real person to email, a legitimate domain, likely deliverable.
• Urgency (0-3): a clear reason to act now.
• Data completeness (0-3): enough fields to personalize and track.

Step 2: Choose weights (make reachability matter most)

Weights stop a “great fit” from sneaking in when you can’t reliably reach anyone. A simple default:

• Reachability x 4
• Fit x 3
• Urgency x 2
• Data completeness x 1

Total score formula: (Fit*3) + (Reachability*4) + (Urgency*2) + (Completeness*1). With 0-3 scoring, the max is 30.

Step 3: Segment into tiers

Turn the total into decisions:

• Tier A (24-30): send now.
• Tier B (16-23): enrich first, then send.
• Tier C (0-15): replace or park.

Step 4: Decide actions per tier (and stick to them)

Tag tiers before launch so only Tier A enters your first sequence. That single habit protects deliverability and focuses your time on accounts that can turn into replies.

A 15-minute pre-send check

This pre-send check is meant to catch the problems that waste sends and cause bounces.

Start by sampling 20 random rows. If you spot obvious issues immediately (wrong industry, missing names, sketchy domains), pause and fix the sourcing rules first. Cleaning a broken list is slow.

Then run a quick pass, in order:

• Remove low-trust domains. Drop obvious junk: typos, parked domains, strange TLDs you don’t recognize, or domains that don’t appear to belong to a real company.
• Cut risky inbox types unless you have a reason. Quarantine role inboxes like info@, support@, sales@, admin@ unless you’re intentionally targeting those.
• De-duplicate accounts and contacts. Remove repeats by domain and by email, and catch near-duplicates (same company with two spellings).
• Add one trigger line for top-tier only. For your best accounts, add a single reason you chose them (recent hiring, a tool in their stack). Don’t force it for everyone.
• Spot-check accuracy before the full send. Make sure company name matches domain, the contact works there, and the title is plausible.

If you’re already sending in LeadTrain, saving this as a recurring preflight step helps you avoid fixing the same hygiene issues every week.

Common mistakes that waste sends and hurt deliverability

Most founders focus on “are these the right companies?” and forget the boring part: can you actually reach the right person. That’s how a list that looks great on paper turns into bounces, spam placement, and the false conclusion that cold email doesn’t work.

Common mistakes:

• Treating fit as the only thing that matters, then discovering too late that emails are invalid or blocked.
• Sending to low-score accounts “just to see what happens,” which damages sender reputation before you learn anything.
• Mixing personas (Founder, VP Sales, RevOps) in one sequence and blaming copy when replies are inconsistent.
• Using stale data (old job titles, wrong company names, outdated domains), which creates instant distrust.
• Skipping a small pilot batch, so you only notice bounce rates after hundreds of sends.

A safer pattern: start with a small, high-score slice, confirm low bounces and steady inbox placement, then expand. Warm-up and reply classification can help you spot issues earlier, but they don’t rescue a weak list.

Example: scoring a 500-account list before a first campaign

A founder is selling a spend-visibility tool to finance leaders (CFO, VP Finance, Controller) at mid-market SaaS companies. They pull a list of 500 accounts from a data provider. The data is mixed: some accounts are too small, some emails are missing, and many titles are vague.

They score a sample of 50 accounts first. Each account gets 0 to 2 points for Fit, Reachability, Urgency, and Data completeness (8 points max). In about 20 minutes, they can see where the list is weakest.

Projected to the full 500, they end up with a plan:

• Tier A (about 180 accounts): send now.
• Tier B (about 220 accounts): enrich first.
• Tier C (about 100 accounts): drop for now.

After cleaning, bounce rate drops because they stop sending to risky or incomplete contacts. Replies improve because Tier A and enriched Tier B are more relevant and easier to personalize.

The key lesson for the next pull: don’t start with 500. Start with the scoring rules. Update filters (SaaS only, size band, finance titles) and require a minimum field set (first name, title, email confidence, and one trigger).

Next steps: make list quality a repeatable habit

Treat list quality like a weekly routine, not a one-time cleanup. Run the same checks every time and your results get easier to predict.

Start with a Tier A test: send a modest first batch, then compare results by tier. If Tier A replies more and bounces less, your scoring is working. If not, adjust one rule at a time so you can see what actually changed.

Write the rules down so the bar doesn’t shift based on urgency.

A simple repeatable flow:

• Pull the list with one clear target (persona plus market)
• Score it, then drop or fix anything under your minimum
• Clean the data (duplicates, bad domains, missing fields)
• Warm up new mailboxes before increasing volume
• Send, review results, and update the rules

If you want fewer moving parts, LeadTrain keeps the outbound workflow in one place: domains and mailboxes, automated warm-up, multi-step sequences, and reply classification (interested, not interested, out-of-office, bounce, unsubscribe). That makes it easier to stick to the habit without juggling several tools.