Your best customers share a stack, a vertical and a size. LeadsQuantum finds every website that matches, ranks them by fit, writes the first email, and warns you about the customers most likely to leave.
Industry and headcount say little about whether a business will use your product. The tools it already runs say a lot, and they are visible on every website.
"Retail companies, 10 to 50 staff" includes thousands of businesses with no need for you and no way to use you.
Everyone mines competitor users. Those buyers are tired of switch pitches arriving every week.
Sites whose tools, vertical and size match your best customers, ranked by fit.
Look at your twenty best customers. Write down what they have in common, using only fields LeadsQuantum can filter on.
Each list comes from a different module and calls for a different pitch. Run them in parallel and let the results decide where to put more volume.
Sites with no tool in your slot whose stack predicts yours.
Sites running a rival product.
Sites running a tool you integrate with.
Sites in categories where your type of tool over-indexes.
Combine fit with size to decide how much personal effort each row deserves. Most of the list gets light-touch outreach; a small top tier gets real research.
| Tier | Who | Treatment | Share of list |
|---|---|---|---|
| Tier 1 | High fit, large sites | Hand-written outreach, research, calls | Top 5 to 10% |
| Tier 2 | High fit, mid-size sites | AI draft plus one personal detail | Next 30% |
| Tier 3 | Good fit, small sites | AI drafts, light review, self-serve offer | Next 40% |
| Hold | Weak fit | Newsletter or ads only | The rest |
Use these to choose tier 1 accounts. The more signs a site shows, the more personal effort it deserves.
Their stack strongly predicts your category, and the driver tool is a clear opener.
Your integration removes setup friction and shortens the trial.
Your type of tool is normal in their category, so no education is needed.
Catalog and price sit in your best-customer band, so pricing fits.
Young domain with strong authority, or a rising catalog that will need more tools.
A rich stack of paid apps signals budget and a habit of buying software.
One login covers the data, the ranking and the first draft. Your email and CRM tools handle the rest.
Four list types, filtered to your ICP and refreshed monthly.
Recommender score and driver tool on each row, ready for tiering.
One-click AI emails, edited by your team before sending.
Risk ranking of current customers, reviewed each quarter.
A realistic plan for a small sales team or a founder doing sales. It assumes a few hours a week, not a full-time team.
| Weeks | Focus | Output |
|---|---|---|
| 1 | Write the ICP as filters, map rivals and partners | One page ICP, rival and partner list |
| 2 to 3 | Build lists A and C, tier them, write openers | First two campaigns live |
| 4 to 5 | Build lists B and D, add a switch offer | Four campaigns running |
| 6 to 8 | Compare reply and meeting rates by list type | A ranking of what works |
| 9 to 10 | Double down on the best list, refresh all lookups | Bigger volume where it works |
| 11 to 12 | Run churn review on current customers | A retention list and owners |
| 13 | Write the playbook for next quarter | A repeatable process |
The list source is the biggest driver of results. Track it on every contact, from first email to renewal, so you can see which source produces customers who stay.
Retention by list source. A list that books meetings but produces churning customers is worse than it looks.
The same four list types apply everywhere, but each software category leans on a different one first.
| Software category | Lead with | Typical filter |
|---|---|---|
| Email and SMS marketing | Competitor users | Product count, country |
| Reviews and UGC | Recommender picks | Fashion, beauty, home categories |
| Live chat and helpdesk | Recommender picks | Average price |
| Subscriptions and loyalty | Partner tool users | Consumable categories |
| Payments and pay-later | Competitor users | Average price |
| Shipping and returns | Vertical sweep | Country, catalog size |
| Analytics and testing | Partner tool users | Popularity rank |
| Site search and merchandising | Recommender picks | Product count |
| Translation and localization | Vertical sweep | Language |
| Publisher and ad tech | Competitor users | IAB category |
Starting suggestions. Test all four list types for your own product.
Illustrative first lines. The structure matters more than the exact wording, so adapt each one to your own product and voice.
"Since you already run [driver tool], stores like yours usually add [category] next."
"You use [rival]. Stores your size switched to us for [specific benefit]."
"We plug into [partner tool] in minutes, so nothing changes for your team."
"Most [category] stores we work with start by fixing [common problem]."
Stack-based outreach gets more replies, including objections. Have answers ready.
Explain that the tools on a website are publicly visible, and that you only reached out because the fit looked strong for their kind of store.
Ask what they use and what they would change about it. Often it does a different job than yours, which opens a real conversation.
Ask when they plan tools for the next season and set a reminder to follow up then.
Send one short page relevant to their category, plus one example from a similar store.
Talk about the outcome in their numbers, or offer a smaller starting plan to prove value first.
Have two examples per vertical ready, ideally from stores of a similar size.
The data is rarely the problem. How it is used usually is, and each of these six habits is easy to fix.
Mixing list types hides which one works. Keep them separate in your tools and reports.
Pricing fits a size band. Filter to it before export, not after replies.
Use the driver tool, rival or category in the very first line of every email.
Small batches protect deliverability and let you learn before you scale.
Stacks change monthly. Re-run lookups and work new rows first.
Use churn signals on new customers from the start, not after they cancel.
An illustrative example of a small helpdesk vendor selling to mid-size online stores.
The same data serves sales, marketing, product, success and partnerships. One subscription gives every team the same view of the market.
A pipeline without hiring an SDR team first, built in a few hours a week.
Lists they trust, with a reason to call written on every row.
Verticals to target and audiences to export for paid campaigns.
Integrations users expect and audiences you currently miss.
Which accounts to protect first, and why they are at risk.
Partners whose users overlap most with your ICP.
All SaaS workflows need the technology features, which start on Advanced. Pick by team size and how many rows each report needs.
| Team | Plan | Why |
|---|---|---|
| Founder or small team | Advanced, $999/yr | 100 recommender rows, 1,000 technology rows, 2 users |
| Larger team or several products | Enterprise, $1,999/yr | 5,000 recommender rows, 10,000 technology rows, 5 users |
Both plans are billed yearly and paid by bank transfer against an invoice. See pricing for details.
No. Store data is strongest for ecommerce tools, but the five million popular domains cover SaaS sold to publishers, services and many other website owners.
Use the closest known product in your category for recommender and market views. Your own customers' stacks will tell you which tools to use as partners.
Store records include public contact details where available. Many teams add named contacts with their own enrichment tool after exporting.
It depends on your ICP. Reports hold 100 recommender and 1,000 technology rows on Advanced, and 5,000 and 10,000 on Enterprise, and you can run many reports per month.
Yes. Export to CSV or Excel and import into any CRM or sequencer. Keep the list type and driver tool as custom fields.
It varies by product. Run all four for a month, compare meetings per 100 contacts, then shift volume to the winner while keeping the others running at a low level.
Yes: over-index ratios for content and campaign planning, and domain exports for account-based advertising and event invitations.
Yes. Send up to 500 domains to [email protected] for cross-sell and prioritization.
Most teams send their first campaign in the first week and have comparable reply data within a month. Win rates take a full sales cycle to read.
Yes, at any time. Access continues to the end of the paid period, and you keep everything you exported.
For teams selling to websites and online stores, it covers the part those platforms are weakest at: stack fit and store context. Many teams use both, with LeadsQuantum deciding who to target.
Yes. Export tier 1 and tier 2 domains as target account lists for ads, events and direct mail.
Give each rep a tiered list with the driver tool or rival column visible, and agree how many tier 1 accounts they work each week.
Filter by country and language. Around one million stores carry a country, and the popular domains are global.
Look for clusters of client sites sharing an agency's tools and themes, then reach out to the agency behind them. See agencies.
The same fit signals that rank prospects also flag risky customers. Using them on both sides means you sign customers who stay.
One person, usually the founder or head of sales, owns the ICP filters and the monthly refresh. Reps own their tiers and the follow-ups.
A few hundred per list type is enough to compare results. Scale the winner once you see which list books meetings.
Yes. Users of complementary tools point to partner companies, and agencies building on your ecosystem show up as clusters of client sites.
Four list types, AI ranking, one-click drafts and churn signals. From $999 per year.