Know the software behind every online store and popular website. Target by the tools a company already runs, the ones it lacks, and the ones it is most likely to add.
Firmographics tell you a company's size and sector. Technographics tell you how it actually operates.
A store that pays for email automation, reviews and a helpdesk is investing in retention. A store with none of them is not there yet.
Most tools give you one direction: domain to stack. LeadsQuantum gives you four.
Open any store or domain and see every technology it runs, grouped by function.
Type a technology and get every site that runs it, filtered by country, vertical and size.
See which verticals over-use a technology, and how it spreads by domain age, popularity and country.
The recommender predicts which technology a site is likely to add, based on everything it already runs.
From the platform a site is built on to the pixel that tracks its ads. A sample of what is tracked:
Each answers a different question. Technographics answer the one closest to the sale: will my product fit here?
| Data type | Answers | Strength | Weakness |
|---|---|---|---|
| Firmographic | How big is the company and what sector is it in? | Easy territory planning | Says nothing about tool fit |
| Technographic | What software does it run, and what is missing? | Direct product fit and timing | Needs context to prioritize |
| Intent | Is someone researching my category right now? | Timing | Small, noisy, short-lived lists |
| LeadsQuantum | Stack + category + size + momentum + likely next tool | Fit, priority and reason in one row | Focused on web-facing businesses |
A tool list alone gives you thousands of rows. Adding category, size and a fit score turns them into an order of attack.
Each play is a filter you can save and re-run monthly. Most teams run three or four of them in parallel, each with its own message and its own owner on the sales team.
Every site on a rival product. Time it with their price changes.
Sites running a tool you integrate with. Lead with the integration.
Sites with no tool in your category but a stack that suggests they need one.
Sites on a budget tool with large catalogs and high prices. They have outgrown it.
Old domains on legacy platforms. Pitch migration services or modern apps.
All sites in the verticals where your category over-indexes, minus current users.
Sites using a tool often paired with yours. The recommender finds the pairs.
Your customers whose stack makes your tool an unusual fit. Call them first.
Five teams, five different first questions. Pick yours to see the two jobs it usually starts with.
Rank territories by stack fit, not by alphabet or headcount.
Name the tools they use and how yours connects.
Export domains by stack for account-based ads.
Write for the verticals where your category is strongest.
Build for the tools your users run most often.
Compare your tool with rivals by age, popularity and country.
Agencies that build on your platform show up as clusters of client sites.
Measure overlap between your users and a partner's users.
Check a SaaS target's real footprint across millions of sites.
See which categories of tool are winning new sites.
Plan for about an hour. By the end you will have three exported lists and a clear A-list to start on tomorrow morning.
Which kind of tool are you? Live chat, reviews, shipping, analytics?
Three rivals, three tools you integrate with.
Rival users, partner users, and recommender picks for your category.
Overlap between partner users and recommender picks is your A-list.
The data is only as good as the way you use it. Six habits separate teams that book meetings from teams that burn lists.
Popular tools return huge lists. Add country, vertical and size first.
Open a few records before a big campaign. It keeps your messaging honest.
Stacks change. Re-run saved searches and work new rows first.
"I noticed you use X" works when it leads to a benefit, not when it sounds like surveillance.
Stack plus momentum plus category beats any single signal.
Follow the email and privacy laws of each country you contact.
Vertical shapes the stack. Knowing the norm tells you what is missing at any one site.
Heavy on email, reviews with photos and size tools.
Subscriptions and SMS sit alongside email.
Subscriptions, delivery scheduling and local shipping rules.
Pay-later financing, freight shipping and live chat.
Helpdesk, product Q&A and warranty tools.
Subscriptions for food, loyalty points and reviews.
Ad network scripts, consent tools and analytics.
Forms, chat, scheduling and CRM tracking scripts.
A fictional reviews app sells to mid-size stores. Here is how its sales lead would use the data in one afternoon.
Look up the three biggest competing reviews apps. Note how many stores run each one.
Check where reviews apps over-index. Fashion, beauty and home usually lead.
Export stores on the rivals in those verticals, with 100 to 1,000 products.
Run the recommender for the reviews category. Keep stores with a high fit score.
Rival users get a switch offer. Empty slots get a "first reviews app" pitch.
Generate a first email per row, edit the best ones, load into the sequencer.
Two clean lists with different messages, both ranked, both ready the same day.
Patterns are not proof, but they are a strong first read before any call.
| Pattern on a site | Likely meaning | Good pitch |
|---|---|---|
| Email + SMS + reviews, no loyalty | Retention-minded team with a gap | Loyalty or referral program |
| Large catalog, no site search tool | Shoppers struggle to find products | Search and merchandising |
| High prices, no pay-later option | Checkout friction on big tickets | Financing at checkout |
| Many ad pixels, basic analytics | Spending on ads, weak measurement | Attribution or analytics |
| Several languages, one currency | International traffic, local friction | Multi-currency and translation |
| Young domain, rich stack | Funded or experienced founders | Premium tools, agency services |
| Old domain, legacy platform | Migration is overdue | Re-platforming services |
| Helpdesk but no live chat | Support is reactive | Live chat or chatbots |
Use these when you compare options, including us.
Coverage of small and mid-size sites matters most for SMB sales.
A short list misses the niche apps that define a modern store.
Category, country and size should come on the same row.
Raw lists need a priority order to be usable by a sales team.
Knowing what a site will add next is worth more than knowing what it has.
Market views by vertical, age and popularity show where you win.
CSV, Excel and PDF without extra fees per download.
Enterprise-only pricing rules out most small teams. Ours starts at $999 per year, all features included.
The words used across this site and inside the dashboard, defined once so your whole team reads lists the same way.
| Term | Meaning |
|---|---|
| Technographics | Data on the software and services a business runs. |
| Stack | The full set of tools on one website. |
| Install base | All the sites that use a given technology. |
| Over-index | When a technology's share in a category is higher than that category's share of all sites. A ratio above 1.0. |
| Co-occurrence | Two tools often found on the same sites. |
| Recommendation | A tool a site does not use yet but is likely to add, given its stack. |
| Explainability | The installed tool that most drove a recommendation. |
| Displacement | Selling against an incumbent tool on the same site. |
| Add-on sale | Selling into an empty slot in a site's stack. |
| Popularity tier | A band of the top 1M sites, in groups of 100,000. |
| Domain age | Years since the domain was first registered. |
| Authority | The link strength of a domain. |
More than 4,000, across ecommerce, marketing, support, payments, analytics, advertising and infrastructure.
More than one million online stores plus the five million most popular domains worldwide.
Both plans. Technology reports allow 1,000 rows on Advanced and 10,000 on Enterprise.
Yes. Every store record lists its technologies and the tools it is likely to add next.
The recommender returns sites that do not use a technology yet but fit its user profile, ranked by likelihood.
That is more useful than a raw "does not use" list, which would include millions of poor fits.
Type it in the technology search on the platform. If it is missing, write to [email protected].
Yes, by domain age, popularity tier and country, and several at once on one chart.
Yes, to CSV, Excel or PDF, within your plan's row limit per report.
We run the recommender on up to 500 of your domains. Contact us for larger enrichment jobs.
SaaS sales and marketing teams, agencies, app developers, partnership teams and investors.
Yes. The five million popular domains include publishers, SaaS companies, services, education and more, each with its technologies and IAB category.
They show the verticals where a type of tool is already normal. Prospects there understand the category, so cycles are shorter.
Yes. Customers whose stack and category make your tool an unusual fit are flagged as higher risk. See churn prediction.
The platform is dashboard-first with exports. For bulk or automated delivery, contact us to discuss options.
Monthly works for most teams. Stores add and drop tools all the time, and new stores open every week.
Work the new rows first. A store that just added a tool next to yours is often in buying mode for the whole category.
Yes. Use product count, average price, popularity rank and authority on any technology result.
A technology list shows who uses a tool today. A recommender list shows who is likely to start using it, ranked by likelihood.
Use the first for switch campaigns and the second for add-on campaigns.
Yes, within your plan's searches and seats. The Enterprise plan includes five users. See the agency page for workflows.
Technographic data, the AI recommender and AI copy are in both plans, from $999 per year.