Agencies use LeadsQuantum as their list factory. Pick the client's technology, vertical and market, rank the sites by fit, generate first drafts, export, deliver. Then do it again for the next account.
Clients judge an agency by reply rates. Reply rates come from lists that match the client's product, not from bigger lists, and that is where stack data makes the difference.
Filter by the exact technology, vertical, country and size each client sells into, so every list is built for one product.
The recommender orders sites by fit, so the best rows get the most effort and the reason travels with them.
From client brief to exported list in an hour, not days of manual research across browser tabs.
Most agency clients fall into a handful of patterns. Each pattern maps to one or two modules, so your team can build a playbook per client type and reuse it.
| Client sells | List to build | Module |
|---|---|---|
| A store app or plugin | Stores likely to add it, plus rival users | Recommender, technology lookup |
| SaaS for websites | Sites in over-indexed verticals without the tool | Market analytics, recommender |
| Fulfilment or shipping | Stores by country and catalog size | Store database |
| Payments or financing | High-price stores on rival checkouts | Technology lookup |
| Wholesale products | Stores selling similar products | Similar stores, keyword search |
| Agency services | Young, growing stores in a vertical | Rising stores |
| Ad tech or monetization | Publishers on a given ad network | Ad-monetized sites |
| Content or affiliate offers | Niche sites by category and authority | Niche research |
A repeatable process your whole team can follow, client after client, so quality does not depend on who builds the list.
Capture the client's product, best customers, rivals, partners and markets in one document.
Turn the brief into filters: technology, vertical, country and size bands.
Run two or three list types for the client so results can be compared.
Use fit scores and size to set the level of effort per row.
Generate AI first emails, then edit them in the client's voice.
Export, launch, and report results by list type every month.
Most bad lists come from a vague brief. Ask these in the kickoff call.
A good delivery explains the logic, not just the rows. Clients who understand the targeting stay longer and argue less about results.
Show which technology, vertical, country and size you used, in plain words.
Mark which rows get personal outreach and which get automated sequences.
The driver tool, rival or category to open each email with, per row.
When you will re-run the list and add the newest stores.
Export a PDF summary for the client meeting and a CSV for the sequencer.
The companies that need lead generation help are visible too. Software vendors, apps and service providers show up in the technology data.
Clients renew when they see results tied to clear choices. Report by list type, not just in total.
| Section | What to show | Why it matters |
|---|---|---|
| Lists built | Filters, row counts, tiers | Shows the targeting logic |
| Activity | Contacts reached per list | Shows the work done |
| Results | Replies and meetings per 100, by list type | Shows what is working |
| Learnings | Best hooks, best verticals, weakest segment | Shows thinking, not just volume |
| Next month | What changes and why | Shows a plan |
Generalist list agencies compete on price. Specialists compete on results. The data makes specializing practical.
Serve only vendors that sell to stores on one platform. Every list starts from the same install base, so your expertise compounds.
Become the agency for, say, retention tools. Rival and partner maps carry over between clients and get sharper each time.
Know pet or beauty stores better than anyone. Category filters keep lists tight and your messages credible.
Own a country or language. Country and language filters do the heavy lifting while your native writers do the rest.
Focus on young, rising stores or on large established ones, each with its own buying style.
Serve ad tech and affiliate clients with publisher lists by category and authority band.
Illustrative examples of how an agency might structure work for different clients.
A new app needs its first 200 installs from stores that match its early users.
A fulfilment company opens a warehouse in a new country and needs local merchants fast.
An ad network wants more mid-size publishers in its strongest categories.
How you package the work shapes what clients expect. These are common models; choose what fits your agency.
Lists, copy, sending and replies handled end to end. Clients usually pay for meetings or a monthly retainer.
Targeting strategy and tiered lists; the client's own team does the outreach and replies.
A one-off report on a market: install bases, verticals and rising stores, as a strategy input.
A focused 30 to 60 day push for a new product, new feature or new country.
If a model involves handing exported data to clients, contact us first so your agency terms cover it.
Each of these quietly lowers reply rates and shortens client relationships. All six are process problems, not data problems.
Reusing one list across similar clients burns prospects and the agency's reputation with them.
Filters guessed from a website, not from the client's best customers and real sales data.
Emailing the client's own customers is an instant trust killer, and easy to prevent.
Big sends from new domains hurt deliverability for every client on shared infrastructure.
Without results by list type, nobody learns what to change next month.
Lists not refreshed for months miss the newest, warmest stores in the market.
It is the targeting layer. The rest of your tools handle contacts, sending and reporting, and the exports connect them all.
| Job | Tool type | LeadsQuantum role |
|---|---|---|
| Who to target | Targeting and list building | Core: stores, stacks, niches, ranking |
| Who to email at each company | Contact enrichment | Supplies the domains to enrich |
| What to say | Copywriting | AI first drafts per row |
| Sending and follow-ups | Sequencer | Exports load straight in |
| Tracking and reporting | CRM and dashboards | List type and driver as fields |
Enterprise includes five users and 50,000 searches per month. Here is how agencies usually split them so each seat has a clear job.
| Seat | Role | Main use |
|---|---|---|
| 1 | Head of data | Owns filters and templates for each client |
| 2 | List builder | Runs lookups, tiers and exports |
| 3 | Copywriter | Generates and edits AI drafts |
| 4 | Account manager | Reviews lists with clients, reports results |
| 5 | New business | Builds sample lists to win new clients |
Agencies need the technology features, which start on Advanced.
Your real need depends on list sizes and refresh frequency. For larger volumes, write to [email protected].
Have clear answers ready. They build trust in your targeting from the first meeting and make renewals easier later.
Show the filters and the fit ranking. Name the driver tool or rival per segment so the logic is visible.
Tell them when you ran it and when you will refresh it, with dates.
Explain your exclusion step using their customer list, and show it in the delivery.
Share install base counts and category splits for their space, plus the rising stores in it.
Because it is filtered to fit. Smaller and relevant beats large and random, every single time.
Point to results by list type and the segment you will expand or drop.
Yes. Build separate lists for each client within your plan's searches, seats and row limits. Most agencies keep a filter log per client.
Seats are for your own team. If you plan to hand exported lists to clients, write to [email protected] first so we can confirm the right agency terms for you.
Advanced includes 2 users and Enterprise 5. Choose by team size, not by client count alone.
CSV, Excel and PDF. CSV suits sequencers and CRMs, PDF suits client meetings.
Generate AI drafts, then edit them in the client's voice before sending. A short style guide per client speeds this up.
Write to [email protected] with your expected volume.
Yes, up to 500 domains per request. Useful for cross-sell campaigns and for prioritizing a client's existing pipeline.
Ask for their customer domains at kickoff and remove matches from every export before anything is sent.
Monthly for most clients. New stores and stack changes add fresh rows each time, and those rows often reply best.
Yes. Agencies and their clients must follow the email and privacy rules of every country they contact.
Agencies with several active clients usually choose Enterprise for the larger report sizes and extra seats. Small agencies often start on Advanced.
Yes. A short sample built for a prospect's product is one of the strongest pitches an agency can send, because it proves skill before any call.
Around one million stores carry a country across all major ecommerce markets, and the five million popular domains are global.
With a clear brief, an hour or less for filtering, tiering and export. Editing drafts takes longer and depends on how personal the campaign is and how many tier 1 accounts it includes.
Keep a simple log of filters and exports per client in your own project tool. It also makes monthly refreshes faster and handovers between team members painless.
Yes. Many agencies serve only vendors that sell to stores on one platform. The platform's install base becomes the starting universe for every client.
That is your call. Most agencies price on outcomes or on a monthly retainer rather than per row, because the value is in the targeting and the copy.
Avoid contacting the same company for two clients in the same month. Split segments by vertical or size, or stagger timing.
Yes. Install base counts, vertical splits and a few sample rows make a strong discovery-call presentation.
Run the ten-question brief in the kickoff call, build two list types the same week, and send the first small batch before the end of week one. Early data beats long planning.
Write to [email protected] and describe your model. We will tell you which plan and terms fit.
Technology, store and niche data with AI ranking and drafts. Up to 5 seats and 50,000 searches on Enterprise.