SaaS growth
Find the accounts already paying for tools next to yours
For B2B SaaS and marketing technology vendors
The problem
Firmographic targeting by headcount, industry and revenue band is a weak predictor for e-commerce SaaS. What predicts a purchase is the stack a merchant already runs, and that is invisible in a standard company database.
How the data solves it
Filter on detected technology: tracking pixels, marketing tools, video players, analytics. A shop already running three tools in your category has budget and buys software. A shop running none, at scale, is a category-education play.
The filter set this maps to
Every field below exists in the shop schema. This is the query, not an illustration.
- Tracking pixels
- specific pixels present or absent
- Other tech
- adjacent tools in your category
- Has any social
- yes
- Domain age
- > 2 years (past survival risk)
Build it
Four steps to this list
Every column named here exists in the schema, and the request below runs as written.
- 1Filter tracking_pixels for a tool adjacent to yours — proof they buy software.
- 2Add age >= 2 so the shop is past the survival window.
- 3Add has_any_social = yes for shops actively marketing.
- 4Invert any filter with not: true to find shops missing your category entirely.
The same query through the API
POST this to /api/v1/search for a count, or /api/v1/rows for the shops themselves.
{
"filters": [
{
"kind": "text",
"col": "tracking_pixels",
"mode": "contains",
"value": "Meta Pixel"
},
{
"kind": "numeric",
"col": "age",
"op": "gte",
"min": 2
},
{
"kind": "dropdown",
"col": "has_any_social",
"values": [
"yes"
]
}
]
}Playbook
What to do once you have the list
A list is not a campaign. These are the three things that decide whether it converts.
Buying software is the signal
Firmographics predict poorly here. A shop running three tools in your category has budget and a procurement habit; a shop running none is an education sale.
Exclude your own customers
Filters work in both directions. Use not: true on your own pixel so an acquisition campaign never lands on an existing account.
Segment by stack maturity
Shops with many tracking pixels want integration depth. Shops with few want simplicity. Same product, two different pitches.
Outcome
What you end up with
Accounts segmented by proven willingness to pay for software
Competitive displacement lists based on detected tools
Stack context so the first touch is specific
Questions
SaaS: frequently asked
Which technologies can you detect?
Themes, installed apps, tracking pixels, video players and other detectable front-end technology. Detection reads what the storefront actually loads, so it reflects what is live rather than what was announced.
Can I exclude shops already running my product?
Yes. Filters work in both directions, so you can require a technology or exclude it, which is how you keep existing customers out of an acquisition campaign.
Why is the installed stack a better signal than company size?
Because it is evidence of a decision rather than a description of the company. A merchant running three marketing apps has proved it buys software, has a budget line, and has somebody whose job it is to evaluate tools. Headcount and revenue band tell you none of that, and for e-commerce they are usually estimates anyway.
Can I find shops running a competitor so I can pitch a switch?
Yes, that is the core query. The apps and other technology fields record what is detectable on the storefront, so you can filter for a named competitor and get the shops already paying for the category. Budget approved, need proven, and the only question left is whether you are better.
What if my competitor is not detectable from the storefront?
Then invert the query and target the gap instead. Filter for shops that clearly have the problem your tool solves, judged by catalogue size, tracking pixels present and category, and exclude every competitor you can detect. The remainder is shops with the need and nothing visible in place.
Start finding better e-commerce leads today
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