If your product only works with Shopify, every email you send to a WooCommerce store is wasted. If you sell a returns tool, a store that already pays for one is a harder conversation than a store that has none. For e-commerce, the technology a merchant runs tells you more about fit than headcount or revenue band ever will.
This guide covers which parts of a store's stack are worth looking at, where to get that data, four targeting plays that use it, and a step-by-step workflow for turning a tech stack filter into a verified contact list with a relevant first line.
Why tech stack beats firmographics for e-commerce
Firmographic filters were built for companies that look like companies on LinkedIn. Plenty of online stores don't: a seven-figure DTC brand might list three employees, or none, and its founder may not have updated a profile in years. The storefront, on the other hand, is public. The platform, the apps loaded on the page, the review widget, and the checkout options are all visible to anyone who inspects the site.
That gives you three things generic lists can't:
Hard qualification. If you integrate with one platform, you can exclude every store that isn't on it.
A reason to write. "You run Klaviyo but no SMS tool" is a specific observation. "We help e-commerce brands grow" is not.
Proof of spend. A store paying for several apps already buys software. A store on a free theme with no apps may not have budget yet.
The layers of an e-commerce stack
Each layer says something different about the merchant. Pick the one or two that matter for your product instead of pulling everything.
Layer | Examples | What it tells you |
|---|---|---|
Platform | Shopify, Shopify Plus, WooCommerce, BigCommerce, Adobe Commerce (Magento) | Which integrations apply, rough merchant size, technical sophistication |
Email and SMS | Klaviyo, Mailchimp, Omnisend, Attentive | Marketing maturity and how much they invest in retention |
Reviews | Yotpo, Loox, Judge.me, Stamped | How much they rely on social proof, and which tier of tool they can afford |
Shipping and fulfillment | ShipStation, Shippo, EasyPost | Order volume and fulfillment complexity |
Analytics and attribution | GA4, Triple Whale, Northbeam | Whether paid acquisition is a serious channel for them |
Payments and financing | Shop Pay, PayPal, Klarna, Affirm | Order value range and checkout focus |
Subscriptions | Recharge, Skio | A recurring revenue model, which changes what they care about |
Where to get e-commerce tech stack data
Detection tools: BuiltWith and Wappalyzer
Both scan websites for technologies that load in the browser: platforms, tracking scripts, widgets, and many apps. Wappalyzer has a free browser extension and a free account with a small monthly lookup allowance, with paid plans for lead lists and API access. BuiltWith is paid and keeps historical data, so you can see when a site added or dropped a technology, which is useful for timing.
The limit is the same for both: they only see what is visible from the front end. Backend tools, private integrations, and apps that don't inject code into the storefront are often missed.
E-commerce specific databases: Store Leads
Store Leads focuses only on online stores. Alongside platform and location, it tracks installed apps with install and uninstall dates, estimated monthly sales, product counts, themes, and store contact details. That install history is what makes app-level targeting practical, and it covers several hundred e-commerce platforms, not just Shopify.
A correction to a common assumption: the Shopify App Store does not publish which stores use a given app. App listings show reviews and ratings, and some reviewers name their store, but there is no public install list. Databases like Store Leads build that view by scanning storefronts.
Enrichment platforms
If you already have a list of domains, running them through tech stack lookups in bulk is faster than checking sites one by one. In Databar you can search e-commerce stores through Store Leads and run BuiltWith or TheirStack tech stack lookups on each domain in the same table, then add contacts and verification in the columns next to it. For the general question of checking a single company's stack, see where to check what tech stack a company uses.
Four targeting plays
1. Platform match
The simplest play: your product works with a platform, so you only target stores on it. Go one level deeper where you can. Shopify Plus merchants are larger businesses with different problems (and bigger budgets) than stores on standard Shopify plans, so they deserve their own list and their own message.
Angle: "You're on Shopify Plus, so [specific constraint that Plus merchants hit] probably comes up. Here's how we handle it."
2. App displacement
Find stores running a competitor's app and give them a reason to switch. This is where install dates matter. A store that installed the competitor last month is still setting up and unlikely to move. A store that has run it for two years and just saw a price change might be.
Time outreach to real events: a competitor price increase, a removed feature, or a sunset announcement. Keep the claim honest; don't promise results you can't back up.
3. Missing piece
Look for a gap in an otherwise mature stack. A store with Klaviyo email flows but no SMS tool. A store with ShipStation and high order volume but no returns app. A store with paid attribution tools but no post-purchase survey.
Angle: name the gap plainly and connect it to something they already use. "You're sending abandoned cart emails through Klaviyo. Have you tried adding an SMS step to that flow?"
4. Stack maturity
Count the paid tools. A store on GA4, Triple Whale, Klaviyo, and Recharge runs a serious operation. A store with a free theme, basic analytics, and Mailchimp is earlier. Match your offer to where they are: an enterprise tool pitched to a two-person store wastes both sides' time, and so does a starter tool pitched to a large brand.
Step-by-step: from tech filter to personalized outreach
This workflow runs in a Databar table, but the logic applies to any stack of tools.
Step 1: Pull stores that match your platform filter
Start with an e-commerce store search. Set the platform (for example, Shopify), the countries you sell into, and category keywords that describe your ICP (skincare, pet supplies, outdoor gear). If your product only suits stores above a certain size, filter on estimated sales or product count too. You should end up with store domains, names, descriptions, and whatever public contact details the store lists.
Step 2: Confirm and expand the tech stack
Run a tech stack lookup on each domain to confirm the platform and pull the rest of the stack. Now you can filter for the play you chose: stores using a competitor's app, stores missing a category of tool, or stores whose stack signals the right maturity. Cut rows that don't fit before you spend anything on contacts.
Step 3: Find the decision maker
Store inboxes like hello@ or support@ are rarely read by the person who buys software. For small stores, you want the founder or owner. For larger brands, look for titles like head of e-commerce, e-commerce manager, head of growth, or director of retention, depending on what you sell.
Hunter's search by company and department (filtered to executive seniority) works well for small brands where the founder is the buyer. For role-specific searches, use a finder that takes a domain plus a job title. Because no single provider covers small merchants well, a waterfall that tries several email providers in order and stops at the first valid result will usually find more contacts than one provider alone.
Step 4: Verify the emails
Run every address through a verifier (Bouncer and ZeroBounce are both options) and keep only the ones marked deliverable. Decide separately what to do with catch-all or risky results; many teams hold those back or send to them from a secondary domain. Sending to unverified addresses is the fastest way to hurt deliverability for the whole campaign.
Step 5: Write a first line from the stack
Now use the stack data you collected. With Databar's AI Researcher you can run a prompt against each row, feeding it the store description and the technologies you found. A starting prompt:
The kind of output you are aiming for:
Shopify with basic analytics only: "Saw the store runs on Shopify with GA4 as the only analytics tool, so repeat purchase rates by product are probably hard to pull."
WooCommerce with several payment gateways: "Noticed you take Stripe, PayPal, and Klarna at checkout on WooCommerce, which usually means reconciling three payout reports every month."
Adobe Commerce with light marketing tooling: "Your Magento store has a big catalog but I couldn't find an email automation tool on the site. Are browse abandonment emails handled some other way?"
Read a sample of 20 or 30 outputs before sending. If the model starts inventing results or making claims about the store it can't know, tighten the prompt. For more on writing these at volume, see our guide to personalized emails in bulk.
Where this goes wrong
Treating detection as certain. Headless storefronts, custom builds, and tag managers can hide or confuse what detection tools see. If a claim in your email depends on one detected app, check a few sites by hand before sending thousands.
Stale data. Stores add and remove apps constantly. Re-run lookups on a list that is more than a month or two old, and don't mention a tool the store dropped.
Agencies and dev shops in the list. Store searches sometimes return agencies, theme demos, and test stores. Filter them out by description and product count.
Creepy specificity. Naming their stack is fine. Listing every script you found on their site reads like surveillance. Mention one tool, tie it to one problem.
If a tech stack list works, the next step is usually finding more stores like your best customers. Our guide on finding lookalike companies covers that.
FAQ
How do I find all Shopify stores in a niche?
Use an e-commerce store database or technographic search filtered to Shopify, then narrow by category keywords, country, and size signals like estimated sales or product count. Expect to clean the results: category tagging is imperfect, so skim store descriptions before building contacts.
How do I find stores that use a specific Shopify app?
Not from the Shopify App Store, which doesn't list installs. Use a database that detects apps on storefronts, such as Store Leads or BuiltWith, and filter by the app. Install dates help you separate new users from long-time ones.
How do I target WooCommerce stores?
WooCommerce runs on WordPress, so detection tools report both. Filter for WooCommerce specifically, since plenty of WordPress sites run no store at all. Extensions and payment plugins give you the deeper signals.
Can I find stores that recently switched platforms?
Yes, with tools that keep technology history. A store that moved to Shopify in the last few months is still choosing apps, which is good timing if you sell one.
How accurate is e-commerce tech stack data?
It is reliable for things that load visibly on the storefront, like the platform, review widgets, and tracking pixels. It is weaker for backend tools and private integrations. When a message depends on a specific tool, confirm with a second source or a manual check.
Databar gives you store search, tech stack lookups, contact finding, verification, and AI Researcher in one table, across 160+ data providers. Paid plans start at $99/month and you are only charged for results. Start a 14-day trial and build your first e-commerce list.
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