If you’re in sales or growth and you need serious data—not just a handful of leads—Similartech can be a goldmine. But pulling and making sense of big datasets from Similartech isn’t exactly plug-and-play. This guide is for sales and ops folks who want to get bulk data out of Similartech, wrangle it, and actually use it for smart targeting—without spending all day cursing at Excel or hitting CSV limits.
Who should care about this guide?
- Salespeople tired of shallow lead lists or manual scraping.
- RevOps or growth teams building big, targeted campaigns.
- Anyone who wants more than just “top 100 sites using X tech.”
If you need quick, small exports, this is overkill. But if you're staring down tens of thousands of rows and want to pull real insights, keep reading.
Step 1: Know What You’re Getting From Similartech
Before you start, it’s worth being clear about what Similartech is—and isn’t. Similartech tracks which technologies websites use (think: Shopify, Hotjar, Salesforce, etc.), along with some company and site metadata. You can filter by tech, location, traffic, and more.
What works: - Finding companies using a specific technology. - Getting lists of domains, tech stacks, and some basic company info. - Filtering by geography, tech, and estimated traffic.
What doesn’t: - Don’t expect verified contact info (emails, phone numbers). - Company data can be spotty, especially for smaller sites. - Some “industry” tags are vague or unreliable.
Pro tip: Don’t treat Similartech data as gospel. It’s good for direction, not for 1:1 targeting.
Step 2: Exporting Large Datasets—How Big is “Big”?
If you’re just downloading a few hundred rows, you’ll be fine. But if you want thousands (or hundreds of thousands) of results, you’ll run into some friction:
- UI exports are capped. The Similartech web UI usually limits CSV exports to 10,000 rows. Sometimes less, depending on your plan.
- API access is better (if you have it). The API lets you paginate through much larger datasets, but requires scripting skills.
- Export speed is slow. Large exports can take ages to generate and download.
What to do:
Option 1: Use the Web UI (for smaller jobs)
- Filter down as much as possible—by country, tech, etc.—before exporting.
- If you need more than 10,000 rows, break your search into chunks (e.g., by country or tech version).
- Download as CSV.
Downside: Manual, tedious, and the risk of duplicates is high if you’re not careful.
Option 2: Use the API (for serious data pulls)
- Ask your Similartech rep for API access. It’s not always included in basic plans.
- Use the documentation to script your exports. You’ll get data in JSON, which you can convert to CSV or load into a database.
- Handle pagination—results come in batches, so you’ll need to loop through “pages” until you’ve got everything.
Pro tip: If you don’t code, get a technical teammate to help. Trying to “hack” this with browser plugins or Zapier is a waste of time and often breaks.
Step 3: Cleaning Up Your Data
Let’s be real: raw Similartech exports are messy.
Common issues: - Duplicate domains (or different URLs for the same company). - Missing fields (like company size or industry). - Inconsistent formatting (http vs https, trailing slashes, etc.). - “Ghost” companies—domains with no real business behind them.
How to clean things up:
- Deduplicate. Use tools like Excel’s “Remove Duplicates,” or—if you’re working with big data—pandas in Python.
- Normalize URLs. Strip out protocols (
http://,https://), lowercase everything, and trim trailing slashes. - Fill in blanks. If company size/industry is missing, consider enriching with another data provider (like Clearbit or LinkedIn).
- Filter out junk. Ditch domains like “test.com”, “blogspot.com”, or anything that looks like a parked site or a spam trap.
Pro tip: Don’t get hung up on perfection. For sales research, “good enough” data beats “perfect but slow” every time.
Step 4: Analyzing the Data for Sales Research
Now the fun part. Here’s how to turn your cleaned Similartech data into something actionable:
4.1. Segment by Technology Stack
- Group companies by what tech they use (e.g., “everyone using Magento in Germany”).
- Look for patterns—do certain tech stacks cluster in certain industries or regions?
- Identify “ripe” targets (e.g., companies using an older version of a technology you can upgrade).
What to ignore: Don’t obsess over edge-case techs with tiny adoption. Focus on techs with enough install base to be worth your time.
4.2. Enrich and Score Accounts
- Add columns for estimated company size, revenue, or employee count (from LinkedIn, Clearbit, etc.).
- Create a simple scoring model—maybe just a weighted sum—to sort high-potential accounts to the top.
- Flag companies that match your ICP (Ideal Customer Profile).
What works: Simple scoring is way better than just “sort by traffic.” Don’t over-engineer it.
4.3. Map to Real People
- Cross-reference domains with LinkedIn to find decision-makers.
- Use tools like Apollo, Hunter, or Seamless.AI to pull emails (if you need them).
- Avoid “spray and pray”—focus on the best-fit accounts.
What to ignore: Don’t waste time chasing every company on the list. Prioritize, then go deeper.
Step 5: Tools That Actually Help (and What to Avoid)
Helpful
- Excel or Google Sheets: Fine for under ~50,000 rows. Anything bigger, you'll want something beefier.
- Python (pandas) or R: Great for deduping, cleaning, and basic analysis.
- Airtable: Decent for light enrichment and tracking, but gets sluggish with big data.
- Data enrichment APIs: Clearbit, Apollo, or LinkedIn Sales Navigator for filling in gaps.
Not Helpful
- Browser plugins for scraping: Usually break or get you banned.
- Zapier/Integromat hacks: Too brittle for big exports.
- Enterprise “AI” tools: Mostly overkill unless you’re at FAANG scale.
Pro tip: Don’t spend days building a perfect data pipeline. Patch it together, get results, then improve as you go.
Step 6: Avoiding Common Pitfalls
- Export limits: If you keep bumping into row caps, try chunking your queries (by region, industry, etc.).
- Data freshness: Similartech data can get stale. Cross-check key accounts—especially if you see odd tech usage.
- Legal stuff: If you’re exporting data on European companies, be mindful of GDPR. Don’t spam people.
Honest take: You’ll never get a “perfect” list. The goal is a workable list that you can filter and improve as you go.
Step 7: Keeping It Simple and Iterating
You don’t need a PhD in data science to get value out of Similartech exports. Here’s what actually works:
- Start small. Pull a chunk, clean it, see if you get results.
- Don’t overthink enrichment—just add what you need to prioritize.
- Iterate: fix what’s broken, ditch what’s not useful.
- Keep notes on what filters and enrichment steps actually help you close deals.
You’ll get more out of your data with a few tight feedback loops than with a 3-month data project.
Wrapping Up
Big datasets from Similartech are powerful, but only if you keep your process simple and practical. Export, clean, enrich, and prioritize—don’t let the perfect be the enemy of the good. Most teams get bogged down trying to make things too fancy. Start scrappy, get some wins, and only scale up when you’re sure it’s worth it. The best sales research is the kind you actually use.