2 posts tagged with "Sales Prospecting"

Your best customer — the one that closed fast, expanded twice, and has never complained — is not unique. There are hundreds, maybe thousands of companies that look just like them: same industry, similar size, comparable tech stack, facing the same problems.

If you could find those companies systematically, you'd have the most productive prospect list in your entire pipeline.

That's the idea behind lookalike company finding: start with your best customers, identify what makes them great, and find more companies that match. It's how Facebook Ads (now Meta) revolutionized advertising — and it works just as well for B2B sales prospecting.

Yet most B2B sales teams still build prospect lists the old way: filtering by industry and company size in a database, then hoping for the best. It's a blunt instrument. You end up with thousands of "technically-fit" companies, most of which will never buy.

This guide explains how lookalike modeling works for B2B sales, compares the tools available, and shows you how to find companies similar to your best customers — starting for free.

What Is Lookalike Company Finding?​

Lookalike company finding is the process of identifying companies that share key characteristics with your existing customers — particularly your best customers.

Instead of defining your ideal customer profile (ICP) from scratch using industry, size, and location filters, you let the data tell you what your best customers have in common. Then you find more companies that match those patterns.

How It Differs from Traditional Prospecting​

Traditional approach:

1. Define ICP manually (e.g., "SaaS companies, 50-500 employees, US-based")
2. Search a database with those filters
3. Get 10,000+ results
4. Manually sort through to find the good ones
5. Discover that most don't respond because the targeting was too broad

Lookalike approach:

1. Start with 10-20 of your best customers
2. AI analyzes what they have in common (beyond obvious firmographics)
3. Get a ranked list of companies that match, sorted by similarity score
4. Focus outreach on the highest-similarity matches
5. Get dramatically higher response and close rates

The difference is signal density. Traditional filters are binary (yes/no on each criteria). Lookalike models consider dozens of weighted attributes simultaneously, producing a similarity score that tells you exactly how closely a prospect resembles your best customers.

What Makes a Good Lookalike Model?​

Not all lookalike tools are created equal. The best ones consider these dimensions:

1. Firmographics (The Basics)​

• Industry and sub-industry (NAICS/SIC codes)
• Company size (employees, revenue)
• Location (HQ, offices)
• Founding year and growth stage

2. Technographics (What They Use)​

• Technology stack (CRM, marketing automation, analytics)
• Development frameworks and infrastructure
• SaaS tools and integrations

3. Business Model Similarity​

• Revenue model (SaaS, marketplace, services)
• Customer type (B2B, B2C, B2B2C)
• Sales motion (PLG, enterprise sales, channel)

4. Growth Signals​

• Hiring velocity (especially in relevant departments)
• Funding history and stage
• Recent news and expansions

5. Digital Presence​

• Website traffic and engagement
• Content marketing activity
• Social media presence

6. Buying Behavior Indicators​

• Previous technology purchases
• Conference attendance
• Content consumption patterns

The more dimensions a tool considers, the more accurate the lookalike matches will be.

Tools for Finding Lookalike Companies​

MarketBetter Lookalike Company Finder (Best Free Option)​

Website: tools.marketbetter.ai/lookalike-finder

How it works:

1. Enter the name or URL of your best customer
2. AI analyzes the company across multiple dimensions (industry, size, tech stack, business model, growth signals)
3. Get a ranked list of similar companies with similarity scores
4. Export results for outreach

Pricing: Completely free. No signup required.

Why it stands out:

• Zero friction — paste a company name or URL, get results instantly
• AI-powered matching — considers more than just industry and size
• Similarity scoring — ranked results so you know which prospects are closest to your ICP
• Free and unlimited for individual lookups
• Actionable output — results you can immediately use for prospecting

Best for: Sales reps, founders, and small teams who want to quickly find companies similar to their best customers without paying for expensive data platforms.

Instantly (SuperSearch)​

Website: instantly.ai

Instantly's SuperSearch feature includes lookalike company discovery as part of their outreach platform.

How it works: Upload your best customer domains, set filters (industry, size, location), and get similar companies ranked by fit.

Pricing: Free trial available; paid plans from $30/month (outreach), data add-ons extra

Pros: Integrated with email outreach platform, verified contacts included, good UI Cons: Lookalike is part of a larger platform — can't use it standalone, data credits have limits

La Growth Machine​

Website: lagrowthmachine.com

LGM's Lookalike Search lets you enter a company URL and get companies ranked by similarity score.

How it works: Enter a company URL, apply filters (industry, company size, location), browse results ranked by similarity.

Pricing: Plans from €50/month (includes outreach features)

Pros: Similarity scoring, integrated with LinkedIn outreach, good European coverage Cons: Can't use lookalike search without the full platform, limited free usage

Surfe​

Website: surfe.com

Surfe offers company search with smart lookalikes, integrated into their CRM-LinkedIn bridge.

How it works: Search from a database of 350M+ companies, with lookalike matching based on firmographic attributes.

Pricing: Free tier available; paid plans from $39/month

Pros: Large database, LinkedIn integration, CRM sync Cons: Lookalike is one feature among many, limited free tier

Coresignal​

Website: coresignal.com

Data provider offering a "Find Similar Companies" tool based on multiple firmographic and growth attributes.

How it works: Enter a company, get similar companies based on employee data, growth patterns, and firmographic matching.

Pricing: Enterprise/API pricing (contact for quotes)

Pros: Deep data (based on 700M+ professional profiles), growth signals, investor-grade data Cons: Enterprise-focused and expensive, not designed for individual sales reps

Clay​

Website: clay.com

Clay's enrichment platform can build lookalike lists using multiple data sources.

How it works: Import your customer list, use Clay's 75+ enrichment sources to find patterns, then search for similar companies.

Pricing: Free tier (100 credits/month); paid plans from $134/month

Pros: Extremely flexible, combines multiple data sources, customizable scoring Cons: Requires setup and configuration, not a one-click solution, credits-based pricing adds up fast

Apollo.io​

Website: apollo.io

Apollo's database of 275M+ contacts includes company search with advanced filters.

How it works: Search companies by industry, size, technology, and other attributes. No native "lookalike" feature, but you can replicate it by analyzing best customers and applying similar filters.

Pricing: Free tier (unlimited emails, limited features); paid from $49/user/month

Pros: Large database, includes contact data, integrated outreach Cons: No true lookalike search — you're manually building equivalent filter sets

How to Build Your Lookalike Prospect List (Step by Step)​

Step 1: Identify Your Seed Companies​

Start with 10-20 of your best customers. "Best" should mean:

• Fastest time to close — they bought without a long sales cycle
• Highest lifetime value — they expanded or renewed
• Lowest churn risk — they're actively using your product
• Best advocacy — they refer others or leave positive reviews

Don't include one-off wins or customers acquired through unusual channels (e.g., a personal connection). You want customers who bought because of genuine fit.

Step 2: Analyze What They Have in Common​

Look beyond the obvious. Yes, they might all be "mid-market SaaS companies," but dig deeper:

• What specific sub-industry? (e.g., not just "SaaS" but "vertical SaaS for healthcare")
• What growth stage? (Series A? Series C? Bootstrapped?)
• What tech stack? Use MarketBetter's Tech Stack Detector to check
• What departments are growing? (Hiring SDRs? Building a marketing team?)
• How do they sell? (PLG? Enterprise? Channel?)

Document the 5-10 attributes that your best customers share. This is your real ICP — not the one your VP of Sales wrote on a whiteboard, but the one validated by actual buying behavior.

Step 3: Run the Lookalike Search​

Enter your seed companies into MarketBetter's Lookalike Company Finder. Review the results, paying attention to:

• Similarity scores — higher is better, but don't ignore medium-similarity companies entirely
• Companies you recognize — if the tool surfaces companies you already know are good fits but haven't prospected, that's validation the model is working
• Surprises — companies you wouldn't have found through traditional filtering are where the real value lives

Step 4: Enrich and Qualify​

For your top lookalike matches, add additional qualifying data:

• Contacts: Use the AI Lead Generator to find buyer contacts
• Tech stack: Verify technology fit with the Tech Stack Detector
• Recent signals: Check for hiring, funding, product launches, or conference attendance

Step 5: Prioritize and Sequence​

Rank your final list by:

1. Similarity score (highest first)
2. Timing signals (recently raised funding, hiring relevant roles)
3. Accessibility (do you have a warm connection? Are they in your territory?)

Then build your outreach sequences — starting with the highest-priority accounts.

Why Lookalike Prospecting Outperforms Traditional Targeting​

The numbers speak for themselves:

• 2-3x higher response rates compared to broadly-filtered cold outreach (Instantly customer data, 2025)
• 40% shorter sales cycles when prospects closely match your ICP (Gong Labs research)
• 30% higher win rates on ICP-matched deals (TOPO/Gartner research)

This makes intuitive sense: if your best customer is a Series B vertical SaaS company with 100-300 employees using HubSpot and Intercom, and you find another company with those exact characteristics, your pitch is already battle-tested. You know the pain points, the objections, and the value proposition that works.

Common Mistakes in Lookalike Prospecting​

1. Using Too Few Seed Companies​

With only 2-3 seeds, the model can't identify meaningful patterns. Use 10-20 for reliable results.

2. Including Bad Customers as Seeds​

That enterprise customer who churned after 3 months? Don't use them as a seed. You want to find more companies like your best customers, not your worst ones.

3. Ignoring the Similarity Scores​

Not all lookalikes are equal. A company with 95% similarity is fundamentally different from one with 60% similarity. Prioritize accordingly.

4. Skipping Enrichment​

Lookalike data tells you which companies to target. You still need to find the right people at those companies, understand their current situation, and personalize your outreach.

5. Set-and-Forget Mentality​

Your ICP evolves as you close more deals and learn what works. Re-run your lookalike analysis quarterly with updated seed lists.

Get Started: Find Your Lookalike Companies​

The fastest way to build a high-quality prospect list is to start with what's already working and find more of it.

Try MarketBetter's free Lookalike Company Finder →

Enter your best customer's name or URL, and get a ranked list of similar companies in seconds. No signup required, completely free.

Found companies you want to prospect? Use our AI Lead Generator to find buyer contacts, or check their Tech Stack to qualify by technology fit. Need help with outreach? The GiftDM Copilot creates personalized gifts and LinkedIn messages for your top prospects.

It's Tuesday morning. Your sales team is prepping for the biggest trade show of the year. You need a list of every exhibitor, speaker, and sponsor — with company names, booth numbers, descriptions, and ideally contact info — so you can prioritize who to visit and who to prospect before the event.

You go to the conference website. The exhibitor list is there: 400+ companies spread across an interactive floor plan with infinite scroll, lazy-loaded cards, and no export button.

So you start copying and pasting.

Company name. Tab. Description. Tab. Website. Tab. Next company. Repeat 400 times.

This is the reality for thousands of SDRs, AEs, and marketing teams every year. Conference and trade show websites are designed for attendees to browse — not for sales teams to extract. The data is right there, but getting it into a usable format (a spreadsheet, a CSV, your CRM) is absurdly painful.

There's a better way.

Why Conference Lists Are Gold for Sales Teams​

Conference exhibitor, speaker, and sponsor lists are among the highest-quality prospecting sources available:

1. Pre-Qualified by Budget​

Companies that pay $5,000-$50,000+ for a booth at a trade show have budget. They're investing in growth. That's a buying signal you can't get from a cold database.

2. Industry-Specific Targeting​

A cybersecurity conference exhibitor list is a curated list of cybersecurity companies. A SaaS conference speaker list is a roster of SaaS leaders. No filtering required — the conference organizer already did it for you.

3. Timely and Relevant​

Conference lists are current. These companies are actively participating in industry events right now, which means they're engaged, investing, and accessible.

4. Multi-Stakeholder Data​

Speaker lists give you names of decision-makers. Exhibitor lists give you company profiles. Sponsor lists tell you who has the biggest budgets. Combined, it's a complete prospecting package.

5. Pre-Event Outreach Advantage​

The most successful conference prospecting happens before the event. If you can email or LinkedIn-message an exhibitor before the show with "I saw you're exhibiting at [event] — I'll be at Booth 342, would love to connect," your meeting rate goes through the roof.

The Pain of Manual Conference Data Extraction​

Let's be honest about what manual extraction looks like:

Time cost: A typical conference with 300 exhibitors takes 4-6 hours to manually copy into a spreadsheet. That's nearly a full working day.

Error rate: Copy-paste errors are inevitable. Company names get truncated, URLs get mangled, descriptions get partially captured.

Format inconsistency: Different conferences structure their data differently. Some have separate pages per exhibitor. Others use interactive maps. Others use accordion-style lists. Each requires a different manual approach.

Dynamic content problems: Many conference sites use JavaScript-heavy frameworks (React, Angular) that render exhibitor data dynamically. You can't even "View Source" to find the data — it's loaded asynchronously.

Repetitive across events: If your team attends 10+ events per year, you're burning 40-60 hours annually just copying exhibitor data. That's a full work week of pure drudgery.

Methods for Extracting Conference Data​

Method 1: Manual Copy-Paste (The Hard Way)​

Process: Open the conference website, manually select each exhibitor's name, description, and details, paste into a spreadsheet.

Pros: No tools required, works on any site Cons: Extremely time-consuming, error-prone, soul-crushing

Time per event: 4-8 hours for 300 exhibitors

Method 2: Browser Extensions (Semi-Automated)​

Tools like Instant Data Scraper (Chrome extension) can detect tables on web pages and export them.

Process: Install the extension, navigate to the exhibitor page, click the extension, hope it detects the right data, export to CSV.

Pros: Free, fast when it works Cons: Only works on simple HTML tables. Fails completely on dynamically-loaded content, interactive maps, and paginated lists. Captures lots of irrelevant data. Requires manual cleanup.

Success rate: Works on maybe 20% of conference websites. Most modern event sites use dynamic rendering that breaks these extensions.

Method 3: Custom Python Scripts (Technical)​

For developers, writing a custom scraper using libraries like Scrapy, BeautifulSoup, or Playwright is an option.

Process: Inspect the conference website's HTML structure, write a Python script to extract the relevant elements, handle pagination and dynamic loading, export to CSV.

# Example: Scraping a simple exhibitor list with BeautifulSoup
import requests
from bs4 import BeautifulSoup

url = "https://example-conference.com/exhibitors"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

exhibitors = []
for card in soup.find_all('div', class_='exhibitor-card'):
    name = card.find('h3').text.strip()
    desc = card.find('p', class_='description').text.strip()
    exhibitors.append({'name': name, 'description': desc})

Pros: Highly customizable, handles complex sites, can be reused Cons: Requires programming skills, breaks when site structure changes, needs to be rebuilt for each conference site, can take hours to debug JavaScript-rendered content

Time investment: 2-4 hours to write and debug per conference site

Method 4: Apify Actors (Platform-Dependent)​

Apify offers pre-built scrapers ("Actors") for specific conference platforms like Map Your Show, 10times, and Xporience.

Process: Find the right Actor for your conference platform, input the URL, run the scraper, download results.

Pros: Pre-built for specific platforms, handles dynamic content, outputs structured data Cons: Only works for supported platforms (many conference sites aren't covered), requires an Apify account, costs credits for large runs, doesn't work for custom-built conference websites

Pricing: Free tier includes some usage; paid plans from $49/month

Method 5: Web Scraping Services (Expensive)​

Companies like WebScrapingExpert and ScrapeHero offer custom scraping as a service.

Process: Submit the conference URL, they build a custom scraper, deliver results in 24-48 hours.

Pros: Hands-off, handles any site Cons: Expensive ($50-$500+ per project), slow turnaround, not practical for frequent use

Method 6: MarketBetter Conference Scraper (Free, Any Site)​

MarketBetter's Conference Scraper takes a completely different approach: paste any conference URL and get structured CSVs of speakers, exhibitors, and sponsors.

How it works:

1. Paste the conference website URL
2. The AI-powered scraper analyzes the site structure, navigates through exhibitor directories, speaker pages, and sponsor lists
3. You get downloadable CSVs with structured data: company names, descriptions, booth numbers, speaker names, titles, and topics

Why it's different:

• Works on any conference website — not limited to specific platforms like Map Your Show or Eventbrite
• AI-powered navigation — handles dynamic content, infinite scroll, paginated lists, and JavaScript-rendered pages
• Multiple data types — extracts speakers, exhibitors, AND sponsors in one pass
• Structured output — clean CSVs ready for import into your CRM or outreach tool
• Completely free — no account, no credits, no per-use charges

Time per event: 2-5 minutes (versus 4-8 hours manually)

The Conference Prospecting Workflow​

Here's the complete workflow for turning conference data into booked meetings:

Step 1: Extract the Data​

Use MarketBetter's Conference Scraper to pull exhibitor, speaker, and sponsor lists from your target conference.

Step 2: Enrich with Contact Data​

Conference lists give you company names, but you need individual contacts. Use tools like:

• MarketBetter AI Lead Generator — find buyer contacts at each company on LinkedIn
• Apollo.io — search for contacts by company and title
• LinkedIn Sales Navigator — find decision-makers at target companies

Step 3: Prioritize by Fit​

Not every exhibitor is a good prospect. Score your list by:

• Company size — do they fit your ICP?
• Industry fit — are they in your target vertical?
• Technology fit — use our Tech Stack Detector to check if they use compatible/competitive technology
• Sponsor level — Platinum sponsors have bigger budgets than basic exhibitors

Step 4: Pre-Event Outreach​

Reach out 2-4 weeks before the event:

Email template:

Hi [Name],

I noticed [Company] is exhibiting at [Conference] — we'll be there too.

[One sentence about what you do and why it's relevant to them]

Would love to grab 15 minutes at the event. Are you available on [day]?

LinkedIn message template:

Hey [Name], saw you're speaking at [Conference] on [topic]. Really looking forward to your session.

We work with companies like [similar company] on [relevant problem]. Would be great to connect while we're both there.

Step 5: At-Event Meetings​

With pre-scheduled meetings, your conference ROI multiplies. Instead of wandering the floor hoping for productive conversations, you arrive with a full calendar.

Step 6: Post-Event Follow-Up​

Within 48 hours of the event, follow up with everyone you met and everyone who didn't respond to pre-event outreach:

Great connecting at [Conference], [Name]. As discussed, [reference specific conversation point].

Here's [the resource/demo/proposal] I mentioned. Would [date] work for a follow-up call?

Real-World Example: SaaStr Annual​

Let's say your team is attending SaaStr Annual, one of the largest SaaS conferences with 300+ exhibitors and 200+ speakers.

Manual approach:

• Time to extract exhibitor list: ~6 hours
• Time to extract speaker list: ~3 hours
• Total data extraction: 9+ hours
• Result: One messy spreadsheet with inconsistent formatting

MarketBetter Conference Scraper approach:

• Paste the SaaStr exhibitor page URL
• Wait 3-5 minutes
• Download clean CSVs of exhibitors, speakers, and sponsors
• Total data extraction: 5 minutes
• Result: Three clean, structured CSVs ready for CRM import

Time saved: 8+ hours per event. Multiply that by 10 events per year, and you've reclaimed 80+ hours of productive selling time.

Tips for Effective Conference Prospecting​

Start Early​

The best conference prospecting starts 4-6 weeks before the event. Exhibitor lists are usually published 2-3 months in advance. Don't wait until the week before.

Focus on Speakers​

Conference speakers are typically senior leaders (VP+) who are active in the industry and open to networking. They're often higher-quality prospects than random booth visitors.

Layer Multiple Data Sources​

Combine conference data with:

• Lookalike Company Finder to find similar companies not at the conference
• Tech Stack Detector to qualify by technology fit
• LinkedIn to research individual contacts

Track Your Metrics​

Measure conference prospecting ROI:

• Pre-event emails sent
• Meetings booked before the event
• Meetings held at the event
• Deals generated from conference contacts
• Revenue attributed to conference prospecting

Get Started​

Stop manually copying exhibitor data from conference websites. Paste any conference URL into MarketBetter's free Conference Scraper and get structured CSVs of speakers, exhibitors, and sponsors in minutes.

Try the Conference Scraper free →

Once you have your conference list, use our AI Lead Generator to find buyer contacts at each company, or try the GiftDM Copilot to personalize outreach gifts for your top prospects.