How to Scrape Data from a Website to Excel for B2B Prospecting

The fastest B2B prospect lists are built from data that already exists on the public web — company directories, job boards, technology review sites, and vendor pages that tell you exactly who is buying what. The challenge is getting that data out of a browser and into a spreadsheet where you can filter, sort, and sequence it. This guide covers every method for scraping data from a website to Excel, from zero-code options to purpose-built prospecting tools, so you can choose the right approach for your workflow.

Why scrape website data for B2B prospecting?

Web scraping for sales is not about collecting email addresses at random. The most valuable use case is extracting structured signals — which companies use which tools, which are hiring for specific roles, which are listed as customers on a competitor's site — and turning those signals into a targeted outreach list.

McKinsey research on B2B digital sales consistently shows that relevance is the primary driver of sales engagement — buyers respond when outreach is tied to a specific context they recognise. Scraping gives you that context at scale: you are not guessing at a prospect's pain points, you are reading signals they have already published publicly.

The concrete workflow looks like this: find a data source that contains a buying signal (a competitor's customer page, a G2 review list, a job posting mentioning a specific tool), extract the company names and any available contact data, clean it in Excel, and feed it into your sequencing tool. Done well, this produces lists with reply rates significantly above cold-list averages because every company on the list has already demonstrated a relevant behaviour.

What are the main methods to scrape data from a website to Excel?

There are four practical approaches, ordered from least to most technical effort required.

1. Copy-paste with Excel table detection

For simple HTML tables — think Wikipedia lists, basic directory pages, or static pricing comparison tables — you can paste the URL directly into Excel's Data tab and it will detect and import structured tables automatically. This takes under two minutes and requires no tools. It breaks immediately on any page with dynamic content or JavaScript rendering.

2. Excel Power Query (Data → From Web)

Power Query is Excel's built-in web data connector. It handles structured pages more reliably than copy-paste, lets you clean and transform data before loading it, and supports scheduled refresh. It is the right tool for recurring data pulls from stable, structured pages. The full walkthrough is in the next section.

3. Browser extension scrapers

Tools like Web Scraper (free Chrome extension), Octoparse, or ParseHub let you point and click to define which elements to extract, then run the scrape and export to CSV. These handle JavaScript-rendered pages that Power Query cannot touch. They require more setup time but can handle complex multi-page scrapes across paginated directories or search results.

4. Purpose-built B2B data tools

For sales prospecting specifically, tools that already hold structured, enriched company and contact data are faster than scraping raw HTML. You define your criteria — company size, location, technology stack, competitor relationship — and export a clean Excel-ready list directly. The scraping has already been done for you, and the data has been normalised and deduplicated. This is covered in the tools section below.

How do you use Excel Power Query to pull data from a website?

Power Query is the most accessible zero-code option for pulling structured web data into Excel. Here is the exact process.

Step 1: Open the web data connector

In Excel, go to Data → Get Data → From Other Sources → From Web. In older Excel versions (2016), this appears as Data → New Query → From Other Sources → From Web. Paste the URL of the page you want to scrape and click OK.

Step 2: Select the table

Power Query will scan the page and display any HTML tables it detects in a Navigator panel on the left. Select the table that contains the data you want. A preview appears on the right. If the page renders data via JavaScript rather than static HTML, Power Query will either show an empty table or no tables at all — in that case, you need a browser extension scraper instead.

Step 3: Transform before loading

Click Transform Data rather than Load directly. This opens the Power Query editor where you can remove irrelevant columns, rename headers, filter rows, and fix data types before the data lands in your spreadsheet. Doing the cleaning here saves significant time compared to cleaning in the sheet after the fact.

Step 4: Load and refresh

Click Close and Load to push the cleaned data into a new worksheet. To keep the data current, right-click the loaded table and select Refresh, or set an automatic refresh under Data → Queries and Connections → Properties. This is useful for monitoring pages that update regularly, like job boards or directory listings.

Limitation to know upfront: Power Query works reliably only on static HTML. Review sites like G2, Capterra, or LinkedIn render their content dynamically — Power Query will return empty results on these, and you will need a browser extension or API-based tool instead.

What are the best tools for scraping B2B company data to Excel?

The right tool depends on what data you are trying to extract and how often you need to do it.

Web Scraper (Chrome extension)

Free and widely used. You build a sitemap that tells the extension which elements to click, scroll, and extract. It handles pagination and JavaScript-rendered pages. The export is a CSV file you can open directly in Excel. The learning curve is moderate — expect to spend 30–60 minutes setting up a non-trivial scrape for the first time.

Octoparse

A desktop application with a visual point-and-click interface. Better suited to non-technical users than raw Web Scraper because it detects page elements automatically. The free tier limits you to 10 scraper tasks and slower run speeds. Exports to Excel, CSV, and Google Sheets. Works well for scraping company directories, review sites, and job boards.

Apify

A cloud-based scraping platform with pre-built scrapers for common sites including LinkedIn, Google Maps, and major job boards. More powerful than browser extensions — it runs in the cloud, handles anti-bot measures better, and can be triggered via API. Pricing scales with usage. The output is JSON or CSV, which loads cleanly into Excel.

Purpose-built sales intelligence tools

For B2B prospecting where the goal is a list of companies matching specific criteria, the most efficient path is a tool that has already done the scraping and enrichment. Stealery is built specifically for one high-value use case: you enter a competitor's name, and it surfaces every company currently using that product, filterable by company size, location, and hiring signals. The output exports directly to a spreadsheet — clean, structured, and ready for sequencing — without touching a single HTML selector. What would take hours of manual scraping and enrichment takes about 30 seconds.

"We were manually scraping G2 review pages and cross-referencing LinkedIn to build competitor lists. It took a full day to build 200 accounts. Now we get a filtered list of 500 in the time it takes to make coffee."

— Head of Sales Development, 60-person SaaS company

The broader point applies regardless of which tool you use: Gartner's research on B2B buying behaviour shows that buyers spend only 17% of their purchase journey talking to sales reps. The other 83% is independent research — which means the signals that matter most are the ones buyers leave in public data. Scraping and acting on that data is not a shortcut; it is the correct strategy.

How do you clean and structure scraped data in Excel for outreach?

Raw scraped data is almost never outreach-ready. The extraction step gets you 60% of the way there. Cleaning gets you the rest.

Remove duplicates first

Scraped lists frequently contain duplicate rows, especially when pulling from paginated sources. In Excel: select the data range, go to Data → Remove Duplicates, and choose the columns that define uniqueness (usually company name plus domain). Do this before any other cleaning step — it saves time on everything that follows.

Standardise company names and domains

Scraped company names come in inconsistent formats: "Acme Corp", "ACME", "Acme Corporation". Use PROPER() to normalise capitalisation and TRIM() to remove leading or trailing spaces. For domains, strip protocols and trailing slashes with a combination of SUBSTITUTE() and text functions so every row has a clean domain you can use for email pattern matching or CRM lookup.

Add ICP filter columns

Create a column for each ICP dimension you care about: company size tier (SMB / Mid-Market / Enterprise), geography, industry vertical, and any technology signals you extracted. Use these to score or filter the list before it goes into your sequencer. Sending 200 well-filtered accounts outperforms sending 1,000 unfiltered ones every time — both in reply rate and in the quality of conversations you get back.

Enrich missing fields

Scraped data typically gives you company name and maybe a domain. For outreach you also need the right contact, their title, and a verified email. Run your cleaned list through an enrichment layer — tools like Apollo, Clay, or Hunter.io take a domain and return contact data. Structure your Excel file with placeholder columns for these fields before enrichment so the data drops into the right place.

Flag outreach-ready rows

Add a status column with values like "Ready", "Needs enrichment", "Exclude". Filter to "Ready" before exporting to your sequencing tool. This keeps your pipeline clean and makes it easy to return to the list later without re-reviewing every row from scratch.

What are the legal and ethical limits of web scraping for sales?

The legal landscape for web scraping shifted significantly with the Ninth Circuit Court's 2022 ruling in hiQ Labs v. LinkedIn, which confirmed that scraping publicly accessible data does not violate the Computer Fraud and Abuse Act (CFAA). Public data — meaning data accessible without a login — is generally fair game under US law.

The practical limits are narrower than the legal ones. Three rules cover the vast majority of cases:

• Do not scrape behind authentication walls. Data that requires a login to access is not public data, regardless of how it is stored. Scraping it likely violates both the CFAA and the platform's terms of service.
• Check robots.txt. A site's robots.txt file signals which paths the site owner considers off-limits for automated access. Ignoring it is not illegal in itself, but it is a term-of-service violation on most major platforms and can result in IP blocks or legal notice.
• Do not scrape at rates that harm the target server. Aggressive scraping that degrades site performance can create liability under computer misuse statutes regardless of data accessibility. Throttle requests and scrape during off-peak hours.

For GDPR compliance in the EU, the calculus is different. Even publicly available personal data — email addresses, names, job titles — is subject to GDPR if the data subject is in the EU. Legitimate interest is a recognised legal basis for B2B prospecting under GDPR, but you must be able to demonstrate that the processing is proportionate, expected, and that you have a clear opt-out mechanism. When in doubt, run scraped EU contact data through a legal review before sequencing.

The cleanest path: use data sources that have already handled consent and compliance, and focus your scraping on company-level signals (which tool a company uses, what they are hiring for, where they are headquartered) rather than personal contact data. Enrich to contacts only when you have a clear ICP match — that approach is both more compliant and more effective.

For more on building targeted outreach lists from public signals, see the Product Guides section of the Stealery blog, or explore how Stealery structures competitor-based prospecting from first principles.