Scrape Capterra Data: how to scrape data from Capterra using ScrapeStorm

Scrape Capterra Data: how to scrape data from Capterra using ScrapeStorm

Capterra is a website that provides reviews and comparisons of different software products. It aims to help businesses easily find and compare the right software for their needs. As a software buyer or reviewer, Capterra is a valuable resource. However, as a data scientist or web scraper, accessing and utilizing all that review and comparison data can be tricky without the right tools. 

ScrapeStorm is a freemium and easy-to-use web scraping tool that allows you to build web scrapers visually without any code. In this article, we will walk through how to use ScrapeStorm to scrape useful data from Capterra including software names, ratings, reviews, and more. By the end, you'll have a working scraper that automatically collects data from Capterra on a schedule. Let's get started!

how to scrape data from Capterra using ScrapeStorm

Sign Up for ScrapeStorm

The first step is to head over to and sign up for a free account. ScrapeStorm offers both free and paid plans, but the free plan has more than enough capabilities for our purposes here. 

Once you've signed up, you'll be taken to your ScrapeStorm dashboard. The dashboard is where you'll build, test, schedule, and monitor all your scrapers. It has a simple and intuitive drag-and-drop interface for constructing web scrapers without writing any code.

Capterra Website Structure 

Before we start building the scraper, it's helpful to understand the basic structure and elements of the Capterra website. This will guide how we design the scraper to extract the necessary data.

Capterra has dedicated category pages that display lists of different software products. For example, the Project Management category page shows software like Trello, Asana, Basecamp, and more. These category pages are a good starting point since they aggregate related software in one place.

Each individual software listing contains key details like:

– Name 

– Description

– Rating (out of 5 stars)

– Number of reviews

– Feature comparison chart

– Pricing information

Clicking through to a software's product page opens up more in-depth information such as: 

– Extended description

– User reviews 

– Screenshots/videos

– Integrations 

– Case studies

– FAQs

– Related/alternative software

Some important things to note:

– Pages are dynamically loaded with JavaScript so content may not load instantly

– Pagination is used to split listings over multiple pages

– Pages use relative URLs so full URLs need to be extracted

Understanding the website structure is important for targeting the right elements and pages to extract the necessary data for analysis. Now let's start building the scraper.

Building the Scraper

Once signed in, click the “Create New Scraper” button to get started. Select “Web Scraper” and give it a name like “Capterra Software Data”. 

The first thing a scraper needs is a starting URL – the entry point for crawling the site. For Capterra, we'll use the main Project Management category page as our start:

Drag the “Start URL” block from the left sidebar and drop it onto the canvas. Then enter the category URL. 

Now we need to define what data we want to extract. At a minimum, we want the:

– Software name

– Rating 

– Number of reviews

Drag three “Extract” blocks from the sidebar and label them appropriately. 

To extract the software names, we can target the “h3” element that contains each name. Capterra uses consistent HTML tags, so this should work reliably across pages.

Select the “Extract” block named “Software Name” and in the Selector field, enter:


This tells ScrapeStorm to scrape all text from <h3> tags it finds on the page. 

For the rating, Capterra displays it as the average number of stars inside a “div” with class “avg-rating”. We can target this specifically:


And similarly for number of reviews, it's inside a “div” with class “review-count”:

This will pull the data we want from each listed software on the starting category page. But we also want to scrape subsequent pages of results and individual software pages for more details.

Scraping Paginated Results

Let's add pagination scraping. Capterra loads up to 10 software listings per page and provides next/previous page links at the bottom. 

Drag a “Find Links” block and a “Follow Links” block onto the canvas. Connect the “Find Links” to the “Start URL” block.

Inside the “Find Links” block, we can search for the pagination links which have a class of “page-link”. Enter this selector:

This will find all the numbered pagination links on each page.

Now we need to tell the scraper to actually follow and scrape those linked pages. Connect the “Find Links” block to the “Follow Links” block. 

In the “Follow Links” configuration:

– Set the Link Type to “Pagination” 

– Check the box to “Follow pagination links recursively”

This tells ScrapeStorm that these are pagination links to follow across multiple pages, scraping each new one.

Finally, connect the “Follow Links” block to each of the “Extract” blocks so the data is scraped from every paginated page.

Now the scraper will:

1. Start on the category page

2. Scrape the initial listings 

3. Find the pagination links

4. Follow each link and scrape additional pages

5. Repeat until all pages are complete

Scraping Software Detail Pages

With paginated results covered, the next step is extracting more data from individual software pages. 

Each listing contains a link to the software's product page. Drag another “Find Links” block and label it “Software Links”. 

Inside, enter a selector to find these product page links:

.product-title a

This targets the anchor tag inside the title element of each listing.

Now drag two more “Extract” blocks for additional details – let's grab the:

– Extended Description

– Number of User Reviews

For the description, Capterra puts it inside a <div> with class “description”:


And reviews are inside another <div> with class “review-count-outer”:

Connect the “Software Links” block to the two new “Extract” blocks so it pulls those fields from each product page.

Finally, add another “Follow Links” block and connect it from “Software Links” to tell the scraper to actually visit those product pages and scrape the additional data:!

Testing the Scraper

Before saving and scheduling the scraper, it's important to test that it's working properly. 

Click the “Test” button at the top to run a live test on your actual scraper. ScrapeStorm will emulate the crawling process and show you the scraped data in real-time.

You should see it:

1. Visit the starting category URL 

2. Scrape the initial listings

3. Follow pagination links

4. Scrape additional pages

5. Click through to software pages

6. Scrape extra product details

Check that the expected fields are being extracted correctly on all page types. Make any necessary adjustments to selector paths if needed.

Once you're satisfied with the results, click “Save Scraper”. This will save your work in ScrapeStorm without running it yet.

Now we're ready to actually run the scraper on a schedule.

Scheduling the Scraper

To automatically scrape fresh data from Capterra over time, the scraper needs to run on a scheduled basis.

Click the “Schedule” button on the scraper page. Here you can select a frequency like daily, weekly or monthly.

For our use case, we probably don't need fresh data more than weekly since software listings don't change that rapidly. Set it to run every Monday at noon.

Click “Save Schedule” and your Capterra scraper is now configured and ready to run automatically each week without any other input needed.

You'll receive email notifications of each successful or failed run. And you can monitor runs, see logs and check scraped data straight from your ScrapeStorm dashboard anytime.

The data will be stored internally by ScrapeStorm until you're ready to export it for analysis. Let's see some examples of what can be done with this scraped software data.

Analyzing the Scraped Data

With fresh Capterra data being collected on a reliable schedule, there are many useful ways we could analyze and apply the insights. Here are a few ideas:

Market Analysis

Compare popularity of different categories over time. Have certain types of software grown or declined? This could give signals on market trends.

Top Software Tracking 

Monitor which individual products are most reviewed, highest rated or gaining the most reviews each period. This visibility into “market leaders” could be valuable for vendors, buyers or analysts.

Sentiment Analysis

The user reviews scraped from each product page present an opportunity for sentiment analysis. Text analysis tools could evaluate reviews for positive or negative tone on a periodic basis, surfacing software that has seen improvements or degradations in customer satisfaction. This type of ‘customer temperature check' data is immensely useful for product teams.

Feature/Pricing Benchmarking  

By comparing the feature lists and pricing models scraped from software pages across categories, meaningful benchmarks can be established. Companies could see how their offerings stack up against competitors for different features and pricing tiers. This kind of competitive intelligence assists with product positioning and strategy.

Sales & Marketing Insights

Changes in scraped metrics like review volume,user adoption scores or monthly active users could provide early indicators on marketing campaign effectiveness or upcoming promotional opportunities for vendors. Strategic partnerships or advertising placements may also correlate to movements in relevant scraped data. This type of attribution helps optimize spending.

Geographic & Industry Insights

With enough data collection over time, geographic and industry segmentation could be revealed. For example, certain software may be more popular in specific regions or verticals. Niche solutions targeting emerging opportunities could be uncovered. Sales and local marketing teams could benefit from visibility into these customer and demand trends. 

Of course, ethics must be considered with any web scraping or data collection project. It's important to respect platform terms of use and individual user privacy when handling personally identifiable information. But by focusing scraper design on openly published review, comparison and metrics data – as we have in the example Capterra scraper – meaningful insights can be gained to help various stakeholders while avoiding ethical issues.

With the right analysis, scraped data has immense potential to provide Strategic, market and customer insights. It requires ongoing scraping to monitor trends, but offers a dimension of intelligence beyond what any single data source could reveal alone. And it can all be automated with a tool like ScrapeStorm.


In this article, we walked through how to build a web scraper for Capterra using the visual scraping tool ScrapeStorm. The scraper is designed to automatically collect software names, ratings, review counts and other key details directly from Capterra on a scheduled weekly basis. 

With this example of scraping Capterra as a template, the same principles can be applied to construct scrapers for any other sites to digitize publicly available data for analysis over time. ScrapyStorm's simple drag-and-drop interface makes it possible even for non-programmers to build powerful scraping robots.

By setting up scheduled, rules-based scraping of targeted data sources, a continuous stream of market insights can be collected and analyzed on an ongoing basis. This ongoing monitoring of metrics, sentiments and trends stands to benefit various business functions from product to sales and beyond. With the right analysis applied, rich strategic intelligence is within reach.