Capterra Scraping: Extract Software Reviews, Ratings and Feature Data

Web scraping Capterra opens up a goldmine of structured data about software products — reviews, ratings, pricing, feature comparisons, and vendor profiles. Whether you're doing competitive analysis, building a SaaS comparison tool, or feeding data into a recommendation engine, Capterra is one of the richest sources of software intelligence on the web.

In this guide, we'll walk through how Capterra is structured, what data you can extract, and how to build a reliable scraper using JavaScript and Apify. By the end, you'll have a working approach to pull thousands of software reviews, ratings, and feature data points at scale.

Why Scrape Capterra?

Capterra hosts reviews and profiles for over 100,000 software products across 900+ categories. Each product listing includes:

• User reviews with ratings, pros/cons, and recommendation scores
• Overall ratings broken down by ease of use, customer service, features, and value for money
• Pricing information including free trials, free versions, and subscription tiers
• Feature lists with checkmarks showing what each product offers
• Vendor profiles with company size, founding year, and deployment options
• Comparison data showing how products stack up against alternatives

This data powers business decisions worth millions. Market researchers, SaaS founders, and investors all need access to this information — and scraping is often the only way to get it at scale.

Understanding Capterra's Structure

Before writing any code, let's understand how Capterra organizes its data. This knowledge is critical for building an efficient scraper.

Category Pages

Capterra organizes software into categories like "Project Management," "CRM," "Email Marketing," etc. Each category page lists software products with basic information:

https://www.capterra.com/project-management-software/
https://www.capterra.com/crm-software/
https://www.capterra.com/email-marketing-software/

Category pages use pagination and typically show 25 products per page. The URL pattern for pagination is:

https://www.capterra.com/project-management-software/?page=2

Product Pages

Each software product has its own profile page with detailed information:

https://www.capterra.com/p/12345/ProductName/

Product pages contain the core data: overview, pricing, features, and a summary of reviews.

Review Pages

Reviews live on dedicated subpages:

https://www.capterra.com/p/12345/ProductName/reviews/

Reviews are paginated and can be filtered by rating, date, company size, and more.

Setting Up Your Scraping Environment

We'll use Apify's Crawlee library, which handles browser automation, request queuing, and anti-blocking measures out of the box.

Initialize the Project

// First, create a new Apify actor project
// npm init -y
// npm install crawlee puppeteer apify

import { PuppeteerCrawler, Dataset } from 'crawlee';

const crawler = new PuppeteerCrawler({
    maxConcurrency: 3,
    navigationTimeoutSecs: 60,
    requestHandlerTimeoutSecs: 120,

    launchContext: {
        launchOptions: {
            headless: true,
            args: ['--no-sandbox', '--disable-setuid-sandbox'],
        },
    },

    async requestHandler({ request, page, enqueueLinks, log }) {
        const { label } = request.userData;

        if (label === 'CATEGORY') {
            await handleCategoryPage(page, enqueueLinks, log);
        } else if (label === 'PRODUCT') {
            await handleProductPage(page, log);
        } else if (label === 'REVIEWS') {
            await handleReviewsPage(page, log);
        }
    },
});

Scraping Category Pages

Category pages are your entry point. They list software products with basic metadata and links to detailed product pages.

async function handleCategoryPage(page, enqueueLinks, log) {
    log.info('Processing category page...');

    // Wait for product listings to load
    await page.waitForSelector('[data-testid="product-card"]', {
        timeout: 15000
    });

    // Extract basic product info from category listing
    const products = await page.evaluate(() => {
        const cards = document.querySelectorAll('[data-testid="product-card"]');
        return Array.from(cards).map(card => {
            const nameEl = card.querySelector('h2 a, [data-testid="product-name"]');
            const ratingEl = card.querySelector('[class*="rating"]');
            const reviewCountEl = card.querySelector('[class*="review-count"]');
            const descEl = card.querySelector('[class*="description"]');

            return {
                name: nameEl?.textContent?.trim() || '',
                url: nameEl?.href || '',
                rating: parseFloat(ratingEl?.textContent) || null,
                reviewCount: parseInt(
                    reviewCountEl?.textContent?.replace(/[^0-9]/g, '')
                ) || 0,
                shortDescription: descEl?.textContent?.trim() || '',
            };
        });
    });

    log.info(`Found ${products.length} products on category page`);

    // Enqueue individual product pages for detailed scraping
    await enqueueLinks({
        selector: '[data-testid="product-card"] h2 a',
        userData: { label: 'PRODUCT' },
    });

    // Handle pagination - enqueue next page
    await enqueueLinks({
        selector: 'a[aria-label="Next page"], [data-testid="pagination-next"]',
        userData: { label: 'CATEGORY' },
    });
}

Extracting Product Details

Product pages contain the richest data. Here's how to extract everything from a single product profile:

async function handleProductPage(page, log) {
    log.info(`Processing product: ${page.url()}`);

    // Extract comprehensive product data
    const productData = await page.evaluate(() => {
        // Basic info
        const name = document.querySelector('h1')?.textContent?.trim();
        const vendor = document.querySelector(
            '[data-testid="vendor-name"], [class*="vendor"]'
        )?.textContent?.trim();

        // Overall rating
        const overallRating = parseFloat(
            document.querySelector('[class*="overall-rating"] [class*="value"]')
                ?.textContent
        ) || null;

        // Rating breakdown
        const ratingBreakdown = {};
        const ratingCategories = document.querySelectorAll(
            '[class*="rating-breakdown"] [class*="category"]'
        );
        ratingCategories.forEach(cat => {
            const label = cat.querySelector('[class*="label"]')
                ?.textContent?.trim();
            const value = parseFloat(
                cat.querySelector('[class*="value"]')?.textContent
            );
            if (label && value) {
                ratingBreakdown[label.toLowerCase().replace(/\s+/g, '_')] = value;
            }
        });

        // Pricing information
        const pricingSection = document.querySelector('[class*="pricing"]');
        const pricing = {
            startingPrice: pricingSection?.querySelector(
                '[class*="starting-price"]'
            )?.textContent?.trim(),
            hasFreeVersion: !!pricingSection?.querySelector(
                '[class*="free-version"]'
            ),
            hasFreeTrial: !!pricingSection?.querySelector(
                '[class*="free-trial"]'
            ),
            pricingModel: pricingSection?.querySelector(
                '[class*="pricing-model"]'
            )?.textContent?.trim(),
        };

        // Features
        const features = [];
        const featureItems = document.querySelectorAll(
            '[class*="feature-list"] li, [data-testid="feature-item"]'
        );
        featureItems.forEach(item => {
            const featureName = item.querySelector(
                '[class*="feature-name"]'
            )?.textContent?.trim();
            const hasFeature = !!item.querySelector(
                '[class*="checkmark"], svg[class*="check"]'
            );
            if (featureName) {
                features.push({ name: featureName, available: hasFeature });
            }
        });

        // Deployment & support
        const deployment = Array.from(
            document.querySelectorAll('[class*="deployment"] li')
        ).map(el => el.textContent.trim());

        // Review summary
        const totalReviews = parseInt(
            document.querySelector('[class*="total-reviews"]')
                ?.textContent?.replace(/[^0-9]/g, '')
        ) || 0;

        return {
            name,
            vendor,
            overallRating,
            ratingBreakdown,
            pricing,
            features,
            deployment,
            totalReviews,
            url: window.location.href,
            scrapedAt: new Date().toISOString(),
        };
    });

    // Save product data
    await Dataset.pushData(productData);

    log.info(`Extracted data for: ${productData.name}`);
    log.info(`Rating: ${productData.overallRating}, Reviews: ${productData.totalReviews}`);
}

Extracting Reviews

Reviews are where the real value lies. Each review contains structured sentiment data that's incredibly useful for analysis.

async function handleReviewsPage(page, log) {
    log.info('Processing reviews page...');

    // Wait for reviews to load
    await page.waitForSelector('[class*="review-card"], [data-testid="review"]', {
        timeout: 15000
    });

    const reviews = await page.evaluate(() => {
        const reviewElements = document.querySelectorAll(
            '[class*="review-card"], [data-testid="review"]'
        );

        return Array.from(reviewElements).map(review => {
            // Reviewer info
            const reviewerName = review.querySelector(
                '[class*="reviewer-name"]'
            )?.textContent?.trim();
            const reviewerRole = review.querySelector(
                '[class*="reviewer-role"], [class*="job-title"]'
            )?.textContent?.trim();
            const companySize = review.querySelector(
                '[class*="company-size"]'
            )?.textContent?.trim();
            const industry = review.querySelector(
                '[class*="industry"]'
            )?.textContent?.trim();

            // Rating
            const rating = parseFloat(
                review.querySelector('[class*="star-rating"]')
                    ?.getAttribute('aria-label')?.match(/[\d.]+/)?.[0]
            ) || null;

            // Review content
            const title = review.querySelector(
                '[class*="review-title"] , h3'
            )?.textContent?.trim();
            const pros = review.querySelector(
                '[class*="pros"]'
            )?.textContent?.trim();
            const cons = review.querySelector(
                '[class*="cons"]'
            )?.textContent?.trim();
            const overallComment = review.querySelector(
                '[class*="overall"], [class*="comment"]'
            )?.textContent?.trim();

            // Metadata
            const dateStr = review.querySelector(
                '[class*="review-date"], time'
            )?.textContent?.trim();
            const recommendScore = review.querySelector(
                '[class*="recommend"]'
            )?.textContent?.trim();
            const usageDuration = review.querySelector(
                '[class*="time-used"], [class*="usage"]'
            )?.textContent?.trim();

            return {
                reviewerName,
                reviewerRole,
                companySize,
                industry,
                rating,
                title,
                pros,
                cons,
                overallComment,
                date: dateStr,
                recommendScore,
                usageDuration,
            };
        });
    });

    // Save all reviews
    for (const review of reviews) {
        await Dataset.pushData({
            type: 'review',
            productUrl: page.url().split('/reviews')[0],
            ...review,
            scrapedAt: new Date().toISOString(),
        });
    }

    log.info(`Extracted ${reviews.length} reviews`);
}

Building Feature Comparison Tables

One of the most valuable things you can extract from Capterra is feature comparison data. Here's a dedicated function for that:

async function extractFeatureComparison(page, productUrls) {
    const comparisonData = {};

    for (const url of productUrls) {
        await page.goto(`${url}/features/`, { waitUntil: 'networkidle0' });

        const features = await page.evaluate(() => {
            const featureGroups = {};
            const groups = document.querySelectorAll(
                '[class*="feature-group"]'
            );

            groups.forEach(group => {
                const groupName = group.querySelector('h3, h4')
                    ?.textContent?.trim();
                const items = Array.from(
                    group.querySelectorAll('[class*="feature-row"]')
                ).map(row => ({
                    name: row.querySelector('[class*="name"]')
                        ?.textContent?.trim(),
                    available: !!row.querySelector(
                        'svg[class*="check"], [class*="available"]'
                    ),
                }));

                if (groupName) {
                    featureGroups[groupName] = items;
                }
            });

            return featureGroups;
        });

        const productName = url.split('/').pop();
        comparisonData[productName] = features;
    }

    return comparisonData;
}

Vendor Profile Extraction

Vendor profiles give you company-level intelligence:

async function extractVendorProfile(page) {
    return await page.evaluate(() => {
        const vendorSection = document.querySelector(
            '[class*="vendor-details"], [data-testid="vendor-info"]'
        );

        if (!vendorSection) return null;

        const getField = (selector) => 
            vendorSection.querySelector(selector)?.textContent?.trim() || null;

        return {
            companyName: getField('[class*="company-name"]'),
            founded: getField('[class*="founded"]'),
            headquarters: getField('[class*="headquarters"]'),
            companySize: getField('[class*="company-size"]'),
            website: vendorSection.querySelector(
                'a[class*="website"]'
            )?.href || null,
            socialLinks: Array.from(
                vendorSection.querySelectorAll('a[class*="social"]')
            ).map(a => ({
                platform: a.getAttribute('aria-label') || a.textContent.trim(),
                url: a.href,
            })),
            supportOptions: Array.from(
                vendorSection.querySelectorAll('[class*="support"] li')
            ).map(el => el.textContent.trim()),
        };
    });
}

Putting It All Together with Apify

Here's the complete Apify actor that ties everything together:

import { Actor } from 'apify';
import { PuppeteerCrawler, Dataset } from 'crawlee';

await Actor.init();

const input = await Actor.getInput() ?? {};
const {
    categories = ['project-management-software'],
    maxProducts = 50,
    scrapeReviews = true,
    maxReviewPages = 5,
} = input;

const startUrls = categories.map(cat => ({
    url: `https://www.capterra.com/${cat}/`,
    userData: { label: 'CATEGORY', category: cat },
}));

const crawler = new PuppeteerCrawler({
    maxConcurrency: 2,
    maxRequestsPerMinute: 12,
    navigationTimeoutSecs: 60,

    async requestHandler({ request, page, enqueueLinks, log }) {
        const { label } = request.userData;
        log.info(`Processing [${label}]: ${request.url}`);

        switch (label) {
            case 'CATEGORY':
                await handleCategoryPage(page, enqueueLinks, log);
                break;
            case 'PRODUCT':
                await handleProductPage(page, log);
                if (scrapeReviews) {
                    const reviewsUrl = request.url.replace(/\/?$/, '/reviews/');
                    await enqueueLinks({
                        urls: [reviewsUrl],
                        userData: { label: 'REVIEWS', reviewPage: 1 },
                    });
                }
                break;
            case 'REVIEWS':
                await handleReviewsPage(page, log);
                const currentPage = request.userData.reviewPage || 1;
                if (currentPage < maxReviewPages) {
                    await enqueueLinks({
                        selector: 'a[aria-label="Next page"]',
                        userData: {
                            label: 'REVIEWS',
                            reviewPage: currentPage + 1,
                        },
                    });
                }
                break;
        }
    },

    async failedRequestHandler({ request, log }) {
        log.error(`Request failed: ${request.url}`);
        await Dataset.pushData({
            type: 'error',
            url: request.url,
            error: request.errorMessages.join('; '),
        });
    },
});

await crawler.run(startUrls);
await Actor.exit();

Handling Anti-Scraping Measures

Capterra employs several protection mechanisms. Here are strategies to handle them:

const crawlerConfig = {
    // Rotate user agents
    launchContext: {
        useChrome: true,
        launchOptions: {
            args: [
                '--disable-blink-features=AutomationControlled',
                '--no-sandbox',
            ],
        },
    },

    // Add random delays between requests
    async requestHandler({ request, page, log }) {
        // Random delay between 2-5 seconds
        const delay = 2000 + Math.random() * 3000;
        await new Promise(resolve => setTimeout(resolve, delay));

        // Set realistic viewport
        await page.setViewport({
            width: 1366 + Math.floor(Math.random() * 200),
            height: 768 + Math.floor(Math.random() * 200),
        });

        // Your scraping logic here...
    },

    // Use Apify proxy for IP rotation
    proxyConfiguration: await Actor.createProxyConfiguration({
        groups: ['RESIDENTIAL'],
    }),
};

Data Output and Analysis

Once you've collected the data, you can export it in various formats:

// Export to CSV
const dataset = await Dataset.open();
await dataset.exportToCSV('capterra_data');

// Or process in-memory for analysis
const { items } = await dataset.getData();

// Calculate category averages
const categoryStats = items
    .filter(item => item.type !== 'review' && item.type !== 'error')
    .reduce((acc, product) => {
        if (product.overallRating) {
            acc.totalRating += product.overallRating;
            acc.count += 1;
            acc.avgRating = acc.totalRating / acc.count;
        }
        return acc;
    }, { totalRating: 0, count: 0, avgRating: 0 });

console.log(`Average rating across ${categoryStats.count} products: ${categoryStats.avgRating.toFixed(2)}`);

Use Cases for Capterra Data

The data you extract can power numerous business applications:

1. Competitive Intelligence: Track how competitors' ratings change over time. Monitor new reviews for sentiment shifts.

2. Market Research: Identify gaps in software categories where user satisfaction is low. Find underserved niches.

3. Lead Generation: Companies actively reviewing software are in a buying cycle. Use reviewer industry and company size data for targeting.

4. Product Development: Analyze pros/cons across a category to understand what features users value most and what pain points exist.

5. Investment Analysis: Track rating trends and review velocity as signals of product-market fit and growth.

Legal and Ethical Considerations

Always respect Capterra's terms of service and robots.txt. Implement rate limiting to avoid overloading their servers. Consider these best practices:

• Keep request rates reasonable (under 15 requests per minute)
• Cache results and avoid re-scraping unchanged data
• Don't scrape personal information beyond what's publicly displayed
• Use the data for analysis, not for republishing reviews verbatim
• Consider reaching out to Capterra about API access for commercial use

Conclusion

Scraping Capterra gives you access to one of the most comprehensive software review databases on the internet. With the approach outlined here — using Puppeteer through Apify's Crawlee framework — you can extract product profiles, pricing data, feature comparisons, vendor information, and thousands of user reviews at scale.

The key to success is respecting rate limits, handling pagination correctly, and structuring your data extraction to capture all the valuable metadata that Capterra provides. Whether you're building a competitive intelligence dashboard or powering a software recommendation engine, this data is invaluable.

Start with a single category, validate your extraction logic, and then scale up to cover the categories relevant to your business. The Apify platform handles the infrastructure — proxy rotation, browser management, and data storage — so you can focus on what matters: turning raw data into business insights.