Review parser: automate collection and import from marketplaces

Review collection and import from marketplaces: parser development

We build parsers for automatic review collection from marketplaces. A client lost 30% conversion due to outdated reviews — manual collection took hours, data went unrefreshed for weeks. Our team (5+ years experience, 50+ projects) created a solution that collects reviews from Wildberries, Ozon, Yandex.Market, and other platforms, normalizes them, and imports them into your store's database. Time savings — up to 70%. Project payback — 2–3 months. Order parser development for your online store.

Typical scenario: a manager manually copies reviews from 5 marketplaces for 200 products — that's 8 hours a day. Data grows stale, customers see no new reviews for weeks, trust drops. A parser grabs reviews every 2 hours, updating product pages in real time. Result: conversion growth by 15% in the first month.

Technically, each marketplace is its own headache. Wildberries offers a JSON API, but with a limit of 1000 reviews per session. Ozon is an SPA on Nuxt, where data is loaded via GraphQL — we have to emulate a browser with Playwright. Yandex.Market changes its structure every six months, so we use adaptive parsing with a fallback strategy. Our stack: Python for high-load platforms, PHP (Laravel) for integration with your site.

Supported marketplaces and methods

Method comparison: JSON-API (Wildberries) processes 1000 reviews in 10 seconds, while Selenium on Ozon takes 5 minutes for the same 1000. That's a 30 times difference. For performance, we choose JSON wherever possible.

How the Wildberries parser works

We use asynchronous httpx to iterate through pagination. Example:

# scraper/reviews/wildberries.py
import httpx
import asyncio
from dataclasses import dataclass
from typing import Optional

@dataclass
class Review:
    external_id: str
    product_nm_id: int
    author: str
    rating: int
    text: str
    pros: Optional[str]
    cons: Optional[str]
    date: str
    photos: list[str]
    helpful_count: int

class WildberriesReviewScraper:
    REVIEWS_URL = "https://feedbacks2.wb.ru/feedbacks/v2/{nm_id}"

    def __init__(self):
        self.client = httpx.AsyncClient(
            headers={
                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
                "Origin": "https://www.wildberries.ru",
                "Referer": "https://www.wildberries.ru/",
            }
        )

    async def get_reviews(self, nm_id: int, take: int = 100) -> list[Review]:
        all_reviews = []
        skip = 0

        while True:
            url = self.REVIEWS_URL.format(nm_id=nm_id)
            params = {
                "immt": nm_id,
                "skip": skip,
                "take": take,
                "order": "dateDesc",
            }

            resp = await self.client.get(url, params=params)
            resp.raise_for_status()

            data = resp.json()
            feedbacks = data.get("feedbacks", [])

            if not feedbacks:
                break

            for fb in feedbacks:
                all_reviews.append(self._normalize(nm_id, fb))

            skip += take
            await asyncio.sleep(1.0)

            # Limit: no more than 1000 reviews per session
            if skip >= 1000:
                break

        return all_reviews

    def _normalize(self, nm_id: int, raw: dict) -> Review:
        photos = []
        for photo in raw.get("photos", []):
            if full_url := photo.get("fullSize"):
                photos.append(full_url)

        return Review(
            external_id=raw.get("id", ""),
            product_nm_id=nm_id,
            author=raw.get("wbUserDetails", {}).get("name", "Customer"),
            rating=raw.get("productValuation", 0),
            text=raw.get("text", ""),
            pros=raw.get("pros"),
            cons=raw.get("cons"),
            date=raw.get("createdDate", ""),
            photos=photos,
            helpful_count=raw.get("feedbackValuation", 0),
        )

Parsing HTML reviews

For sites without an API, we parse HTML using PHP and Symfony Crawler. Example for iHerb:

// app/Services/ReviewScraper/HtmlReviewScraper.php
class HtmlReviewScraper
{
    public function scrapeIherb(string $productUrl, int $pages = 5): array
    {
        $reviews = [];

        for ($page = 1; $page <= $pages; $page++) {
            $html = $this->fetch("{$productUrl}?p={$page}&is=1&s=6");
            $crawler = new Crawler($html);

            $items = $crawler->filter('[itemprop="review"]');
            if (!$items->count()) break;

            $items->each(function (Crawler $node) use (&$reviews) {
                $reviews[] = [
                    'external_id' => $node->attr('data-review-id'),
                    'author'      => trim($node->filter('[itemprop="author"]')->text('')),
                    'rating'      => (int) $node->filter('[itemprop="ratingValue"]')->attr('content'),
                    'date'        => $node->filter('[itemprop="datePublished"]')->attr('content'),
                    'title'       => trim($node->filter('[itemprop="name"]')->text('')),
                    'text'        => trim($node->filter('[itemprop="reviewBody"]')->text('')),
                    'helpful'     => (int) $node->filter('.helpful-yes')->text('0'),
                    'verified'    => $node->filter('.verified-buyer')->count() > 0,
                ];
            });

            sleep(rand(2, 4));
        }

        return $reviews;
    }
}

Deduplication and import into Laravel

After collection, reviews go through a Job with deduplication by external_id and source. Example:

// app/Jobs/ImportProductReviews.php
class ImportProductReviews implements ShouldQueue
{
    public int $tries = 3;
    public int $backoff = 120;

    public function handle(ReviewImportService $service): void
    {
        $mapping = ProductReviewMapping::where('product_id', $this->productId)
            ->where('source', $this->source)
            ->firstOrFail();

        $reviews = $this->scrape($mapping->external_id);

        $imported = 0;
        $skipped = 0;

        foreach ($reviews as $reviewData) {
            $exists = ProductReview::where([
                'source'      => $this->source,
                'external_id' => $reviewData['external_id'],
            ])->exists();

            if ($exists) {
                $skipped++;
                continue;
            }

            $service->import($this->productId, $this->source, $reviewData);
            $imported++;
        }

        Log::info("Reviews imported", [
            'product_id' => $this->productId,
            'source'     => $this->source,
            'imported'   => $imported,
            'skipped'    => $skipped,
        ]);
    }
}

Filtering and moderation

Stop-words (spam, ads, profanity), too-short reviews (<20 characters), and author anonymization — all customizable. Below is a service example:

// app/Services/ReviewImportService.php
class ReviewImportService
{
    private array $stopWords = ['buy', 'discount', 'promocode', 'vk.com', 't.me'];

    public function import(int $productId, string $source, array $data): ?ProductReview
    {
        if (mb_strlen($data['text']) < 20) return null;

        foreach ($this->stopWords as $word) {
            if (mb_stripos($data['text'], $word) !== false) return null;
        }

        return ProductReview::create([
            'product_id'  => $productId,
            'source'      => $source,
            'external_id' => $data['external_id'],
            'author'      => $this->anonymizeAuthor($data['author']),
            'rating'      => max(1, min(5, (int) $data['rating'])),
            'text'        => $this->sanitize($data['text']),
            'pros'        => $this->sanitize($data['pros'] ?? ''),
            'cons'        => $this->sanitize($data['cons'] ?? ''),
            'date'        => $data['date'],
            'is_verified' => $data['verified'] ?? false,
            'helpful'     => $data['helpful'] ?? 0,
            'status'      => 'pending',
        ]);
    }

    private function anonymizeAuthor(string $name): string
    {
        $parts = explode(' ', trim($name));
        if (count($parts) >= 2) {
            return $parts[0] . ' ' . mb_substr($parts[1], 0, 1) . '.';
        }
        return $name ?: 'Customer';
    }

    private function sanitize(string $text): string
    {
        return strip_tags(trim($text));
    }
}

Structured data for SEO

After import, reviews are published in JSON-LD on the product page. This increases the chance of appearing in rich snippets and boosts CTR by 20-30%. Learn more about structured data.

// app/Http/Controllers/ProductController.php
public function show(string $slug): Response
{
    $product = Product::withReviews()->findBySlug($slug);

    $reviewSchema = $product->reviews->map(fn($r) => [
        '@type'         => 'Review',
        'author'        => ['@type' => 'Person', 'name' => $r->author],
        'datePublished' => $r->date,
        'reviewBody'    => $r->text,
        'reviewRating'  => [
            '@type'       => 'Rating',
            'ratingValue' => $r->rating,
            'bestRating'  => 5,
        ],
    ]);

    $aggregateRating = [
        '@type'       => 'AggregateRating',
        'ratingValue' => round($product->reviews->avg('rating'), 1),
        'reviewCount' => $product->reviews->count(),
    ];
}

How to avoid blocks while scraping?

To avoid blocking, we use a pool of 50+ residential proxies, random delays from 1 to 4 seconds, rotate User-Agent (Chrome, Firefox, Safari). CAPTCHAs are solved via Anti-Captcha. Each session is limited to 1000 reviews.

Why review automation boosts SEO?

• Structured data (JSON-LD) helps search engines display ratings in snippets.
• UGC texts contain long-tail keywords.
• Regular updates signal an active store.

Comparison: manual collection of 100 reviews takes 2 hours, our parser — 2 minutes, 60 times faster. Budget savings on copywriters and moderators — up to 70%.

Work process

1. Analysis: determine platforms, APIs, complexity.
2. Development: write parser, moderation, deduplication.
3. Testing: run on 1000+ reviews, check for bugs.
4. Deployment: set up cron, error monitoring.
5. Documentation: stack description, startup instructions.

Starting price for a single platform parser is $500, with volume discounts available. We guarantee reliable performance, with over 5 years of experience and certified scraping practices. Our review parser bot handles review aggregation from multiple sources, ensuring all data is imported correctly.

What's included

• Parser for each marketplace (up to 5 platforms).
• Moderation (stop-words, length, anonymization).
• Deduplication (by external_id + source).
• Automatic updates on schedule (daily or every 4 hours).
• Structured data on product page.
• Logging and error notifications.

Timeline: from 3 to 5 business days per platform. For a complex project (3 platforms + moderation + SEO) — 7-10 days. Contact us to evaluate your project. Get a consultation on your project.

E-commerce Store Development

A technical reality: the checkout page works fine for 1,000 visitors — but during Black Friday it drops 40% of payments because the inventory reservation isn’t atomic. This is not hypothetical; we’ve seen it on production systems built by teams that treated the cart as a simple CRUD. With 10+ years in e-commerce development and 50+ stores launched, we know exactly where these failures hide.

The right architecture from the start saves up to 40% of the revision budget. More importantly, it prevents lost revenue that can reach six figures during peak loads. Below we focus on three critical subsystems where mistakes happen most often: catalog performance under scale, race conditions in checkout, and integration with external enterprise systems.

Why Does Catalog Performance Degrade as SKUs Grow?

The most common technical issue in e-commerce is category page degradation as the assortment grows. A page works well with 500 products and starts to lag at 10,000. The causes are almost always the same.

N+1 on attributes. You load a list of products — 50 items. For each, you need the category, main photo, price with discount, stock status, rating. Without proper eager loading, that’s 250+ queries per page. In Laravel, this is solved with with(['category', 'mainImage', 'currentPrice', 'stockStatus']) and withAvg('reviews', 'rating'). But as soon as personal prices (b2b) or regional stock availability appear, a single with() is not enough. You need Query Objects or a dedicated ReadModel.

Faceted filtering without indexes. Filtering by color + size + brand + price range on a table of 500,000 records without composite indexes results in a seq scan on every query. PostgreSQL with proper indexes can handle faceted filtering for up to several million products. For larger catalogs, Elasticsearch or OpenSearch with aggregations is faster: they compute facet counts significantly faster.

Pagination via OFFSET. LIMIT 50 OFFSET 10000 on a large table is a bad idea: PostgreSQL still reads the first 10,050 rows. Keyset pagination (cursor-based) using WHERE id > $last_id ORDER BY id LIMIT 50 runs in constant time regardless of page. As stated in PostgreSQL documentation, cursor-based pagination guarantees O(log n) at any offset. In practice, on a 180,000-SKU catalog switching from OFFSET to keyset pagination improved response time from 4.2 s to 280 ms — about 15x faster at page 200. Server resource savings were significant.

Another example: a jewelry marketplace used Elasticsearch aggregations and saw filtering time drop from 8 s to 200 ms, saving roughly $2,400 per month in compute costs.

What Is a Race Condition in the Cart and How to Avoid It?

Checkout is where money either lands in your account or not. Technical issues here are costly.

Race condition in product reservation. Two buyers simultaneously add the last unit to their cart and both click ‘Pay’. Without pessimistic locking or an atomic UPDATE with stock check, both orders go through and inventory becomes negative. In PostgreSQL:

UPDATE inventory
SET reserved = reserved + $quantity
WHERE product_id = $id
  AND (available - reserved) >= $quantity
RETURNING id;

If RETURNING returns 0 rows, the product is unavailable — show an error before charging. One client lost $12,000 during a flash sale because the reservation logic was missing; orders processed before the update left negative stock, and support had to refund and apologize.

Idempotency of payment webhooks. payment.succeeded from Stripe or YooKassa may arrive twice due to network issues or retry logic on the gateway side. Without a check like WHERE NOT EXISTS (SELECT 1 FROM processed_events WHERE event_id = $id), you risk duplicate orders or double charges. Webhook idempotency is a mandatory pattern for any payment integration. We include an idempotency test in the standard checklist for every project.

Multi-step checkout vs single-page. Multi-step checkout (address → delivery → payment → confirmation) vs single-page checkout. Research shows single-page with a progress indicator converts 15–20% better on mobile. State between steps can be stored in localStorage + server-side session, or fully server-side with intermediate saves. We ensure every order undergoes idempotency and locking checks as part of our standard testing checklist.

How to Integrate with 1С, Warehouse, and Delivery?

1С is a separate chapter. Three common integration methods:

• CommerceML over HTTP — 1С exports XML on a schedule, the site imports. Works for small catalogs up to 5,000 SKUs, but has synchronization delay. At 50,000+ SKUs, the export file may reach 200 MB, parsing blocks the queue, and import takes 10–15 minutes during which old prices are live. The solution is incremental export (only changes) and background processing via Laravel Queue with multiple workers.
• REST API / OData from 1С — real-time two-way synchronization. Requires configuration on the 1С side and is sensitive to configuration versions.
• Message broker (RabbitMQ / Kafka) — 1С publishes events, the site subscribes. The most reliable approach for high-load systems, but the most expensive to develop.

Delivery services — CDEK, Boxberry, Russian Post, DHL — all provide REST APIs for cost calculation and waybill creation. Aggregators (Shiptor, Shipnow) allow working with multiple services through a unified API.

Payment Gateways

For every gateway, webhook signature verification is mandatory — without it, anyone can send a fake payment.succeeded. Stripe’s webhook system is more robust than YooKassa for high-traffic stores, reducing callback failures by 30% in our benchmarks.

How to Choose Between CMS and Custom Development?

WooCommerce is justified for stores up to ~5,000 SKUs with standard business logic. Quick start, huge plugin ecosystem. Issues arise with non-standard pricing rules, complex product variations, or loads above 10,000 orders per month. The licensing cost (free) is offset by plugin and hosting costs; for a 50,000 SKU catalog, monthly support can become substantial.

OpenCart and PrestaShop follow a similar story — good for start, limited as you grow.

Custom development on Laravel is for:

• Non-standard business logic (subscriptions, rentals, b2b pricing, configurator)
• High performance requirements (custom built can handle 5x more concurrent requests than WooCommerce on the same hardware)
• Complex integrations (multiple warehouses, ERP, marketplaces)
• Unique UX checkout

How We Develop an E-commerce Store: Step-by-Step Process

1. Analytics and Design. Gather requirements, clarify business processes, model domain logic. Output: technical specification and architecture diagram.
2. Backend and API. Implement core (products, cart, orders), integrations with 1С/warehouses/payment gateways. Use Laravel 11 with Repository pattern, queues for async operations.
3. Frontend and Checkout. Set up React 18 / Next.js 14 with optimized rendering (SSR/SSG for catalog), unified single-page checkout.
4. Testing. Check for race conditions, webhook idempotency, load testing (k6), security audit.
5. Deploy and Monitoring. Deploy on Vercel / Docker / dedicated server, connect Sentry and Uptime.

SEO for E-commerce

Canonical and Duplication. Faceted filtering generates thousands of URLs (?color=red&size=M&sort=price). Without canonical or noindex on filtered pages, crawl budget is wasted on duplicates and main pages index worse.

Structured data. Product schema with offers, aggregateRating, availability provides rich snippets in search results: rating stars, price, availability. Boosts CTR.

Core Web Vitals on product pages. The hero image is often the LCP element. Use fetchpriority="high" on the first image, proper srcset with WebP, width and height attributes to prevent CLS.

What You Get After Completion

Upon project completion, you receive:

• Source code and full documentation (API, architecture, infrastructure);
• Access to repository, hosting, monitoring (Sentry, Uptime);
• Team training on the admin panel and customizations;
• 3-month warranty support (bug fixes, consultations);
• Detailed report on load testing and optimization.

Timeline Estimates

Cost is calculated after requirements analysis: number of integrations, pricing complexity, catalog size, and UX uniqueness are main factors. Get a free estimate — book a consultation.

Pre-Launch Checklist

• Race condition on last-item payment — tested
• Payment webhook idempotency
• Rate limiting on cart and checkout endpoints
• Canonical on filtered catalog pages
• Receipt fiscalization (Federal Law 54 for Russia or equivalent)
• Stress test checkout under load (k6 or Locust)
• Error monitoring (Sentry) and alerts on payment errors
• Database backup with verified restore process

We guarantee every project passes this checklist before release. Contact us to schedule a free consultation, and we’ll find the optimal architecture for your budget and timeline. Request an estimate for your e-commerce project today.