We extract product details, macronutrient profiles, subscription pricing, and review sentiment from Magic Spoon. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Products & Bundles objects from magicspoon.com. All fields typed and schema-versioned.
"product_id": "MS-CER-VAR-01", "title": "Variety Pack Cereal", "product_type": "Bundle", "price_one_time": 39.0, "price_subscribe": 29.25, "in_stock": true, "bundle_size": 4, "flavour_notes": "['Fruity', 'Peanut Butter', 'Cocoa', 'Frosted']"
| # | product_id | title | product_type | price_one_time | price_subscribe | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Nutritional Macros objects from magicspoon.com. All fields typed and schema-versioned.
"product_id": "MS-CER-FRU-01", "flavour": "Fruity", "serving_size_g": 38, "calories": 150, "protein_g": 13, "net_carbs_g": 4, "sugar_g": 0, "allergens": "['Milk']"
| # | product_id | flavour | serving_size_g | calories | protein_g | net_carbs_g |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Subscriptions objects from magicspoon.com. All fields typed and schema-versioned.
"product_id": "MS-CER-VAR-01", "base_price": 39.0, "subscribe_price": 29.25, "discount_pct": 25, "delivery_frequencies": "['Every 4 weeks', 'Every 8 weeks', 'Every 12 weeks']", "cost_per_bowl": 1.95, "currency": "USD"
| # | product_id | base_price | subscribe_price | discount_pct | delivery_frequencies | cost_per_bowl |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from magicspoon.com. All fields typed and schema-versioned.
"review_id": "REV-982341", "product_id": "MS-CER-VAR-01", "author": "Sarah J.", "star_rating": 5, "review_text": "Tastes exactly like my childhood favorites but without the sugar crash.", "verified_buyer": true, "date_posted": "2023-10-14", "helpful_votes": 12
| # | review_id | product_id | author | star_rating | review_text | verified_buyer |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Availability objects from magicspoon.com. All fields typed and schema-versioned.
"product_id": "MS-CER-BRP-01", "sku": "BRP-4PK-01", "stock_status": "OUT_OF_STOCK", "limited_edition": true, "restock_date": "2023-11-01", "max_order_qty": 5, "scraped_at": "2023-10-24T08:15:00Z"
| # | product_id | sku | stock_status | limited_edition | restock_date | shipping_delay |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Magic Spoon relies on complex Shopify themes, headless commerce APIs, and dynamic subscription widgets. We bypass the frontend rendering to extract raw product, pricing, and nutritional data directly from the source.
Extract all active SKUs, bundle configurations, descriptions, and high-resolution imagery across the entire Magic Spoon storefront.
Map complex nutritional labels into structured fields: calories, protein, net carbs, sugar, ingredients, and allergen warnings per flavour.
Capture one-time pricing versus subscribe-and-save tiers, including discount percentages and available delivery frequencies.
Paginate through customer reviews to extract star ratings, text, verified buyer status, and helpful votes for sentiment analysis.
Monitor stock levels, out-of-stock flags, and maximum order quantities to gauge product velocity and supply chain health.
Track the lifecycle of limited-batch flavours from launch to sell-out, capturing availability windows and pricing premiums.
Deconstruct variety packs and build-your-own-box options to calculate implied per-box and per-bowl pricing metrics.
Bypass DOM scraping where possible by intercepting underlying Shopify GraphQL queries for cleaner, more reliable data extraction.
Run extractions on a daily or weekly cadence to build historical datasets of pricing changes, review accumulation, and stock availability.
Brief in. Clean data out.
Select the data points required: product catalogues, nutritional macros, pricing tiers, or customer reviews.
We configure Playwright crawlers to handle Shopify's dynamic rendering and subscription widgets.
Schema validation, null-rate checks, and macro consistency verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Modern DTC brands like Magic Spoon use sophisticated frontend frameworks and anti-bot protections. Here is how we ensure reliable data extraction.
Shopify employs advanced bot mitigation. We utilise US-based residential proxies and realistic browser fingerprinting to maintain high success rates without triggering rate limits or block pages.
Pricing data is often injected dynamically via third-party subscription apps. We run full Playwright sessions to execute JavaScript, ensuring we capture the exact pricing displayed to the end user.
Where possible, our crawlers intercept the underlying GraphQL requests used by the headless frontend. This provides cleaner, strictly typed data compared to parsing HTML DOM structures.
DTC brands frequently update their themes and layouts for marketing campaigns. Our extraction logic relies on multiple fallback selectors and data attributes to prevent pipeline breakage during site updates.
Every extraction run is monitored for schema drift, null-rate spikes, and missing fields. If a layout change affects data quality, our system alerts our engineers for immediate remediation.
CPG brands track Magic Spoon's bundle pricing, discount strategies, and cost-per-bowl metrics to inform their own pricing models.
Food scientists and product developers analyse macronutrient ratios and ingredient lists to understand the formulation of high-protein, low-carb products.
Investors and analysts monitor product launch frequency, limited edition drop velocity, and review volume to estimate brand growth and market penetration.
Marketing teams mine customer reviews to identify popular flavour profiles, common complaints, and language used by the target demographic.
Supply chain analysts track out-of-stock events and restock timelines to estimate production cycles and inventory management efficiency.
Strategy teams study the discount delta between one-time purchases and subscriptions to optimise their own recurring revenue funnels.
"Magic Spoon represents the modern DTC playbook: highly optimised subscription funnels, dynamic bundle pricing, and rich nutritional metadata that standard crawlers miss entirely."
Extracting data from headless Shopify builds requires intercepting GraphQL queries and rendering dynamic subscription widgets. DataFlirt handles the JavaScript execution and proxy rotation, delivering clean nutritional and pricing datasets so your team can focus on market analysis.
Everything supported by our magicspoon.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles the crawl orchestration and deduplication, while Playwright executes the JavaScript required to render headless frontend components.
We utilise high-quality residential IP pools to distribute requests, preventing IP bans and ensuring consistent access to the target site.
Pipelines run on containerised infrastructure managed by Kubernetes. Apache Airflow handles scheduling, retries, and delivery to your data warehouse.
Data delivered to where your team already works — no new tooling required.
About magicspoon.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible data such as product prices, nutritional information, and customer reviews is generally permissible. We do not extract personally identifiable information or bypass authentication walls to access gated wholesale data.
We use a combination of residential proxies, realistic browser fingerprinting via Playwright, and randomised request intervals to avoid triggering Shopify's automated security measures.
Yes. We execute the necessary JavaScript to trigger the subscription widget, allowing us to capture the base price, the discounted subscription price, and the available delivery frequencies.
We parse the nutritional tables and ingredient lists into discrete, structured fields (e.g., calories, protein_g, sugar_g) rather than returning a single block of text, making the data immediately ready for analysis.
Pipelines can be scheduled at your required cadence: daily, weekly, or monthly. For limited edition drops or inventory tracking, we can configure higher-frequency runs.
Yes. We paginate through the entire review history for each product, capturing the text, rating, author, and verified buyer status for comprehensive sentiment analysis.
Yes. We provide sample extracts during the scoping phase to ensure the schema matches your analytical requirements before moving to a production pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off nutritional database extract or continuous competitor price monitoring, we scope, build, and operate the pipeline. Tell us what you need.
