Core services
Enterprise Data Extraction

Scalable web, app and AI-powered collection across 40+ countries.

All 58 services →
New 2026
AI Training Data

Corpus building with provenance and opt-out compliance.

Learn more →
Free pilot
24-hour sample

We run collection on your own sources before you commit.

Get a sample →
58Services
40+Countries
DEVELOPER

Ready-Made Scrapers

Pre-built for top platforms. Self-serve, no setup.

View All →
TRY FREE

API Playground

Test endpoints instantly. No credit card.

Start Free →
28Tools
2SDKs
icons Delivery & SDKs
Streaming Crawl API Scheduler Realtime Alerts Webhook Delivery 🐍 Python SDK 💚 Node.js SDK
Need it managed instead?

Fixed monthly retainer, named engineer, no per-request metering.

Managed Data API →
HOT

Case Studies

How brands use Actowiz, with named outcomes.

Read →
FREE

Sample Datasets

Real output, no signup.

Download →
NEW

ROI Calculator

Model the return on a data engagement.

Calculate →
Platform · Jumbo

Jumbo Data Scraping

A published price guarantee means some price movements are someone else's decision arriving late.

Jumbo data scraping collects pricing, promotions and availability across this Dutch grocer. The feature that shapes the analysis: the retailer operates a published lowest-price guarantee. When a price moves, it may be the retailer's own decision or a mechanical response to a competitor, and nothing in the listing distinguishes them.

This is the same problem our Colruyt page describes in Belgium, and it has the same two consequences: attribution stays open, and the refresh has to be higher.

Free pilot on your own Jumbo list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

jumbo_2026-08-25.jsonl LIVE FEED
{"retailer":"jumbo","product_id":"jb-44120", "price":2.19,"prior_price":2.49, "price_changed_at":"2026-08-25T11:20+02:00", "price_change_attribution":"not_determined", "panel_schedule_id":"nl-grocery-sync", "observation_interval_minutes":120} {"retailer":"competitor-a","price":2.19, "price_changed_at":"2026-08-25T08:05+02:00", "note":"moved 3h15m earlier. THAT is the finding — and only a shared schedule shows it"} {"guarantee_invoked":"not_determinable", "deposit_amount":0.25, "caution":"price alignment is NOT evidence of a match. two retailers can land there independently"}
3 of 884,220 product rows · Netherlandsattribution constant not_determined · sub-daily · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Jumbo or its owners. Jumbo and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall
Jumbo at a glance

How we handle Jumbo specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Retailer
Jumbo — Netherlands
The feature
A published lowest-price guarantee
Consequence one
Price change attribution is not determinable
Consequence two
A higher refresh than usual, because sequence is the analysis
Competitor set
Must be observed on the same schedule
Own label
Several ranges, strong penetration
Statiegeld
Deposit as a separate field, never folded in
Refresh
Daily minimum. Sub-daily where sequence matters
Platform specifics

What a price guarantee does to a dataset

These are the reasons a Jumbo dataset needs its own handling rather than a shared retail schema.

Attribution is not determinable, and we say so

Where a retailer publicly commits to matching or beating competitor prices, a price movement has at least two possible causes:

  • An own pricing decision, reflecting its own strategy.
  • A mechanical response to a competitor's move, arriving with whatever lag the process has.

A listing shows the new price. It does not show which. So we set price_change_attribution to a constant not_determined rather than letting an analyst assume the first.

Why that constant matters

Without it, a competitive analysis reads a series of price cuts as a strategic campaign. Some of them are a guarantee executing, which is a different thing — it says something about the competitor rather than about this retailer.

This is the same position our Colruyt page takes, and it generalises: any retailer with a published matching policy needs this field, and most datasets do not carry it.

Which means the competitor set and the schedule are the engagement

If some movements are responses, then the sequence is the analysis — who moved first, and how long the response took.

That has two design consequences:

  • The competitor set must be collected too. Observing this retailer alone shows you responses with no stimulus.
  • They must be on the same schedule. Retailers observed hours apart produce apparent leads and lags that are entirely timing artefacts.

So a Jumbo engagement is usually scoped as a synchronised panel rather than a single retailer, with panel_schedule_id shared and observation_interval_minutes recorded.

And a higher refresh than we would normally recommend

Our cadence page argues that frequency should follow observed price movement, and that we frequently recommend lower than a client asks. This is the opposite case.

Where response lag is the question, a daily observation can only resolve lags to the nearest day — which for a same-day matching process is no resolution at all. Sub-daily is genuinely warranted here.

Dutch conventions, and what we do not produce

Statiegeld

Container deposits apply and are refundable. deposit_amount and deposit_type are separate fields, never folded into the price — as on our Albert Heijn page.

Unit pricing

Displayed unit price captured, ours computed alongside, disagreement flagged.

Own label

Several ranges across tiers, flagged with tier from range naming, unmatched across retailers.

What we do not produce

  • An attribution for a price change. The constant says not determined, and we do not model one.
  • Whether a guarantee was invoked on a specific product. Not published.
  • Margin impact of the guarantee. Requires cost data nobody publishes.
  • Loyalty account data, sales, customer or employee data.

The second one is worth naming. It would be easy to infer a match from a price aligning with a competitor's. Alignment is not evidence of a match — two retailers can arrive at the same price independently, and frequently do on branded lines.

Scope

What we collect on Jumbo, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

  • price_change_attribution as a constant not_determined
  • Competitor set collected on the same schedule where the engagement needs sequence
  • panel_schedule_id shared across the panel
  • observation_interval_minutes recorded
  • Sub-daily recommended where response lag is the question
  • deposit_amount and deposit_type as separate fields
  • Unit price computed by us alongside the displayed one
  • Own label with tier, unmatched across retailers
  • Promotional mechanics as displayed

❌ What we do not, and why

  • A price change attributed to the retailer's own strategy
  • A guarantee invocation inferred from price alignment
  • Margin impact of the guarantee, which needs cost data
  • A competitor set observed on a different schedule
  • Deposit folded into the product price

Core Jumbo fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field What it is on this platform
retailer / store_id / surface The retailer, store and channel
product_id / ean Identifiers where published
price / price_vat_basis As displayed
price_change_attribution Constant not_determined
prior_price / price_changed_at The movement, with its timestamp
deposit_amount / deposit_type Separate. Never in the price
pack_size / price_per_unit / unit_basis Computed by us
displayed_unit_price / unit_price_matches Theirs, and whether it agrees
is_own_label / own_label_tier From range naming
panel_schedule_id / observation_interval_minutes So sequence is interpretable
observed_at Timestamp, precise enough to sequence
Use cases

What teams do with Jumbo data

Response-lag analysis across a synchronised panel

This retailer and its competitor set on one schedule with precise timestamps, which is the only design that resolves who moved first and how long the response took.

Competitive pricing without false attribution

Attribution left open as a constant, so a series of cuts is not read as a strategic campaign when some of it is a guarantee executing.

Dutch market benchmarking

Deposits separated and unit prices computed on a stated basis, so Dutch prices compare correctly against non-deposit markets.

Own-label positioning in the Netherlands

Own label flagged with tier, matched within the retailer only, in a market where own label is a primary competitive lever.

The 24-hour sample — run on your sources, not ours

Send us a Jumbo item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

  • Real extraction from your actual sources
  • Returned within 24 hours
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Jumbo is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Jumbo data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what grocery data scraping covers, and a Jumbo-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Jumbo data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because the listing shows the new price and not the cause. With a published price guarantee, a movement may be the retailer's own decision or a mechanical response to a competitor.

We set price_change_attribution to a constant not_determined so an analyst does not assume the first. Some of those cuts say something about the competitor rather than about this retailer.

By collecting the competitor set on the same schedule, so the sequence is visible — who moved first and how long the response took.

Observing this retailer alone shows you responses with no stimulus, which is not an analysis.

Because retailers observed hours apart produce apparent leads and lags that are entirely timing artefacts. On a matching question that artefact can exceed the lag being measured.

We share panel_schedule_id across the panel and record the observation interval.

Yes, which is unusual for us — our cadence page argues that we frequently recommend lower than a client asks.

This is the opposite case. Where response lag is the question, a daily observation resolves lags only to the nearest day, which for a same-day process is no resolution at all.

No. It is not published, and we do not infer it from price alignment.

Alignment is not evidence of a match — two retailers can arrive at the same price independently, and frequently do on branded lines.

We quote individually, and this usually costs more than a single-retailer engagement because the competitor set and a higher refresh are both part of making it useful.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Jumbo data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

Social Proof That Converts

Trusted by Global Leaders Across Q-Commerce, Travel, Retail, and FoodTech

Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.

4,000+ Enterprises Worldwide
50+ Countries Served
20+ Industries
Join 4,000+ companies growing with Actowiz →
Real Results from Real Clients

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

1 min
★★★★★
"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"
TG
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
2 min
★★★★★
"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."
II
Iulen Ibanez
CEO / Datacy.es
1:30
★★★★★
"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."
FC
Febbin Chacko
-Fin, Small Business Owner
icons 4.8/5 Average Rating
icons 50+ Video Testimonials
icons 92% Client Retention
icons 50+ Countries Served

Join 4,000+ Companies Growing with Actowiz

From Zomato to Expedia — see why global leaders trust us with their data.

Why Global Leaders Trust Actowiz

Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.

icons
7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
icons
4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
icons
200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
icons
9.2M
Automated Workflows
Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
icons
270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
icons
380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
icons Product Matching icons Attribute Tagging icons Content Optimization icons Sentiment Analysis icons Prompt-Based Reporting

Connect the Dots Across
Your Retail Ecosystem

We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

icons
Analytics Services
icons
Ad Tech
icons
Price Optimization
icons
Business Consulting
icons
System Integration
icons
Market Research
Become a Partner →

Popular Datasets — Ready to Download

Browse All Datasets →
icons
Amazon
eCommerce
Free 100 rows
icons
Zillow
Real Estate
Free 100 rows
icons
DoorDash
Food Delivery
Free 100 rows
icons
Walmart
Retail
Free 100 rows
icons
Booking.com
Travel
Free 100 rows
icons
Indeed
Jobs
Free 100 rows

Latest Insights & Resources

View All Resources →
thumb
Blog

How the US Grocery Price Inflation Tracker 2026 Helps Retailers Manage Rising Food Costs and Pricing Decisions

Track the US Grocery Price Inflation Tracker 2026 to monitor food price trends, category changes, and inflation insights for smarter decisions.

thumb
Case Study

How Brands Leverage KSA Hungerstation Menu Pricing Scraping API for Real-Time Food Delivery Intelligence

Explore KSA Hungerstation Menu Pricing Scraping API to track menu prices, competitor changes, and food delivery market trends in Saudi Arabia.

thumb
Report

Namshi Fashion & Beauty Data Intelligence

Namshi Fashion & Beauty Data Intelligence helps brands track prices, products, availability, assortment, and trends for smarter MENA market decisions.

Start Where It Makes Sense for You

Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

icons
Enterprise
Book a Strategy Call
Custom solutions, dedicated support, volume pricing for large-scale needs.
icons
Growing Brand
Get Free Sample Data
Try before you buy — 500 rows of real data, delivered in 2 hours. No strings.
icons
Just Exploring
View Plans & Pricing
Transparent plans from $500/mo. Find the right fit for your budget and scale.
Get in Touch
Let's Talk About
Your Data Needs
Tell us what data you need — we'll scope it for free and share a sample within hours.
  • icons
    Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment.
  • icons
    Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
  • icons
    US-Based SupportOffices in New York & California. Aligned with your timezone.
  • icons
    ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.
Request Free Sample Data
Fill the form below — our team will reach out within 2 hours.
+1
Free 500-row sample · No credit card · Response within 2 hours

Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

+1
Free 500-row sample · No credit card · Response within 2 hours