Scrape Bol.com Product and Review Data 2026 - How Netherlands Brands Track Products, Prices, and Customer Reviews.jpg
                     Scrape Bol.com Product and Review Data 2026 - How Netherlands 
Brands Track Products, Prices, and Customer Reviews
Introduction
The Netherlands has become a highly digital retail market where product 
assortment, price changes, seller competition, ratings, and customer 
reviews can change quickly. In 2025, 80.4% of people aged 12 and over in 
the Netherlands reported buying something online during the previous 
three months, compared with 71.2% in 2020.
For brands selling through major online marketplaces, manually 
monitoring these changes is difficult. Scrape Bol.com Product and Review 
Data 2026 provides a structured approach to collecting product 
attributes, seller information, prices, ratings, review content, availability, 
and related marketplace signals for analysis.
The opportunity is supported by the wider growth of Dutch online 
commerce. 
Dutch companies generated almost €31.7 billion in online consumer sales 
in 2020, while online retail continued to expand afterward. CBS data also 
shows that online retail turnover grew 5.8% in 2024, while online-only 
retailers recorded 9.0% growth.
A scalable Bol.com data scraping strategy can therefore help 
brands create continuously refreshed datasets for competitive 
intelligence, pricing decisions, assortment planning, customer 
sentiment analysis, and marketplace performance monitoring.
Building a Reliable View of Marketplace Assortment
Brands need more than product names when monitoring a marketplace. 
They need structured information covering SKU identifiers, product titles, 
categories, specifications, prices, discounts, sellers, availability, ratings, 
review counts, and other attributes. Bol.com Product Data Scraping in the 
Netherlands can transform these marketplace signals into datasets suitable 
for product intelligence and competitive analysis.
Price monitoring is particularly important because consumers can compare 
similar products within seconds. Price scraping allows businesses to record 
current prices and historical movements, helping them identify price gaps, 
promotional activity, and potential repricing opportunities.
The 2020–2026 development of Dutch e-commerce shows why continuous 
monitoring matters. Online purchases rose sharply during the pandemic and 
remained structurally high afterward. By 2025, 80.4% of Dutch residents 
aged 12+ had purchased online in the preceding three months.
Year Netherlands online purchase rate / market indicator Business implication
2020 71.2% online buyers Rapid digital adoption
2021 77.4% Strong marketplace engagement
2022 73.5% Normalisation after pandemic peak
2023 78.5% Online purchasing regained momentum
2024 80.8% Very broad digital shopping adoption
2025 80.4% Sustained high adoption
2026 Outlook: continued high adoption Greater need for automated monitoring
The figures above come from CBS; 2026 is an outlook rather than a 
reported full-year result.
For Netherlands-focused brands, the value is not simply collecting 
a larger volume of records. The objective is to create a repeatable 
product-monitoring layer that reveals which SKUs are changing, 
which competitors are gaining visibility, and where pricing 
opportunities exist.
Turning Customer Feedback into Competitive Intelligence
Product reviews provide a direct source of customer feedback. They can 
reveal recurring complaints, product strengths, packaging issues, delivery 
concerns, feature preferences, and expectations that may not appear in 
structured product specifications.
Bol.com Review Data Scraping for E-Commerce Intelligence enables 
businesses to organize review text, ratings, review dates, review counts, 
product identifiers, and other available review attributes. Once 
standardized, this information can support sentiment analysis and 
product-quality research.
The wider Dutch market provides a strong reason to monitor customer 
feedback continuously. Online buying remained above 70% of the 
population throughout 2020–2025, with the rate reaching 80.8% in 2024 
and 80.4% in 2025.
Year Online buyers in Netherlands Review-intelligence opportunity
2020 71.2% Capture pandemic-era preference shifts
2021 77.4% Identify rapidly changing expectations
2022 73.5% Track post-pandemic normalisation
2023 78.5% Detect renewed category demand
2024 80.8% Expand customer feedback monitoring
2025 80.4% Maintain continuous sentiment tracking
2026 Outlook Automate review intelligence at scale
For example, a brand can classify reviews into themes such as quality, 
usability, sizing, durability, delivery, packaging, value for money, and 
product performance. Comparing those themes across competing SKUs 
creates a much stronger intelligence layer than relying only on average 
star ratings.
Review monitoring can also identify changes earlier than sales reporting. A 
sudden increase in negative comments may indicate a product-quality 
issue, packaging problem, inaccurate product description, or customer 
expectation gap. Conversely, repeated positive comments about a 
particular feature can support product positioning and marketing decisions.
Connecting Product Records with Commercial Signals
Marketplace analysis becomes more useful when product information, 
seller information, pricing, availability, and customer feedback are 
connected at SKU level. Bol.com Product and Review Data Extraction 
creates the foundation for such a dataset, while 
E-commerce data scraping can extend the same methodology across 
other relevant digital retail sources.
The historical development of Dutch online retail demonstrates the 
importance of structured data. CBS reported almost €31.7 billion in online 
consumer sales by Dutch companies in 2020. Online retail turnover 
continued to fluctuate as the market moved beyond pandemic conditions, 
with 2024 online turnover 5.8% above 2023.
Year Relevant Dutch digital-retail signal Data priority
2020 €31.7B online consumer sales Build baseline datasets
2021 Online purchasing reached 77.4% Expand SKU coverage
2022 Online purchasing at 73.5% Track normalised demand
2023 Online purchasing at 78.5% Increase competitive monitoring
2024 Online turnover +5.8% YoY Strengthen pricing analytics
2025 80.4% online buyers Expand review intelligence
2026 Research outlook Move toward real-time datasets
The 2020 turnover figure and later online-purchase statistics are reported 
by CBS; 2026 represents a forward-looking research period rather than a 
reported full-year statistic.
A unified dataset can connect product IDs with price history, sellers, 
ratings, review volume, and availability snapshots. This allows analysts to 
ask more meaningful questions: Which products became cheaper while 
review sentiment deteriorated? Which sellers repeatedly offered lower 
prices? Which products gained reviews fastest? Which categories show 
high ratings but limited availability?
This connected approach turns isolated marketplace records into a 
decision-support dataset.
Understanding What Consumers Actually Value
Average ratings provide a quick quality signal, but they do not explain 
why customers like or dislike a product. Bol.com Review Data for 
Consumer Insights allows brands to examine the underlying language 
and themes contained within customer feedback.
The 2020–2026 period is particularly useful for longitudinal analysis 
because consumer purchasing behaviour changed substantially during 
and after the pandemic. Dutch online purchasing rose from 71.2% in 
2020 to 77.4% in 2021, fell to 73.5% in 2022, and then increased to 
80.8% in 2024.
Year Online purchase rate Potential consumer-insight focus
2020 71.2% New digital-shopping expectations
2021 77.4% Convenience and availability
2022 73.5% Value and price sensitivity
2023 78.5% Product comparison behaviour
2024 80.8% Review-driven purchase decisions
2025 80.4% Mature marketplace behaviour
2026 Outlook Predictive sentiment and demand signals
Review analytics can classify customer comments into positive, neutral, 
and negative sentiment. More advanced analysis can identify specific 
topics associated with sentiment. For instance, a product may receive 
four-star or five-star average ratings while repeatedly attracting 
complaints about packaging. Another product may have a slightly lower 
average rating but receive strong praise for durability.
This distinction matters to product managers. A simple rating tells a 
business how customers are responding; review-text analysis can help 
explain why.
Brands can also compare review themes between competing products. If 
competitor products consistently receive positive comments about 
battery life, material quality, ease of use, or delivery experience, those 
themes can influence product development and positioning.
In 2026, the strongest review-monitoring programs will increasingly 
combine structured attributes with unstructured text, enabling businesses 
to move from basic rating tracking toward customer-experience 
intelligence.
Establishing Transparent Price Benchmarks
Price competition is one of the most visible marketplace dynamics. 
Consumers can compare similar products rapidly, while sellers can adjust 
pricing based on demand, competition, inventory, promotions, and 
marketplace conditions.
Bol.com Price Benchmarking Across Sellers helps businesses build 
historical comparisons instead of relying on occasional manual checks. A 
benchmark dataset can contain product ID, seller, listed price, discount, 
promotional price, availability status, timestamp, and other relevant 
commercial fields.
Dutch online retail has experienced considerable year-to-year movement 
since 2020. CBS reported particularly strong online-retail growth during 
parts of 2020 and 2021, followed by declines in some quarters of 2022 
aPenriodd  renewed growth duriOnnlgine  2ret0ail2 sig3na–l2024. Benchmarking implication
2020 Annual online-retail index 285.3 Establish pandemic baseline
2021 Annual index 356.3 Monitor rapid expansion
2022 Quarterly volatility Increase price-change frequency
2023 Moderate recovery Compare competitive movement
2024 Q4 online sales +6.0% YoY Strengthen promotional tracking
2025 80.4% online buyers Monitor mature marketplace demand
2026 Outlook Automate continuous benchmarks
CBS data shows the online-retail index reached 356.3 in 2021 from 285.3 
in 2020, using 2015=100. The 2026 figure is intentionally presented as an 
outlook because a complete annual 2026 result is not yet available.
Price benchmarking becomes more powerful when historical records are 
preserved. A business can calculate minimum price, maximum price, 
average observed price, price volatility, promotional frequency, and seller-
level price gaps.
For manufacturers and brands, this can also support pricing governance. 
Businesses can identify potential channel conflicts, monitor reseller 
behaviour, evaluate promotional periods, and understand whether their 
products remain competitively positioned.
Measuring Reputation Beyond a Single Star Rating
Ratings and written reviews should be treated as complementary signals. 
A four-star product with thousands of reviews can represent a very 
different customer experience from a five-star product with only a handful 
of ratings.
Ratings, reviews and sentiment analysis can combine numerical scores 
with textual feedback to provide a more complete reputation picture. 
Useful metrics include average rating, rating distribution, review volume, 
review velocity, sentiment ratio, recurring complaint themes, positive-
feature frequency, and changes over time.
The Dutch e-commerce environment makes this increasingly important. 
Online purchasing remained widespread from 2020 through 2025, while 
online turnover also continued to develop after the pandemic.
Year Market indicator Reputation-analysis opportunity
2020 71.2% online buyers Establish baseline sentiment
2021 77.4% Track rapid customer adoption
2022 73.5% Identify changing expectations
2023 78.5% Compare evolving product preferences
2024 80.8% Scale review-volume analysis
2025 80.4% Strengthen sentiment benchmarking
2026 Outlook Integrate predictive reputation signals
A robust analysis can calculate the proportion of positive, neutral, and 
negative reviews and then connect those results with product categories, 
prices, sellers, and availability. This makes it possible to identify 
relationships between commercial performance and customer experience.
For example, if a product experiences a sharp increase in negative 
sentiment after a packaging change, the business can investigate the 
issue. If a competing SKU gains positive sentiment after introducing a new 
feature, product teams can evaluate whether that feature deserves greater 
emphasis.
The long-term objective is not simply to count reviews. It is to create an 
intelligence system that transforms marketplace feedback into measurable 
signals for product development, pricing, marketing, merchandising, and 
customer-experience teams.
Why Choose Product Data Scrape?
Product Data Scrape helps businesses convert marketplace information 
into structured, analysis-ready datasets that can support competitive 
research and e-commerce intelligence. The approach can combine product 
attributes, seller information, pricing, availability, ratings, and customer 
feedback into consistent records.
With Competitor Review Monitoring, brands can track how customers 
respond to their own products and competing SKUs, identify recurring 
complaints, compare sentiment trends, and discover product attributes 
frequently mentioned by customers.
For businesses planning Scrape Bol.com Product and Review Data 2026, 
the focus can extend beyond one-time extraction toward scheduled 
collection, historical datasets, data validation, normalization, and 
analytics-ready delivery.
A structured data pipeline can also support dashboards, pricing 
intelligence, catalog analysis, review monitoring, and category-level 
research. Instead of manually visiting product pages, analysts receive 
organized information that can be filtered, compared, enriched, and 
integrated into internal workflows.
The result is a scalable marketplace intelligence framework designed 
around business questions rather than raw page collection.
Conclusion
The Netherlands' strong online-shopping adoption makes marketplace 
intelligence increasingly valuable. From 2020 to 2025, the share of Dutch 
residents aged 12+ purchasing online rose from 71.2% to 80.4%, 
demonstrating how deeply digital commerce has become embedded in 
consumer behaviour.
For brands, marketplace monitoring should therefore combine product 
information, prices, seller activity, availability, ratings, and review 
content. Turn Customer Reviews into Actionable Business intelligence by 
connecting customer feedback with product and commercial signals 
rather than analyzing reviews in isolation.
A structured approach to Scrape Bol.com Product and Review Data 2026 
can help businesses benchmark competitors, understand customer 
sentiment, identify pricing opportunities, monitor assortment changes, 
and improve marketplace decision-making.
As e-commerce competition becomes increasingly data-driven, the 
advantage will belong to businesses that can transform continuously 
changing marketplace information into reliable, timely, and actionable 
intelligence.
Start building a structured Bol.com product, pricing, and review dataset 
with Product Data Scrape to turn marketplace signals into smarter e-
commerce decisions!