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GeoIp2\Model\City Object
(
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    [continent:protected] => GeoIp2\Record\Continent Object
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
                    [2] => connectionType
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                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
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        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
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                            [zh-CN] => 哥伦布
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            [validAttributes:protected] => Array
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    [location:protected] => GeoIp2\Record\Location Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [latitude] => 39.9625
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            [validAttributes:protected] => Array
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                    [1] => accuracyRadius
                    [2] => latitude
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                    [7] => postalConfidence
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        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [validAttributes:protected] => Array
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                    [0] => code
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    [subdivisions:protected] => Array
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [iso_code] => OH
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                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
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                    [validAttributes:protected] => Array
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)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
How-AI-Tracks-Cross-Platform-Price-Anomalies-in-UAE-Noon-vs-Amazon-ae-01

Introduction

As Japan’s digital economy continues to thrive, Rakuten has emerged as a major force in online retail. For businesses looking to scale in this region, real-time data from Rakuten is a goldmine of insights. From pricing trends to seller strategies, the ability to Extract Rakuten Website Data can drastically improve strategic planning. But manually tracking data from thousands of listings is impractical and inefficient. That’s where automated Rakuten product scraping comes into play.

With a focus on Product Availability Solutions, companies can extract and analyze live data to optimize inventory, pricing, and competitor positioning. From seller ratings to shipping trends, marketplace intelligence Japan depends heavily on actionable, granular Rakuten data.

In this blog, we’ll explore how to leverage Rakuten data scraping tools effectively, backed by real use cases, 2020–2025 eCommerce growth statistics, and proven strategies for building competitive advantage in Japan.

Extracting Product Listings: The First Step to eCommerce Intelligence

The foundation of marketplace intelligence in Japan begins with comprehensive Rakuten product listings extraction. As Japan's leading eCommerce platform, Rakuten offers millions of products across categories, making it a prime source for consumer behavior insights and product demand forecasting.

Manually collecting listing data is time-consuming and incomplete. Instead, automated Rakuten product scraping enables businesses to gather structured product information such as titles, SKUs, descriptions, categories, brand details, and listing formats. With this structured data, companies can quickly analyze product availability, performance, and category saturation.

According to Statista, Japan’s eCommerce revenue grew from $114.7 billion in 2020 to a projected $170 billion in 2025. Rakuten alone contributes over 25% of this volume. Tracking what’s listed—and how—is critical for companies entering or scaling in Japan.

Table 1: Growth of Japan's eCommerce Market (2020-2025)
Year eCommerce Revenue (USD Billion)
2020 114.7
2021 126.5
2022 138.3
2023 150.1
2024 160.0
2025 170.0

Using a product scraper for Rakuten Japan, businesses can detect new product launches, identify top-selling SKUs, and assess the popularity of features like free shipping or bundle offers. This is particularly valuable for brands conducting competitive product analysis and assortment benchmarking.

Another benefit of listing extraction is discovering under-served niches. For instance, in 2023, high-growth categories included eco-friendly home products and domestic skincare brands. By analyzing these trends from Rakuten data, brands can optimize listings, improve SEO, and position their products where demand is rising.

Furthermore, structured listings data enables mapping category depth and diversity. For example, some categories may have thousands of listings with high saturation, while others remain relatively underserved.

Table 2: Sample Category Saturation on Rakuten (2023)
Category Total Listings Top Brand Share (%)
Mobile Accessories 56,000 22%
Organic Skincare 12,500 18%
Home Decor 33,200 25%
Gaming Accessories 24,700 30%

In summary, extracting Rakuten product listings is the critical first step in building a strong data-driven strategy. It sets the stage for deeper analysis in pricing, seller activity, and consumer engagement—delivering the insights needed to compete effectively in Japan’s eCommerce market.

Price Monitoring: Stay Competitive with Real-Time Rakuten Pricing

Understanding market-driven pricing is essential for retailers to stay competitive. Through automated tools for Rakuten price and inventory tracking, companies can monitor the latest price changes across categories and sellers. This data becomes a foundation for strategic pricing decisions that drive profitability.

In Rakuten’s dynamic environment, prices are updated frequently based on seasonality, competitor actions, and stock availability. Relying on static snapshots or manual methods results in missed opportunities and slow responses to market shifts. Instead, Rakuten product scraping enables real-time monitoring of thousands of SKUs daily.

By comparing historical price trends from 2020 to 2025, brands can gain foresight into promotional cycles and price sensitivity.

Table 3: Average Price Drop % by Category (Rakuten 2020-2025)
Year Electronics Apparel Home Goods Skincare
2020 14% 10% 12% 9%
2021 16% 12% 13% 11%
2022 18% 13% 14% 12%
2023 20% 15% 16% 13%
2024 22% 16% 17% 14%
2025 24% 18% 18% 15%

Retailers using scrape Rakuten product data tools have been shown to react 40% faster to competitor promotions and can increase conversion rates by 22% on average.

Using Extracting Product Prices from Rakuten, retailers can:

  • Detect underpriced competitors in real-time
  • Identify seasonal pricing drops
  • Benchmark against category leaders

In addition to price, inventory tracking enables smarter restocking decisions. For example, if a high-selling product frequently goes out of stock with competitors, a well-stocked inventory creates a sales opportunity.

Table 4: Stockout Incidence by Product Type (Rakuten 2023)
Product Category Stockout Rate (%)
Smartwatches 18%
Organic Groceries 21%
Bluetooth Speakers 16%
Japanese Cosmetics 25%

As Japan’s eCommerce sector grows, staying ahead with accurate, real-time pricing data will define who leads and who lags. Marketplace intelligence Japan begins with competitive pricing awareness, and Actowiz Solutions delivers the tools that make that possible.

Stay ahead of the curve—use real-time Rakuten price monitoring to outsmart competitors, optimize margins, and boost your eCommerce performance in Japan today!
Contact Us Today!

Seller Benchmarking: Analyzing Competitor Strategies

One of the most overlooked opportunities in data extraction is evaluating the behavior and performance of top competitors. Extracting seller data from Rakuten Japan enables businesses to identify which sellers are dominating specific categories, how they differentiate themselves, and what their customer feedback reveals.

Understanding seller patterns is essential for scraping Rakuten for competitor analysis. By extracting data points like seller rating, shipping time, return policy, and frequency of updates, businesses can benchmark their operations and improve customer satisfaction.

Table 5: Top Seller Attributes Comparison (Rakuten 2023)
Seller Category Avg. Rating Avg. Delivery Time Return Window
Electronics 4.6 2.3 days 10 days
Health & Beauty 4.7 1.9 days 14 days
Home Appliances 4.5 3.0 days 7 days
Fashion 4.4 2.5 days 15 days

Between 2020–2025, Rakuten’s seller network has grown by 40%, with top-tier sellers consistently outperforming smaller competitors by 2x in revenue.

By using marketplace scraping techniques, businesses can:

  • Identify the top-performing sellers by niche
  • Benchmark delivery times and policies
  • Analyze frequency of product updates

Marketplace intelligence Japan is enriched when you know who you’re competing with and how they operate. These insights not only allow you to close performance gaps but also help tailor your strategy to mirror or surpass leading sellers.

Inventory & Availability Monitoring: Never Miss a Sales Opportunity

Monitoring stock availability is a crucial part of ensuring sales readiness. Frequent stockouts lead to lost revenue and dissatisfied customers. With Rakuten price and inventory tracking, businesses can track not only their own availability but also detect gaps in competitor listings.

When combined with price data, inventory insights offer a complete picture of supply-demand dynamics. For example, if a competing seller is out of stock for a high-demand product, maintaining your own inventory can allow you to capture additional market share.

Table 6: Inventory Advantage Opportunity Index (2023)
Category Avg. Stockouts (per month) Revenue Opportunity Increase
Baby Products 5.1 17%
Supplements 6.3 21%
Small Kitchenware 4.7 14%
Laptop Accessories 5.8 18%

Using Product Availability Solutions, brands are able to:

  • Automate alerts for low-stock competitors
  • Adjust prices dynamically based on supply
  • Maximize product visibility in high-opportunity categories

For brands operating on thin margins, optimizing inventory based on Rakuten product scraping not only safeguards revenue but boosts market share during competitor shortages.

Pricing Trend Analysis: Forecasting for Smarter Promotions

Strategic forecasting is only as good as the historical data feeding it. With deep insights from Rakuten pricing trends, companies can uncover how prices fluctuate across time, seasons, and shopping events like Singles Day or Rakuten Super Sale.

Using Rakuten Datasets, brands can map pricing trajectories across years to plan discount events more effectively. Predictive models built on this data enable brands to anticipate when consumers are most price-sensitive.

Table 7: Rakuten Category Pricing Index (2020–2025)
Year Electronics Fashion Home Goods Beauty
2020 100 100 100 100
2021 96 97 98 98
2022 94 95 96 96
2023 92 93 95 94
2024 90 92 94 93
2025 88 90 92 91

Analyzing these trends allows brands to:

  • Time discounts for maximum ROI
  • Predict competitor moves
  • Align promotions with consumer expectations

Leveraging pricing intelligence within marketplace intelligence Japan delivers a clearer roadmap for when to launch or adjust offers, ultimately protecting profit margins while maintaining competitiveness.

Leverage Rakuten pricing trend analysis to forecast demand, plan smarter promotions, and drive conversions with data-backed strategies tailored for Japan’s eCommerce market.
Contact Us Today!

Tools and APIs: Automate and Scale Your Rakuten Scraping

Manual scraping simply isn’t scalable for businesses that want to stay competitive. Advanced tools for Rakuten scraping and monitoring enable businesses to extract large volumes of real-time data with minimal manual effort.

Through API-based systems like Extract Rakuten.jp API, companies can fetch price, availability, reviews, and seller data across thousands of SKUs daily.

Benefits of automation include:

  • Reduced human error
  • Faster data collection cycles
  • Seamless integration with BI platforms
Table 8: Scraping Efficiency: Manual vs Automated (2023)
Method Avg. Listings Processed/Day Error Rate Update Frequency
Manual 1,000 12% Weekly
Automated API 100,000 1% Hourly

By implementing automation, businesses can:

  • Maintain data freshness
  • Support daily pricing and inventory sync
  • Enable predictive analytics at scale

Rakuten product scraping at scale ensures uninterrupted insights and fuels every layer of strategic decision-making. For any company serious about marketplace intelligence Japan, automation is the backbone of a successful data strategy.

How Actowiz Solutions Can Help?

Actowiz Solutions specializes in delivering structured, real-time Rakuten datasets tailored to your business needs. Whether you're a brand, analytics firm, or digital marketer, our team provides complete solutions—from Rakuten product scraping to seller insights and competitor tracking.

We don’t just scrape Rakuten product data—we deliver it in ready-to-use formats, integrate it into dashboards, and ensure it aligns with your KPIs. Our expertise in marketplace intelligence Japan means we understand what matters—timing, accuracy, and relevance.

Whether you need daily inventory updates, category-specific pricing trends, or region-based availability metrics, Actowiz offers a full suite of scraping and automation tools that scale with your operations.

Let us handle the data—so you can focus on strategy.

Conclusion

Gaining actionable marketplace intelligence Japan requires more than just collecting raw data—it demands clean, contextual, and timely insights. With Rakuten product scraping, businesses unlock a real-time view of pricing, seller strategies, and product dynamics within one of Asia’s most competitive marketplaces.

From Rakuten product listings extraction to competitive tracking and stock analysis, Actowiz Solutions enables you to Extract Rakuten Website Data with precision and speed.

Whether you're trying to predict market trends, improve product positioning, or benchmark against local sellers, our tools give you the edge. Ready to unlock Japan’s eCommerce secrets? Contact Actowiz Solutions to get started today! You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

GeoIp2\Model\City Object
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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    [locales:protected] => Array
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            [0] => en
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.160
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

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From Raw Data to Real-Time Decisions

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Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Coffee / Beverage / D2C

Result

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Operations Manager, Beanly Coffee

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Real Estate

Result

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Real-time RERA insights for 20+ states

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“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

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“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

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Industry:

Quick Commerce

Result

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Inventory Decisions

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“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

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See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

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