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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [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
                        )

                )

        )

    [location:protected] => GeoIp2\Record\Location Object
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            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

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

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
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                    [validAttributes:protected] => Array
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                            [0] => confidence
                            [1] => geonameId
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                            [3] => names
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                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                            [0] => en
                        )

                    [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
                                )

                        )

                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
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                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

        )

    [country:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
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                            [zh-CN] => 美国
                        )

                )

        )

    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                )

        )

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

    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
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                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
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                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
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                    [19] => staticIpScore
                    [20] => userCount
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.110
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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        )

    [raw:protected] => Array
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                    [names] => Array
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                            [de] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
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                )

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                    [geoname_id] => 6255149
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                            [de] => Nordamerika
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
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                )

            [postal] => Array
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                    [code] => 43215
                )

            [registered_country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                        (
                            [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
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                )

            [subdivisions] => Array
                (
                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
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                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.110
                    [prefix_len] => 22
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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
)
Revolutionizing-Global-Tire-Business-with-Tyre-Pricing-and-Market-Intelligence

Introduction

Sainsbury's, one of the UK’s leading supermarket chains, offers a wide range of products in categories such as Food Cupboards, Drinks (non-alcoholic), Health and beauty, Household, Pet, and Home. Businesses seeking to remain competitive in retail and eCommerce often require detailed insights into these diverse categories. This case study explores how Actowiz Solutions utilized advanced data scraping techniques to provide valuable data intelligence for these categories.

Objective

Objective

To scrape and analyze product data from Sainsbury's for six key categories, enabling clients to:

  • 1. Understand pricing strategies.

  • 2. Analyze product availability and demand trends.

  • 3. Benchmark against competitors in specific categories.

Challenges

Challenges

1. Diverse Categories: Each category required different scraping methodologies to capture relevant data, such as nutritional information for Food Cupboard items or product ingredients for Health & Beauty.

2. Dynamic Web Pages: Sainsbury's frequently updates its website with new products, promotions, and stock status.

3. Regulatory Compliance: Ensuring scraping adheres to ethical guidelines and legal frameworks.

Methodology

Solution

Actowiz Solutions deployed its proprietary data scraping platform, designed for handling complex and dynamic websites like Sainsbury's.

1. Data Points Captured:
  • Food Cupboard: Product names, pricing, nutritional values, and shelf-life details.

  • Drinks (Non-Alcoholic): Product flavors, package sizes, and promotions.

  • Health & Beauty: Ingredients, certifications, and reviews.

  • Household: Product usage, eco-friendly attributes, and stock availability.

  • Pet: Pet-specific items, feeding instructions, and bundle deals.

  • Home: Furniture dimensions, warranties, and seasonal offers.

2. Technology Stack:
  • Python-based scraping tools with headless browsers for seamless data extraction.

  • Cloud-based storage solutions for handling large datasets.

  • AI-powered algorithms for data cleaning and structuring.

Key Findings

1. Food Cupboard Trends:

Subcategory Avg. Price Organic Growth (%) Gluten-Free Growth (%) Top-Selling Brand Share (%)
Cereals 2.20 +12% +15% 40%
Sauces 1.80 +8% +10% 35%
Ready-to-Eat Meals 3.50 +10% +14% 45%
  • Popular subcategories included cereals, sauces, and ready-to-eat meals.

  • Organic and gluten-free products showed consistent price increases.

  • Top-selling brands highlighted customer preference for well-established names.

2. Drinks (Non-Alcoholic):

Beverage Type Seasonal Demand (%) Bulk Promo Sales (%) Avg. Price
Flavored Water +20% 15% 1.50
Soft Drinks +30% 25% 1.20
Juices +25% 10% 2.00
  • Seasonal variations in demand for beverages like flavored waters and soft drinks.

  • Promotions for bulk purchases influenced customer buying patterns.

3. Health & Beauty:

Product Type Vegan/CF Growth (%) Private Label Share (%) Avg. Price
Skincare +18% 30% 5.00
Haircare +22% 35% 4.50
Makeup +15% 25% 7.00
  • Increased demand for vegan and cruelty-free beauty products.

  • Price-sensitive segments gravitated towards private-label items.

4. Household Products:

Product Type Eco-Friendly Share (%) Seasonal Spike (%) Avg. Price
Cleaning Supplies 40% +25% 3.00
Air Fresheners 20% +30% 2.50
Detergents 35% +20% 5.00
  • Eco-friendly cleaning products outperformed traditional counterparts.

  • Seasonal spikes in sales were noted for items like air fresheners and detergents.

5. Pet Products:

Product Type Premium Demand (%) Seasonal Spike (%) Avg. Price
Pet Food 60% +15% 4.00
Grooming Tools 20% +30% 6.00
Pet Toys 25% +25% 3.50
  • High demand for premium pet food.

  • Specialty items like grooming tools and toys gained traction during festive seasons.

6. Home Category:

Product Type Compact Furniture Sales (%) Décor Promo Sales (%) Avg. Price
Space-Saving Furniture 50% +20% 100.00
Home Décor 30% +25% 30.00
Seasonal Items 20% +15% 40.00
  • A clear rise in sales of compact furniture and space-saving solutions.

  • The popularity of home décor items surged during promotional periods.

Benefits Delivered

Outcome
1. Data-Driven Decision Making:

Clients used the insights to fine-tune their product offerings and pricing strategies.

2. Competitive Benchmarking:

The extracted data enabled businesses to understand how Sainsbury's positioned itself against competitors.

3. Improved Marketing Strategies:

Promotional campaigns were tailored based on real-time demand insights across different categories.

4. Enhanced Product Innovation:

Trends identified in categories like Health & Beauty and Pet helped clients develop new product lines.

Testimonial

"Actowiz Solutions has been an invaluable partner in helping us unlock actionable insights from Sainsbury's product data. Their advanced data scraping techniques provided us with comprehensive details across multiple categories, including Food Cupboard, Health & Beauty, and Home. The insights enabled us to refine our pricing strategies, adjust our marketing campaigns, and stay competitive in the market. Actowiz’s attention to detail, especially in capturing complex data points, ensured that we received high-quality, accurate information. We are highly impressed with their expertise and look forward to working with them on future projects."

—Johnathan Miller, Director of Market Research

Conclusion

Actowiz Solutions’ expertise in web scraping and data extraction transformed complex datasets from Sainsbury's into actionable business insights. By focusing on six major categories, the project empowered clients to stay ahead in a competitive market while maintaining agility in product and pricing strategies.

Actowiz Solutions specializes in comprehensive data scraping services tailored to your business needs. Contact us today to unlock the power of actionable insights from leading retailers like Sainsbury's! You can also reach us for all your mobile app scraping , data collection, web scraping, and instant data scraper service requirements!

From Raw Data to Real-Time Decisions

All in One Pipeline

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

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“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

★★★★★

“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

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“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

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
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 highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

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

Blinkit (Delhi NCR)

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

Appzon AirPdos Pro

Price
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Monitor Prices, Availability & Trends -Live Across Regions

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✔ Scraped Data: Price inights Top-slling SKUs

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"Actow's helped us reduce out of ststack incidents by 23% within 6 weeks"

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

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actow's helped us reduce out of ststack incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

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