Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
Actowiz Metrics Now Live!
logo
Unlock Smarter , Faster Analytics!
 
GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

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

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

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

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

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

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [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
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

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

            [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:protected] => Array
        (
            [0] => en
        )

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

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

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

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

            [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
                        )

                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

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

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

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [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
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.184
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

        )

    [raw:protected] => Array
        (
            [city] => 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
                        )

                )

            [continent] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [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] => 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
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

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

            [registered_country] => 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
                        )

                )

            [subdivisions] => Array
                (
                    [0] => 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
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.184
                    [prefix_len] => 22
                )

        )

)

 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
)

Start Your Project

+1 ▼
  • Home
  • Work
  • Web Scraping for Lean Six Sigma Data - HelloFresh Datasets

Case Study - Web Scraping for Lean Six Sigma Data - HelloFresh Grocery Datasets

A case study on using web scraping for Lean Six Sigma data from HelloFresh grocery datasets for process optimization insights.

Download Case Studies

✓ Valid

Note: You’ll receive it via email shortly after submitting the form.

Web-Scraping-for-Lean-Six-Sigma-Data-HelloFresh-Grocery-Datasets

Overview

In the competitive food service industry, operational efficiency and quality control are crucial for success. Lean Six Sigma methodologies focus on improving processes, reducing waste, and enhancing customer satisfaction. HelloFresh, a leading meal-kit delivery service, requires vast amounts of operational and grocery data to refine its services and maintain high standards. Businesses can gather valuable insights from HelloFresh’s data to drive Lean Six Sigma initiatives by leveraging web scraping.

This case study explores how Actowiz Solutions helped a company perform Lean Six Sigma data scraping by scraping HelloFresh internal data. The project involved HelloFresh operational data extraction to collect data on ingredients, pricing, order patterns, and delivery performance. Analyzing this data enabled the company to optimize processes, minimize waste, and improve overall efficiency. Organizations can gain actionable insights to enhance service quality and operational effectiveness through effective web scraping for Lean Six Sigma data.

Challenge

Challenge

HelloFresh operates a complex system of grocery deliveries, meal kit preparation, and supply chain management. To manage its operations more efficiently and stay ahead of competitors, HelloFresh needed a data-driven approach. Traditional methods of gathering data were time-consuming and insufficient to provide real-time insights.

The goal was to collect detailed data on:

Grocery inventory and supply patterns

Order fluctuations across regions

Delivery time performance

Pricing strategies for ingredients and meal kits

This data was necessary to apply Lean Six Sigma principles, aiming for process improvements, cost reduction, and enhanced customer satisfaction.

Solution

Solution

Actowiz Solutions deployed web scraping technology to extract critical operational data from HelloFresh’s grocery datasets. Using advanced scraping techniques, we were able to collect structured and unstructured data on:

Order volumes and ingredient usage to track demand trends

Pricing of grocery items and meals to refine pricing strategies

Delivery times and regional performance to optimize logistics

Menu updates and cooking instructions to ensure consistency and quality control

When scrape HelloFresh internal data such as product lists, meal configurations, and delivery schedules, we provided the client with real-time operational data crucial for Lean Six Sigma process improvements.

Approach

Approach

Actowiz Solutions followed a structured approach to gather Lean Six Sigma data through:

Data Extraction: Using advanced tools for Web Scraping HelloFresh Food Delivery Data from their grocery and meal kit platforms, focusing on ingredients, pricing, and delivery details.

Data Cleaning and Structuring: After extraction, the data was cleaned and formatted to ensure it was ready for analysis. This included normalizing grocery data, filtering delivery times, and categorizing meal kits.

Integration with Lean Six Sigma Tools: The extracted data was analyzed using Lean Six Sigma software. Metrics such as order accuracy, waste reduction, and delivery performance were monitored.

Ongoing Monitoring: Web scraping was set up as a continuous process, allowing the client to receive updated data in real time for ongoing process improvements.

By leveraging this structured methodology, Actowiz Solutions enabled the client to extract food delivery and menu data and scrape it, facilitating continuous improvement in their operational processes.

Results

Results

By leveraging Actowiz Solutions’ web scraping services, the client achieved significant improvements in operational efficiency:

Waste Reduction: By analyzing ingredient usage data from HelloFresh’s grocery datasets, the client minimized overstocking, which reduced food waste by 18% in the first quarter.

Improved Delivery Performance: Scraping delivery performance data allowed for route optimization, reducing average delivery time by 12%.

Pricing Strategy Optimization: Data on ingredient costs and customer preferences enabled the client to adjust pricing strategies, resulting in a 9% increase in profit margins.

Customer Satisfaction: Through data-driven adjustments in menu offerings and delivery schedules, customer satisfaction improved by 15%.

Case Study: Lean Six Sigma in Action

Case-Study-Lean-Six-Sigma-in-Action-01

A Lean Six Sigma project using HelloFresh operational data uncovered inefficiencies in the company’s grocery supply chain. By extracting real-time grocery data and integrating it with Lean Six Sigma methodologies, the client identified patterns of over-ordering certain ingredients while understocking others.

By implementing the insights from the HelloFresh grocery store dataset, the client reduced stockouts by 22%, improved order accuracy by 17%, and optimized their supply chain, ultimately boosting efficiency and customer satisfaction.

Use Cases

Optimizing Ingredient Supply: Using food data scraping services, the client could accurately forecast ingredient demand, leading to a balanced inventory and reduced waste.

Improving Delivery Times: By scraping HelloFresh food delivery data, the client identified regions where delivery times were consistently slower. This led to improved delivery scheduling and faster service.

Pricing Strategy Adjustments: Data on ingredient costs from HelloFresh’s grocery datasets enabled the client to implement dynamic pricing strategies, enhancing profitability.

Menu and Recipe Adjustments: Food cooking data scraping from HelloFresh menus helped refine recipe offerings based on customer preferences and ingredient availability.

Key Takeaways

Data-Driven Insights for Lean Six Sigma: By extracting HelloFresh operational data, businesses can streamline operations, reduce waste, and enhance process efficiency using Lean Six Sigma techniques.

Scraping for Real-Time Monitoring: Continuous web scraping allows businesses to stay ahead of operational challenges by receiving real-time grocery inventory, deliveries, and pricing data.

Cost Savings and Efficiency: Data scraping in the food service industry can significantly reduce costs related to overstocking, delivery delays, and inefficient pricing strategies.

Conclusion

Web scraping has proven valuable for businesses implementing Lean Six Sigma methodologies in the food service industry. With access to real-time data from platforms like HelloFresh, businesses can optimize their operations, reduce waste, and improve customer satisfaction.

Actowiz Solutions provides industry-leading web scraping services tailored to the food delivery industry. These services allow businesses to extract critical data for operational efficiency and success. From HelloFresh grocery store datasets to food delivery and menu data, we help companies achieve Lean Six Sigma goals with accurate, real-time data.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize
Explore Solutions Get a Custom Demo

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.

Ecommerce
Q-Commerce
Travel & OTA
Food Delivery
Real Estate
Social Media
Trusted by Global Leaders – Secured by International Standards

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
₹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

Request Demo Access icon

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

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All
Blog
Case Studies
Infographics
Report
thumb

Tracking 5M+ SKUs Across 50 E-commerce Sites for Real-Time Price & Stock MonitoringSystem

tracked 5M+ SKUs across 50 e-commerce sites for real-time price & stock intelligence to empower global retail strategy.
thumb

Enterprise Price Intelligence: Building a Global Multi-Platform Scraping System

Actowiz Solutions builds global multi-platform scraping systems for enterprise price intelligence, enabling real-time competitive and market insights.
thumb

K-Beauty Market Intelligence: How Naver & Coupang Data Shaped a Turkish Importer’s Product Strategy

weekly tracking of job role demand via Indeed & LinkedIn in Chicago, analyzing hiring trends, role popularity, and market demand shifts.
thumb

How Compare Product Prices Weekly For Local Retail Stores Improved Sales and Customer Retention

Discover how our solution to Compare Product Prices Weekly For Local Retail Stores helped boost sales, enhance customer loyalty, and maintain competitive pricing effectively.

Aug 15, 2025

Enterprise Price Intelligence: Building a Global Multi-Platform Scraping System

Actowiz Solutions builds global multi-platform scraping systems for enterprise price intelligence, enabling real-time competitive and market insights.

Aug 14, 2025

Healthcare Review Analytics – Turning Patient Feedback into Insights

Actowiz Solutions turns patient reviews into actionable healthcare insights using AI-powered review scraping, sentiment analysis, and trend tracking.

Aug 13, 2025

How AI Job Market Data Scraping is Shaping U.S. Hiring Trends

Discover how AI-driven job market data scraping is transforming U.S. hiring trends with real-time insights, salary benchmarks, and demand forecasting from Actowiz Solutions.

Aug 12, 2025

E-commerce Price Wars: Amazon vs Walmart – Weekly Price Analytics

Discover how weekly price analytics reveal the Amazon vs Walmart e-commerce battle. Actowiz Solutions shares real-time price trends and competitive insights.

Read More
thumb

Tracking 5M+ SKUs Across 50 E-commerce Sites for Real-Time Price & Stock Monitoring

tracked 5M+ SKUs across 50 e-commerce sites for real-time price & stock intelligence to empower global retail strategy.

thumb

K-Beauty Market Intelligence: How Naver & Coupang Data Shaped a Turkish Importer’s Product Strategy

See how Actowiz Solutions used Naver & Coupang data to identify trending K-Beauty SKUs, prices, and stock insights for the Turkish cosmetics market.

thumb

How Compare Product Prices Weekly For Local Retail Stores Improved Sales and Customer Retention

Discover how our solution to Compare Product Prices Weekly For Local Retail Stores helped boost sales, enhance customer loyalty, and maintain competitive pricing effectively.

Read More
thumb

India's Holiday Travel Surge Raksha Bandhan to Independence Day 2025

Explore India's holiday travel trends during the 2025 festive season—from Raksha Bandhan to Independence Day. Discover top destinations, travel tips, and booking insights.

thumb

2025 Buying Behavior Trends: Smarter Retail Decisions Backed by Data

Explore retail buying behavior trends for 2025 with data-driven insights on Gen Z, AI, omnichannel shopping, and smart decisions from Actowiz Solutions.

thumb

Real-Time Price Monitoring & Benchmarking on Amazon & Walmart for Smarter eCommerce

Use real-time price monitoring to benchmark Amazon & Walmart prices, avoid MAP violations, and power your eCommerce intelligence with Actowiz Solutions.

Read More
thumb

Weekly Tracking of Job Role Demand via Indeed & LinkedIn in Chicago

weekly tracking of job role demand via Indeed & LinkedIn in Chicago, analyzing hiring trends, role popularity, and market demand shifts.
thumb

Monthly Tracking of Property Prices in NYC via Realtor.com

monthly tracking of property prices in NYC, using Realtor.com data to analyze market trends, price shifts, and neighborhood-level changes.
thumb

Weekly Uber Eats Data Tracking of Vendor Activity in New York

Analyze vendor trends with Weekly Uber Eats data in New York, tracking menus, pricing, and activity for strategic food delivery insights.
Read More