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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
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                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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        )

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

        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [validAttributes:protected] => Array
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => 43215
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    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [iso_code] => OH
                            [names] => Array
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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
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                        )

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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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                    [2] => names
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6255149
                    [names] => Array
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                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
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                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

        )

    [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
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                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
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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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                    [iso_code] => US
                    [names] => Array
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                            [en] => United States
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                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [validAttributes:protected] => Array
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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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                    [5] => type
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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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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                    [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
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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        )

    [raw:protected] => Array
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                            [de] => Columbus
                            [en] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [continent] => Array
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                    [geoname_id] => 6255149
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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
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                )

            [country] => Array
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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
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [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
                        )

                )

            [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
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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
)
Case-Study-AI-Models-for-Predicting-Inventory-Restocks-on-Blinkit-&-Zepto

Overview

In the fast-paced world of quick commerce (Q-commerce), efficient inventory management is vital. Blinkit and Zepto, two leading players in India’s 10-minute grocery delivery ecosystem, face constant pressure to keep shelves stocked without overburdening storage. Actowiz Solutions deployed advanced AI models backed by real-time web scraping and historical pattern analysis to accurately forecast inventory restocks—ensuring zero missed sales and reduced wastage.

Challenges

Before partnering with Actowiz Solutions, both retailers faced:

The-Client
  • Stockouts during high-demand periods, leading to revenue losses.
  • Excess inventory of non-moving SKUs due to inaccurate demand prediction.
  • Lag in vendor restock alerts, especially in pin code-based delivery zones.
  • Lack of SKU-level granularity in forecasting replenishment windows.
  • Manual coordination with warehouse ERP for perishable goods.

Objective

The-Client
  • Predict restocking needs of high-frequency SKUs across cities.
  • Build a zone-wise AI model trained on pricing, order rate, and stock visibility.
  • Reduce product unavailability alerts on Blinkit and Zepto apps by 70%.
  • Improve vendor coordination based on near-term demand patterns.

Actowiz’s Approach

1. Real-Time Scraping Engine
The-Client

Using Blinkit and Zepto's front-end interfaces, Actowiz set up a high-frequency scraping system that extracted:

  • Product availability by pin code
  • Stock status (e.g., "Only 2 Left", "Out of Stock")
  • Price changes and discounts
  • Reappearance timestamp of previously out-of-stock SKUs
  • Location-wise category rank
2. Historical Dataset Modeling

Actowiz gathered 6 months of SKU availability snapshots (every 30 minutes) for key cities like:

  • Delhi NCR
  • Mumbai
  • Bengaluru
  • Pune
  • Hyderabad

Sample data structure:

Date City SKU In Stock Quantity Tag Reappeared After (hours)
2025-05-12 Mumbai Amul Toned Milk 1L No - 4
2025-05-12 Mumbai Amul Toned Milk 1L Yes "Only 3 left" -

This data was used to train time-series models for each product + pin code combination.

3. Machine Learning Models Used
  • LSTM Neural Networks for SKU restock time prediction
  • Random Forest Classifiers for binary classification (restock likely/not likely in next 12 hours)
  • Gradient Boosted Trees for multi-variate regression on stockout duration
  • Bayesian Inference for estimating restocking frequency during promotions
4. Integrating External Signals

The AI models were enhanced with:

  • Blinkit & Zepto app promotions
  • Festival dates & traffic spikes (e.g., Diwali, Independence Day)
  • Weather data for fresh produce perishability
  • Vendor-wise replenishment cadence

Key Features of Actowiz’s AI System

Feature Description
SKU-Level Prediction Hourly probability of restock for individual SKUs by city
Stockout Alert System Dashboard alert for high-demand SKUs nearing depletion
Price vs. Restock Analysis Predict restocking lag based on recent discounts or flash sales
Vendor Mapping Correlate restock speed with past vendor delivery times
City-wise Heatmaps Visualize restocking rates across hyperlocal zones
Dashboard Snapshot (Sample)

A dynamic dashboard powered by Actowiz displayed:

For Blinkit – Andheri West, Mumbai
  • Top 10 SKUs predicted to be out-of-stock in next 4 hours
  • Estimated replenishment time (in hrs)
  • Historical avg. restock window
SKU Name Stock Status ETA to Replenish Demand Surge (%)
Tropicana Orange 1L Low (Only 2) 3.5 hours +42%
Britannia Bread 400g Out of Stock 5.2 hours +37%
Dettol Handwash 250ml Low 2.0 hours +18%

Business Impact

Operational Wins:
Metric Before Actowiz After 90 Days
Average Stockout Rate 17.3% 4.5%
SKU-Level Forecasting Accuracy 62% 89%
Reduction in Missed Orders - 31% increase
Vendor Coordination Delay 8 hours avg. 2.5 hours
Manual Alerts 100+ daily Auto AI-based

Client Testimonial

"Actowiz’s predictive inventory system transformed how we plan replenishments. We no longer chase restocks manually. Instead, we’re proactively ready for every demand surge."

– Inventory Head, Blinkit (Mumbai Ops)

Expansion Plan

Actowiz is now extending this AI restocking solution to:

  • Regional warehouses of Zepto in Tier 2 cities like Indore, Kochi, Lucknow
  • Integration with dark store partners
  • Predictive insights for perishable SKUs with <24hr shelf life
  • Zepto Café’s inventory for combo meals & snacks

Conclusion

This case highlights how real-time data scraping, when paired with AI/ML modeling, can unlock powerful restocking predictions in hyperlocal delivery models. For Blinkit and Zepto, Actowiz Solutions helped move from reactive inventory handling to predictive precision—ensuring consumers always find what they need when they want it.

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
₹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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