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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [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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                            [zh-CN] => 美国
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                    [longitude] => -83.0061
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            [postal] => Array
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [subdivisions] => Array
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                                    [fr] => Ohio
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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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                    [code] => NA
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                    [names] => Array
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                            [de] => Nordamerika
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                            [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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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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                    [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
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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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
                (
                    [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
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [0] => confidence
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        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.141
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
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                    [10] => isp
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
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                )

        )

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

    [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
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                    [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
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        )

    [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
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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
)
Actowiz Solutions – Hyperlocal Price Shift Detection on Instamart Using AI-Powered Scrapers

Introduction

In India’s fast-moving quick commerce space, pricing isn’t static—it changes by the hour, city, and even pin code. Instamart (Swiggy’s Q-Commerce vertical) serves millions with localized pricing strategies. But for FMCG brands and competitors, understanding these fluctuations at a hyperlocal level is complex.

Actowiz Solutions developed and deployed an advanced AI-powered scraping system to detect, track, and analyze SKU-level price shifts on Instamart in real-time—enabling accurate benchmarking, dynamic pricing, and competitive market response.

Challenges Faced by Brands & Retailers

The-Client
  • No visibility into hyperlocal price differences across cities or delivery zones
  • Manual price audits failed to capture hourly or daily price fluctuations
  • Brands couldn’t track their own SKU price changes across Instamart zones
  • Lack of structured data for pricing trends vs. stock vs. promotional offers
  • No alerts for sudden price drops or flash deals

The Client

  • Track product pricing by pin code on Instamart across top 10 cities
  • Detect real-time price fluctuations across SKUs and time windows
  • Visualize pricing trends, price wars, and promotional timelines
  • Enable alerts for competitor price drops or changes
  • Deliver hourly updates via dashboards or APIs

Actowiz’s AI-Driven Scraping and Analytics Approach

Actowiz’s-AI-Driven-Scraping-and-Analytics-Approach
1. High-Frequency Scraping Infrastructure

Deployed scrapers that extract data every 30–60 minutes from Instamart’s public-facing interfaces, capturing:

  • Product Name + Brand
  • SKU Unit & Variant
  • Listed Price & Offer Price
  • Discount %
  • Stock Status
  • Pin Code / City
  • Timestamp
2. Pin Code–Level Intelligence

Instamart offers different prices even within the same city based on delivery pin codes. Actowiz’s system structured this data by:

Timestamp SKU Pin Code MRP Offer Price Discount In Stock
2025-06-10 08:00 Surf Excel 1kg 400001 ₹235 ₹199 15% Yes
2025-06-10 08:00 Surf Excel 1kg 400053 ₹235 ₹225 4% Yes
3. AI Models Used for Price Pattern Detection
  • K-Means Clustering – Grouped pricing patterns by product category & geography
  • Time-Series Analysis (ARIMA, LSTM) – Forecasted future price shifts & spike detection
  • Anomaly Detection (Isolation Forest) – Flagged outliers like flash price drops
  • Classification Models – Predicted promotion-driven vs. demand-driven price changes
4. Sample Data – Real-World Case

Tracking Maggie 12-Pack Instant Noodles:

Date City Pin Code Offer Price % Drop Restock Frequency Stock Status
2025-06-10 Mumbai 400054 ₹135 -10% 12 hrs In Stock
2025-06-10 Mumbai 400076 ₹150 -2% 8 hrs Low Stock
2025-06-11 Mumbai 400054 ₹140 +3.7% 12 hrs In Stock

Insights showed that pricing volatility was higher in high-demand zones near colleges and offices.

Key Dashboard Features

Actowiz provided the client with a rich dashboard interface powered by hourly data and AI insights:

Feature Description
Price Heatmaps City-wise and pin code-wise color-coded price variance
SKU Price Timeline Hour-by-hour price chart for each product
Competitor Alerts Real-time notifications when rival brand SKUs drop prices
Promotion Detection Flags algorithm-driven pricing likely linked to campaigns
Export & API Feed CSV export and JSON API integration for client tools

Supported Cities and Zones

Top cities covered with hyperlocal granularity:

  • Mumbai (Dadar, Andheri, Bandra, Powai, etc.)
  • Delhi NCR (South Delhi, Noida, Gurugram, Rohini)
  • Bengaluru (Koramangala, Whitefield, Indiranagar)
  • Pune, Hyderabad, Ahmedabad, Kolkata, Chennai

Total pin codes monitored: 1,100+

Business Impact Delivered

Metrics After 60 Days of Integration:
KPI Before Actowiz After Actowiz
Price Monitoring Frequency Weekly Manual Hourly AI-based
SKU-Level Price Drop Alerts None 2,500+ flagged
Average Pricing Accuracy (by SKU) 45% 94%
Brand Response to Competitor Drops Delayed <1 hour
Campaign Optimization Uplift - +22% ROI

Sample Insights Delivered

  • Nestlé detected Instamart price drops on competing brands every Friday evening in tech hubs
  • Parle saw its biscuit pack listed at 12% less in Ahmedabad zones than Mumbai—adjusted campaign regionally
  • Fortune Oils prices dipped only in Bengaluru on Sundays—Actowiz helped align influencer campaigns accordingly

Client Testimonial

“Actowiz's real-time Instamart tracking saved us from losing market share. Their AI alerts are faster than anything we’ve used before—pin code intelligence is a game-changer.”

— Category Manager, Leading FMCG Brand India

Extending the Use Case

Now expanding this model to:

  • Detect Zepto and Blinkit hyperlocal pricing
  • Integrate WhatsApp-based alerts for field sales teams
  • Add regional language labels for city-specific marketing
  • Train AI models on discount campaign predictions based on past patterns

Conclusion

Actowiz Solutions is redefining hyperlocal intelligence with AI and data scraping. In a market where prices change every hour and by every neighborhood, having access to real-time, pin code–specific price detection is invaluable for brands, marketing teams, and sales operations.

From pricing strategy alignment to smarter campaigns, this case study proves how AI scraping helps brands win the quick commerce race—one pin code at a time.

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

Actowiz Insights Hub

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