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GeoIp2\Model\City Object ( [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] => 216.73.216.126 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [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 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.126 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
We live in an era where the hassle of cooking after a late workday or sudden cravings for a quick snack are quickly resolved by ordering food from our favorite eateries. This convenience has never been more accessible.
In terms of numbers, the food delivery market is projected to reach $5 billion in 2019 and is expected to surge to $15 billion by 2023. With Swiggy and Zomato leading in India's Tier-I and Tier-II cities, the food delivery market is set for rapid expansion. Leverage Zomato Food Delivery Data Scraping Services for a competitive edge and valuable insights in this dynamic industry.
According to a report from the data intelligence platform KalaGato, as of the first half of 2018, Swiggy held a dominant market share of 36.40%, with FoodPanda closely following at 32.02%. Zomato, despite having a smaller market share of 23.78%, has made its global presence felt by operating in more than 300 cities.
Several variables, such as current traffic conditions, the speed of order preparation, and the efficiency of delivery personnel, play a pivotal role in shaping the growth of food-tech companies. The crucial importance of time and distance becomes evident at different junctures within the delivery process.
The era of using maps solely for finding locations is long gone. Nowadays, food-tech companies harness location data to enhance different aspects of their operations. Let's explore a few of these applications!
FoodTech firms leverage location data to assist customers in discovering new restaurants in their vicinity. They achieve this by identifying restaurants capable of accepting orders and delivering them within a specified maximum delivery time, a concept known as "serviceability."
The key challenge lies in pinpointing the optimal time for guaranteed delivery and offering a diverse selection of restaurants to calculate delivery times swiftly. These companies prioritize enhancing the customer experience above all else. If the estimated delivery time is excessively lengthy or users must be presented with sufficient restaurant options, they may opt against placing an order altogether.
After placing an order via your mobile device, the closest delivery driver is assigned to your order. Numerous calculations are involved in this process, including determining the distance between the delivery executive's current location and the restaurant and estimating the time required to reach your delivery destination.
Occasionally, a scenario arises where a restaurant receives two orders from customers near each other. In such instances, "batching" is employed, and a single delivery driver is designated to deliver both orders.
Two orders are considered batchable when the estimated delivery times for all orders within a batch align with the delivery time commitments made to each customer.
Last-mile delivery encompasses more than just the route from the restaurant to the delivery destination. Food-tech enterprises utilize their proprietary mapping systems to precisely compute the projected delivery time, which encompasses even the duration taken by the Delivery Executive to cover the "penultimate mile" (for instance, the time needed to travel from the community entrance to the customer's door).
To meet their requirements at scale, they harness historical data and real-time signals to develop and enhance their mapping solutions.
Food delivery demands a delicate balance between customer satisfaction and operational efficiency. Food Delivery Data Intelligence helps streamline operations and enhance customer experiences in this complex industry. This equilibrium extends to optimizing time, expenses, and routes, even when confronted with unavoidable obstacles such as inclement weather, traffic disruptions, and decreased available delivery personnel.
This objective is attained by minimizing the idle time of delivery executives, achieved through reduced waiting times at restaurants (while meals are being prepared) and minimizing the duration during which delivery personnel awaits their next assigned order.
The advent of "cloud kitchens" has brought a significant revolution to the market, particularly in terms of their size and technological enhancements, particularly in last-mile delivery.
The combination of location intelligence and artificial intelligence empowers cloud kitchens to forecast customer purchasing trends effectively and strategically position inventories within the nearest cloud kitchens to areas with a higher demand for home delivery.
The rise of "cloud kitchens" has stirred a significant transformation in the market, both in scale and technological advancements. Last-mile delivery has become a focal point of innovation.
Leveraging location intelligence and artificial intelligence, these cloud kitchens are now adept at forecasting customer purchasing patterns. They strategically position their inventories in the nearest cloud kitchens to cater to areas with higher demand for home delivery.
Burger King implemented real-time geofencing in conjunction with location intelligence to identify customers within a 600-foot radius of their outlets. This approach allowed them to provide digital coupon discounts for orders placed at any of their locations.
In this manner, they optimize customer engagement, effectively boosting awareness and efficiently acquiring new customers.
As consumers become increasingly comfortable sharing their data, food-tech companies are capitalizing on this opportunity by leveraging real-time location information to target customers at precisely the right moments.
Location-based advertising offers several advantages, including access to better, real-time data and a remarkably high level of engagement. For instance, in regions primarily known for lunch orders, such as college campuses or commercial areas, these companies can promote breakfast offers from various food establishments.
Moreover, companies are harnessing location intelligence to determine zone-specific cancellation rates, analyze the balance between food demand and supply, evaluate the availability of delivery personnel in an area, and gain insights into the fluctuating volumes of deliveries and restaurant foot traffic.
Eatigo, a renowned restaurant reservation platform, harnesses location intelligence to provide discounts to customers, taking into account their geographic location, time of day, and additional factors such as the day of the week and prevailing weather conditions.
The objective is to ascertain whether a customer is inclined to travel a certain distance in order to avail substantial discounts. For instance, an individual might be willing to journey approximately 10 kilometers for a significantly attractive discount offer.
Businesses actively monitor real-time traffic data to gain insights into the comparative levels of customer foot traffic at their establishments versus their competitors.
These widely recognized "quick-service" brands are utilizing real-time traffic information to obtain enhanced insights into factors such as the allocation of consumer spending, visits to competitor stores, and customer loyalty trends.
In conclusion, Actowiz Solutions is at the forefront of revolutionizing the food delivery industry by strategically implementing geospatial data. By providing food delivery companies with cutting-edge tools and solutions, Actowiz empowers businesses to optimize operations, increase efficiency, and enhance the overall customer experience. Using geospatial data has become an indispensable asset in the competitive food delivery landscape, enabling companies to make data-driven decisions, improve route planning, and precisely target customers. As we look towards the future, it is clear that Actowiz Solutions is paving the way for food delivery companies' continued growth and success, offering them the tools they need to thrive in a dynamic and evolving marketplace.
For more information about Food Delivery and Geospatial Data, contact Actowiz Solutions now! You can also reach us for all your mobile app scraping, instant data scraper and web scraping service services requirements.
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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.”
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Real Estate
Real-time RERA insights for 20+ states
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Data Analyst, Aditya Birla Group
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Organic Grocery / FMCG
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
Quick Commerce
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
3x Faster
improvement in operational efficiency
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Business Development Lead,Organic Tattva
✓ Weekly competitor pricing feeds
Beverage / D2C
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
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.”
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Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
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
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
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