DEV Community: Crawler Bros

Travel demand intelligence: tracking Airbnb, search, and news signals with Apify

Crawler Bros — Wed, 02 Sep 2026 11:08:26 +0000

Travel demand intelligence: tracking Airbnb, search, and news signals with Apify

Travel demand usually becomes visible online before it appears in a quarterly tourism report.

It shows up in Airbnb prices. It shows up in new listings. It shows up in Google searches. It shows up in local news about events, airline routes, hotel openings, and visa changes.

Travel demand intelligence is the practice of combining these public signals — short-term rental supply and pricing, search intent, and local news — into a single destination-level dataset, instead of waiting for a lagging industry report. An Apify workflow can build that dataset automatically using three Actors: Airbnb Scraper, Google Search Results Scraper, and Google News Scraper.

Quick answer

Use Airbnb Scraper to collect short-term rental supply, prices, ratings, host data, coordinates, and photos. Combine it with Google Search Results Scraper for search visibility and Google News Scraper for event and market signals. Schedule runs to compare demand indicators across locations over time.

The travel signal stack

The workflow pulls from:

Airbnb Scraper — listings, prices, ratings, host info, coordinates, photos, and date-specific availability.
Google Search Results Scraper — travel-related SERP visibility and People Also Ask questions.
Google News Scraper — local events, tourism news, and market mentions.

The signal chain:

\Target destinations -> Airbnb Scraper -> Google Search Results Scraper -> Google News Scraper -> travel demand snapshot \\

Short-term rental investors, travel startups, tourism marketers, and market researchers can use the same dataset to compare destinations without waiting for lagging reports.

Key facts

3 Actors, 1 schedule: Airbnb Scraper, Google Search Results Scraper, Google News Scraper can all run on a recurring Apify schedule.
Date-aware pricing: passing explicit checkIn/checkOut dates to Airbnb Scraper is what makes prices comparable across destinations — undated scraping only gives directional numbers.
Neighborhood beats city: coordinate-level clustering surfaces local demand patterns that city-wide averages hide.
Three signal types, one dataset: supply (Airbnb), intent (Search), and context (News) — combining all three avoids mistaking a cheap destination for a good market.

Actor configuration that matters

For Airbnb, start with a location or search URL, then keep the date window explicit. Date-aware scraping matters because travel prices move by season, weekend, and event. Set a listing cap during discovery and only expand once the location query looks right.

\json { "locationQueries": ["Lisbon", "Porto", "Valencia"], "checkIn": "2026-05-15", "checkOut": "2026-05-18", "maxListings": 100, "proxyConfiguration": { "useApifyProxy": true } } \\

For Google Search Results Scraper, use location-specific travel queries and country/language settings. For Google News, pair destination names with event and tourism keywords, then use a recent dateRange such as 7d or 1m.

What the output looks like

For Airbnb, the base row needs price, location, and quality signals:

\json { "title": "Sunny apartment near the old town", "location": "Porto, Portugal", "price": 142, "currency": "EUR", "rating": 4.86, "reviewsCount": 128, "hostType": "Superhost", "coordinates": { "lat": 41.1579, "lng": -8.6291 }, "url": "https://www.airbnb.com/rooms/..." } \\

Search and news rows add intent and context. The same destination can have average rental prices, high search interest, and weak news coverage — seeing those signals together is what makes the workflow useful.

In a small Airbnb Scraper run for Austin with a two-night September date window, the Actor returned 18 listing rows, including id, url, name, type, price, originalPrice, priceQualifier, rating, latitude, longitude, photos, searchUrl, and scrapedAt.

Airbnb as a supply and pricing signal

The Airbnb Scraper extracts listings for a location and can include check-in and check-out dates for more accurate pricing. Useful fields include:

property title
nightly or trip price
rating
review count
host details
Superhost status
coordinates
photos
property type

For market research, density and price distribution matter most. A neighborhood with many high-rated listings and rising prices may indicate strong demand. A location with lots of listings but weak reviews may indicate opportunity or oversupply.

Search results as an intent signal

Search data helps explain what travelers are trying to figure out. Example queries worth running:

"best area to stay in Lisbon"
"things to do in Medellin"
"family hotels in Orlando"
"Airbnb near Formula 1 Austin"

Google Search Results Scraper returns organic results, related queries, People Also Ask questions, country and language targeting, and pagination. People Also Ask data is especially useful because it shows recurring traveler concerns.

News as an event signal

News often explains demand spikes. Google News Scraper can monitor:

festivals
conferences
sports events
new flight routes
visa policy changes
safety issues
hotel openings
tourism board campaigns

If Airbnb prices rise and local news shows a major event, the story becomes clearer.

The destination scorecard

Turn the raw data into a simple destination scorecard. It isn't meant to be a perfect forecast — it's meant to rank markets for deeper investigation. The scorecard includes:

number of Airbnb listings found
median listed price for the selected dates
share of listings with strong ratings
review count distribution
density by neighborhood or coordinate cluster
number of recent news mentions
major events mentioned in news
People Also Ask questions from Google Search
visible travel guides ranking in search

This mix helps avoid one-source thinking. A destination with high Airbnb prices but weak review volume may be thinly supplied. A destination with lots of listings and many recent event mentions may be entering a busy season. A destination with many search questions about safety or transportation may need better traveler education.

For short-term rental investors, this doesn't replace underwriting — it narrows the map. For travel marketers, it shows which questions to answer in content before the season peaks.

Example workflow

Suppose you want to compare Lisbon, Porto, and Valencia for a spring travel campaign. Run Airbnb Scraper for the same date window in each city, collect Google Search results for "where to stay" and "things to do" queries, then pull Google News results from the last 30 days.

The output shows more than average price — it shows which neighborhoods have dense listing supply, which destinations have recent event coverage, and which traveler questions appear repeatedly. If "is Porto walkable" keeps appearing in People Also Ask, that question belongs in the content plan. If Valencia has a sudden cluster of news around a festival, that belongs in the demand notes.

The workflow works because it combines supply, intent, and context — that combination keeps you from mistaking a cheap destination for a good market.

Production notes

Use dates. Travel pricing without check-in and check-out dates is directional. Date-specific scraping gives better price comparisons.

Compare neighborhoods, not only cities. City-level averages hide local patterns. Coordinates make neighborhood clustering possible.

Track review counts over time. Review growth can indicate booking activity even when occupancy isn't public.

Watch news for shocks. Weather, regulation, safety, and events can change demand quickly.

Keep raw data and derived metrics separate. Store original listing prices and calculate averages downstream.

Compare neighborhoods, not cities, from the start. An early version of this workflow compared cities too broadly — Lisbon vs. Porto looked interesting, but neighborhood-level clusters were far more useful. Travel demand is local: a city average hides the difference between a tourist center, a residential neighborhood, and an event district.

Cost comparison

Approach	Best for	Weakness
Manual travel research	One destination	Slow and subjective
STR analytics SaaS	Occupancy estimates	Can be expensive
Apify workflow	Custom location and event monitoring	Requires an analysis model

Check the current Pricing tabs for Airbnb Scraper, Google Search Results Scraper, and Google News Scraper before running at scale — date windows, listing limits, and full-text extraction settings affect run cost.

FAQ

Can Apify scrape Airbnb listings?

Yes. Airbnb Scraper (built by CrawlerBros) extracts public listing data, pricing, ratings, host info, coordinates, and photos.

Can I track travel demand with scraping?

Yes — you can track public demand proxies such as listing prices, review growth, search questions, and news events. This doesn't replace official tourism statistics, but it's available in near-real time, unlike quarterly reports.

Is this only useful for Airbnb investors?

No. Tourism marketers, travel startups, hotel analysts, and event planners can all use the same three-signal approach (supply, intent, context) for their own destination research.

What's the fastest way to try this?

Pick three destinations and one date window. Scrape Airbnb listings, run travel-related Google searches, and collect local news from the last 30 days, then put the data side by side.

Try it yourself

Pick three destinations and one date window. Scrape listings with Airbnb Scraper, run travel-related queries with Google Search Results Scraper, and pull recent coverage with Google News Scraper. Put the data side by side — the strongest market is rarely the one with the loudest travel blog posts.

Unlocking Boxing Insights: Scraping Fighter Stats & Records

Crawler Bros — Wed, 02 Sep 2026 09:36:45 +0000

The Challenge: Getting Actionable Boxing Data Without the Headache

For sports analysts, fantasy sports enthusiasts, content creators, or even developers building boxing-related applications, accessing comprehensive and structured boxing data can be a major hurdle. Imagine you're a sports journalist preparing an article comparing top heavyweights, or a developer needing a reliable list of all boxing weight divisions for an app. Manually digging through various sports websites is tedious, time-consuming, and often leads to inconsistent data formats. How do you efficiently gather detailed fighter profiles, their nationality, fight records, and even their physical attributes like height and weight, without writing complex custom scrapers or paying for expensive premium APIs?

The answer lies in specialized data extraction tools that simplify this process. Rather than wrestling with web scraping complexities, you can leverage pre-built solutions designed to deliver the exact information you need, fast and reliably.

Introducing the Boxing Stats Scraper: Your Ringside Data Assistant

The Apify Boxing Stats Scraper is a powerful and easy-to-use actor designed to extract professional boxing fighter profiles, weight divisions, and career data directly from TheSportsDB's free public API. This actor streamlines the data collection process, allowing you to focus on analyzing and utilizing the information rather than spending hours on data acquisition.

It's categorized under 'AUTOMATION', 'DEVELOPER_TOOLS', and 'INTEGRATIONS', making it a versatile tool for various professional needs.

How Does it Solve Your Boxing Data Problems?

The Boxing Stats Scraper offers two distinct modes to address common data needs:

1. Need to Find a Specific Boxer's Profile? Use 'search' mode.

If you're looking for detailed information on a particular fighter, the search mode is your go-to. By providing a searchQuery (the boxer's name), the actor will find the specified fighter and return their profile data. This is incredibly useful for researchers tracking individual careers, journalists fact-checking fighter statistics, or developers needing specific fighter details for a database.

Example Use Case: A fantasy boxing league creator wants to get the latest stats for "Tyson Fury". They would set the mode to 'search' and searchQuery to "Tyson Fury".

2. Need a List of All Boxing Weight Divisions? Use 'teams' mode.

For those requiring a comprehensive list of boxing weight divisions, the teams mode is perfect. This feature allows you to quickly list all available boxing weight classes without needing to scour multiple sources. This is invaluable for app developers creating categorization systems, analysts studying trends across divisions, or content creators explaining the structure of professional boxing.

Example Use Case: A sports analytics firm is building a new application that needs to display all official boxing weight divisions. They would set the mode to 'teams'.

What Data Can You Expect?

When you run the Boxing Stats Scraper, the output records are rich with essential information. Each record includes:

fighterId
fighterName
team
nationality
position
height
weight
birthDate
imageUrl
profileUrl

This structured data makes it incredibly easy to integrate into your spreadsheets, databases, or applications, saving you the effort of manual data entry and formatting.

Input Parameters: Tailoring Your Data Extraction

The actor's input schema makes it simple to customize your data requests:

mode (string, required): This is the fundamental choice between 'search' (for individual boxers) and 'teams' (for weight divisions).
searchQuery (string): Use this field when your mode is 'search' to specify the boxer's name.
weightClass (select): This allows you to filter your results by a specific weight class, further refining your data extraction.
maxItems (integer): Control the number of records returned, from 1 to 200. This is useful for managing the volume of data you receive.

It's important to note that no API key is required to use this actor, as it leverages TheSportsDB's free tier. This means you can get started immediately without any additional setup or registration.

How to Use the Boxing Stats Scraper

Getting started with the Boxing Stats Scraper is straightforward:

Navigate to the Actor: Find the "Boxing Stats Scraper" actor on the Apify platform.
Select Your Mode: Choose either 'search' or 'teams' from the mode dropdown.
Enter Search Query (if applicable): If you selected 'search' mode, type the boxer's name into the searchQuery field.
Set Max Items (optional): Specify the maxItems you wish to retrieve (between 1 and 200).
Run the Actor: Click the "Run" button to start the data extraction process.
Download Your Data: Once the run is complete, you can download your structured boxing data in various formats.

Beyond the Ring: Why Structured Data Matters

The power of an actor like the Boxing Stats Scraper extends far beyond just retrieving a list. It provides structured data, which is the cornerstone for:

Building powerful applications: Developers can integrate this data into fantasy sports apps, historical boxing archives, or news aggregators.
Conducting in-depth analysis: Researchers can analyze trends in fighter careers, compare stats across different eras, or study the impact of various physical attributes.
Creating engaging content: Journalists and content creators can quickly gather accurate data for articles, infographics, and social media posts, enhancing the quality and credibility of their work.
Automating data workflows: Marketers can automate the collection of competitor data or industry trends if boxing data is relevant to their niche.

By offering a reliable way to access information like fighterName, nationality, height, weight, and profileUrl, this actor empowers users to unlock valuable insights into the world of professional boxing.

Ready to Dive In?

Stop wasting time on manual data collection or struggling with complex API integrations. The Apify Boxing Stats Scraper offers a simple, efficient, and robust solution for all your boxing data needs. Whether you're a seasoned developer, a sports data analyst, or just a passionate fan, this actor provides the tools you need to access accurate and comprehensive boxing statistics with ease.

Explore the Boxing Stats Scraper today and discover how effortlessly you can turn raw data into actionable intelligence.

Ready to try it yourself? Run *Boxing Stats Scraper** on the Apify Store -- no setup required.*

How to Scrape Skiplagged Flight Data for Fare Intelligence

Crawler Bros — Wed, 02 Sep 2026 03:49:55 +0000

Tracking dynamic flight prices across multiple airlines and online travel agencies is a major challenge for travel analysts, agency operators, and developers building travel comparison tools. Traditional airfare aggregators often lock their data behind heavy bot-detection screens or expensive API agreements, making programmatic market research difficult. Furthermore, standard search engines frequently overlook creative self-transfer or hidden-city itineraries that offer substantial savings.

The skiplagged-scraper (Skiplagged Flight Search Scraper) on Apify solves this by automating data extraction directly from Skiplagged's public flight-search results. With no login, proxy, or cookies required, it delivers structured fare data, flight segment details, layover times, and booking provider comparisons across single or batched routes.

Why Scrape Skiplagged for Flight Fare Intelligence?

Skiplagged is widely known for surfacing unique fare combinations, including self-transfer itineraries where outbound and return legs are priced independently. Capturing this data gives travel platforms and analysts access to real-time market pricing that standard bundled-fare searches might miss.

The scraper extracts live data at the moment of execution, returning structured fields such as:

Itinerary details: originAirport, destinationAirport, departureTime, arrivalTime, and duration
Route structure: stops, direct, segments (including flightNumber, airlineCode, and durationMinutes), and layovers (including layover airport and durationMinutes)
Pricing & providers: price, currency, minRoundTripPrice (for round-trip searches), and bookingProviders (showing which travel site offers the lowest listed fare)
Cabin and metadata: fareClass, requestedCabinClass, recordType: "skiplaggedFlight", and scrapedAt

Key Capabilities for Developers and Analysts

1. Flexible Route Searches and Metro Code Support

You can search specific airport pairs using IATA codes like JFK or query entire metropolitan regions with metro codes like NYC (covering JFK, LGA, and EWR) or LON (covering LHR, LGW, STN, LTN, and LCY). The scraper handles single searches, batched route lists, or direct URL inputs.

2. Built-In Itinerary Filtering

Refine your data extraction before output generation using precise schema controls:

directOnly: Set to true to capture only nonstop flights.
maxStops: Limit itineraries to a maximum number of connections (e.g., maxStops: 1).
airlines: Pass an array of 2-letter IATA airline codes to restrict results to specific carriers.
minPrice and maxPrice: Filter out fares outside of your target budget thresholds.
sortBy: Order the output by price, duration, departureTime, or stops.

3. Comprehensive Leg and Segment Breakdown

For round-trip queries, Skiplagged prices itineraries as independently booked tickets. The actor emits separate records for outbound and return legs, identified by legType: "outbound" and legType: "inbound". Outbound records also include minRoundTripPrice to reflect the total combined fare. Each individual flight segment is parsed into the segments array with flight numbers and operating airline names.

How to Set Up the Skiplagged Flight Search Scraper

You can configure and run the actor on the Apify platform in a few simple steps:

Step 1: Select Your Search Mode

Choose one of the three supported values for the mode parameter:

search: Run a single route query.
byRoutes: Provide an array of route objects in the routes parameter for batch processing (e.g., passing multiple {origin, destination, departDate} objects).
byUrls: Supply direct Skiplagged flight search URLs in the startUrls array.

Step 2: Configure Route Parameters and Dates

For a standard single route search (mode: "search"), define your origin and destination:

Set origin to your departure airport or metro code (e.g., NYC).
Set destination to your arrival code (e.g., LON).
Define departDate in YYYY-MM-DD format (leaving it blank defaults to 6 weeks from the run date).
Provide an optional returnDate for round-trip fare comparisons.

Step 3: Apply Filters and Limits

Set your passenger parameters (adults, children, infantsLap, infantsSeat) and desired cabin using cabinClass. Define the maxItems property to establish a hard cap on the total number of emitted records across your run.

Step 4: Run the Scraper and Export Data

Execute the actor. Once complete, you can download the clean dataset in JSON, CSV, or Excel formats, or route the structured data directly into internal databases, dashboards, or pricing pipelines via API.

Practical Use Cases

Fare Monitoring Dashboards: Schedule regular runs across priority business routes to track price fluctuations over time and generate low-fare alerts.
Corporate Travel Analysis: Audit employee travel routes in bulk using the byRoutes mode to identify cost-effective carrier options and direct flight availability.
Online Travel Tools: Ingest live airfare data, flight segments, and bookingProviders information to benchmark your own travel platform against current market rates.
Route Competitiveness Research: Aggregate primaryAirline patterns, duration metrics, and layover hubs across international corridors to assess airline network efficiency.

The Skiplagged Flight Search Scraper provides a reliable, direct path to public flight search data without complex proxy configurations or session management. Explore the skiplagged-scraper actor on Apify to automate your airfare data collection workflows today.

Ready to try it yourself? Run *Skiplagged Flight Search Scraper** on the Apify Store -- no setup required.*

How to Scrape Boxing Fighter Profiles and Career Stats Instantly

Crawler Bros — Tue, 01 Sep 2026 20:51:06 +0000

Why Is Gathering Boxing Data So Difficult?

For sports analysts, boxing journalists, app developers, and fantasy sports platforms, keeping track of professional boxers is a major challenge. Unlike team sports with centralized league databases, boxing data is scattered across various promotional companies, sanctioning bodies, and regional commissions.

If you are building a boxing news aggregator, a fighter comparison tool, or a sports analytics dashboard, manually copy-pasting career records is incredibly inefficient. You need structured, clean data on demand—such as a fighter's nationality, height, weight division, and profile images.

The Boxing Stats Scraper is a specialized automation tool designed to solve this problem. By leveraging the free public API from TheSportsDB, this actor allows you to programmatically extract comprehensive boxing profiles and weight class structures without writing complex scrapers from scratch.

What is the Boxing Stats Scraper?

The Boxing Stats Scraper is a pre-built web scraping and automation tool available on Apify. It falls under the Automation, Developer Tools, and Integrations categories, making it easy to incorporate into your existing developer workflows or data pipelines.

The tool operates by querying TheSportsDB free public API. Because it uses this free public tier, you do not need to obtain or configure your own API key to start pulling data.

Supported Features and Scraping Modes

The scraper is designed with two distinct modes to help you get the exact datasets you need:

search mode: This mode allows you to find any professional boxer by their name. It is ideal for targeted searches when you need to build individual fighter profile cards or update a specific athlete's stats in your database.
teams mode: In the context of TheSportsDB boxing data, this mode lists the different boxing weight divisions. This is highly useful for mapping out weight classes to structure your sports application or website navigation.

Key Input Fields and Search Parameters

To get the exact boxing datasets you need, the Boxing Stats Scraper provides a simple input schema with a few key parameters:

mode (string, required): Choose between search to look up specific boxers, or teams to retrieve weight division structures.
searchQuery (string): The name of the boxer you want to search for (used when the search mode is active).
maxItems (integer): Control your data budget by limiting the maximum number of records returned (supports a range from 1 to 200).

What Data Fields Can You Extract?

Once the scraper completes its run, it delivers structured data directly to your dataset. The output fields returned for boxing profiles include:

fighterId: The unique identifier for the boxer.
fighterName: The full name of the professional athlete.
team: The associated team or promotional entity.
nationality: The athlete's home country.
position: The role or specific category of the fighter.
height: The height of the fighter.
weight: The weight or weight class.
birthDate: The fighter's date of birth.
imageUrl: A direct link to the athlete's profile or headshot image.
profileUrl: The official profile page link on the source database.

With access to image URLs and key physical stats, developers can instantly populate user interfaces with rich visual and textual content.

How to Use the Boxing Stats Scraper: Step-by-Step

Getting started with the scraper is straightforward, even if you do not have extensive programming experience. Follow these steps to run your first task:

Access the Actor: Navigate to the Apify platform and search for the boxing-stats-scraper in the store.
Select Your Mode: In the input configuration, set the mode field to either search (to find a specific boxer) or teams (to list weight divisions).
Define Your Search: If you selected the search mode, enter the boxer's name in the searchQuery field.
Set Limits: Specify the maximum number of records you wish to retrieve in the maxItems field.
Run the Scraper: Click the "Start" button. The actor will query the API and process the results.
Download Your Data: Once the run is finished, you can export your clean structured data in formats like JSON, CSV, or Excel directly from the Apify console.

Frequently Asked Questions

Is an API key required to run this scraper?

No, an API key is not required. The Boxing Stats Scraper utilizes the free public tier of TheSportsDB to pull fighter profiles and weight divisions, meaning you can start gathering data immediately without registering for external API credentials.

Can I filter the results by weight class?

Yes, the input schema allows you to filter your queries by selecting a weight class to refine the returned records.

How many records can I retrieve in a single run?

The actor allows you to set the maxItems parameter to retrieve up to 200 records per run, making it quick and easy to gather bulk datasets on multiple fighters or divisions at once.

Ready to try it yourself? Run *Boxing Stats Scraper** on the Apify Store -- no setup required.*

Master Coupon Data: How Marketers Boost Deals & ROI

Crawler Bros — Tue, 01 Sep 2026 16:20:12 +0000

The Marketer's Edge: Unlocking Competitive Coupon Intelligence

In the fast-paced world of e-commerce, staying ahead means more than just offering great products; it means offering great deals. For marketers, affiliate managers, and business intelligence analysts, understanding the coupon landscape is crucial. How do your competitors' offers compare to yours? Are there trending deals you're missing out on? Manually tracking thousands of coupons across hundreds of stores is a monumental, if not impossible, task. This is where data scraping comes from being a competitive advantage.

Imagine having a daily, updated stream of coupon codes, discounts, and promotional offers from your competitors and related brands. This isn't just about finding a good deal for yourself; it's about gaining strategic insights that can inform your pricing, promotional calendars, and partnership strategies.

Introducing the Groupon Coupons Scraper: Your Data-Driven Discount Detective

Apify's Groupon Coupons Scraper is designed to automate the tedious process of collecting coupon data from Groupon Coupons, one of the largest aggregators featuring over 10,000 brands. This powerful tool extracts essential details like coupon codes, discount values, verified status, and usage counts, providing you with actionable intelligence without requiring a proxy.

With this actor, you can:

Monitor Competitor Deals: Keep a close eye on promotional activities from specific stores. Are your rivals offering free shipping, a percentage off, or a buy-one-get-one deal?
Identify Trending Promotions: Discover popular deals across various categories to spot market trends and identify high-performing coupon types.
Optimize Your Own Offers: Benchmark your discounts against what's currently available, ensuring your promotions are competitive and appealing to customers.
Power Affiliate Marketing: Access a fresh database of working coupon codes for various brands to enhance your affiliate content and drive conversions.

How Can Businesses Leverage Groupon Coupon Data?

Let's dive into practical scenarios where timely coupon data can make a significant difference.

Competitive Analysis for E-commerce Brands

A common challenge for e-commerce brands is understanding competitor pricing and promotional strategies. Let's say you run an electronics store. You need to know what deals major players like Best Buy and Amazon are pushing.

Using the Groupon Coupons Scraper in byStore mode, you can input specific storeNames like best-buy and amazon. The scraper will return all active coupons and deals for these retailers. You'll get details like:

couponTitle: "Save up to $500 on Select 4K TVs"
discountValue: "20% Off"
couponType: "Code" or "Deal"
isVerified: true/false (indicating if Groupon editors manually checked it)
usedToday: The number of times the coupon was used today, offering a proxy for popularity.

This data allows you to quickly identify if competitors are offering steeper discounts, free shipping, or special bundled deals. You can then adjust your own promotions to remain competitive or even strategically counter their offers.

Identifying Niche Market Opportunities for Affiliates

Affiliate marketers constantly seek fresh content ideas and high-converting offers. Imagine you specialize in the health and beauty niche. Manually sifting through hundreds of brands on Groupon for relevant coupons is inefficient.

With the Groupon Coupons Scraper, you can switch to byCategory mode and select health-beauty as your category. The scraper will fetch coupons from various stores within that niche. Furthermore, you can use the couponTypeFilter to only return "Code" type coupons, ensuring you're getting redeemable codes rather than just general deals. This allows you to quickly populate your affiliate site or social media channels with verified, actively used coupon codes, driving traffic and potential earnings.

Trend Spotting for Business Intelligence Analysts

Business intelligence analysts often look for broader market trends. Are consumers gravitating towards percentage-off deals or free shipping? Are certain product categories seeing a surge in promotional activity?

By running the scraper in popular mode, you can obtain a snapshot of today's most trending and featured deals across Groupon. This provides insights into what consumers are actively seeking and using. Coupled with discountType and usedToday metrics, you can identify:

Which discountType (e.g., "Percent", "Dollar", "Free Shipping") is most prevalent or popular.
Categories that are experiencing high coupon usage.
The expiresAt field helps understand the urgency and duration of trending promotions.

This aggregated data can inform broader marketing strategies, inventory decisions, and even future product development by revealing what kind of value propositions resonate most with the market.

How to Use the Groupon Coupons Scraper

Getting started with the Groupon Coupons Scraper is straightforward:

Find the Actor: Search for "Groupon Coupons Scraper" on the Apify platform.
Choose a Mode: Decide if you want to scrape byStore, byCategory, or get popular deals.
Configure Input:
- For byStore mode, provide a list of storeNames (e.g., nike, target). Remember to use store slugs as found in Groupon URLs.
- For byCategory mode, select one of the available categories (e.g., electronics, travel).
- (Optional) Refine your results with couponTypeFilter (Code, Deal, Reward) or verifiedOnly to ensure quality.
- (Optional) Set maxItems to control the number of coupons returned (1-500).
Run the Actor: Click "Start" to execute the scraper.
Export Data: Once the run completes, download your extracted coupon data in your preferred format (JSON, CSV, Excel, etc.).

Each output record provides a wealth of information, including couponId, storeName, couponTitle, couponCode, discountValue, isVerified, and usedToday.

The Power of Daily Data: Stay Updated, Stay Ahead

Groupon updates its coupons daily. To ensure your competitive intelligence and promotional strategies are always based on the freshest data, it's recommended to run the Groupon Coupons Scraper daily. This continuous stream of information allows marketers and analysts to react swiftly to market changes, optimize campaigns, and ultimately drive better results.

Don't let valuable coupon intelligence slip through your fingers. Integrate the Groupon Coupons Scraper into your daily workflow and transform how you approach competitive analysis and promotional strategy.

Ready to try it yourself? Run *Groupon Coupons Scraper** on the Apify Store -- no setup required.*

Powering Your Content Engine: Mixkit Media Scraper for Seamless Asset

Crawler Bros — Tue, 01 Sep 2026 11:24:16 +0000

The Challenge: Content Creation Demands a Constant Stream of High-Quality Media

In today's fast-paced digital landscape, content creators, marketers, and developers are under immense pressure to produce engaging and high-quality material. Whether you're building a YouTube channel, designing an app, creating social media campaigns, or developing training videos, the need for diverse and compelling media assets – videos, background music, and sound effects – is relentless.

The manual process of browsing, filtering, and downloading individual media files from stock sites like Mixkit can be incredibly time-consuming. Imagine needing a hundred 4K vertical nature clips for a TikTok campaign, a library of uplifting cinematic music for a podcast, or a comprehensive collection of "whoosh" sound effects for a game. Manually searching, previewing, and downloading each asset can quickly become a bottleneck, diverting valuable time from your core creative tasks.

What if you could automate this entire process, building a tailored library of royalty-free media assets without spending hours on repetitive tasks?

The Solution: Automate Your Mixkit Media Sourcing with the Mixkit Stock Media Scraper

That's where the Mixkit Stock Media Scraper comes in. This powerful Apify actor provides a streamlined way to programmatically scrape free stock videos, background music, and sound effects directly from mixkit.co. It's designed to give you direct, ready-to-download media URLs and rich metadata without needing a login, dealing with watermarks, or requiring attribution.

The actor supports three primary media types via the mode input: video, music, and soundEffect. This flexibility allows you to target specific content needs, whether you're a video editor, podcaster, or app developer.

Bulk-Source B-Roll for Video Editors and YouTubers

Video content thrives on diverse and high-quality visuals. Imagine a scenario where you need a large quantity of specific video clips – say, 4K vertical clips related to "sea" for a social media campaign. Manually browsing hundreds of videos to find the right resolution and orientation can be a huge drain on resources.

With the Mixkit Stock Media Scraper, you can specify your exact requirements. For instance, you could configure the actor with:

mode: video
videoCategory: sea
resolutionFilter: 4k
orientationFilter: vertical
maxItems: 20

This configuration would efficiently gather up to 20 direct MP4 download URLs for 4K vertical videos categorized under "sea," along with essential metadata like title, description, tags[], durationSeconds, and licenseType. The downloadUrl field provides a direct link to the highest available resolution, while availableQualities[] shows all options. This saves countless hours compared to manual searching, ensuring your content pipeline is always flowing with fresh, relevant visuals.

Build Curated Music and SFX Libraries for Podcasters and Developers

Podcasters and game developers constantly need new audio assets. Finding the perfect background music that matches a specific mood or genre, or a comprehensive collection of sound effects for in-game actions, can be a major project in itself.

For a podcaster looking for uplifting cinematic background music, the actor allows precise filtering:

mode: music
musicMood: uplifting
musicGenre: cinematic
minDurationSeconds: 60
maxItems: 25

This will fetch up to 25 full-quality MP3 downloadUrls for music tracks that fit the "uplifting" mood and "cinematic" genre, ensuring they are at least 60 seconds long. You'll also get genres[], moods[], instruments[], and tags[] for each track, helping you further organize your library.

Similarly, an app or game developer needing sound effects for explosions and impacts can use:

mode: soundEffect
sfxCategory: explosion
maxItems: 30

This run will provide up to 30 direct WAV downloadUrls for sound effects in the "explosion" category, complete with categories[] and durationSeconds. This significantly speeds up the process of building robust audio asset libraries for your projects.

Dynamic Content Integration with Keyword and Duration Filters

Beyond categories and genres, the Mixkit Stock Media Scraper offers even more granular control. You can use the searchQuery input for a broad full-text search across all media types, matching titles, tags, and descriptions. For example, searchQuery: "lofi piano" will find relevant music, videos, or sound effects.

The minDurationSeconds and maxDurationSeconds inputs are universal across video, music, and soundEffect modes, letting you filter media by length. Additionally, the containsKeyword input provides a case-insensitive match against title, description, artist, or tags, ensuring you only retrieve assets truly relevant to your specific needs. This is invaluable for integrating scraped content into automated workflows or dynamic content pipelines where precise asset matching is crucial.

How to Use the Mixkit Stock Media Scraper

Using the Mixkit Stock Media Scraper is straightforward:

Find the Actor: Navigate to the Mixkit Stock Media Scraper page on the Apify platform.
Choose Your Media Type: In the input form, select your desired mode (e.g., video, music, or soundEffect).
Specify Your Criteria: Fill in the relevant input fields based on your mode. For video, you might use videoCategory, resolutionFilter, or orientationFilter. For music, musicGenre or musicMood. For soundEffect, sfxCategory. You can also use searchQuery for site-wide search, and minDurationSeconds, maxDurationSeconds, or containsKeyword for additional filtering.
Set Item Limits: Define maxItems to control the number of records the actor will emit.
Run the Actor: Click the "Start" button to initiate the scraping process.
Download Your Data: Once the run completes, you can download the results, which will include direct downloadUrls for your desired media, along with comprehensive metadata.

The output will include detailed fields like videoId, title, description, tags[], downloadUrl, downloadResolution for videos; trackId, title, artist, genres[], moods[], downloadUrl for music; and sfxId, title, categories[], downloadUrl for sound effects. Every record is guaranteed to have real data for all present fields.

Start Powering Your Content Engine

The Mixkit Stock Media Scraper empowers professionals to move beyond manual media sourcing. By automating the collection of high-quality, royalty-free videos, music, and sound effects from mixkit.co, you can significantly accelerate your content creation workflows, streamline asset management, and ensure your projects are always fueled with fresh and relevant media.

Ready to transform your content pipeline? Try the Mixkit Stock Media Scraper today and experience the efficiency of automated media asset sourcing.

Ready to try it yourself? Run *Mixkit Stock Media Scraper** on the Apify Store -- no setup required.*

Optimize Your Fantasy Football with ESPN NFL Stats

Crawler Bros — Tue, 01 Sep 2026 04:27:07 +0000

Dominate Your Fantasy Football League with Data-Driven Insights

Are you tired of making fantasy football decisions based on gut feelings or outdated information? In the competitive world of fantasy sports, access to fresh, comprehensive data is the ultimate equalizer. Imagine having real-time player stats, team performance metrics, and scheduling insights at your fingertips, allowing you to make smarter trades, optimize your lineup, and predict breakout stars.

Traditional methods of data collection – manually browsing websites, copying and pasting, or relying on limited free tools – are time-consuming and often incomplete. They leave you guessing when you should be making informed decisions. This is where web scraping and automation become indispensable for serious fantasy football managers, sports analysts, and even casual fans looking for an edge.

The Solution: ESPN NFL Stats Scraper for Unrivaled Data

The ESPN NFL Stats Scraper is a powerful tool designed to extract a wealth of NFL statistics directly from ESPN's public API. It bypasses the limitations of manual data gathering, providing structured, up-to-date information on all 32 NFL teams without requiring an API key. This means you get access to the same rich data ESPN uses, but in a format you can easily analyze and integrate into your own models and dashboards.

Whether you're looking to scout potential draft picks, analyze opponent strengths and weaknesses, or track the latest league developments, this actor provides the raw data you need.

What Data Can You Scrape for Fantasy Football and Analysis?

The ESPN NFL Stats Scraper offers several data modes to help you build a comprehensive understanding of the league:

Team Rosters (mode=roster): Get detailed player profiles for any team. This includes playerName, position, age, weightLbs, heightInches, and college. Imagine building a database of potential waiver wire pickups, filtered by age, position, or physical attributes.
League Standings (mode=standings): Track team performance across conferences and divisions. Key fields like wins, losses, winPercentage, pointsFor, pointsAgainst, and streak are invaluable for understanding team momentum and strength of schedule.
Team Schedules (mode=schedule): Access a full season's game schedule for any team, complete with scores, venues, and dates. This allows you to identify bye weeks, predict easy or tough matchups, and analyze historical game performance.
Game Scoreboards (mode=scoreboard): Get current and recent NFL game scores and statuses, perfect for real-time tracking during game days.
NFL News (mode=news): Stay updated with the latest articles, headlines, and descriptions, often including related teams and athletes. This can be crucial for identifying injury news or strategic shifts that impact player performance.
All Teams (mode=teams): Obtain a full list of all 32 NFL teams, along with their ESPN team IDs, logos, and URLs, serving as a handy reference for your scraping efforts.

For instance, to analyze the Green Bay Packers' roster for the 2024 season, you would set the mode parameter to 'roster', teamId to 9 (for Green Bay Packers), and season to 2024. The actor would then return detailed data for each player, including their playerName, position, and age. This allows you to quickly assess the youth and experience of their squad, informing your draft strategy or trade negotiations.

Real Use Case: Building a Predictive Fantasy Football Model

Let's say you're building a sophisticated fantasy football model. You need more than just weekly scores; you need granular, historical data. Here's how the ESPN NFL Stats Scraper empowers you:

Player Scouting and Valuation: Use the 'roster' mode to scrape detailed player bios for every team. Combine position, age, and college data to identify emerging talent or potential decline in veteran players. For example, you might look for younger players (low age) in skill positions (like WR, RB) with significant heightInches and weightLbs who played at top college programs, signaling high upside.
Matchup Analysis and Strength of Schedule: Scrape 'schedule' data for all teams for past and upcoming seasons. Analyze pointsFor and pointsAgainst from 'standings' data for opposing teams. This allows you to identify favorable matchups for your fantasy players and forecast potential performance spikes or dips. If your running back is facing a team with a historically high pointsAgainst value, that's a good sign for their fantasy output.
Injury Tracking and News Analysis: Regularly scrape 'news' to stay abreast of injuries, team changes, and coaching decisions. Integrating this unstructured text data with structured player stats can help you predict sudden changes in player roles or overall team performance. A headline about a key player's injury could immediately impact the value of their backup.

By combining these data points, you move beyond simple statistics and start to build a nuanced understanding of the NFL landscape, giving you a significant competitive edge in your fantasy league.

How to Use the ESPN NFL Stats Scraper

Getting started with the ESPN NFL Stats Scraper is straightforward. Here’s a quick guide:

Find the Actor: Navigate to the ESPN NFL Stats Scraper page.
Input Parameters: Specify your desired data using the input fields.
- For example, to get standings for the 2023 season, set mode to 'standings' and season to 2023.
- To get the Kansas City Chiefs' roster for 2024, set mode to 'roster', teamId to 12, and season to 2024.
- You can also specify maxItems if you only need a limited number of records.
Run the Actor: Click the "Start" button to initiate the scraping process.
Export Your Data: Once the run is complete, you can download your scraped data in various formats, such as JSON, CSV, or Excel, ready for analysis in your preferred tool.

Frequently Asked Questions

Is an API key required?
No. ESPN's site API is publicly accessible without authentication, meaning you can start scraping immediately.

How often is data updated?
ESPN updates game scores in real time during games, while rosters and standings are typically updated daily. This ensures you're working with fresh data.

What seasons are available?
The ESPN API provides historical data dating back to 2000, allowing you to perform extensive historical analysis by simply adjusting the season parameter.

Take Your Fantasy Football and Sports Analysis to the Next Level

The ESPN NFL Stats Scraper empowers you with the data you need to make informed decisions, whether you're a fantasy football guru, a sports data analyst, or a developer building a sports application. Stop relying on incomplete information and start leveraging the power of automation.

Try the ESPN NFL Stats Scraper today and unlock a new dimension of NFL data analysis.

Ready to try it yourself? Run *ESPN NFL Stats Scraper** on the Apify Store -- no setup required.*

Uncover Audiobook Trends with Audible Scraper

Crawler Bros — Fri, 14 Aug 2026 18:59:57 +0000

The Challenge: Navigating the Vast Audible Landscape

For publishers, authors, marketers, and researchers, understanding the dynamic world of audiobooks is crucial. Imagine you're a marketing manager at a publishing house. You've just released a new fantasy audiobook, and you need to answer critical questions:

How is your new title performing against competitors in the "Science Fiction & Fantasy" category, not just in the US but globally?
Which authors are consistently topping the charts in specific genres, and what are their books' key metadata points (ratings, runtime, series info)?
Are there pricing trends or discount cycles for similar titles that you could leverage for your next promotion?
How can you identify emerging trends in narration languages or formats?

Manually sifting through Audible's storefronts across 11 different locales to collect this information is an arduous, time-consuming, and often incomplete process. You need a systematic, scalable way to gather rich, real-time data directly from Audible.

The Solution: Apify's Audible Scraper

Enter the Apify Audible Scraper. This powerful Actor is designed to systematically extract comprehensive audiobook metadata from Amazon's audiobook marketplace. It offers six distinct modes to suit various data extraction needs, covers 11 Audible storefronts, and provides a wealth of filtering and sorting options, making it an indispensable tool for market intelligence, content curation, and competitive analysis.

How Can Publishers Track Competitor Performance?

For publishers and authors, competitive intelligence is paramount. The Audible Scraper allows you to monitor the performance of competing audiobooks and identify key success factors.

You can use the bestSellers mode combined with the category input field to pull top-ranking titles in specific genres. For instance, to see the top 50 "Science Fiction & Fantasy" audiobooks in the US, you would configure the Actor with:

mode: "bestSellers"
category: "Science Fiction & Fantasy"
locale: "us"
maxItems: 50

The output will provide detailed metadata for each audiobook, including title, authors, rating, ratingCount, regularPrice, series information, and coverImageUrl. This allows you to benchmark your own titles against the market leaders, identify popular trends in series structure or narration, and track price points. By running this for multiple locale values, you can observe regional popularity differences.

How Can Researchers Build Comprehensive Author Catalogs?

Researchers, literary agents, or even dedicated fans often need complete lists of works by a particular author. Manually browsing an author's page on Audible is fine for a few titles, but for prolific authors, it quickly becomes unwieldy.

The byAuthor mode of the Audible Scraper makes this process effortless. You can provide an authorQuery (e.g., "Stephen King") or a direct authorId to retrieve their entire Audible catalog.

For example, to get all audiobooks by Stephen King from the US storefront, you'd set:

mode: "byAuthor"
authorQuery: "Stephen King"
locale: "us"
maxItems: 500 (to ensure comprehensive coverage, as the Actor can paginate up to 500 results per query)

The Actor automatically resolves the authorQuery to the correct author ID and then scrapes all associated audiobooks. The output includes crucial details like asin, title, releaseDate, and series information, allowing you to reconstruct an author's complete bibliography or analyze their publishing history. The audibleUrl field links directly back to the product page for further manual investigation if needed.

How Can Marketers Monitor Price and Promotional Changes?

Understanding pricing strategies across different regions and over time is vital for marketing and sales teams. The Audible Scraper provides regularPrice, salePrice, and priceCurrency for each audiobook.

By running the byAsins mode with a list of specific ASINs, or using byCategory and sortBy: "price-asc" or sortBy: "price-desc", you can periodically track pricing fluctuations for titles of interest. For example, to monitor the pricing of a set of competitor books:

mode: "byAsins"
asins: ["1473565421", "B0CSTCJZN1", "B07ND8GVR8"]
locale: "us" (and then run again for uk, de, etc. to compare regional prices)

This allows you to identify when competitors are running sales, understand regional price disparities, and inform your own promotional scheduling. The Actor also includes flags like isPlusMembership and isAudibleOriginal, which are useful for understanding different promotional tiers and content strategies.

How to Use the Audible Scraper

Getting started with the Audible Scraper is straightforward:

Find the Actor: Navigate to the Apify platform and search for "Audible Scraper".
Define Your Input: Choose a mode that fits your goal (e.g., "bestSellers", "byAuthor", "search").
Specify Parameters: Based on your chosen mode, fill in relevant fields. For example, use category for best-sellers, authorQuery for author catalogs, or searchQuery for keyword searches. Don't forget to select your desired locale (e.g., "us", "uk", "de").
Add Filters (Optional): Refine your results using filters like minRating, maxLength, releaseDate, or containsKeyword to focus on specific criteria.
Run the Actor: Click "Start" to initiate the data extraction. The Actor will process your request and collect the data.
Download Your Data: Once the run is complete, you can download the structured data in various formats (JSON, CSV, Excel, etc.).

Practical Use Cases for Your Data

The data collected by the Audible Scraper can power a wide array of applications:

Market Intelligence Dashboards: Create interactive dashboards to visualize best-seller trends, category performance, and author prominence across different regions.
Recommendation Engines: Feed the rich metadata (authors, narrators, categories, series, ratings) into your custom recommendation systems for audiobooks.
Content Gap Analysis: Identify underserved niches by analyzing categories with fewer highly-rated titles or by spotting emerging keyword trends.
Translation and Localization Strategy: By scraping across multiple locale options, you can see how the same book performs or is priced in different markets, informing your international publishing strategies.
Audible Plus & Originals Tracking: Monitor the growth and impact of Audible's exclusive content by filtering for isPlus or isAudibleOriginal titles.

The Audible Scraper is a robust, flexible tool that provides granular access to Audible's vast marketplace data. By leveraging its various modes and filtering capabilities, you can unlock valuable insights and make data-driven decisions in the rapidly evolving world of audiobooks. Try it today and transform how you understand the audiobook market.

Ready to try it yourself? Run *Audible Scraper** on the Apify Store -- no setup required.*

Grocery Price Tracking & Competitive Analysis with Apify

Crawler Bros — Fri, 14 Aug 2026 13:16:38 +0000

The Challenge of Real-Time Grocery Market Intelligence

In the fast-paced world of retail, staying ahead means knowing not just what you're selling, but also what your competitors are offering. For product managers, data analysts, and marketing strategists in the grocery sector or related industries, a critical challenge is gaining real-time insights into competitor pricing, promotional strategies, and product availability. Manually sifting through thousands of weekly flyers from different retailers across various regions is an impossible task.

Imagine you're a product manager at a growing CPG brand, launching a new line of organic yogurts. You need to understand how major retailers like Target, Kroger, and Walmart are pricing similar products, what discounts they're applying, and when these promotions go live. Or perhaps you're a data analyst for a grocery delivery service, aiming to optimize pricing algorithms and identify arbitrage opportunities. The sheer volume of weekly ad data, combined with its localized nature (prices varying by ZIP code), makes traditional data collection methods impractical and inefficient.

This is where automation becomes indispensable. The ability to programmatically access and analyze weekly grocery deal data can transform how businesses make strategic decisions.

Solving the Grocery Data Dilemma with the Flipp Weekly Deals Scraper

The Apify Flipp Weekly Deals & Grocery Ad Scraper offers a powerful solution to this problem. It allows you to programmatically collect current weekly flyer items with prices, discounts, and validity dates directly from Flipp, North America's leading grocery deals platform. This actor provides a structured, machine-readable output of publicly available deal data, eliminating the need for manual data entry or complex web scraping setups.

What Data Can You Collect?

The scraper delivers a wealth of detail for each deal record, including:

name: The product's name.
merchantName: The name of the store offering the deal (e.g., "Lidl", "Walmart").
currentPrice: The current sale price in USD.
originalPrice: The price before discount, if available.
discount and discountPercent: The savings amount and percentage.
saleBadge: Promotional text like "Buy 1 Get 1 Free" or "Save 25%".
category and subcategory: Product classifications (e.g., "Food, Beverages & Tobacco", "Beverages").
validFrom and validTo: The start and end dates of the deal.
imageUrl: A direct link to the product image.
sourceUrl: A direct link to the deal on Flipp.

This comprehensive dataset empowers you to perform detailed competitive analysis, track pricing fluctuations, and identify emerging promotional trends.

Key Use Cases for Retail Professionals

Competitive Price Monitoring: Track how competitors are pricing key products week-over-week. By regularly scraping currentPrice and originalPrice for specific merchant names, you can build a historical database of competitor pricing strategies. This is invaluable for dynamic pricing adjustments and market positioning.
Promotional Strategy Analysis: Understand the types of discounts and promotions (e.g., saleBadge content) competitors are running. Filter deals by category or subcategory to analyze promotional intensity within specific product segments. This insight can inform your own marketing campaigns and product launches.
Market Trend Identification: Identify patterns in deal availability across different merchants and postalCodes. Are organic products frequently on sale in urban areas? Are specific brands consistently offering deeper discounts? This data can reveal broader market trends and consumer preferences.
Automated Deal Aggregation: For consumer-facing applications, automatically aggregate the best deals on popular items. Use the searchQuery input to find "chicken breast" or "yogurt" across all stores in a given postalCode and present users with the most attractive discountPercent.

How to Use the Flipp Weekly Deals Scraper

Getting started with the Flipp Weekly Deals Scraper is straightforward. Here's a quick guide:

Find the Actor: Navigate to the Apify Console and search for "Flipp Weekly Deals & Grocery Ad Scraper".
Choose Your Mode: Select from weeklyDeals, search, or byMerchant depending on your goal.
- To track all items in a specific store's current weekly ad, use mode: "weeklyDeals". You'll need to specify the merchant (e.g., "Walmart") and a postalCode (e.g., "90210") for localized results.
- To find deals on a specific product across all available stores, use mode: "search". Provide a searchQuery like "detergent" and your postalCode. You can also add a category filter.
- To simply list all active flyers for a given retailer, use mode: "byMerchant" with the merchant name and postalCode.
Define Your Scope: Specify your target merchant, postalCode, or searchQuery according to your chosen mode. You can also set a maxItems limit to control the number of records returned.
Run the Actor: Click "Start" to run the scraper.
Export Your Data: Once the run completes, you can download your scraped data in various formats like JSON, CSV, or Excel for further analysis.

For example, to get the top 50 chicken breast deals in Los Angeles:
{
"mode": "search",
"searchQuery": "chicken breast",
"postalCode": "90210",
"maxItems": 50
}

Or to get all current weekly deals from Lidl in New York City:
{
"mode": "weeklyDeals",
"merchant": "Lidl",
"postalCode": "10001"
}

The data is publicly accessible, so no login or API keys are required, simplifying the setup process.

Transform Your Grocery Retail Strategy

The Flipp Weekly Deals Scraper empowers professionals to move beyond manual data collection and embrace data-driven decision-making. Whether you're a market researcher needing to analyze promotional patterns, a product manager monitoring competitor pricing, or a developer building a consumer-facing deal aggregator, this actor provides the granular, real-time data you need to gain a competitive edge.

Stop guessing and start analyzing. Leverage the power of automated data scraping to unlock critical insights from the grocery retail landscape today.

Ready to try it yourself? Run *Flipp Weekly Deals & Grocery Ad Scraper** on the Apify Store -- no setup required.*

How to Track Tech Trends with the Hacker News Scraper

Crawler Bros — Fri, 14 Aug 2026 07:38:37 +0000

Uncovering Emerging Tech Trends from Hacker News

In the fast-paced world of technology, staying ahead of the curve is not just an advantage – it's a necessity. For product managers, market researchers, venture capitalists, and even individual developers, understanding what’s gaining traction, what's generating buzz, and what the community truly thinks can be the difference between innovation and obsolescence. But how do you efficiently sift through the constant deluge of information on platforms like Hacker News to extract actionable insights?

Manually tracking the "Top Stories" or "New Stories" is tedious and time-consuming. You miss critical context from comments, struggle to filter out irrelevant noise, and can't easily perform historical analysis. Imagine needing to:

Identify the most discussed programming languages or frameworks over the last month.
Monitor competitor mentions and community sentiment around their new product launches.
Pinpoint specific domains that consistently make it to the front page, signaling new players or influential content sources.
Analyze the depth of engagement and specific concerns within "Ask HN" threads related to a particular industry challenge.

This is where the Hacker News Scraper from Apify comes in. This powerful Actor automates the process of extracting stories, comments, jobs, and user profiles directly from Hacker News via its public APIs, providing structured data for your analysis needs.

How the Hacker News Scraper Works for Trend Monitoring

The Hacker News Scraper is designed to interact with Hacker News's public Firebase and Algolia APIs. This means it doesn't rely on proxies or logins, making the data extraction process robust and efficient. It offers various mode options to target specific data types:

topStories: Capture what's currently most popular.
newStories: Discover the latest submissions as they appear.
bestStories: Focus on high-quality, recently trending content.
askStories / showStories / jobStories: Isolate specific types of posts for targeted analysis.
search: Perform full-text queries across all Hacker News history using the Algolia HN API.
item / user: Look up specific stories, comments, or user profiles by ID or username.

Let's explore some concrete examples of how you can use this Actor to track tech trends and gather market intelligence.

1. Identifying Dominant Domains and Emerging Topics

You want to understand which websites or content sources are consistently reaching the Hacker News front page, indicating influence or high-quality content.

The Problem: Manually checking the front page daily and recording domains is impractical. You need a systematic way to aggregate this information over time and filter out irrelevant content.

The Solution: Use the topStories mode to regularly scrape the top stories. The Actor's output for each story includes the domain (parsed host) and url. You can then aggregate and count these domains over a specified period.

To refine your results, you can use input fields like:

maxItems: Cap the total number of stories to fetch, for example, maxItems: 100 for the top 100 stories.
minScore: Filter out stories that don't meet a certain engagement threshold, e.g., minScore: 50.
domainBlocklist: Exclude domains that are not relevant to your research, such as social media sites. For instance, domainBlocklist: ["twitter.com", "x.com"] will prevent stories linking to these platforms from being emitted.
dateRangeFrom and dateRangeTo: Analyze trends within specific timeframes by setting these to ISO-formatted dates.

By running this setup weekly or daily, you can build a historical dataset of prominent domains, revealing which companies or content types are consistently gaining attention.

2. Analyzing Community Sentiment Around New Technologies or Products

Understanding how the developer community reacts to a new framework, tool, or product launch is invaluable. You need to capture not just the announcement, but the detailed discussions and opinions.

The Problem: Reading through hundreds of comments on a popular story to gauge sentiment is time-consuming and subjective. You need structured access to comment content and authorship.

The Solution: First, identify the relevant story ID (either manually or by scraping newStories or search for keywords). Then, use the item mode with that itemIds to pull the story and its comments.

Crucially, set maxComments to a high value (e.g., maxComments: 500) to ensure you fetch all relevant discussions, and enableCommentHierarchy: true to get nested replies, which is essential for understanding conversation flow. The maxDepth field (e.g., maxDepth: 5) lets you control how many levels deep the replies go.

The output for each comment includes text, author, createdAt, and parentId, allowing you to reconstruct discussion threads. You can even filter comments by commentAuthorFilter if you're interested in specific influential voices.

Once you have this structured comment data, you can apply natural language processing (NLP) techniques to analyze sentiment, identify recurring themes, and track community concerns or enthusiasm over time.

3. Monitoring "Ask HN" and "Show HN" for Industry Pain Points and Innovations

"Ask HN" threads are a goldmine for understanding the challenges and questions developers face, while "Show HN" posts reveal new projects and solutions being built.

The Problem: It's hard to consistently track "Ask HN" posts related to a specific niche or to get a comprehensive view of new "Show HN" projects without manual scanning.

The Solution: To monitor industry pain points, use mode: "askStories" and combine it with searchQuery if you have specific keywords (e.g., "AI ethics" or "serverless challenges"). For new innovations, use mode: "showStories".

You can use minCommentCount (e.g., minCommentCount: 10) to focus on discussions that have generated significant engagement, signaling more impactful problems or solutions. Enabling maxComments and enableCommentHierarchy will give you the full context of the discussions around these posts, similar to the sentiment analysis use case.

How to Use the Hacker News Scraper

Here’s a quick guide to getting started with the Hacker News Scraper on Apify:

Find the Actor: Navigate to the Apify Store and search for "Hacker News Scraper."
Start a New Task: Click the "Try for free" or "Start new task" button.
Configure Input: The "Input" tab is where you define what you want to scrape.
- Select a Mode: Choose your desired mode (e.g., topStories, search, item).
- Add Filters: Use fields like maxItems, minScore, domainAllowlist, domainBlocklist, dateRangeFrom, dateRangeTo, maxComments, and enableCommentHierarchy to refine your data.
- Specify Search Query/Item IDs: If using search mode, enter your searchQuery. For item mode, provide itemIds.
- Example Input: To get the top 50 stories with at least 100 points, excluding Twitter links, you'd use:
  
  {
  "mode": "topStories",
  "maxItems": 50,
  "minScore": 100,
  "domainBlocklist": ["twitter.com", "x.com"],
  "excludeDeadOrDeleted": true
  }
Run the Actor: Click the "Start" button to initiate the scraping process.
Download Results: Once the run completes, go to the "Storage" tab and download your data in your preferred format (JSON, CSV, Excel, etc.).

The output records will include fields like title, url, domain, score, numComments, author, and createdAt for stories, and text, author, createdAt, parentId for comments, providing a rich dataset for your analysis.

Beyond Basic Trend Monitoring

The Hacker News Scraper is a versatile tool that extends beyond these examples. You can use it to:

Build a weekly digest of YC job ads by setting mode: "jobStories".
Perform user research by fetching user profiles to understand karma, account age, and submittedCount for potential outreach.
Feed Algolia search results into downstream AI models for automated tagging or summarization, leveraging the search mode to gather relevant content.

By transforming unstructured web content into clean, structured data, the Hacker News Scraper empowers you to make data-driven decisions, stay competitive, and discover the next big thing in tech. Give it a try and unlock the insights hidden within Hacker News!

Ready to try it yourself? Run *Hacker News Scraper** on the Apify Store -- no setup required.*

Uncover Food Delivery Trends with Wolt Scraper

Crawler Bros — Fri, 14 Aug 2026 02:20:03 +0000

Cracking the Code of Food Delivery Markets

In the hyper-competitive world of food delivery, understanding market dynamics is crucial for success. Whether you're a new restaurant planning expansion, a food tech startup validating a concept, or a market researcher looking for granular insights, getting reliable data on local venues, cuisines, and their performance on platforms like Wolt can feel like searching for a needle in a haystack.

Manual data collection is tedious, error-prone, and simply not scalable. API access can be restrictive, and building custom scrapers requires significant technical expertise and ongoing maintenance. So, how can you efficiently gather actionable intelligence from Wolt's vast network of restaurants and venues across 22 countries?

This is where the Wolt Restaurant & Venue Scraper comes in. This powerful Apify actor provides a streamlined solution for extracting comprehensive venue data directly from the Wolt platform, helping you make data-driven decisions without the hassle.

What is the Wolt Restaurant & Venue Scraper?

The Wolt Restaurant & Venue Scraper is an automation tool designed to extract detailed information about restaurants, cafes, grocery stores, and other venues listed on the Wolt food delivery platform. It operates across 49 supported cities in 22 countries, offering a wide geographical reach for your data collection needs.

With this actor, you can retrieve a wealth of information, including:

Venue ID, name, and description: Essential identifiers for any establishment.
Cuisine categories: Such as 'Pizza', 'Sushi', 'Burger', 'Grocery', allowing you to analyze dietary trends and market segmentation.
Rating score and review count: Key indicators of customer satisfaction and popularity.
Estimated delivery time, currency, and price range: Crucial for understanding logistical performance and pricing strategies.
Online status and delivery availability: To identify currently operational venues.
Location data: Including country, city, street address, and GPS coordinates (latitude/longitude).
Wolt URL slug and direct venue link: For easy navigation and referencing.
Minimum order amount and tags/badges: Like 'Popular', 'New', 'Exclusive', providing deeper insights into venue characteristics.
Cover image and logo URLs: For visual analysis and branding efforts.

Real-World Use Case: Uncovering Market Entry Opportunities

Imagine you're a restaurant chain planning to expand into a new city, say, Athens, Greece. Your goal is to identify underserved cuisine categories or areas with high demand but limited competition. Manually sifting through Wolt for hundreds of venues and their details would be impractical.

Using the Wolt Restaurant & Venue Scraper, you can automate this research. By setting the mode to 'byCity' and specifying 'athens' for the city input, you can scrape all available venues (up to 2000 results).

Once you have this raw data, you can:

Analyze Cuisine Density: Identify which cuisine types are most prevalent and which are less common. If you notice a high demand for, say, vegan options (based on local market research) but very few "Vegan" or "Plant-based" categories in the scraped data, that could be a significant market gap.
Assess Competitor Performance: Examine the rating and ratingCount for existing restaurants in your target cuisine. Are there established players with consistently high ratings, or are there opportunities to outcompete with superior offerings?
Evaluate Pricing Strategies: The priceRange (1=budget to 4=luxury) allows you to understand the pricing landscape. Is the market saturated with budget-friendly options, or is there room for a premium offering?
Identify High-Demand Locations: By cross-referencing latitude and longitude with local population data, you can pinpoint specific neighborhoods within Athens that show promise for your new location.

This comprehensive dataset empowers you to make an informed decision about market entry, minimize risk, and tailor your offering to maximize success.

How to Use the Wolt Restaurant & Venue Scraper

Getting started with the Wolt Restaurant & Venue Scraper is straightforward. Here’s a quick walkthrough:

Find the Actor: Navigate to the Apify Store and search for "Wolt Restaurant & Venue Scraper".
Start a New Task: Click the "Try for free" or "Add to project" button to open the actor's console.
Configure Input: You'll be presented with input fields.
- Set the mode to 'byCity'.
- Choose your target city from the dropdown (e.g., 'athens').
- Optionally, use categoryFilter (e.g., 'pizza', 'sushi') to narrow down results to specific cuisines.
- Set onlineOnly to 'true' if you only want venues currently accepting orders.
- Adjust maxItems to control the number of results (up to 2000).
- Ensure proxyConfiguration uses a residential proxy, which is recommended for reliable results.
Run the Actor: Click "Start" to initiate the scraping process.
Download Data: Once the run is complete, you can download your scraped data in various formats like JSON, CSV, or Excel.

Why Residential Proxies are Key for Wolt Data

It's important to note that Wolt's API employs measures to block automated requests from bare datacenter IPs. To ensure reliable and consistent data extraction, the Wolt Restaurant & Venue Scraper leverages residential proxies. These proxies route your requests through real user IP addresses, making them indistinguishable from regular user traffic. The actor is pre-configured to use Apify Residential proxies for optimal performance.

Beyond Market Entry: Other Powerful Use Cases

The utility of the Wolt Restaurant & Venue Scraper extends far beyond market entry research:

Food Delivery Market Research: Gain a macro-level understanding of cuisine distribution, popular restaurants, and delivery times across different cities and countries.
Competitor Analysis: Track specific competitors by filtering venueUrls or categoryFilter and monitor their rating, priceRange, and online status over time.
Location Intelligence: Identify specific neighborhoods or areas where certain cuisines are popular or where there are gaps in the market.
Brand Monitoring: If you operate a restaurant on Wolt, you can track your own venue's rating and position relative to competitors.
Price Benchmarking: Compare priceRange across different cuisine types or cities to understand regional pricing dynamics.

Unlock Wolt Insights Today

The Wolt Restaurant & Venue Scraper is a versatile and indispensable tool for anyone looking to gain a competitive edge or deep insights into the food delivery landscape. By automating the data collection process, you save valuable time and resources, allowing you to focus on analyzing the data and making strategic decisions.

Ready to uncover hidden opportunities and gain a deeper understanding of the Wolt ecosystem? Try the Wolt Restaurant & Venue Scraper on Apify today and start transforming raw data into actionable intelligence.

Ready to try it yourself? Run *Wolt Restaurant & Venue Scraper** on the Apify Store -- no setup required.*

How to Discover Emerging Tech Skills in Southeast Asia

Crawler Bros — Thu, 13 Aug 2026 19:07:28 +0000

Navigating Southeast Asia's Dynamic Tech Job Market: A Data-Driven Approach

The tech landscape in Southeast Asia is exploding, but for job seekers, recruiters, or even educational institutions, understanding the exact skills and roles in highest demand can feel like chasing a moving target. Are data scientists in Singapore still prioritizing Python, or is Scala gaining traction? What's the remote work availability for frontend developers in Vietnam? Sifting through countless job boards manually to answer these questions is tedious, time-consuming, and frankly, impossible to scale.

Imagine a hiring manager in Jakarta trying to understand the competitive salary range for a "Senior Backend Developer" with specific microservices experience, across multiple countries. Or a recent graduate in Kuala Lumpur wanting to see which "Computer & Software" skills are most frequently requested for "Fresh Grad" roles in Singapore. Manually aggregating and analyzing this kind of granular data from platforms like Glints.com, Southeast Asia's leading job platform, is a monumental task.

This is where automation becomes your superpower. By systematically extracting structured job data, you can transform overwhelming raw information into actionable insights.

The Solution: Glints Jobs Scraper

The Glints Jobs Scraper is a powerful tool designed to extract detailed job listings from Glints.com across Singapore, Indonesia, Malaysia, Vietnam, and Taiwan. It provides structured data including job title, company, salary information, required skills, employment type, work arrangement (on-site, hybrid, remote), and even direct application URLs.

This actor allows you to:

Search for jobs by keywords: Use the searchQuery input field to look for specific job titles, skills, or any relevant keywords.
Filter by country: Target specific markets using the country field (e.g., 'SG' for Singapore, 'ID' for Indonesia, or leave empty for all supported countries).
Filter by employment type and work arrangement: Pinpoint "FULL_TIME" or "REMOTE" opportunities using the employmentType and workArrangement filters.
Browse by job category: Explore broad industry sectors like 'Computer & Software' using the categoryName field.
Scrape specific URLs: If you already have a list of Glints job URLs, you can directly scrape their full details using the mode: byUrl input.

Real-World Use Case: Uncovering Emerging Tech Skills

Let's explore how a career coach or an HR analyst could use the Glints Jobs Scraper to identify emerging tech skills and understand market demand across Southeast Asia.

Problem: A career coach wants to advise their clients on the most in-demand programming languages and frameworks for "Backend Developer" roles in Singapore and Indonesia, especially those offering "REMOTE" or "HYBRID" work arrangements, to help them tailor their skill development.

How Glints Jobs Scraper Solves It:

Define Search Parameters:
- Set mode to "search".
- Set searchQuery to "backend developer".
- Specify country as "SG" for Singapore, then run again for "ID" (Indonesia).
- Filter by workArrangement to "REMOTE" or "HYBRID".
- Set seniorityLevel to "ONE_TO_THREE_YEARS" or "THREE_TO_FIVE_YEARS" to focus on mid-level roles.
- Set maxItems to a sufficiently high number (e.g., 500) to get a comprehensive dataset.
Extract Data: The scraper will run and output a dataset containing hundreds of job records. Each record includes a skills array, description, salaryMin, salaryMax, employmentType, workArrangement, and countryName.
Analyze Results:
- By aggregating the skills arrays across all relevant job postings for both countries, the coach can create a frequency distribution of required skills. They might discover that "Node.js" and "AWS" are frequently requested in Singapore, while "Java" and "Spring Boot" are more prevalent in Indonesia.
- Comparing the salaryMin and salaryMax fields for similar roles across "REMOTE" vs. "HYBRID" in different countries can reveal salary benchmarks and compensation trends.
- Analyzing the employmentType and workArrangement fields for different categoryTop (e.g., "Computer & Software") can provide insights into the prevalence of full-time remote opportunities versus on-site contract roles.

This data-driven approach allows the career coach to provide precise, evidence-based advice to their clients, helping them invest in skills that truly align with market demand.

Another Use Case: Competitor Analysis for Recruiters

A recruitment firm specializing in tech roles across Southeast Asia wants to understand what their competitors (other companies hiring similar roles) are offering in terms of benefits and salary for "Data Scientist" positions in Vietnam.

Targeted Search:
- Use mode: search and searchQuery: "data scientist".
- Filter by country: "VN" for Vietnam.
- Specify seniorityLevel: "THREE_TO_FIVE_YEARS" for experienced roles.
Data Extraction: The actor will return detailed job postings, including company, salary (display string, salaryMin, salaryMax, currency), and benefits (a list of benefits offered).
Insights: The recruitment firm can then analyze the benefits and salary fields to benchmark their own offerings against those of other companies, identifying areas where they can become more competitive to attract top talent. They can also track the company field to see which organizations are most actively hiring for these roles.

How to Use the Glints Jobs Scraper

Getting started with the Glints Jobs Scraper is straightforward:

Find the Actor: Navigate to the Glints Jobs Scraper page on Apify Store.
Input Parameters: Fill in the input fields according to your needs. For instance, to find remote Python developer jobs in Indonesia:
- Set mode to "search".
- Set searchQuery to "python developer".
- Select country as "ID".
- Select workArrangement as "REMOTE".
- Optionally, set maxItems to control the number of results.
Run the Actor: Click the "Start" button to execute the scraper.
Download Results: Once the run completes, download your structured job data in your preferred format (JSON, CSV, Excel, etc.).

The Glints Jobs Scraper provides a robust, flexible, and efficient way to extract invaluable data from Glints.com. Whether you're a job seeker, a recruiter, a market researcher, or an educational institution, leveraging this actor can provide a significant competitive edge in navigating Southeast Asia's thriving job market.

Ready to try it yourself? Run *Glints Jobs Scraper** on the Apify Store -- no setup required.*