Databases for business intelligence and lead generation
We aggregate, process and enrich information so that our clients can make better decisions about their businesses. Our goal is to provide them with the most accurate and up-to-date data possible.
Web scraping can be used to collect a wide variety of data, including contact information, product details, prices and much more. It is an efficient way to get the information you need without having to manually search through websites or databases. In addition, web scraping can be used to automate repetitive tasks such as monitoring competitor prices or checking for new product releases.
Our team of experienced developers are experts at extracting data from even the most difficult websites. We have successfully completed projects for clients in a variety of industries including ecommerce, real estate, finance and more. Contact us today to learn how we can help you get the data you need to make better business decisions
Check out our products for SMB companies.
Analytics for e-commerce
Our scraping services
Research of market, audience, competitors
Extracting internet data of competitors, probable leads and consumers
Is done in a chain-like process: catalog → website → social media → another source that has to be accessed for one entity. All the data obtained from objects in the chain are organized into a pivot table for assessment.
Possible application include:
- Getting further details about present buyers
- Accumulating data on prospective customers
- Obtaining knowledge about competitors.
Scraping of goods and prices from the websites
of competitors and suppliers
Scraping goods and prices from websites can be an extremely valuable tool for any business owner. By gathering data from your competitors, you can gain insights into their pricing strategies and see where there may be opportunities for your own business.
By tracking price dynamics over time, you can make sure that your prices are always competitive and adjust them accordingly if necessary.
It can help you find suppliers who are offering lower prices than others.
- Analysis of the product range
- Comparison of competitors’ prices and price dynamics tracking
- Getting content to fill the online store
- Supplier price comparison
Scraping search results
We specialize in Scraping search results from various online sources:
Google, Yandex, aol.com, Ask.com, Bing, Baidu, Dogpile, DuckDuckGo, Mail.ru, Rambler, seznam.cz, startpage.com, Yahoo.
We get all possible snippets without limiting requests.
- Search engine competition assessment
- Retrieve ad blocks of competitors for key queries
- Collection of keywords
Getting links to sites from the search results for key queries
- Getting keywords in Google Trends
- Search for mentions
- List parsing People also ask
- Craping Google Maps
Scraping social networks data
Instagram, Telegram, Pinterest, YouTube, TikTok, Facebook, Linkedin
Scraping social networks data is a very useful tool for anyone who wants to keep track of what their competitors are doing on social media. You can easily see which social networks your competitors are using, what kind of content they are posting, and how successful that content is. This information can help you improve your own marketing strategy, or even give you some ideas for new things to try.
Tracking the marketing activity of competitors in social networks and content analysis.
Gathering data to test your machine learning models
Gathering data is an essential part of testing machine learning models. Without the right data, a model cannot be trained properly and will not produce accurate results. Webscraping is one of the most useful tools for gathering large amounts of data quickly and efficiently.
Webscraping involves using automated scripts to extract information from websites or other online sources such as web APIs or RSS feeds. This can involve downloading entire pages, extracting specific elements from a page, or even parsing HTML code in order to find relevant information that can then be used for training models.
Webscraping allows users to gather more diverse datasets than would otherwise be possible with manual methods such as manually entering values into spreadsheets or searching through databases by hand; it also makes it much faster and easier to collect large amounts of data at once rather than having to do so piecemeal over time which could take days if not weeks depending on the size of the dataset needed .