Web data extraction for ECommerce
We can monitor prices and optimise product costs by analysing competitors.
With the help of web scraping you can gain access to all the information about customers and then offer them targeted products.
Case study: We gathered information from more than 7 resources on the best mattresses and sleep products for our client, analysed the data and prepared them in a report form. As a result, the company got full information on their competitors' best products and prices.
Lead generation with data scraping
We can find the sources where your potential customers communicate and we can extract their public contact data.
As a result, you get a client base in a ready format for marketing activities. You can also get out help for writing Linkedin inmails and emails templates or even letter chains.
Case study: How we generated the first leads for a new company
The startup company Elmy turned to us with an idea to generate leads using advanced web data scraping. The startup idea was to unite freelancers, that were ready to work during a short period of time. They wanted to find the first users of the platform via social networks.
We suggested to use the LinkedIn network and helped to choose the best instruments for the search. After we scraped data we analysed the results and prepared the inmails for he potential clients. We provided the company with special tools for automatical email sending and as a result, generated the first users of a new platform.
Top lists and other engaging content
By extracting data relevant to your industry it is possible to generate an engaging content.
For example, you can find out what people googled the most or what they wrote on social media on a certain topic.
You can use these fascinating insights to create top lists, make researches or even academic studies.
Case study: 35 best online marketing degrees
Our client needed to find the best online marketing degrees of the USA to make a TOP list. We made research and determined 5 websites with aggregated data with statistics on different higher education institutions. The websites were chosen to evaluate the courses from the following aspects: online quality, online presence, earnings potential, student satisfaction, affordability, acceptance, retention.
For example, we chose the PayScale website to extract web data on the earnings potential of the student after getting a certain online marketing degree. We scraped web data from the websites of the best colleges in the USA and special sources with student reviews, such as Niche.com.
As a result, we gathered up to 20 different characteristics of every online course. Then we gathered them in 7 aggregated metrics that were the base for counting one total index of the online degree. It was equal to 100 points for the best course. The results of our research were published in an online schools report that provides students with practical and accurate information about every online college degree program available.
Data extraction for predictive models
If you want to use machine learning and build predictive models for improving your business processes, we are ready to provide you with quality data and statistically significant results.
Case study: Web scraping for predictive models is widely used in a fantasy sport. We extracted data from more than 50 websites on tennis and gathered data in one table for further usage in predictive models. We were also supporting the data extraction interface during a long period of time.