Case study: tracking crowd-marketing activities using web scraping

Our client uses crowd-marketing to get backlinks and increase their brand awareness. Their crowd-marketing campaign includes the following activities:

• Finding industry-related review websites, forums, blogs, Q&A services, and social media platforms.
• Writing answers to the users’ questions and posting comments on articles, videos, and other content.
• Promoting their own comments by upvoting them or commenting on them or asking their customers to do so.

The challenge: how to track crowd-marketing activities

When the marketers posted their comments, it was hard to track how many of them were upvoted by other users, stayed visible (not collapsed or hidden), and were not deleted. Also, the marketers wanted to know how promoting their comments through likes, upvotes and replies would help them push those comments to the top of the discussion.

As the number of comments published every week was very high, it was hard to track them manually. That’s why it was decided to try data scraping technology to track and monitor crowd-marketing activities.

Utilizing web scraping to parse crowd-marketing comments on any website or service

Our client had a list of URLs where their marketing department posted their comments. Also, they had a list of user profiles under which they posted their comments. The eScraper service parsed the given URLs and searched for the specified user comments. One file/list per one marketing platform.

Here’s an example of the file with the results of such scrape for one marketing platform:

Based on the collected data, eScraper engineers made a report to help the store owner and CMO analyze the results. The report shows the number of URLs for each comment position. For example, 127 URLs have remained in the first position, 71 URLs in the 2nd position, and 24 URLs – in the 3d position. There were also a number of comments not available anymore.

If we want to see the list of URLs where the comments remained, say, in the 1st position, we switch back to the previous sheet and filter positions by “1”:

Repeated scraping for performance tracking

The client wanted to check how the comments positions have changed overtime. In a month, the eScraper engineers scraped the positions for the comments from the initial file. The result was presented in the following report.

From the report, it could be seen that 13.21% of comments lost their positions and 33.57% comments improved their positions. Based on this report, the client could work on the comments with dicreased positions. They deleted the existing comment and added a new ome so that it appeared at the top.

The result. How will web scraping for crowd-marketing be used further

Thanks to the web scraping technology, our client was able to get the number of existing comments and their positions in a matter of one hour. Now that they have the scraping tool at hand, they will be able to use it further to solve even more tasks:

• Tracking the work of several crowd-marketers.
• Promoting their own comments and tracking how their positions change over time.
• Tracking comments on other platforms where like: forums, Reddit, Quora, eCommerce communities, and others.

Hope this article was helpful and brought you new ideas on how to make data-driven decisions in crowd-marketing. If you have any questions on how web scraping can assist in your particular case, don’t hesitate to write us in the comments below. We’ll explore your tasks and offer your possible solutions.

Ready to try automated data extraction for your crowd-marketing activities tracking? Order a free scrape and receive a test file or download eScraper software and set up the scraper on your own!