(Reconstructed based on actual project)
"Finished a data collection task that could take months in just one week"
Blank Corporation sells over 20 brands and hundreds of products.
With a large number of products and brands, sales were high, but they were deeply concerned as the review scores were lower than expected.
Through a marketing meeting, we thought, "Wouldn't we be able to find out why the review scores are low by collecting and analyzing all the reviews on the shopping mall?"
It was too difficult to gather customer reviews in one place as we sell products not only on open markets like Coupang, WeMakePrice, and 11th Street but also on our own brand mall.
When we asked part-time workers to copy and paste 1,000 reviews, it took them a full two days.
Moreover, due to the repetitive nature of the task, their concentration wavered, leading to frequent mistakes.
So we searched for "Is there a place that can automatically collect reviews?" and found out that using web scraping technology can make data collection easy and fast.
Hashscraper has a unique advantage compared to other companies.
Advantage of being able to scrape data very quickly using hundreds of servers
Convenience of being able to directly check the data collection process on the dashboard
Transparent pricing policy where you only pay for the amount collected
Thanks to this, our marketing team was able to easily and quickly collect reviews of our products and complete the project.
"Confirmed consumers' true feelings based on data"
Simply emphasizing frequently mentioned words with word clouds and analyzing based on the positivity/negativity of words alone did not help us achieve our goal.
This is because many words have multiple meanings.
For example, when expressing a car, the word "heavy" has two meanings as follows:
"Stable"
"Poor fuel efficiency"
Therefore, a technology that analyzes sentences rather than words was essential to understand human emotions.
Hashscraper first processed a spell check function tailored to the nature of reviews that are prone to spelling errors, significantly increasing the accuracy of data analysis. As a result, we were able to accurately identify what aspects of our product were lacking and what were its strengths based on data.
By analyzing various indicators such as product price, design, and convenience from multiple perspectives, we were able to determine what aspects of the product needed improvement.
As a result, we were able to deliver a convincing report to our team leader and CEO.
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