A great amount of data scraping and analytics is aimed at the existing workflow improvement, brand awareness, and market impact enhancement, or providing cutting-edge customer service and experience.
To achieve these aims you should:
Set clear business goals
Your goals should be specific and tangible, you should have a clear picture of what you want and what you must do to achieve it. You can, for instance, set a goal to increase sales and try to figure out what product your target customers prefer through the analysis of your clients’ feedback to surveys, their activity on social media, and various review platforms. With the received insights in mind, you can then alter your product mix accordingly.
Choose relevant data sources
To guarantee credible results, extract data from relevant web pages and sources. It’s also vital to check the target websites for the credibility of their data.
Check data completeness
Before analyzing the received data set, make sure it covers all the essential metrics and characteristics from at least one relevant source. When this is done, a proper Machine Learning algorithm should be applied to provide the expected outcomes.
Verify the applicability of the Big Data analysis results
On receiving the Big Data analysis results, you should take action based on these results to reach the business goals you have set. Having a certain product in stock in abundance, for instance, or by considering a relevant promo or giveaway.
It’s vital to act while your Big Data analysis results are still current, or you risk having gone through the whole process in vain.
Set the indicators of data mining success
To check the effectiveness of your decisions and actions grounded on Big Data mining analysis, set certain KPIs (Key Performance Indicators)—the level of sales growth, a decrease in marketing expenses, logistics costs going down, etc. This will help you evaluate the efficiency of your data scraping and continue moving your work on business workflow improvement and optimization in the right direction.