…and what we are collectively trying to create is “increasingly data-informed and data-aware businesses and business people”
- Eric T. Petersen from the “The Myth of the Data-Driven Business”, published September 6, 2011 in Web Analyatics Demystified
Strategists are always touting the importance of data to businesses. The conventional thinking is to measure everything, from the most sublime to the very obscure; the more data the better. Well here’s the rub - data can be expensive: collecting it, analyzing it and figuring out what it really means.
One exception to this notion of data collection being an expensive undertaking, is data that is generated and collected through more passive means.
This kind of data is available just by virtue of the fact that you have a web page, an e-newsletter, a Facebook page, or a Twitter account. It’s data that comes to businesses in the form of ‘likes’, comments, product or service reviews, retweets, webpage visits, or any other digital feedback. Of course, just because the data is collected passively, the analysis is not passive.
Tips to Analyze Your Passive Data
As a matter-of-fact, it’s probably a bit more challenging translating likes and retweets into hard analytics. Here are just a few questions you can use to explore your data during analysis to provide you some interesting insights:
What is the relationship between ‘likes’ and sales?
What is the relationship between negative online ratings and reviews and sales?
How much do negative ratings and reviews correlate with your overall brand ratings?
How many bad reviews does it take for your brand image to be negatively impacted?
Do customers ‘like’ before they buy or because the buy (In all fairness, this is a question that might be best-suited for good old-fashioned survey research)?
One point of caution: Remember to take a motion picture rather than a snapshot approach when analyzing any kind of data whether passively or actively generated. It’s easy to make wrong conclusions based on a few data points. The more data points you have typically, the more confident that you can be in your conclusions.
A pretty good practical example of using this passively collected data is a paper titled “Social Media Analytics: Data Mining Applied to Insurance Twitter Posts” by Roosevelt C. Mosley Jr. Mosley's paper provides examples and ideas about the potential of using this type of data that will help your organization get started collecting and effectively analyzing your online data from social networks, your website, and your private online community.
The good news is that the data is relatively cheap, if not free. So don’t be afraid to wade into your data!
- Eric T. Petersen from the “The Myth of the Data-Driven Business”, published September 6, 2011 in Web Analyatics Demystified
Strategists are always touting the importance of data to businesses. The conventional thinking is to measure everything, from the most sublime to the very obscure; the more data the better. Well here’s the rub - data can be expensive: collecting it, analyzing it and figuring out what it really means.
One exception to this notion of data collection being an expensive undertaking, is data that is generated and collected through more passive means.
This kind of data is available just by virtue of the fact that you have a web page, an e-newsletter, a Facebook page, or a Twitter account. It’s data that comes to businesses in the form of ‘likes’, comments, product or service reviews, retweets, webpage visits, or any other digital feedback. Of course, just because the data is collected passively, the analysis is not passive.
Tips to Analyze Your Passive Data
As a matter-of-fact, it’s probably a bit more challenging translating likes and retweets into hard analytics. Here are just a few questions you can use to explore your data during analysis to provide you some interesting insights:
What is the relationship between ‘likes’ and sales?
What is the relationship between negative online ratings and reviews and sales?
How much do negative ratings and reviews correlate with your overall brand ratings?
How many bad reviews does it take for your brand image to be negatively impacted?
Do customers ‘like’ before they buy or because the buy (In all fairness, this is a question that might be best-suited for good old-fashioned survey research)?
One point of caution: Remember to take a motion picture rather than a snapshot approach when analyzing any kind of data whether passively or actively generated. It’s easy to make wrong conclusions based on a few data points. The more data points you have typically, the more confident that you can be in your conclusions.
A pretty good practical example of using this passively collected data is a paper titled “Social Media Analytics: Data Mining Applied to Insurance Twitter Posts” by Roosevelt C. Mosley Jr. Mosley's paper provides examples and ideas about the potential of using this type of data that will help your organization get started collecting and effectively analyzing your online data from social networks, your website, and your private online community.
The good news is that the data is relatively cheap, if not free. So don’t be afraid to wade into your data!
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