Even Jeff Bezos, the CEO of Amazon, is a passionate proponent of using qualitative data to drive strategy. “The thing I have noticed is when the anecdotes and the data disagree, the anecdotes are usually right. And there’s something wrong with the way you are measuring (your data),” he explained during an onstage interview at George Bush Presidential Center.
Bezos’ love for customer feedback shouldn’t compel you to supplant quantitative data with qualitative data when strategizing your next marketing campaign, though. It should compel you to inform your strategy with both sources of data. By combining the insights pulled from web metrics and customer feedback, you can get a full understanding of your marketing program’s effectiveness.
If you want a deeper explanation of what qualitative or unstructured data and quantitative or structured data is, check out this quick rundown of what both data sources exactly are and which tools you can use to store and analyze them.
Most often referred to as qualitative data, unstructured data is usually subjective opinions and judgments of your brand in the form of text, which most analytics software can’t collect. This makes unstructured data difficult to gather, store, and organize in typical databases like Excel and SQL.
It’s also difficult to examine unstructured data with standard data analysis methods and tools like regression analysis and pivot tables.
Since you can’t store and organize unstructured data in typical databases, you need to store them in Word documents or non-relational (NoSQL) databases, like Elasticsearch or Solr, which can perform search queries for words and phrases.
Additionally, since you can’t use standard data analysis methods and tools to pull insights from unstructured data, you can either manually analyze or use the analysis tools in a NoSQL database to examine unstructured data. However, to use these tools effectively, you need a high level of technical expertise.
If you can successfully extract insights from unstructured data, though, you can develop a deep understanding of your customer’s preferences and their sentiment toward your brand.
The most common examples of unstructured data are survey responses, social media comments, blog comments, email responses, and phone call transcriptions
Every time you gather feedback from your customers, you’re collecting unstructured data. For example, surveys with text responses are unstructured data.
While this data can’t be collected in a database, it’s still valuable information you can use to inform business decisions.
If you’ve ever received social media comments with feedback from your customers, you’ve seen unstructured data.
Again, this can’t be collected in a database, but you’ll want to pay attention to this feedback. You can even store it in a Word document to track.
Similar to survey responses, email responses can also be considered unstructured data.
The feedback you receive is important information, but it can’t necessarily be collected in a database.
Your customer service and sales team are always collecting unstructured data in their phone calls.
Since these calls often include some critiques of your company, it’s important feedback to collect. However, as with all unstructured data, it’s hard to quantify.
Any business document such as presentations, or information you have stored on a Word document, is an example of unstructured data.
Since unstructured data is essentially the information you have that can’t be stored neatly in a database, any miscellaneous documents you have can be considered unstructured data.
Most often referred to as quantitative data, structured data is objective facts and numbers that most analytics software can collect, making the data easier to export, store, and organize in typical databases like Excel and SQL.
Even though structured data is just numbers or words packed in a database, you can easily extract insights from structured data by running it through data analysis methods and tools like regression analysis and pivot tables. This is the most valuable aspect of structured data.
The difference between structured and unstructured data is that structured data is objective facts and numbers that most analytics software can collect, while unstructured data is usually subjective opinions and judgments of your brand in the form of text, which most analytics software can’t collect.
Structured data is easy to export, store, and organize in typical databases like Excel, Google Sheets, and SQL.
On the contrary, unstructured data is difficult to export, store, and organize in typical databases. Most of the time, you must store unstructured data in Word documents or NoSQL databases.
With structured data, you can easily examine the information with standard data analysis methods and tools like regression analysis and pivot tables.
However, with unstructured data, you can’t. You’ll have to manually analyze it or use the analysis tools in a NoSQL database to examine this type of data.
While structured data is easier to store and collect, unstructured data gives analysts more freedom since it’s in its native format.
Additionally, companies usually have more unstructured data since the data is adaptable and not restricted by format.
In a world where Google Analytics can spit out every metric under the sun, you must remember that qualitative data, such as customer feedback, is just as crucial for informing your marketing strategy as web metrics.
Without unstructured data, you won’t have a clear understanding of how your customers actually feel about your brand. And that’s crucial for every marketer to know.
Editor’s note: This post was originally published in February 2019 and has been updated for comprehensiveness.
Originally published Apr 9, 2020 4:30:00 PM, updated April 10 2020