Quinnipiac, impression management online, virtual groups, persuasive industry, locative media, what is information, role of social media, ICM top 5, strategic planning, defining publics, strategic planning to nonprofits, strategic plan implementation, Center for Science in the Public Interest, Wal-mart, project management styles, future, journalism, reflections, NESN SEO, onward to Quinnipiac, A Day in My Life in Social Media, Viral Videos, Qualitative and Quantitative Analytics in my Life, social media monitoring tools, Media Convergence, Basic Web Analytics, A Crash Course in SEO, Semantic Search, Monopoly, Algorithmic Surfacing, Ambient Awareness, Polarization, Television, Participation, Physician Boundaries, Ethical Dilemmas, Charlie Hebdo, Premium Service, Spiderman, Brian Williams, Dark Patterns, Content Moderation, Big Data, Net Neutrality, Privacy and Big Data, Forgotten, Most Important Role of a Community Manager, Influencer Impact and Networks, Harrison Parrott, Content Marketing for Community Managers, Authentic Brand Voice in Social Media, Best Practices in Using Social Media for Customer Service, Highly Regulated Industries, Sabra Hummus, SWOT and PEST Analyses, Message Strategies, Communication Tactics, Program Evaluation, Continuing Program Evaluation, Strategic Campaign Plan Formatting, RPIE, Biblical Texts, Disruption, Facebook network, Qualitative and Quantitative Analytics, NESN Key Indicators, Writing Ethics, Spiderman, Wireframing, Sabra Hummus, Lonely Writer, Final Project ICM 522, reinvention,

Quinnipiac Assignment #02 – Qualitative and Quantitative Analytics

Quinnipiac Assignment #02 – Qualitative and Quantitative Analytics

This week’s assignments at Quinnipiac were centered around the differences between quantitative and qualitative analytics. I had a couple of essays to write for Assignment #02 – Qualitative and Quantitative Analytics.

I have decided to reprint one of my essays here, in its entirety.

Qualitative versus Quantitative

I think that a choice between the two is, perhaps, misplaced.

English: Interactive Visualization of Qualitat...
English: Interactive Visualization of Qualitative and Quantitative data in a web based mixed methods application (Photo credit: Wikipedia)

Aren’t both of these necessary, in order to really see the big picture?

We Love Quantitative Data

Probably the best part of quantitative data is that it’s relatively easy to get, particularly online. Consider this – do we, given the current state of technology, know everyone who comes into, say, a department store?

Web analytics framework
Web analytics framework (Photo credit: Beantin webbkommunikation)

Even when we break this down to hourly increments, and even if we look at closed-circuit cameras, we still might miss someone.

After all, if a person leaves and comes back later, we might not notice that it’s the same person.

How Does the Web Differ?

Not so with the web. Cookies and other tracking codes tell us a device is back; and an account if our site allows for user accounts. That still doesn’t help us if everyone in a household uses the same account, but it’s a start.

We look at our web data and we think – aha! User #12345 has returned four times in one day!

And then we have no idea why that happened, and no way to capitalize on it. It’s the ultimate in vanity metrics, e. g. it’s stuff that can be measured but it isn’t necessarily actionable, or even desirable information.

We Love Qualitative Data

With qualitative data, we get more into the whys and wherefores.

coding cat-egories, qualitative and quantitative data
coding cat-egories (Photo credit: urbanmkr)

Why did User #12345 return four times in one day? If a purchase is made on the fourth go-‘round, that’s terrific. But why were there three other visits? Even someone performing research and then returning later might not necessarily visit two more times. What’s up with that?

Maybe the website was slow those two other times. Maybe User #12345 got busy and abandoned the cart for Visit #2 and Visit #3. Some of this is inferential.

Some of it can be proven, such as site slowness or at least traffic spikes that could imply speed issues. We can’t get into User #12345’s head (at least, not yet).

We REALLY Love Them Together

I think we’ve got to look at the two types together.

In the Huffington Post article, The Big Data Craze Is Just as Qualitative as It Is Quantitative?, Sean Donahue writes, “But for brands, political campaigns and advocacy organizations that aim to have data-driven conversations with audiences, it will be more important than ever to apply qualitative logic and human reasoning to online analytical models.

In short, subject matter expertise and deep knowledge will matter more than ever before given the rise of big data.

As communicators, even with what we have at our fingertips today, we need to immerse ourselves in the substance that contextualizes big data and allows us to make sense out of it. This means committing more time, asking more questions, consuming more content and never losing sight of the fact that data without actionable insights is meaningless.”

I believe that what Donahue is saying is that we can and will be getting great big garbage bags full of data, and soon even more of it will be at low or no cost. But without contextual analysis, it’s somewhat meaningless.

Anmol Rajpurohit

Further to that is Anmol Rajpurohit’s point in Qualitative Analytics: Why numbers do not tell the complete story?, wherein he writes,

“Quantitative analytics still needs more manual intervention and the results are often fuzzy. In absence of a clear-cut approach and thus automation, it is not as time and energy efficient as the traditional quantitative analytics. But, qualitative analytics is still indispensable as it provides deep, actionable insights about the ‘why’ and ‘how’ aspect, which often gets ignored as we continue to be inundated with the ‘what’ ‘where’ and ‘when’ of statistics.”

As Rajpurohit indicates, qualitative data is fuzzy and manual and not automated. It’s a slow process (and perhaps a less exact science than quantitative), yet it remains necessary to a holistic understanding of online data.

To me, this is peanut butter and jelly. They’re fine separate, but they work best together. With purely quantitative data, we can know that a particular literary passage has 278 words. We can know that two of its longest words are twelve letters long: consummation and undiscovered. We can find that the (probably) most frequently-used word is ‘the’, with twenty occurrences.

With qualitative analysis, we learn that the mystery passage is Hamlet’s To Be or Not to Be soliloquy. With qualitative analysis, it stops being a laundry list of words, and its context affords a meaning that goes beyond bare statistics.


Tags: ,