I’ve been working through a series on the creation of a Customer Intelligence System to track and report on customer attitudes. It’s a sprawling subject that touches on quite a range of topics – from online survey research to unstructured data to social media – each of which could consume a book-length treatment. For much of the series, I’ve focused on the power of online survey research and how under-utilitized it is in the enterprise. But in my last post in the series, I described a set of research questions that really couldn’t be addressed with online survey research and for a fair number of those problems, Social Media research was a likely alternative.
The advantages/disadvantages of Social Media Research vs. Site Intercept Surveys are pretty easily understood. Here’s a little cheat sheet I’ve put together that captures the key advantages of each:
Many of the key differences can be summed up in two simple differences. Social Media lets you sample against the entire universe of prospects and customers while site intercept surveys are limited to the population of your site visitors. Social media also provides a less guided, more open research experience. Conversation in Social Media is open-ended and can (and will) cover topics you’ve never considered. With survey research, you can frame very precise questions that allow you to explore differences that would be almost impossible to capture in social chatter, but you have to know what you want to investigate.
These two differences tend to combine and reinforce the advantages of each channel for different kinds of research.
All this tends to assume, however, that HOW to do Social Media research is fairly obvious. There’s no mystery, certainly, about how to do online survey research. I’ve been extremely critical of the way most enterprises conduct online survey research and the uses they make of it. Still, the basic methods of doing survey research are understood by almost everyone.
With Social Media I don’t think that’s true. It’s such a new research
channel that I’m not sure anyone really understands how it can be used or what exactly are the best ways to do that research.
At a very basic level, Social Media Research involves four steps:
- Sampling: identifying conversations from the population of interest
- Classification: grouping those conversations into meaningful bucket
- Interpretation: building models or analyzing relationships between classifications
- Presentation: contextualizing the data for decision-makers
Each of these is rather unique in social. Sampling, for example, involves some steps that simply have no analog in traditional research. You have to figure out where to listen, you have to figure out your high-level gating keywords (that demarcate potentially interesting conversations from the firehose), and you have to figure out how to separate your target population from the rest of the conversations in the resulting stream. It’s hard.
It’s in the classification step, however, that most of the rare magic of Social Media Research has to happen. Classification is the step that changes unstructured text data into analytically useful, structured data. It’s only when you know what a post was about that you have a means of analyzing it.
So how do you classify social media data?
From a technology standpoint, this is a job for text analytics systems. Most listening tools have built-in capabilities for doing this and those capabilities range from machine-learning systems to simple keyword classifications. Regardless of the method or technology however, it’s the analyst’s job to decide on what type of classification is appropriate.
The most obvious answer (and the one we’ve used the most) is by topic. It just seems perfectly natural. You write (or read) a post about Product X. That’s how you classify it. At a high-level, topic classifications might be things like:
- About our Advertising
- About our Brand
- About our Products
- About our Service
- About our Competitors
- About our Industry
Depending on your research needs, a topic taxonomy can be endlessly extended and broadened. About our Products can be expanded into About Product X, About Product Y and About Product Z. This can, in turn, be further deepened into a set of feature classifications: About Product Speed, About Product Form Factor, About Product Reliability, etc.
Topic classifications obviously add tremendous value. They take unstructured data and make it far more meaningful. But (and there’s always a “but“ isn’t there?), it doesn’t seem to me that topic classifications meet the need of every Social Media analysis.
If you run a Facebook community, one of the basic measurement needs is to understand the popularity and impact of various types to postings. We call this editorial support – and it has a direct counterpart in traditional media.
Suppose you’ve posted a picture contest, a vote on a video, a push to a mobile app, a sweepstakes drive, a reference to National Pi day, and a discount offer. A topic classification of these posts isn’t likely to be particularly revealing. Nor will it help to establish comparability or to build segmentations. Knowing that a visitor re-tweeted National Pi day doesn’t seem all that useful unless you have a steady stream of Math-oriented postings. You might classify this as a holiday posting, but does it really suggest that a Valentine’s post would perform similarly or would appeal to the same audience? I think not.
The fact is that while a small percentage of social media content does classify along interesting topic lines, a huge part of what gets communicated isn’t necessarily topic oriented. It’s not unreasonable to suggest that the topic-oriented content is the more important stuff, but it would be wrong to suggest that the rest of the communication is just noise.
Particularly for a community manager creating non-topical posts, that doesn’t seem like a useful answer.
To handle this type of classification, we’ve borrowed one of our old Web analytics tricks – Functional classification. In Functional analysis, you classify pages not by their content but by their function on the Website. So Web pages have functional categories like Engager, Router, Convincer, Informer, and Closer. These Functional categories turn out to be very useful for establishing the proper measurement of pages (if a page is a Router, you measure how well it moves visitors to the appropriate sections of the Website) and for establishing comparability of Web pages. Pages that perform similar functions can and should be compared. Pages that perform different functions shouldn’t be compared.
It’s a simple, elegant solution to a significant categorization problem around Web pages and it seems to apply equally well to Social Media.
Community posts, after all, are designed with similar functions in mind. Some are supposed to attract new members. Some are supposed to drive to products. Some are supposed to put a human face on the brand. Some are supposed to give community members a bit of a laugh and keep them engaged.
Just as you wouldn’t expect a Router page on a Website to perform (or be measured) in the same way as a Product Detail Page, you wouldn’t expect a post designed to give folks a bit of a laugh to perform (or be measured) in the same way as a post designed to drive to a mobile app download.‘
Keep in mind that a Functional and Topic Categorization aren’t mutually exclusive. They are complementary and can exist simultaneously. A post can be About Product X and be Informational. Or it can be about Hurricane Sandy and designed to put a human face on a brand.
For most community managers (and for a great deal of Social segmentation), I think the Functional Taxonomy is probably more interesting than a topic-based grouping. It creates a categorization that can be used to compare posts that, from a topical perspective, seem very distinct. Even better, just as with Websites, it creates a natural path to measurement. When you know what something is for, you are half-way to understanding how to measure it!
In my next post, I’m going to show a sample Functional Taxonomy for Social Media and then describe how we’re using that classification to tackle one of the holy of holiest questions in marketing – how to measure the impact of brand advertising.
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