Social media strategists like to portray Facebook, Twitter, and other social networks as great marketing venues, but, frankly, they fall short of being able to measure campaign success effectively.
Effective social media measurement is much more difficult than it seems, mainly because it is almost impossible to get a representative sampling from social media, as Gary Angel, president of Semphonic and SemAngel blogger, discusses here. While social media measurement may prove rewarding when using social media sites to conduct online focus groups for market research, as representative samples wouldn't be required in such instances, that's not the case so much when measuring social media for brand sentiment, brand awareness, share of voice, share of sentiment, and other such activities.
As a result, I think we are largely measuring the wrong things and going about measurement the wrong way.
As I wrote in my book on social media analytics, I saw an opportunity to disambiguate the choices businesses need to make about which social media analytics platforms, analytics software, and processes they need consider for their social media measurement needs because it's hard to look under the hood and see what's really there. In that sense, I was trying to map a better approach that also put the analyst in charge, to give him or her a seat at the table as the person who is doing most of this measurement work, instead of the business development, sales, or brand person (although this is the conventional wisdom and DNA in many marketing communications departments).
One big issue in measuring social media is that businesses are capturing just a fraction of the data needed to calculate return on investment, and most of the rest is what I call "ultraviolet," which is either data that you can capture but don't, or data that you do capture but is not usable for analysis and business insight. As a result, capturing enough contextual data in social media with its vast troves of unstructured data is highly difficult.
Most social media platforms end up falling back on measuring friends, shares, tweets, retweets, and other quantitative measures that are relatively easy to get and calculate. But what we're really after is a qualitative assessment of effectiveness -- and then, by how much. In general, the formulas for calculating effectiveness do not map well to social media. This is because part of the formula for social media ROI, the cost of the investment in social media, often involves soft costs such as the value of time spent.
Common issues coming up with measuring social media are the confusion surrounding what to measure in social, along with the sheer volume of unstructured data and the lack of any universal standards on what and how to measure social. To make matters worse, many vendors and agencies obscure their marketing messages, misrepresenting offerings in order to sell more of their products and services. Clients and stakeholders often lack the necessary vocabulary and lexicon to articulate their real needs and wants. Another issue is finding the right people to do the work, along with clients willing to pay for the right systems to collect, clean, and structure the data being analyzed for social media effectiveness.
Distilling signal from noise in social media is pretty expensive, when you stop to think about it -- so much of it must be manually culled if you care about accuracy on sentiment and topic analysis -- and the already sizable data volume is growing constantly as the sources and reach of social media expand, too. As an analyst, I believe that measuring the effectiveness of social media requires having the right data at our disposal -- and if we don't have great data to work with, it's hard to get past that.
Many aspects of the businesses evolve rapidly, but others clearly do not. If regulators target the fixed portion of the business models, they would have more success. For instance, if the regulators of privacy were to require that users be aware of the privacy level, then a combination of random user surveys and checks on actual adherence would be, I think, surprisingly effective at gauging the effectiveness of online social privacy regimes. And if the regulators targeted this as a metric, it would be effective at changing the transparency about privacy.
The issue is one of motivations, as I have pointed out in the past. Comapnies have exactly one purpose, and that is to maximize profit. If you want to enforce standards, you need to really understand what it means that the only goal of a company is to maximize profit; it means that unless the standards are able to break through the singleminded pursuit of profit, they will fail. The way this is done can vary - perhaps it would be by imposing fines more than commensurate to the cost for the business to comply, but more likely subtler measures ould be more effective.
The question for regulators is what leverage they have in the business model of the companies they regulate. The simplest leverage point is useless, and most frequently employed - they can create additional burden, therby driving up the overall cost, whether the business is compliant or not.
@Broadway @Shawn I agree with you both, setting up regulatory bodies for privacy standards on major sites like FB and Google will not in my opinion be very easy to enforce. These "privacy standards" seem to be subject to the whims of a particular company.
Shawn, you are correct. FB and Google regularly make a mockery of privacy "standards." At this point, I think standards come down to what the user body tolerates. Anger the users, and even all-mighty FB bows before them.
Just to play devil's advocate for the moment, don't standards face the same problem as technology, in this case setting up rules and proceedures for a medium evolving so rapidly that most might be obsolete by the time they are agreed upon? Consider how linking standards in online media have been slow to adjust to reality including one proposed regulation suggesting that deep links, to a place other than a site's main landing page, was bad etiquette. (I'm not making this stuff up. I can remember bloggers when I started in 2006 trying to promote these crazy ideas!) I might add that online privacy standards, which you mentioned in your comment, have hardly kept up with Facebook's or even Google's tinkering, at least online, and standards seem to vary from Website to Website. I must confess I'm skeptical of much of this.
The answer is Standards - and Standards bodies such as the Web Science Trust - taking on defining how to measue Social Media, how to collect it, much as the there have been standards set up for Web Caching, Privacy, IPv6, and so on.
Well, not sure about that (comprhenensiveness) for a couple of reasons:
1. About 20% of humanity has access to the internet - and it's growing very quickly.
2. More people online and active = much more content
3. Cloud and Big Data considered, it's very hard to get much of the data in any single repository and categorized in useful ways
4. More powerful mobile devices (mainly smartphones) are multiplying like jackrabbits.
5. As people are multiplexing their attention and taking their super powerful mobile devices with them everywhere they go - they are constantly "on" Social Media - creating more and more content which the analytics platforms are trying to store.
6. As time goes on, not only is the collection of tweets, blog posts, pubically shared Facebook posts, Photos, Video sites, Podcasts, RSS feeds going to muliply - but they will mushroom faster than any platform can index and categorize them in useful ways.
7. As more of the world that is non English speaking become content creators and content consumers - demands on the analytics plaforms to index and understand all this data will shoot past any abilities even the most comprhesnsive platform of them all, Google, to contain and represent.
The answer is - it's going to get worse as time goes on, not better.
Beth, actually no. No DATA is better than bad data.
I'll reference the last paragraph of @gangel latest post at SemAngel - see http://semphonic.blogs.com/semangel/2011/10/pr-social-media-measurement.html
"When organizations don't understand how terribly biased their samples are, they will use them in the most inappropriate ways (such as using data sampled by "Influence" to measure brand sentiment or consumer interests). As I've said many times before, when it comes to making decisions, it's really much better to have no data than bad data."
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