When people talk about social data, they usually focus on two dimensions that are relatively easy to measure and articulate—volume and source growth. More data coming from more places. But something else is happening with a lot of social data that makes it difficult to draw conclusions from on its own—noise is increasingly making it difficult to achieve true signal strength. A lot of companies may be experiencing this without even realizing it.
Let’s say I’m a corporate user of the average listening tool, and I’m trying to trend some of the standard social metrics over time. Specifically, I’m looking at number of mentions, follower/fan/subscriber numbers, and sentiment for Twitter, Facebook and blogs.
Imagine that hundreds or thousands of tweets and blog posts mention my brand every week, and that number went up this week. In raw form, what does that number tell me? On the surface, it tells me we’re doing something right…right? Not necessarily. How many of the accounts that tweeted it are actually human, controlled by humans and followed by other humans? Estimates of the prevalence of Twitter spam accounts are difficult to come by, but some are as high as 48% and even 57%. It’s clear that spam mentions shouldn’t factor into your assessment of your brand’s social performance, but that’s just the tip of iceberg. Redundancy matters, too. If two accounts for the same entity tweet the same positive thing about your brand, for instance, that doesn’t mean you have two advocates. The best you can hope for is that different sets of real people follow them, and that same message thus reaches more people.
Within that spike of activity, you notice that blog mentions of your brand have gone up. You dig a little deeper and see that the blogs are actually saying the same exact thing—down to the letter. It’s scraped content that has been duplicated over and over across the web, usually without the original author’s permission. Maybe a keyword in the text triggered it, or maybe your own content has been added to a feed that disseminates it into hundreds of nearly-identical (and totally useless) scraping sites across the web. If only one of these mentions is original content, and 100 of them are scraped content, the raw data tells you that your presence on blogs has increased a hundred times over. It hasn’t. In fact, if it’s your content, you’re likely being hurt because duplicate can hurt your search rankings.
Now let’s talk sentiment. Most tools out there today for assessing sentiment from unstructured social data aren’t very accurate, but that’s not a problem with the data itself. One of the biggest problems is that most companies want to know the sentiment of people toward their brand and products, and raw, unstructured social data is full of data from non-people, like automated RSS feeds. For example, if my company puts out a press release—which of course will contain a lot of positive text—and it’s picked up by 10 different automatic Twitter or blog feeds that post things from the various press release wires out there, this tells me absolutely nothing about how people feel about my brand. If an actual human reads the release and posts something negative about it, my aggregate sentiment data is going to reflect something completely false: that positive sentiment is 10 times higher than negative sentiment.
On to follower/fan/subscriber numbers. Would you rather have100 followers/fans/subscribers that never interact with you in any way, or one follower that does? The only thing those 100 followers can do for you is provide a tiny amount of social proof by making you look more important to people that use follower count as a proxy for importance. But if your single follower actually pays attention to you, responds to your calls to action, or shares your content with their followers on occasion, he or she is way more valuable to your brand—and you’ll have to earn it.
What’s a brand to do?
Tom Foremski outlines the problem well:
“Accurate data on social media users is essential. It’s the foundation of all successful social media marketing and advertising campaigns: the precise targeting of related groups of users with their interests.”
The best solution to this problem has three parts.
- Raw, unstructured social data needs to be processed, filtered and cleaned up before it means much of anything
- Once signal is separated from noise, it should be paired with reliable data from other sources to create a more accurate, holistic view of your customers. For example, you can match your social data to your CRM records
- Look for direct results, not proxies. Are visitors from Facebook converting at higher or lower rates than other visitors? How much did revenue increase after a product change was made based on your analysis of social feedback?
None of these things are particularly easy. All of them are totally worth it.
Tweets, Facebook updates and news headlines are strikingly similar in two ways:
- They convey limited information at breakneck speed
- Their value is entirely contextual
Social updates are short by design. They each tell part of a larger story. Their accuracy and usability depends on the credibility of the person creating them, and the content they reference and link to. People get this, mostly. But no one can read the piece behind every link, or separately assess the authority of the people sharing it with them.
Headlines work the same way. We can’t get the full story from them alone. We can’t truly say whether we agree or disagree with an article if we’ve only read its headline. The article and author behind the headline give it almost all of its value.
But we take shortcuts, because it’s impossible to fully evaluate every piece of content aimed our way. If I trust The Economist, I’m not going to be as skeptical of its articles as I would be of the “news-repurposing turbine” that is HuffPo. If my friend is an amazing cook, the opinions she tweets about food will carry more weight than tweets from my culinarily-challenged college roommate. And if enough people point to a headline, people start to think the headline tells more of the story than it actually does. It’s human nature.
When the conversation is about something as complex as social, that effect is amplified. How do headlines like these shape our understanding of social’s value?
- Social Media Impact On E-Commerce Called Overrated
- Facebook, Twitter Fail to Promote Brands; Offline Works Better
- Most Consumers Still Don’t Talk About Brands on Social Sites
- M-Commerce Rocked the Holiday Season. Still No Word About F-Commerce
- Why Social Isn’t Helping Online Retailers Find Customers
Together, these headlines appeared in thousands of tweets, Facebook and Google Plus posts. The research and reporting behind them is actually pretty solid and nuanced. But headlines and nuance don’t mix well. As a result, the conversation has a difficult time evolving past the narrowly-defined, shortsighted understanding of social’s value.
Traffic, conversion, followers, likes, mentions (we might as well add pins to the list now)—all good things to celebrate, monitor, analyze and even worry about. There’s a whole other universe of value to unlock. We get there by trying our damnedest to answer questions like:
- What do my customers actually care about?
- What are they getting out of sharing their data with me?
- How can we build social experiences without building barriers?
- How can better data move “influencer engagement” past sending people free stuff, and hoping that they’ll blog about it?
One of the unfortunate realities of the conversation about social is its tendency to swing between hyperbole and incredulity. It’s just like political coverage in this way:
- Fake SM headline: Drop everything and get on [new social network or tool] now—or you’ll be sorry
- Fake political headline: [Candidate name] is unstoppable
- Fake SM headline: What’s so great about [new social network or tool]?
- Fake political headline: [Candidate name] wins [state name], but does she really have a chance?
If there’s one thing we in new media love talking about—and writing about—it’s how different “our” new media is from “their” old media, how much better we are. In a lot of ways, we are better. But on this point, whether we’re really less prone to sensationalism and superficial exploration of topics that deserve deeper digging, I think we’re far from proving it. Let’s change that.
This one’s going to be short. Here we go…
Social professionals don’t get to decide that ROI isn’t important. Our clients and bosses decide that, and it’s usually coming from the right place. We can joke among ourselves about ROI-crazed executives and managers, but I think we all know that it’s pretty cool that our companies are putting any money into social–into us–at all. Of course they want to know what they’re getting for it.
That’s one end of the ROI attitude spectrum, the scoffers. I’m not going to devote this post to proving that social ROI can be calculated–that’s something they’ll need to do for themselves (before someone higher up asks for it).
At the other end of the ROI attitude spectrum are the obsessed. They believe in social ROI, as they should. But the way they think about it is neither sustainable nor scalable. To them, ROI is something that justifies what they’re doing. Sometimes it’s even a defensive calculation, as in, “I can’t believe they’re shrinking the social budget–just look at this ROI!” Most of the ROI-obsessed rarely have to play that card, because they’ve always got their finger on the number, which figures into all of their reporting, etc. But why tread water when you can swim? Both will keep you from sinking, but swimming gets you somewhere.
Reporting is good. ROI is good. They both have so much more to offer. Truth is, if we obsess on reporting-as-justification we only get a sliver of the ROI we could see if we used reporting as a basis for optimization. That’s right, improvement.
Social is the most dynamic, interesting development to hit business in the last few decades, and we’re using numbers in a static, one-dimensional quest for approval. Imagine if we decide to devote 10% of our reporting to justification, and the other 90% to improving what we do, and delivering more ROI than before.
Who would disapprove of that? Let’s stop treading water and see where swimming takes us.
This post started as a comment on Brian Solis’ blog.
n. A choice between two positive or ideal things; a problem that actually demonstrates one’s good fortune.
It’s hard to think of something that I would write about here, but not on my company’s blog. Let me assure you that this is a champagne problem, not a case of low standards. But it still feels like I’m letting someone down by not updating this more often; perhaps that someone is you.
The truth is that social media exaggerates our sense of self-importance. I admit that I feel a tinge of guilt when thinking about the dearth of content on this blog, as if you’re sitting there twiddling your thumbs, just waiting for my next burst of genius. Ha.
At the beginning of 2011, I knew that I wasn’t going to be able to consistently update this blog with quality content, because in reality, that would be at the expense of quality content for my employer. Here’s the math I used:
- I like when people read my writing
- My employer benefits (inbound contacts, 3rd party coverage, etc.) when I post on their blog, and also when I guest post on 3rd party blogs
- Tons of people read Bazaarblog and blogs like Convince and Convert, SME and MarketingProfs
- This blog’s audience is far, far more…intimate
- So, it makes more sense for me to write original content for other blogs
Add to that the book I’m trying to write and the wedding I’m trying to plan, and tons of fresh posts here just aren’t in the cards. Instead, here’s a roundup of some of what I’ve been writing about lately elsewhere.
- I wrote about brands that let their customers drive, and brands that are too afraid to give up the wheel.
- I unfairly divided everyone working in social into two species: foxes and hedgehogs. Everyone thinks they’re the fox, but just like geniuses, we can’t all be one.
- I tempered our current enthusiasm for social data with a dose of reality—most companies won’t be able to harness its value until they understand the three major impediments to proper social data analysis.
- I explored what I know best—b2b social media, and argued that only data will assuage the doubters.