Tuesday, October 25, 2011
Many are reliant on technology to do all the work; to spoonfeed insight into the perfect, executive-friendly dashboard.
As a result, what I see today is rampant platform-hopping. Listening in 2011 has largely been marked by the quest for the ultimate platform. Clients switch listening providers for prettier user-interfaces, better-slicing-and-dicing, ease of direct engagement, access to the Twitter API... the list goes on, and many of the reasons for switching are warranted.
The error is simply in expecting the technology to do all the work.
This error has also led many to make the pessimistic statement: "Listening platforms are commoditized."
Is this the case? Is there really no qualitative differentiation? Partial fungibility? Can a listening platform really be a commodity in the absence of standards?
I would argue, not yet.
Further, have they jumped the shark? Is the pessimism warranted? No.
I see platforms vary widely on the surface, and then in more meaningful ways like data coverage, data quality, and mining methodology. But despite these differences, they all do a pretty good job in serving up insight; several have an impressive edge.
The problem is that the insight delivered will not be useful for your organization until you expend some effort, personally. In my Innotech talk last week with Kate Rush Sheehy, I argued there are three main things that matter when trying to be a good listener. The first, the most important, is a precursor and a contingency:
Get involved in the data. Physically. This task is not below anyone. Change up your Boolean operands and see the impact. Read through your results. Know what “spam” really means in your landscape. You must immerse yourself in the data before you can warrant switching. It’s not fair to you or the technology.
Listening platforms are growing in every sense-- in number, in prowess, in prevalence, and in importance. There are amazing advances in NLP, sentiment analysis, geo-location, and data warehousing enabling faster, more precise analyses to occur. But no matter how good the technology, good listening will always be effortful.
Photo credit: flickr.com/CarbonNYC
This post also appears in the Dachis Group Collaboratory
Tuesday, September 13, 2011
The noise out there can be infuriating. Too much clutter; no sense of what goes with what; what's a "need to know" vs. a "nice to know" vs. a "never wanted to know."
I often find myself talking clients off the ledge as they attempt to prioritize social media efforts, given inexplicable differences in results across listening platforms, varying calculations of influence, and the age-old question of "what's good" when it comes to buzz and sentiment. Further, knowing 'how well' you're doing in social media has only become more problematic with the increasing shift in interest from amassing eyeballs to mobilizing and rewarding them.
As an industry, we're plagued by inefficient categorizations, unstable rankings of authority, and unpredictable, black box algorithms guessing what matters most.
Perhaps as a result, we see more organizations shifting in the same way that we, as people, do as we go through adolescence-- from giving disproportionate weight to what others say about us, to being more concerned with our own actions. That which we can control.
Being in control of our actions requires a different type of measurement and management. To help organizations in said efforts, we're announcing today our public launch of the Social Business Index (SBI), the first application on our Social Business Intelligence as a Service (SBIaaS) data services platform.
Having worked in social media analysis for seven years, it's become clear to me, the trick to finding meaning in social media is to be intimate with your dataset (for context), and to monitor relative comparisons to yield meaning. We've thoroughly taken this to heart with the SBI.
Our dataset includes the social accounts of thousands of companies, their subsidiaries, and brands, in addition to the social accounts of their engaged market (e.g. anyone who interacts with a given account). Using natural language processing and machine learning algorithms, powered by our own strategists, we identify specific activities that are being executed by a given brand -- emanating from their social accounts. This is a critical distinction from the way things are being measured today. We're not exclusively monitoring reactions, or buzz in response to a real or perceived tactic. Instead, we're starting with the action per se. We're measuring what you, as marketers, are doing-- in addition to the way your market responds.
We've captured specific behaviors correlated with outcomes such as Brand Awareness, Brand Love, Brand Mindshare, and Advocacy. In aggregate, this gives us a company's Social Business Index Score-- a ranking, analysis, and benchmarking of Social Business adoption and performance.
Go to socialbusinessindex.com, learn more, and sign up.
We're excited about our progress in digging out of the black hole of social media measurement. We acknowledge our approach will evolve over time and we look forward to your collaboration to do so. If you work at a company covered by the index, register for private access to deeper analytics. If your company is not currently covered, request coverage.
If you're just curious, take a look at our ranking and best in class analysis by browsing at www.socialbusinessindex.com.
This post also appears on the Dachis Group Collaboratory, where you can find my colleagues' related posts as well.
Tuesday, June 14, 2011
People are "winging it" with measurement in social today. Marketers embarrassedly tell me this daily.
In my social psychological opinion, those of you who aren't abiding by any standards, measuring a little sentiment here, a little influence there, and a lot of buzz everywhere are falling prey to a cognitive bias: The Imposter Effect. You're denying yourselves the credit of being bona fide experimenters.
With no standards yet, and no evidence that a global social media metric standard spans all business goals and outcomes, the best thing you can do in measurement today is effectively operationalize your variables. Operationalize in the scientific sense: define the ambiguous concept you're trying to get at by coming up with a relevant way to measure it. We're an industry without gold standards. You have no choice but to wing it, but you can still do so empirically.
When social scientists study things like "health," "happiness," and "satisfaction" with marriage, jobs, or life, we rely on proxies. You often hear things like "health, as defined by number of doctor visits in a month," or "happiness, as defined by size of smile." In lieu of (or sometimes in addition to) getting self-reports of "how healthy/happy you are," these objective measures act as a best bet or starting point, eventually with some validation as to why that operationalization was selected.
Take a typical desire in social business measurement today: "we want to tie engagement to business results." So, how do we operationalize engagement? Importantly, this doesn't mean you should settle for a kitchen sink approach and add up everything to yield engagement, instead chose wisely - a realistic manifestation of what it means to be engaged. As I've said before, think about:
- Objectivity-- Really think about what engagement means; don't arbitrarily involve variables simply because they’re available (e.g. # friends).
- Reliability - Look for something that gets at engagement over time. Don't incorporate variables that measure the same thing multiple times (e.g. friends on Facebook + followers on Twitter + connections on LinkedIn).
- Validity - show that your variables predict a meaningful behavior (e.g. statements of connection with the brand and likelihood to purchase, etc.). A great trick in defining variables is to think of the inverse. Get positive results of what you want (i.e. engagement) and negative results of what you don't want (i.e. disengagement).
This is also posted on the Dachis Group collaboratory. Join the conversation there to access a wider network of social business professionals.
Photo Credit: flickr.com/mlrogers
Wednesday, March 9, 2011
I left my hometown of NYC for ATX to cure myself of pedestrian rage (NYers version of road rage). This shocks people-- to have left New York City (said with a Texas accent like in the old El Paso commercials) for a funky little Texan town. Truth is, all that NYC offered didn't stack up so well for me, compared to Austin's riches.
I came for the intellectual curiosity UT Austin uniquely offers-- academic freedom most schools don't manage to breed. I came for the start-up spirit Austin Ventures nurtures-- premium value on entrepreneurialism unseen elsewhere. I came for the work ethic that preserves the best of east coast ambition with the backdrop of west coast yoga retreats. I came to raise my family in a place where people genuinely want you to succeed in life.
Quality of life in Austin is simply higher than in the more fast-paced, cut-throat, nail-biting enclaves of the US. Austin is the perfect mix of intellect, athleticism, family-friendliness, creativity, and entrepreneurial spirit. And like attracts like: this unique combination makes us the most ripe breeding ground for social business - thinkers and doers. You won't believe the people you run into at Whole Foods headquarters...
People often dream of moving to NYC. Living in today's Austin makes me wonder whether people will soon dream of someday making it in Austin with the same tenacity. Austin is a great place to work and live. If you're smart, we're hiring; join us!
Monday, January 3, 2011
Listening (née social media monitoring) used to be a means to uncover known unknowns like this-- predictable sets of things known to be possible.
The future of listening was the promise of evolving to unknown unknowns-- things we didn't even know could be out there. For example, the prospect of happening upon unexpected audiences (i.e. Dads?) talking about your product being used in unintended ways (i.e. eye cream for cellulite!).
Six years of listening has turned up hundreds to thousands of those kinds of anecdotes, yet still there is no precise science to uncover unknown unknowns. We still somewhat systematically rely on a backbone of metrics such as discussion volume, sentiment, and topics. Sometimes we try to identify "influence," although there is no agreed upon algorithm to capture "influencers." There is also no clear winner/best of breed technology with 100% accurate sentiment mining or topics analysis.
I think it's time for us to agree that isn't the future of listening. Technology will not get to the point where we can algorithmically detect weak signals in real (enough) time to prevent crises or perfect product development-- much more than we can today.
The future of listening will transcend technological advancements. The future of listening-- the near future-- is making it work in an organization. Operationalizing listening as a standard business process. The future is a flow chart that integrates people (e.g. customer service, product), process (e.g. escalation, resolution), and technology (e.g. from listening to CRM) and disseminates results to a wider group of stakeholders.
I don't think the fundamental challenges of listening have been solved; and, don't want to encourage stagnation. We should forever challenge ourselves to better understand the complexities of language via semantic analysis and capture and classify new types of data (e.g. check-ins, metadata), but we should go ahead and make listening part of everyone's daily life without waiting for perfect technology and standardized metrics.
That is the future. Serve the coffee in the potentially stained mugs. People need their caffeine to function.
Photo credit: cudmore on Flickr