Today I’m going to invent a new metric.
I’m sure everyone out there remembers when their high school math teachers introduced them to set theory and the hierarchical classification of numbers. Right?
complex / \ imaginary real / \ irrational rational / \ integers fractions | | whole integers | naturals
(courtesy Ask Dr. Math)
Most of these sets have fancy representations of letters to designate them. For example, ℚ is shorthand for the set of rational numbers (it’s ℚ because you can express any rational number as a Quotient, not because a mathematician wanted to mess with people). While there isn’t such shorthand for the set of imaginary numbers, you can always spot a member because it contains some multiple of i, where i2 = -1.
The point of this math refresher is not to make your eyes to glaze over (sorry about that), it’s to set up a point. Folks in marketing and communications are increasingly being asked to justify costs associated with participating in social media. People with responsibility for budget allocations and profits and losses want to know their return on investment (ROI), or return on marketing investment (ROMI), or a host of other abbreviations involving R’s and O’s and I’s. The problem, as beautifully articulated by Olivier Blanchard, is that asking “What’s the ROI of social media?” isn’t a meaningful question.
The question, then, is not what is the ROI of social media, but rather what is the ROI of [insert activity here] in social media?
When it comes to measuring the return on investment of social media activity, the reality is that not all actions have an outcome that results in direct financial impact. Which is not to say an action may not have value, just that said value may not translate right into dollars and cents. Can you measure efficiency from a financial standpoint? Sure. If you spent X dollars and got Y retweets, then you spent X/Y dollars per retweet. If you spent Q dollars performing social customer support and measured a 5% increase in customer satisfaction, you can legitimately say you spent Q/5 dollars per point of improvement. Congrats!
The problem is that I see lots of people (and agencies) present financial efficiency as ROI. It’s not. The most charitable thing you could call it is ROi, for it exists as return on investment only in the imaginary domain of unicorns and Little Nemo.
I apologize for the recent lack of activity; I had some unexpected travel, a dying computer, and other distractions keep me from stringing together meaningful thoughts. In the short term, I wanted to share some recent content that caught my attention:
Pew’s hot-off-the-presses report addresses changing sharing behavior in social media. Their survey seems to have been fielded in April and May of last year, which makes me suspect the degree of access restrictions is even higher now. Yes, Facebook is not the entirety of social media, but this story syncs up well with my analysis of the huge drop in public Facebook content a few weeks back.
Speaking of Facebook, it seems like this Wednesday’s fMC event is going to be a Really Big Deal. While the recent addition of People Talking About This and other engagement-based measures was a step in the right direction, the growing reporting lagtime was not.
The Journal tries to parse some confusing math. The bottom line is that data usage is exploding, no matter how you measure it:
You can always rely on the Altimeter Group to package the current social media wisdom into a format digestible by people not immersed in the digital space. Anyone asking themselves “What is my Pinterest strategy?” should read this before their local social media strategist embeds their palm in their forehead.
I love that the URL to this article is “Be yourself.” The gaps between content consumption, content sharing, and content creation are well-established at this point (if you hear someone talk about 90-9-1, this is what they’re referring to) and it’s something that companies who peer into those chasms make noise about when they want to get some attention. Here’s bitly’s spin on it from a few months ago.
Finally, I really wanted to highlight a quote from this article:
“The real purpose of big data is to enable big analytics. The most compelling companies out there, I think, are those that attack that problem,” Palmer told me this week.”I really do believe that big data is, in and of itself, a tool. The real story is more about big analytics. Once you aggregate the data you then have to ask really hard questions.”
I feel a little late to the party in linking to this, but let’s pretend my analysis is so incisive that it merits the delay.
Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a “predictive analytics” department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them. “But Target has always been one of the smartest at this,” says Eric Siegel, a consultant and the chairman of a conference called Predictive Analytics World. “We’re living through a golden age of behavioral research. It’s amazing how much we can figure out about how people think now.”
What’s not included in this article is almost as fascinating as what is. I’m genuinely curious about what factors went into determining the focus here. Was it that the author only had one juicy source, whose output was direct mail marketing, and one substantial case study, involving TV commercials? Was it an attempt to differentiate from the Wall Street Journal’s “What they know” series?
Here’s a fun exercise: Search the text for the word “Google.” Zero results. This is especially odd if you remember the Google Search Stories ad campaign. Searching for “Amazon” gives you one quote in the 9th paragraph. Online advertising and ecommerce are mentioned in passing; there are more references to the web in the first few comments than in the article itself.
It really feels like this should have been addressed. Behavioral ad targeting is at least as sophisticated as any of the work described here. PRIZM data is good, but PRIZM and BlueKai data together is better.
Even looking strictly at offline tracking, there were some notable omissions. For true creepiness, consider what Target may already be doing: Think fun buzzwords like RFID or NFC. Tie a GuestID to second-by-second in-store location mapping and BAM! Baby, you’ve got a stew goin’.
Social media warrants consideration as an ingredient in this data stew (or maybe it’s more of a data gumbo?), but I’m a bit skeptical of how valuable it is at the present time. Consumer packaged goods are what are politely referred to as a “low interest category.” There are a handful of products that provide excellent segmentation cues (think diaper conversation on TheBump revealing a mom). But segmentation is a Hard Problem if you’re not Facebook, Google, or a data management platform. Getting access to those data sources is either expensive or closely guarded, which is why most social media listening exercises try to wriggle out of doing it. What’s more, social customer relationship management is not mature enough yet for Target to effectively offer a coupon to someone who (in a highly unlikely event) tweets “I love Kmart.” An analytics team lacks sufficient data to tell you the precise value of said coupon to get that tweet’s author to defect.
The reality is that there is (an assumed) level of privacy associated with browsing the pharmacy aisle or a conducting a Google search that is not present in social media. As convenient as it would make marketers’ lives if people publicly stated their intent at all times, it is not the world that you, I, or John Wanamaker inhabit. As he famously said, ”Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”
I’ve been concerned by these same issues lately. Using tools like SimplyMeasured and SocialBro to analyze accounts for large brands, the proportion of followers that have 0 tweets, suspiciously identical account names, or sketchy bio info (do US-focused brands care about engaging with thousands of followers in Jakarta?) is shocking. And that doesn’t even take into consideration the legions of devoted Justin Bieber fans :-p
But more seriously, this is yet another reason to cultivate quality and not just quantity as you grow a social media community.
The short answer: Abysmal Facebook data quality. For the longer answer, read on…
Image caption: I fight for the user
First, I want to reiterate the considerations I shared in Part I:
I am going to call out some specific scenarios. I freely acknowledge that the plural of anecdote is not data, but I believe these situations to be representative of serious issues, based on my personal experience.
And about that personal experience… I’ve been using social media data as a research input for the better part of a decade. I’ve led studies conducted across multiple countries, in multiple languages, in multiple use cases, for multiple Fortune 50 companies. A large portion of that time was spent at a Radian6 competitor, in various research and client service roles. I would understand if you thought I had an axe to grind against them. The reality is that this is a space with hundreds of competitors, and the issues I’m going to cover are by no means exclusive to one vendor. If you ask any of the product managers I’ve collaborated with, they’ll tell you I am an equal opportunity offender when it comes to criticism of tools.
I’ve been providing recommendations and insight to my clients based on Radian6 data since I joined my employer a year ago. So it’s at no small amount of professional risk that I call out Radian6 by name when I bring these issues to your attention. Hopefully, it also means they will take meaningful and rapid steps to address these problems.
Next up: Data access, data manipulation, and customer service. I promise it gets less snarky after I address data access. Really.
The short answer: Abysmal Facebook data quality. For the longer answer, read on…
Everyone seems to be gushing lately about how social media can serve as the world’s largest focus group.
Companies are high on social media for a number of reasons, but perhaps chief among them should be that social platforms provide the opportunity to create focus groups at a scale never before possible. Millions of people talk about all sorts of things online, and with the right systems and algorithms in place, it’s possible to decipher how they actually feel about the topics they’re discussing. If you want to know how the web-savvy world feels about a product, movie, team, you name it, millions of data sources should trump interviewing a few hundred people in malls across the country.
While I agree with the general sentiment here (ha-ha), it does leave out one crucial point: the difference between prompted and unprompted opinions. You may get a read on the most passionate responses if you passively monitor social media, but then you’re ignoring the huge group of people out there who wouldn’t care enough to say anything…unless you asked them first. To get around this, you would still need to use an old reliable survey — or the new shininess of an MROC.
NetBase, one of the social-media-analysis companies, said that it, essentially, does have a sarcasm detector. For instance, company officials say they can detect that “Thank you [wireless provider name] for messing up my bill” is not a customer rave. … NetBase also claims it can handle slang and Internet-age codes. “We have assigned meaning to all of the emoticons out there,” said Lisa Joy Rosner, chief marketing officer for NetBase. “When something is sick, either they have the flu or it’s the hot new thing.”
“Sucks” doesn’t mean something bad… if you’re Hoover. But seriously, the major issue I’ve seen with automated sentiment algorithms is how they deal with comparisons. When people talk about two companies/brands/products so closely together, algorithms can get very confused about which one is preferred, and why.