New Report, New Service Offerings for Social Data

Screen Shot 2014-03-17 at 8.13.38 PM 1Late last year, I started wondering about social media command centers. Salesforce had launched one, as had Brandwatch, but I wondered: were they really still relevant? Were companies investing in command center deployments, or had interest subsided since their heyday in 2010?

I started talking to clients and vendors to take a pulse on how people were thinking about command centers, what they looked like, what were the use cases, and how they were calculating value. I looked at several deployments: some were “pop-ups,” intended to support conversation during sporting or other events, and some were day-to-day operations.

I decided to dig a little deeper. Soon, Wells Fargo, MasterCard and eBay agreed to speak with me about what they were doing and connect me with the social technology companies powering their deployments. I subsequently spoke with Dell, arguably the pioneer of the command center concept, about their original deployment, and their current service offering. The result is the report embedded below. It’s not intended to be a buyer’s guide or technology evaluation; rather, the intent is to lay out the most salient use cases and provide an inside view into how three leading brands are approaching social data in the enterprise:

At the same time, I began working with my colleague Jess Groopman on an Altimeter Group service offering based on our Social Data Intelligence report from last summer. Like Altimeter’s Social Readiness Roadmap, the Social Data Intelligence Roadmap is a diagnostic tool to help enterprise organizations evaluate their readiness (in this case, related to  social and other digital data), and use that to plan roadmaps, resources and investments.

Fortuitously, the two projects converged into the two announcements we’re making today: one, a new report on the social media command centers, and its potential to become a digital intelligence hub for the enterprise. The other, a diagnostic tool that illuminates the issues–regarding scope, context, strategy, governance, metrics and data–that large organizations must address to extract insight from social and other digital data at scale.

My deepest thanks to everyone who contributed ideas and insights for this report; any mistakes are mine alone. I hope you’ll find these tools useful, provide feedback on them and share them with others in the industry.

We will be hosting a webinar on this new report and offering on April 3 at 10:00 am PST. To register, please click here.

Box link to PDF.

To speak with Altimeter about the Social Data Intelligence offering, click here.

We are happy to cross-link to discussions on this report and service offering below.

Hootsuite posts on the report here.

Posted in Uncategorized | 1 Comment

Superbowl Ads and Social Data: Where’s the End Zone?

585061_85722682Let me preface this  by admitting that I’m not an American football fan. Sorry. But I do love the window-dressing of the Superbowl: the inane commentary, the screams of joy and agony from the living room (and neighboring houses), the lame excuse to eat fried food and stuff wrapped in bacon, the over-the-top ads. And what goes better with fried food and extended primal screams than cold, bubbly beverages, whether of the alcoholic or non-alcoholic variety? Apparently nothing, if this data (supplied by Brandwatch) is any indication. (NB: If you’re just looking to see who won on the conversation volume front during the game itself, you can stop reading in 3, 2, 1…)

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But when you dig in a bit, you can see that there was very little momentum from that social conversation once the game ended: it went…flat. Unless you’re Coca-Cola…

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…in which case you also had to contend with the highest negative sentiment in terms of pure volume. So, aside from the obvious rout on conversation volume, and the high amount of negative feedback about the Coca-Cola ad, who would you say are the winners and losers here?

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My answer: it depends. I’m sure the folks at Budweiser are thrilled to have dominated the conversation during the game. I’m not so sure they would have expected that conversation to extend past the final seconds of play. Awareness and brand preference, pure and simple.

I’d like to think that The Coca-Cola Company, which is firmly defending its ad (along with many others) might be happy to have reached a segment of Americans who it might otherwise be at risk of losing based on health concerns. But let’s take a look at some of the other contenders on the sentiment front.

Screen Shot 2014-02-03 at 2.51.34 PMWhile AdAge ranked Radio Shack’s ad the top of the pack from a creative standpoint, the people’s choice was T-Mobile. And it was Axe, not Coca-Cola, that led in negative sentiment as a percentage of total volume.

Bottom line: does any of this matter? The truth is, we’d need a whole lot of proprietary data to know that for sure. But here are some questions I’d ask:

  • What was Budweiser’s conversation volume last year? Is this higher or lower? Do they care that conversation dropped off after the game, or were they hoping for a longer tail?
  • Was Coca-Cola surprised by negative reations to their ad? After all, they did make the classic “I’d Like to Teach the World to Sing” commercial back in 1971, which in many ways is internally consistent with the current one.
  • Does–or should–Axe care that its ad offended a whole bunch of people? If we could drill into the demographics, I’d look for correlation between age, gender and sentiment. Middle-aged mothers of four are not too relevant to this segment, unless Junior isn’t old enough yet to buy his own body spray. Did Axe simply do a brilliant job of speaking to its audience, damn the consequences?
  • How’s Chobani’s creative team doing today? Are they feeling whomped by Dannon/Oikos, or did their results correlate decently with expectations based on creative budget and ad spend?
  • What about the middle-of-the-pack brands? What were their goals and how will they evaluate their performance? Was it too much to be drowned out by puppies, Clydesdales and David Beckham’s abs, or was it worth it for an–abeit brief–shot at reaching such a huge audience?
  • Finally, as Ken Wheaton asks in his Budweiser review, “Has a Clydesdale ad ever sold a single Budweiser?” I don’t know, and I don’t know if Budweiser knows either. That is an awfully long (and interrupted) chain of causation. In the end, does it matter?

This is what makes The Advertising Superbowl so different from The Football Superbowl: in football, you know where the end zone is. It’s always in exactly the same place and you pretty much know when you get there. But the end zone in advertising is a moving target, and sometimes you don’t know whether you reached it–if ever–until many months later.

Posted in Analytics, Big Data, Real-Time Enterprise, Sentiment Analysis, Social Analytics, Social Data, Social media, Social media measurement, Uncategorized | Leave a comment

Time to Change: Disruptive Innovation Panel at Xerox PARC

I had the opportunity to participate on a panel recently called “Time to Change – Culture and Brand Disruption Leading to Innovation.” It was hosted by Xerox PARC as part of their PARC forum series, and featured Todd Wilms of SAP (@toddmwilms), Michela Stribling of IBM  (@mstribling) and Bryan Kramer (@bryankramer) of PureMatter.

I probably shouldn’t admit this, but every time I hear the phrase “Time to Change,” it reminds me of a famous Brady Bunch episode, in which Peter’s voice begins to change, just as the family is about to record the classic “Sunshine Day.”

Poor Peter; he’s so embarrassed. But Peter’s disruption sparks Greg’s idea, which is to write the now-classic “Time to Change,” which celebrates Peter’s challenge and turns it into an opportunity.

So, drawing from both the lofty inspiration from the PARC panel and the more earth-bound lessons of The Brady Bunch, here are five quick points on disruptive innovation:

  1. Disruption is supposed to be painful. It challenges people, processes and established plans. But that disruption is a signal–like pain is in the body–that we need to attend to the underlying issues.
  2. It takes time. For example, we expect, less than a decade into social business, that it should be broadly accepted, mature and scalable. But it takes years–decades sometimes–for us to truly understand the characteristics of new technologies and media types; if you doubt this, take a look at this article published last year in MIT Technology Review.
  3. It requires rigor and discipline. Just because something is new doesn’t mean it requires less rigor to adopt. In many cases, it requires more clarity, as my colleague Charlene Li so clearly stated in Open Leadership. That means bounded experiments with hypotheses, and clear business plans with anticipated returns and outcomes.
  4. Learning is and should be a desired outcome. The reason trends are disruptive is that we don’t yet know how to unlock their value. How else can we discover that without experimenting and learning from the results? We need to act less like managers and more like scientists.
  5. Stupid ideas can be brilliant, or inform other, better ones. The US Patent Office is full of failed ideas that became celebrated innovations or provided unexpected insight. A few years ago, Netflix tried to split its business, a wildly unpopular decision. Now they’re creating award-winning programming.

Here’s the video:

For more on the panel, see Michelle Killebrew’s excellent recap in Click Z.

For more on what’s going to disrupt us in 2014, see Charlene’s post on trends to watch.

As always, I welcome your thoughts.


Posted in Altimeter, Innovation, Social media | 4 Comments

2014: The Year of Data Disruption

542192_61276739Linguist Geoff Nunberg’s annual “Word of the Year” posts offer an instructive peek into the American psyche. In 2012, he chose “Big Data”. In 2013, his pick was (no, not “twerk”) “selfie.” Nunberg makes his selections based on dominant news stories, or words that he believes tell us something important about the culture at a particular point in time. What appeals to me about the 2012 and 2013 choices is that they illustrate the increasing tension between our fascination with data, and our profound unease at its implications.

This plays out from pop culture to organizational culture, from The Economist to TMZ. In 2014, I’ll be looking at the increasing tension in several areas, as technology continues to overtax our ability to understand it (sentiment, video and image analysis) assimilate it (filter failure), act on it (business disruption) and define rules and ethics around it (security and privacy). Here’s what I’ll be thinking about throughout the year:

1. Data Diversity Requires Diversity of Expertise

The biggest “Big Data” challenge will continue to be the sheer variety of data types. Large brands want to know when their products or logos are used on the social web. Sentiment analysis, image recognition in both still and moving images, as well as text-to-speech and speech-to-text will continue to confound technologists, until and unless they more aggressively include linguists, social scientists, even neuroscientists, in their R&D processes. That isn’t to say that will solve everything, but as we bring technology and human communication closer together, it stands to reason that we need a far more multidisciplinary approach to understanding signals.

2. Clean Data is Happy Data 

With multiple data types comes increased demand for consistent interpretive standards, particularly as the need to view disparate data sets in tandem increases. We’ve seen the challenges of this with text-based social data but have not even scratched the surface for other data types, or the impact when they are viewed in conjunction with other data sets. Consistent sourcing, transparent methodology and interpretive standards will become a must-have for 2014. It may not be sexy, but it’s mission-critical.

3. Machine Learning is Table Stakes

The ability to deliver ever-more massive and heterogeneous data streams from devices, enterprise and social apps and other sources–often in real time–will place increasing pressure on organizations. Rather than continuing to segregate analysts, hand-code posts and manually interpret these data sets, machine learning will need to become an expectation rather than an exotic and costly addition to data analysis tools. We’re not talking Scarlett Johanssen in “Her,” (sorry, folks) but rather the ability to infuse learning into data processing technologies to reduce filter failure, improve relevance and move to higher-order analysis–at scale.

4. Data is the New Disruption

As data makes its way around increasingly permeable organizations, we’ll see  waves of disruption follow in its wake. While corporate initiatives can spark quite a bit of controversy over “who owns it” and “who funds it,” data is so elemental to organizational culture and operations that these questions will predominate. The next wave, “who gets to see it, interpret it and administer it” will only increase the need for direct, timely and clear agreements and governance as these data streams become business critical.

5. Contextual Privacy: the Useful/Creepy Conundrum

There has been too much of an inclination to treat privacy as a one-size fits all proposition, but what we are learning is that the complexity of data gathering and data sharing means that privacy can be a very situational concept. Thinkers like Danah Boyd deeply understand the contextual nature of privacy, and how one small adjustment can erode or even build trust. I’ll be focusing on this in 2014, with an emphasis on helping organizations and technology developers deliver relevant experiences without undermining the social contract between individual and organization.

This is one in a series of posts on Altimeter Group’s 2014 research focus. For more from my colleagues on what they’re planning for the year, please click here.

Posted in Altimeter, Big Data, Predictive Analytics, Quantified Self, Real-Time Enterprise, Research, Social Analytics, Social media measurement, Uncategorized | Tagged , , , , , | 9 Comments

An Industry Association for Social Data: The Big Boulder Initiative

Screen Shot 2013-12-06 at 10.09.37 AMA few weeks ago, I had the opportunity to participate in a working session of The Big Boulder Initiative, an industry association founded to promote understanding and development of the emerging  social data market.

It’s been an eventful week in the industry; Topsy was acquired by Apple earlier this week, and DataSift raised an impressive $42 million in their series C round of funding. With increasing momentum comes increasing complexity, and The Big Boulder Initiative has been convened  to identify, prioritize and begin to address the most pressing technology, business and consumer concerns affecting the future of the social data industry.

Here’s the video summarizing the event:

The Big Boulder Initiative from Gnip on Vimeo.

The issues we discussed were:

  • Privacy, Trust & Regulation
  • ROI & Value
  • Data Access
  • Data Standardization
  • Cost of Data
  • Data Quality & Validity

Here is a summary of what was discussed and agreed to at the meetings in Seattle, New York, Washington D.C. and San Francisco.

I’m honored to announce that I’ve been elected to the board, along with some of the most knowledgeable and thoughtful folks in the industry:

In the interest of transparency, I should state that while Gnip convened this group and Chris Moody, CEO of Gnip, is interim chairman, the first order of business for the board will be to elect a new chairperson from among the members and actively recruit more members–explicitly including direct competitors–to join the initiative. [Update from Chris: his role is currently interim but he intends to throw his hat in the ring for full-time chair.  The decision will be made by the board as a whole.]

The goal is for this to be an association that serves the industry rather than individual companies or agendas.

The first board meeting will occur in  January; in the meantime, if you have questions, would like to participate or would like to be included in future communications, please contact Chris Moody at Gnip.

I’m honored to be working with such an impressive group of people and look forward to rolling up my sleeves in 2014. More to come!

Posted in DataSift, Gnip, Social Analytics, Social Data, Topsy, Uncategorized | 4 Comments

Social Data Market Momentum: It’s Not About the Firehose

1034138_89844170In the past year, social data has continued to wend its way into organizations of all types, from large enterprise to small business to media and entertainment and the public sector. We’ve seen use cases far past marketing into product and service quality, entertainment programming, customer service, fraud detection and a host of other examples.

Yet the idea of social data as an asset that requires real enterprise rigor–quality control, curation and integration with other data sources—-is still nascent.

This week, Apple purchased Topsy, one of Twitter’s certified partners and a company that both resells and analyzes Twitter data. The acquisition had many scratching their heads initially, but a quick review of Apple’s acquisitions this year includes, according to AppleInsider, at least two companies with complementary technologies: AlgoTrim, a Swedish data compression company, and, a second-screen startup. The combination of data compression, social data analysis and predictive capability suggest intriguing potential applications in the area of personalized recommendation, whether in iTunes or radio, or TV, or some other medium not yet revealed.

While Apple’s acquisition arguably takes Topsy out of the social data reseller business, the $42 million in Series-C funding raised by DataSift today demonstrates that the business of social data is gaining serious momentum. But this market, as it’s evolving, is not just a game of “Capture the Firehose”; it’s about taking this enormously complex, rich and challenging data set and turning it into insight that can be used to suggest trends that real people in real organizations can act on. It’s not about the firehose; it’s not even about the water. It’s about the fires the water can put out, and the things it can cause to grow.

This small collection of companies, which now effectively includes DataSift, Gnip and NTT Data in Japan, is forming the embryo of a market that will, for the first time, enable organizations to incorporate the customer’s voice–the raw, the spontaneous, the immediate–as a legitimate input into organizational decision-making. This is not a simple proposition: it requires tremendous expertise in big data processing and an ecosystem to promote growth and experimentation and leadership, among many other things. And, as Gnip has clearly understood, it requires educating the market as to the challenges and opportunities of social data.

All of these companies, in their different ways, have played a critical role in getting us to the starting line for social data. Now that 2013 is coming to a close, and 2014 is about to be upon us, I predict the following:

  • More demand among organizations for “enterprise ready” social data streams
  • Experimentation with new use cases for social data
  • Collaboration among IT and marketing as social data becomes a more valued enterprise asset.
  • Less emphasis on the “social” aspect of social data; after all, it really is the most authentic, vivid and vast collection of the voice of the customer, partners, consumers, investors; the community at large.
  • Acceptance of social data as a valued enterprise asset
  • A greater emphasis on social data ethics, compliance and best practices.

Congrats to Topsy and DataSift on their news this week. More to come.

Posted in DataSift, Gnip, NTT, Predictive Analytics, Real-Time Enterprise, Sentiment Analysis, Social Analytics, Social Data, Social media, Social media measurement, Topsy, Twitter | 3 Comments

From Shopping Carts to Poisoned Names, Every Data Point Tells a Story

542471_10200508386412185_1177284541_nEvery so often, I’d like to profile someone who’s doing interesting things with data. Meet Hilary Parker of Etsy (yes, that’s her in the photo).

While at Strata & Hadoop World last week, I had the chance to attend Ignite, a pecha-kucha-like event in which speakers present one idea, on twenty slides, in five minutes (no pressure). One of my favorites was by Hilary Parker, a data analyst at Etsy and Ph.D. in biostatistics who spends her days trying to understand how people use Etsy, guiding experiments, consulting with development teams and generating new hypotheses for further investigation.

One example of the types of questions Parker tries to answer is whether new features are performing as expected (do they increase conversions?) or whether they are causing other, unanticipated outcomes. The goal, essentially, is to get at the root of the user’s behavior; how she’s interacting with the website, and whether that’s different from what the team expected. It requires an open mind and a lot of curiosity, perseverance and attention to detail, not to mention some serious statistical modeling skills.

For example, one of the metrics that ecommerce companies like to measure is average shopping cart value, compared to average order value. If your shopping cart value (let’s say $250) consistently exceeds your actual order value (let’s say $75), that means that items are being added to carts but are being removed before checkout. Why would that be?

One possible reason, Parker posits, could be that people are adding items to the cart to bookmark them for later viewing. Another one (and one that I am personally guilty of) is inadvertently adding the same item multiple times. Either way, the end result should be a user interface change; perhaps to add a way to bookmark items or, in my case, alert me that I am about to spend the equivalent of the national debt on a standing army of home appliances.

Hilary’s talk at Strata, intriguingly entitled “Hilary: The Most Poisoned Baby Name in US History,” documents her investigation into the popularity (or extreme lack thereof) of her given name.  As a seven-year-old in 1992, she suddenly found herself being teased by other children, who called her “Hillary Clinton.”  Later, in college, she Googled her name and came across a blog post that said that Hilary was the most poisoned baby name, meaning that it had been severely undermined by the unpopularity of the then First Lady.

So she got curious. Earlier this year, Parker decided to perform her own analysis using data from the Social Security Administration, which initially revealed that Hilary was, in fact, only the 6th most poisoned baby name. So I asked Hilary what made her suspicious that this wasn’t telling the whole story. Her answer: “the names, for one. It was a somewhat peculiar list.”

To wit: numbers one through five were, in order, Farrah, Dewey, Catina, Deneen and Khadija. So she decided to graph the data to see what was going on.  Once she could visualize the data, she says, “I saw a crazy pattern. I started Googling the names and seeing why they were popular.” You can probably guess why and when Farrah became popular. Khadija was a no-brainer for me, as I clearly remember Queen Latifah in that role on the sitcom “Living Single” from 1993-1998. The rest you’ll have to read for yourself on Hilary’s blog

But, says Parker, “‘Hilary’…was clearly different than these flash-in-the-pan names. The name was growing in popularity (albeit not monotonically) for years.” So she decided to re-run the analysis using only names that were in the top 1000 for more than 20 years, and updated the graph accordingly. Here’s what she found:


So, says Parker, “I can confidently say that, defining “poisoning” as the relative loss of popularity in a single year and controlling for fad names, “Hilary” is absolutely the most poisoned woman’s name in recorded history in the US.”

I love this experiment because it shows the value of following hunches. It also shows the beauty of visualization for large data sets.  As Parker says, “statistics is as much an art as it is a science.”

You can read her full analysis here.

Her slides from Ignite are here.

You can ask her about the roller derby photo yourself.

Find her at

Posted in Analytics, Data Science, Research, Uncategorized | Tagged , , , , , | 1 Comment