Polygraph Media Blog

Data Proliferation and the rise of the Chief Data Officer

Quick update today -- the "data proliferation problem" is just a theme I've heard from a lot of conversations recently.

A fellow "data" startup CEO mentioned to me the other day that he lamented just how hard it is to sell "data" and "data services" to people in brands and the enterprise.  Marketing departments have millions of dollars to spend on print advertising that they know does not work... but a modest $100,000 investment to help people understand their data opportunity requires significant explanation and is often met with blank stares.

We're in an odd point in time kinda like we were in with social media in 2007. Evolved enough for people to recognize the promise, but too early for budgets and organizational alignment to have taken place. This does not frustrate me all that much -- but it is a growth opportunity for small companies such as ours and a chance or visionary bigger companies to jump out to a quick, early lead.

I have a lot to say on this subject and will follow up here and in some of my guest blogging on how business is evolving. Suffice it to say that winners will be data-driven decision makers. They'll have context about what is important and what is not, and they'll execute well. They will know statistics, business goals/metrics, and they'll understand the interplay between operations, customer data, and their own systems.

Companies will increasingly hire or appoint Chief Data Officers -- and not just those in data-intensive businesses either.  A single person and/or office will handle issues related to making data useful and actionable for a business but will also be careful to not expose private data to people who should not see it. It's a tough problem, but a fascinating opportunity.

Deloitte discussed this recently in a fantastic brief paper. I think it was interesting that Alibaba just appointed their former Chief Data Officer as CEO of the entire business. Expect that trend to continue.

What Lance Armstrong and Facebook Have in Common

Today's blog post comes to you from our Business Development Manager, Craig Carter, who had a little fun speculating about Lance Armstrong and Facebook. I thought it was pretty amusing, so here goes...

(Preface—Except for the Facts and Figures charted below, all statements are pure speculation and have not been confirmed by the World Anti-Doping Agency.)

What do Lance Armstrong and Facebook have in common?

The short answer is they have both given their teammates Performance Enhancing Drugs (PEDs).   Now before you go running off to the mountain top, screaming that Mark Zuckerberg is peddling concentration steroids to his coders, let me explain.  Large brand pages and Facebook are all on the same team.  In one way or the other, they are working together to improve marketing performance, brand image, and make a profit.   So WHEN, HOW, and WHY did Facebook, not only begin to give brands PEDs, but dare I say, try to get them addicted?

It all started around June 6 (conveniently after the FB stock price declined 30 percent in 20 days) when all engagement metrics on large brands and company pages started to track upwards faster than the Cliffhangers Game on “The Price is Right.” 

 

I am sure this is partly because of some great new content, more posts, insights from Facebook Ninjas/Gurus/Rock Stars, and increased ad spending on Facebook, but more so, Facebook hooked up the engagement IV to all its pages and started pumping them full of likes, comments, and shares. Just imagine Lance secretly giving his racing team wondrous enhancers, and then the team started winning!

Ahh the glory days!  Little did brands know Facebook was giving them a PED better than Michael Jordan’s Special Stuff in Space Jam. 

As Social Rock Stars became the one direction of the office, Facebook took the hit as their stock “plummeted.”  I even heard rumors that “Zuck” was selling his couches on Craigslist to pay for dinner.  But just as Lance was the genius behind the doping of his many Tour de France wins, Facebook had a plan—give away marketing, get brands hooked on engagement, and then slowly stop trickling the drug through the IV. 

 

What happened next … just ask any local drug dealer. 
Give away the drug, get ’em addicted, then start charging for it.  Low and behold, right around the time when massive free engagement stopped, Facebook’s stock value started going up!  I feel this really started happening around mid-November—actually after the Facebook pundits started yelling from the rooftops about EdgeRank changes.  

As we are now over a month into 2013, it will be interesting to see what happens with these two pieces of data.  All I can say is ask Lance Armstrong or Barry Bonds if PEDs are just like Pringles, “Once you pop, you can’t stop.”  Meaning these brands are going to be pulling out their pocketbooks to get back to their former glory. 

Data Note: All data presented came from Polygraph Media’s Facebook Data Analytics Engine. The 62,682 posts analyzed were from 125 large brand and company pages from Jan 1, 2012, to Jan 28, 2013.  In order to remove outliers and smooth the data, I removed 599 posts for being 3-standard deviations greater than average and opted to use a 7-day rolling average of the total likes received on all the pages. Give me a shout if you want to see some of this data and analysis first hand!

The folly of analytics and value of big data analytics

Analytics has become a hot topic over the last 12 months as middle-managers are asked tough questions by their executive stakeholders.

For all the insanity around social marketing, mobile apps, and "conversations", many CMOs, CIOs, and CFOs want to know exactly what they're getting for their money.  Budgets are growing and advertising spend is fluid.  People want to know the impact of strategy changes and tactics tweaks. But don't worry.  This pattern is not just familiar to you in your business, it's happening everywhere.

Middle managers have turned to analytics to rationalize what has happened -- to measure success and failure.  But I'd contend that some of the early analtyics offerings are doing a bit of a disservice to the very clients who rely upon them.  Charts, graphs, and data visualizations are compelling -- so they must be authoritative.

But are they really?

The one question you should ask of any analytics offering is simple:

Do I truly understand the metrics presented in this chart?
As a marketer, do I understand how to improve the metrics presented in this chart?
And finally, do I understand clearly how these metrics impact my company's ability to make more money, save money, and generate a return on investment (ROI)?

As I talk with corporate marketers about social marketing, content, and advertising measurement, I'd say that at least half the people do not understand the data presented to them by Facebook Insights.  Few, if any people, understand what Facebook's Reach and PTAT (People Talking About This) metrics really mean.  Almost none understand how those metrics should be used and interpreted.  And fewer know exactly how they'd improve upon those numbers.  The same goes for abstractions on hard metrics provided by third-party analytics providers.

The only way to get a true understanding of your performance is for your metrics to match the things you know how to do.  On Facebook, it's simple:

• Page Likes -- how many Fans do you have, and is that number growing?
• Posts -- how often do you post?
• Post Type -- how often do you post different types of content to keep your Page fresh?
• Shares -- how often are people inspired to share content with their friends?
• Comments -- how often do people comment on your Page?
• Likes -- how often do people like content that you post?
• Engagement Rate -- by time period or post, how well does your content truly perform?
• Clicks -- how do your ads and/or Page Posts generate clicks to a web site?

Facebook is the top of the funnel for many consumer marketers -- it's an awareness vehicle that can help you create a better conversion rate for other online activities.  It can also identify your top fans and people who advocate for you online and offline.

But (and I'll pick on Facebook Insights for a moment) if you're using metrics such as Reach and PTAT as religious metrics that you watch feverishly, I think you're missing the point.  You're succumbing to the folly of analytics as opposed to the data-driven answers that big data analytics can provide.

If you go to the effort of measurement, you're ahead of the game.  But you're really ahead of the game if you take a data-driven approach.  Be sure to measure the very things you know you can affect.  Otherwise, you're just guessing.

Polygraph: Solving & more importantly Explaining Big Social Data, Facebook

Howdy everyone -- it's been awhile since I've posted here.  Lots of work on Polygraph and a new addition to my family in the form of a baby.  Margot Ellen Treadaway was born on December 14th and is our first child.  Lots of life changes -- all good.  But the blog has taken a bit of a back seat recently.

That's a-changin'.  Regular blogging is back for us as we bring Polygraph v2 to market.  I'll tell you more about that later.

But for now, I thought I'd better explain how our business has evolved and where it is going.  For those of you who have followed the Notice Technologies ride, you know that we've had quite the journey.

We started in 2009 with a couponing platform for local businesses.  We hit the streets and talked with as many local businesses as possible -- in an effort to build something perhaps a little less one-sided than Groupon in terms of who benefits from the transaction.  Local businesses were tough to work with because they demanded immediate results -- and didn't have the time or energy for anything that failed to generate immediate, incremental revenue.  Hence Groupon.  Groupon built tremendous value for itself, at least early... but much of it was at the *expense* instead of the *benefit* of the local business.  That's why Groupon remains a largely unsustainable business model -- and why we didn't (right or wrong) pursue group purchasing aggressively.  It's also why local businesses haven't gotten a revolutionary marketing platform yet.  It takes iteration, which local businesses simply aren't willing/able to do unlike other classes of customers.

We tried salvaging those couponing assets by offering them to newspaper groups.  But that became its own set of problems as you can imagine, and local media didn't prove to be much of a gainful opportunity for us.

All the while, we'd been hearing from people in agencies, brands, and other larger companies that measurement of social media was a huge pain point.  Facebook Insights didn't do the job well and still doesn't.  We saw an opportunity with our v1 of Polygraph to jump into the analytics fray.  And that's been a very positive move for us.

But we see a bigger trend and a bigger pain point in a lot of companies.  Businesses large and small are trying to better understand what value they're driving from social media.  It isn't enough for the world's leading companies to conduct best practices, adopt a social media management system, hire a consultant, or outsource social media to an agency.  The C-Suite really wants to know a few things:

• What business metrics are being affected by good social media execution?
• Is there a statistical relationship between social data and revenue or other mission-critical business outcomes?
• Do Facebook Ads and the like really work?
• How are competitors grappling with Facebook for customer outreach, customer service, and other marketing purposes?
• Is the social channel truly worthwhile?
• Should social earn a greater or lesser investment moving forward?

Who do CEOs and CMOs turn to for clear, data-driven answers to these questions?  They certainly can't turn to existing agencies of record -- they're too invested in a positive answer.  C-suite execs need a BS detector to tell them what the data says, and we're increasingly that BS detector.

Polygraph does an inventory of available data and collects it from Facebook for a company, its brands, its competitors, and its portfolio of Facebook assets.  We cache it, analyze it, and find relationships in the data.  Companies will sometimes give us other data sets that we'll add to our analysis -- such as web metrics, e-commerce results, or other mission-critical data.  We look at it all holistically to understand the customer engagement system -- how people discover the brand and make decisions to engage, interact, and make purchases (where applicable).  We seek understanding of the customer walk and conflict resolution -- are people using the brand's social assets as a customer service vehicle.

Ultimately, all of this data tells us a compelling story that can drive next steps.  Should you keep your existing agency or find a new one?  Does your portfolio of assets provide a return on investment?  Does social data correlate to real-world business metrics that matter to you?  Can we find evidence of causation?  Does social data move the needle or vice versa?

Understanding is what the C-suite craves today.  But the C-suite doesn't have it today. Agencies aren't independent arbiters aligned with the interests of a C-suite exec responsible for driving revenue and profit.  Unfortunately, very few marketers are equipped with the analysis chops necessary to make the case effectively also.  Sometimes, a marketer is equipped but isn't viewed as an independent, disinterested judge.

We're doing some fascinating projects right now in several major consumer industries that I wish we could talk more about -- but we're under NDA.  It's admittedly a little frustrating in that regard.  But you should know that the data unlocks fascinating insights that leading brands are beginning to uncover today. Truth lies in the social data. That's what we do -- collect, analyze, and drive insights from social data.

Social Media Measurement & ROI - the next big thing

According to The Center for Marketing Research at The University of Massachusetts.

We agree.  Link below:

http://www.customerthink.com/blog/social_media_measurement_and_roi_next_focus_of_inc_500_fortune_500

Big Social Data -- Explaining Offline Behavior

Over the past six months or more, you've probably heard the term "big data" floating around as if it is supposed to solve all your problems in business, marketing, operations, etc.  Just "access the data" and you'll be smarter than your competition.  You'll react to customer feedback.  You'll be a visionary.  Blah blah blah

All of this creates a situation where they hype is through the roof, but few people can really speak to real-world scenarios and useful applications of big data.  That's probably why "Big Data" is high on Gartner's Hype Cycle 2012 Chart.  Lots of possibilities, but not a lot of answers just yet.

We work primarily in the social data realm at the moment, although we have been getting more & more requests to integrate offline data with social data to understand patterns.  Alongside first-cousin ROI (return on investment), the big question today is whether or not social media is a scorecard for how you're doing or if it can influence offline behaviors, purchase patterns, etc.

The takeaway for us so far is that different businesses certainly have different dynamics.  Only with full access to the data can you really draw any firm conclusions.

Contact us to learn more about how we can help harness big data for your company or brand.

Data Mining... a Primer for Marketers & Decision-Makers (Part 1)

A bit of a dry post today, unless you're a data geek like we are.

Since our launch, we've entered a few customer and partner conversations that would've been best served with an introductory primer in data mining.  I would estimate that a majority of marketers don't have a firm grasp on the requirements and/or what the opportunities are in the field of data & data-driven decision making.

So here's my "business guy shorthand" on data mining, what's available today, and the opportunities all of this affords.

First of all, content on the Internet has exploded as others have chronicled over the last few years. From the linked article above: 

"...compliments of Dave Turek, the guy in charge of supercomputer development at IBM: From the year 2003 and working backwards to the beginning of human history, we generated, according to IBM’s calculations, five exabytes–that’s five billion gigabytes–of information. By last year, we were cranking out that much data every two days. By next year, predicts Turek, we’ll be doing it every 10 minutes."

How did this happen, you ask?  Look no further than the social media movement, which democratized publishing to the web via blogs, put our videos online via YouTube and Vimeo, broadcasted our short-form opinions via Twitter, and chronicled our life through Facebook.  And alongside those services are a series of other services that all collectively help us chronicle our lives online.

I personally love this infographic below that illustrates just how much data we're all creating today.  It's a far cry from when we needed a web developer to post content for us.  Additionally, check out this compendium of stats from 2011.

In addition to the revolution in self-publishing, we've simultaneously had a revolution in the collection of data in the corporate realm.  Gone are the days of Day Timers, Day Minders, etc.  In with the world of Salesforce.com.  New Point of Sales (POS) systems are no longer analog but rather "digital", which allow for the collection much more data that can be analyzed and explained. Every part of a modern business can be digitized and recorded today for data analysis -- and smart businesses are doing just that even if they don't have the analysis capability just yet.

Additionally, government agencies are publishing data to the Internet now with increasing frequency -- making records public that were once published to books, stored on microfiche, or available only upon specific request in-person.

The greater point is that we're all creating data about ourselves every day -- personally, as citizens of our locality, as employees, etc.  Although the "quantified self" movement is relatively young, we're already uncovering introspective ways that we can get smarter about the things that we're doing using data as our guide.

Through collecting and analyzing data appropriately, all stakeholder groups can benefit.  A few quick examples...

• Self -- quantifying exercise, meals, etc. for better health. 
• Business -- quantifying operations to understand efficiencies in staff, travel, etc.  Mining customer data to understand market segments & purchase intent.
• Public Sector -- combing through the data to better isolate fraud potential.  Using data to optimize tax receipts.

The common term across all of them is "efficiency" -- using data to become more efficient.  In a hypercompetitive world, efficiency is the way to unlock more profits through decreased costs, increased revenues, and/or improved experiences.

Additionally, data can be predictive and prescriptive.  Let's say you are in a family of four and have bought two cars over the last 8 years.  You have a luxury car with 20,000 miles and an SUV with 200,000 miles.  It's a simplistic case, but the data in this example suggests that you might be in the market for an SUV soon and years away from another luxury car purchase.  That's the predictive part.

Odds are if you got the SUV serviced by a data-driven car dealership, you've heard from them recently about trading in that old clunker in exchange for a shiny, new SUV.  That's the prescriptive part -- send everyone likely to buy a new car a new advertisement.  Like it or not, but it happens all the time already... all while many corporate marketing departments are not fully optimized to take advantage of the opportunities available via data.

As we've talked with a number of companies about our work in big, social data, we've discovered a wide range of companies in terms of adoption of data analysis -- collection, normalizing, analysis, data-driven decision making.  Some are leading the way, and others still make analog decisions.  To be expected, although we do think the shift towards data is a long-term trend that will be impossible to ignore for most.

In the next blog post, I'll talk more about the mechanics of making sense of it all -- from data normalization to analysis and informed decision-making.

How Facebook can inform a Presidential Race

Today, we thought we'd use Facebook data to tackle one of the most anticipated decisions of 2012. Who will Mitt Romney choose as his running mate?  And can Facebook data be used to help make the decision?

Data is great and all, but what good is data if you can't make decisions with it?

First, a little context.  A number of studies have already shown that social media data is helpful to gauge voter sentiment:

• Crimson Hexagon analyzed Twitter data in 2010 to accurately predict a victory for Harry Reid.
• Social data (Twitter, Facebook, Google+) was also used to accurately predict Boris Johnson's victory in the London mayoral race last month (June 2012)
• Facebook themselves pointed out some correlations in the data as well following the 2010 US mid-term election.  Candidates with more fans won Congressional races 74% of the time and Senate races 81% of the time.  Not bad!

Now this field is not without detractors -- some academics have focused on the flaws in using social data to predict the future.  We agree that Twitter data is dirty relative to Facebook data, as it's easier to create dummy accounts for Twitter and many people use Twitter primarily for marketing purposes and not to share what they're really doing/saying/thinking.

But it's also sour grapes in our opinion.  Anything short of asking every single man, woman, and child is in essence a shortcut.  There are going to be flaws in using data to make assumptions about the behavior of a large audience.  There are flaws in traditional market research, statistical analysis, etc.  This is no different -- in fact, it may be better.

With social data, we have the largest self-contributed data sets the world has ever seen... on par with the financial and personal data owned by major credit card companies (Visa, MasterCard, AmEx, etc.) and marketing companies (Acxiom, Dun & Bradstreet, and Harte Hanks). Additionally, people don't really have the motivation to falsify information here as they would perhaps when talking with a market research professional.  Facebook is capturing the unvarnished truth of who everyone is.

As such, we think that Facebook data is the most accurate data set in the world.  And it's relatively unmined at present.

-- 

Anyhow, we wanted to show an example of how reactions on Facebook can give decision-makers in politics a clear sense of who is "known", what they've done, and how they've managed to keep their supporters engaged.  In that sense, politics is not that unlike what social marketers do all the time -- keep the brand front & center and something worth rallying around.

In this admittedly simplistic example, we looked at a number of factors in an attempt to narrow down the Field from a large number (8) to a top-priority running mate.  We collected data on the following:

• Current Facebook Fan count
• Percentage of “Likes” over 5 quarters of posts
• Pro or negative “Romney” in page posts
• Pace of new Facebook fan acquisitions predicted between now and election day based on previous pace (how many new Friends per day)
• Polarization – the activity of each candidate on “pro” or “against” 3^rd party Fan pages

Each of these things was selected in order to test something relevant for a national election.  Is a candidate a national or regional candidate?  Is a candidate doing a good job of keeping the base engaged?  Is the candidate extending his/her reach to a broader base of enthusiastic supporters? Does the candidate create controversy or negative passion in other voters that might improve the prospects of the other political party?

The infographic is below.  We'd love to know what you think.  Also check out our YouTube video where we walk through the findings and explain the numbers in more detail.

Video - walkthrough of our Romney Veepstakes Infographic - Facebook Data

In this quick video below, we review the recent infographic that we released about how Facebook data can be used to inform political campaigns.

 

 

Social Marketing & The Quantified Self

The "Quantified Self" movement is something you've probably heard of over the last few years -- the idea that you can quantify previously mundane activities that you've otherwise ignored to attach a number to the things that you do.  There's plenty of reading about it on Wikipedia if you'd like to dig deeper.

In real life, the movement has manifested itself in successful consumer products in/around the fitness industry, such as the FitBit, numberous nutrition apps on the iPad, Garmin products, and the Nike+ line of products.  

In fact, Nike is releasing a product specifically for basketball so you can quantify your vital stats on the basketball court.  Allow me to let the expert, LeBron James, explain it.  

 

Sidebar -- but as a 38 year old, aging weekend warrior, I just don't want to know how bad my numbers are and especially as they compare to friends... but I digress.

Others have added a social element to tap into how we respond to peer pressure and/or social humiliation -- such as Strava and the Withings Scale that tweets out your weight.  Yikes!  

Why do people quantify themselves?

• People quantify themselves when something is wrong and they want to fix it.
• People quantify themselves when they're doing something right, and want to make sure they continue to do things right.  
• People quantify themselves when they want discipline to do the right thing, even though their natural inclination is to not do the right thing.
• People quantify themselves when they want an objective measurement of how they are performing at something, relative to peers or competitors.
• People quantify others when they want to use data & milestones to understand things they cannot understand, or as a proxy for heavy-handed, micro-management.

Numbers have a way of cutting through the clutter and capturing only what really matters... outcomes.  Combining numbers from different sources can give you a quick, objective look at everything that really matters to you.

If you take numbers the next step to analysis, you can begin to draw conclusions based on the data but also including human judgment where it's appropriate.  Leave analysis to others, and you're getting their interpretation of the data.

We found it interesting that Jeff Ma, formerly of the famed MIT Blackjack team, recently ventured into this space to quantify what people are doing in the workplace with his new startup, TenXer.  Right place, right time -- like Facebook, this would've never flown even a decade ago.  But times change and now it's a brilliant idea.

We're seeing it in social marketing and we're seeing it move into other areas of marketing that were thought to be a "black hole" previously.

We're at the beginning of a bigger trend where the "Quantified Self" moves into areas beyond personal improvement, fitness, etc.  People are learning the value of numbers, metrics, and how to apply them to make better decisions.  We're happy to be here at this critical time.