Facebook, Facebook, Facebook.

Ours is a tumultuous relationship of unrequited frustration, is it not? I am an analyst, therefore (apparently), you scorn me. And, by “scorn,” I mean “ignore.”

You never responded to my letter last year. You don’t return my calls. (Well, that’s not entirely true: you put salespeople on my calls whose general response to any question is, “Buy Facebook media.” I get it. That’s their job, but they act like they’ve parachuted straight out of Mad Men and are pushing traditional mass-blast advertising. Ironic, no?)

Facebook, I’ve dug into the data. Your own documentation states:

Posting regularly with engaging content gets more people to talk about your business with their friends. As a result, you end up reaching more people overall.

Yet, the data you provide us tells a very different story. We debunked this particular claim — that getting people to talk about your content leads to greater reach – a month ago.

So, What Can We Debunk This Month?

Lately, I’ve been digging into a more basic mystery: you claim that, the more someone engages with a page’s content, the more likely that person is to get presented with more of that page’s content in the future. That seems pretty reasonable. Of course, you hedge at the same time:

No matter how engaging your Page posts are, not all of your fans will see them in their News Feed. In order to make sure that more of your fans see your posts, you should create a Page Post Ad

Can we quantify that “not all of your fans…” statement? AllFacebook.com did just that when they published a pretty alarming article last week based on Edgerank Checker data. Their study showed that, on average, across 4,000 pages, only 17% of total fans were being reached per individual post by the brand. “Zoiks!” were the cries that echoed through the halls of community managers the world over!

To be fair, not everyone is on Facebook all the time, and, while that number matches data we’re seeing overall, it also leaves out the fact that these don’t appear to be the same 17% day in and day out. When it comes to looking at the 28-Day Total Reach from Page Posts measure you provide, we see numbers that are more in the neighborhood of half of a page’s Lifetime Total Likes (when there is no Facebook media running — it’s much higher than that if that exposure is being purchased from Facebook).

Is 17% really all brands can expect, or is it all they can expect if they’re doing a lousy job posting content?

Are Brands Simply Not Publishing Engaging Content?

We’ve been working pretty hard to learn what kind of content our clients’ fans like, as well as how often and when to post. That put us in a good position to dig into the data to see how we were doing, especially in light of the drop we felt we were seeing in the Reach of posts across a range of our clients’ pages.

We looked at data from a half-dozen pages. These pages were all devoted to major consumer brands, had Lifetime Total Likes ranging from the low 100,000s to multiple millions, and cut across a range of different verticals. Is “6 pages” on the order of the “4,000 pages” from the Allfacebook.com study? Well, no, but we were working with over 600 status updates, and it quickly became apparent that we’d dug in enough to draw some pretty sound conclusions..

For the chart below, we removed the handful of posts that were clearly data anomalies (skewing both wildly high and wildly low) and then, for each post, took the Lifetime Engaged Users for the post (the number of unique people who clicked anywhere in the post within 28 days of it being posted, regardless of whether the click generated a story or not) and divided it by the Total Reach for the post.

It’s not the cleanest of graphs, but it seems pretty clear that, if anything, these pages are, overall, making some headway when it comes to producing more engaging content.

The idea here is that the only people a post has a chance of engaging are people that it reaches. So, we have Total Reach as the denominator. This is similar to the Post Virality calculation that you, Facebook, generate for me…but we’re looking at a lower level of engagement than “generated a story” — just looking to see if fans are interacting with the post in any way. Because, in theory, if they are, then you will be more likely to present them with subsequent posts from the same page.

So, Engagement Isn’t Dropping. Presumably, Reach Isn’t, Either?

In the post engagement chart, there’s nothing all that shocking. What does get alarming, though, is when we look at the average Organic Reach (unique users who saw the post directly as a result of the page posting it — not because a friend talked about it, and not because the brand ran paid media to extend the reach of the post). We divided that organic reach by the Lifetime Total Likes for the page to see what % of the total fans were reached by the post organically.

Again, outliers (high and low) were removed (this included locally-targeted posts, where the reach, obviously, was very low relative to the total likes for the page). Each point on the chart represents all of the status updates on that day from our sample:

Wow. I’m not a data scientist, so the above doesn’t have any true statistical rigor applied to it. Rather, it is an exercise in what a stats professor once preached to me: “Start off by plotting the data! That’s going to tell you a lot!”

It’s pretty conclusive, I think, that a Facebook algorithm change (and related UI changes — but the algorithms drove what content appears anywhere for a user, regardless of the UI) in late September gave brands a temporary ability to reach a higher proportion of their fans. That, undoubtedly, led to any number of community managers thinking they had been listening and learning and publishing more engaging content.

Then, (alas!) November arrived. And, suddenly, Reach plummeted.

WTF?

It’s not that I’m opposed to paying you for reach, Facebook. I’m totally okay with paid media being part of my social media mix. But, if I have to pay you each time I want to reach someone, the numbers start to get hard to justify. If someone likes my page, and then they engage with my content, why don’t they keep getting my content for some period of time?

Here’s what I think happened (and, frankly, I’d respect you a bit more in the morning if you just came out and admitted it):

1. You put some sharp people in a room and told them to come up with a good EdgeRank/GraphRank algorithm
2. While you have “a lot of data,” that algorithm still was largely driven by that team’s instincts around what weighting should be given to different factors
3. There was a fair amount of teeth-gnashing, and the team even tried to do some testing of the algorithm before rolling it out. But, that’s a taller order than it sounds.
4. The algorithm got rolled out.
5. You had no idea what was going to happen. What looked good on paper looked, well, different in practice.
6. For various reasons — none of which have been openly stated — the algorithm has been quietly tweaked a couple of times. In one case, it was related to the Timeline rollout, but, by this time, the algorithm had become the red-headed stepchild of Palo Alto. No one really wants to own it, because no one can really figure out what will make it “work.” After all…the algorithm-heads are all just down the street in Mountain View! (zing!)

How close am I with the above speculation? I don’t have inside knowledge (as noted earlier, you don’t call, you don’t write), but I’m not sure what other explanation makes sense.

Know that you’re killing us — the analysts who are trying to drive learning and optimization! At least set up some sort of open dialogue. We don’t need to see the full formula. But, we need to have useful information about how to do things better. And we need to know when you’re tinkering with the algorithm and what the likely result of that tinkering will be. Otherwise, we can’t trust the data, which means we can’t learn from it. Without data we can use, it’s hard to justify investment and action.

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | One comment | Add to del.icio.us
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So, Why a New Design?

I updated the design for two reasons:

• The old design was starting to wear on me. There were a number of little alignment/layout/wrapping issues that I had never quite managed to fix, even as I tinkered with the blog functionality (for instance, my social icons never quite lined up well). I also figured out last fall that the nested table structure pretty much precluded me from getting the mix I wanted of fixed and liquid elements. In short, a redesign just seemed in order.
• Responsive web design is here. This was more of the direct-tie-to-my-day-job reason for the overhaul. Various sharp people at Resource Interactive have started pushing responsive web design as something that should be actively considered for our clients. As I dug into the topic, I realized that: 1) this blog is a good candidate for a responsive design, and 2) there are some analytics implications to a responsive design, and I needed somewhere to experiment with them.

So, this site is now using a fully responsive WordPress theme.

What Is Responsive Design, Exactly?

As I understand it, responsive design is an “Aha!” that grew out of the increasing need for web sites to function across a wide range of screen sizes and experiences and platforms: laptop monitors, desktop monitors, tablets (iOS and Android), and smartphones (also iOS and Android). The idea is that, rather than having a “desktop site” and a “mobile-optimized site,” you can have “a site” that works effectively on a wide range of devices.

There are two keys to this:

• The site needs to be viewable in different devices – 3 columns that display on a desktop monitor may need to become a single set of stacked content on a smartphone. Or, a list of links in the sidebar on the desktop may need to become a dropdown box at the top of the page on an iPhone.
• The site needs to support the most likely use cases in different devices – this is a stickier wicket, because it forces some strategic thought (and possibly research and testing) to think through what a visitor to your site who is using an iPhone (for instance) is likely looking to do and how that differs from a visitor to your site who is using a desktop.

Both of these are questions that have always been asked when it comes to developing a “mobile-optimized version of the site,” but they’re a bit more nuanced given that responsive design isn’t a “separate site.”

Wow, Tim, I’m Impressed with Your Coding Skills!

Don’t be impressed with my coding skills.

I did a little research and then shelled out $35 to buy the Rising theme. That doesn’t mean there wasn’t a fair amount of tinkering (and more tinkering yet to be done — I certainly have not fallen prey to a need to have the perfect site designed before pushing it live!), but the end result is an improved site. And, more importantly, having a site that actually works well across devices (Try it! Just resize your browser window and watch the sidebar at the right. Or, fire up the site on your smartphone and compare it to your desktop.)

Now, of the “two keys” above, I really focused on the first one. This is a blog, after all. Regardless of what device you’re on, presumably, you’re here to consume blog post content.

I’m still working with the palette (too little contrast between the hyperlink color and the plain text color), the font selection (I’m not in love with it), and the header logo (pulling what strings I can to get a professional to contribute on that front), but I’m reasonably content with the change. Let me know if you have any tips for improving the design (I’m not proud!).

Where Does Analytics Come into All of This?

While I have access to tons of different web analytics accounts across a range of platforms through our various clients, I don’t actually have a great sandbox for trying things out (you would think our company’s site would be a good testbed, but the reality is that there are so many competing agendas for competing resources there that it’s seldom worth the effort). Luckily, this site has built up enough content and enough of a presence to get a few hundred visits a day, which is enough to actually do some tinkering and get some real data as a result.

Here’s my list of what I’ll be toying with over the coming weeks:

• Responsive design analytics — we’ve had “screen resolution” and “device” reporting for years, but responsive design introduces a whole new twist, because it’s truly experience-centric. I’ve done a little digging online and haven’t found much in the way of thinking on this. While I don’t think it’s possible to directly pull CSS media query data into the web analytics platform, it should be possible to use Javascript to detect which responsive layout is being used for any given visitor and then pass that information to the web analytics platform (as a custom variable or a non-interaction event in Google Analytics). And, it should be possible to record when an onresize event occurs. In both cases, using this data to segment traffic to determine if a particular layout is performing poorly or well, as well as how visitors move through the site in these different experiences, seems like a promising thought.
• Facebook Insights for Websites — I’ve had this running for a while, but, as part of another experiment, I switched over from using my Facebook user ID in the meta data to authenticate my ownership of the site to using a Facebook app ID. That’s a better way to go when it comes to “real” sites, and I’m now actually doing some tinkering on some client sites to fully validate what happens, so look for some thoughts on that front in the future.
• Detecting the Facebook login status of visitors to the site – this is some experimentation that is actively in work. It’s the implementation of some code that Dennis Paagman came up with to use Facebook Connect and Google Analytics non-interaction events to detect (and then — my thinking — segment) visitors based on whether they’re logged into Facebook or not at the time of their visit to the site. This seems like it has intriguing possibilities when it comes to determining what types of social  interactions should be offered and how prominently. I’ve hit a minor snag on that front and am hoping Dennis will be able to help get to the bottom of it (see the comments on his blog post). But, if I get it figured out, I’ll share in a post down the road.
• Site performance – anecdotally, it seems like this site is now loading more slowly than it did with the old design. The Google Analytics Site Speed report seems to indicate that is the case, but I don’t feel like I have enough data to be conclusive there just yet. I have signed up for a site24x7.com account, which is a platform we use with some of our clients for a couple of reasons: 1) to see what it reports relative to Google Analytics (it’s a fundamentally different data capture method, so I’m not going to be surprised if the results are wildly divergent), and 2) to get more reliable data if I start playing with changes to reduce the site load time. In hindsight, I wish I’d signed up a month or so ago so I had good pre- and post- data. If I had a nickel for every time I wanted to have had that, I’d be a wealthy man!

In a nutshell (a gargantuan, artificial nutshell, I’ll grant you), I’ve got a backlog of topics, some of which will require some additional experimentation. This blog post, I realize, is almost more of a “to do” list for me than it is a “how to” list for you! Oh, well. They can’t all be winners!

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | 2 comments | Add to del.icio.us
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As many, many, many people pointed out before the latest update to Facebook Insights, Page Likes (or “fan count”), while easy to measure, is not a particularly meaningful metric. As John Lovett would say, it is simply a “counting metric.”

Below are the metrics I’m gravitating to these days as KPIs for a page:

• Reach and Impressions – pick one or the other, but, if one of your goals for Facebook is to gain exposure for your brand, these are much better measures of exposure than Page Likes. If you’re running Facebook media, you may want to use Organic Reach (or Impressions) to measure the exposure you’re generating through non-paid means while the ads or Sponsored Stories are running (this will undercount the overall exposure slightly, as some of your viral reach is from non-paid activity, but there simply is no way to really tease that out)
• Engaged Users – if one of your goals for Facebook is to foster dialogue with users, then engaged users is a good measure, because it is a measure of how many people took any actual action related to your page (regardless of whether it “generated a story”); again, if you’re running paid media, you may want to adjust this metric by subtracting out New Page Likes from Ads.
• Average Post Engagement Rate – this is a second potential KPI for the goal of fostering dialogue with users; you have to get this from the post-level data, but it is simply a matter of dividing the number of engaged users by the total reach of the post and then averaging this for all posts in the reporting period.  This metric does not need to be “adjusted” when paid media is running. It is also a metric for which a page owner really can take direct action to affect by analyzing the virality of the individual posts in the reporting period and developing hypotheses as to what made the posts with the highest/lowest engagement rates different from each other (type of post, time of day, day of week, content, etc.). Those  hypotheses can then be tested with subsequent posts to see if they are validated.
• People Talking About or Stories Generated – if you are aiming for your users to spread the word about your page through their social graph, then these are KPIs to consider. Keep in mind that a person who generates a story by liking your page is producing a much broader reaching “story” than a person who simply comments on a page post. And, as with Engaged Users, subtracting out New Page Likes from Ads when you’re running paid media will give you a better picture of the non-paid results from the page in the same time period (although there will still be some spillover impact that is not currently possible to eliminate).
• Average Post Virality – Facebook reports the “virality” of any single post as the number of people talking about the post divided by the reach of the post. It’s a good metric, if something of a misnomer, because “Virality” is really “potential virality but minimal real virality due to Facebook’s EdgeRank algorithms…unless the post is a Facebook Question.”

It’s pretty easy to engineer much more involved metrics by diving into the organic, viral, and paid breakouts…but then you wind up with metrics that are hard for the typical business user to understand.

That’s our take. What metrics are you finding most useful with the new Facebook Insights? What measures are you least able to get that you wish Facebook would add (for me, it’s the ability to break out “viral” metrics into “triggered by paid media” and “not triggered by paid media”)?

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On the one hand, Facebook would have us thinking that it’s all about virality. That’s one of the reasons they’ve made “Friends of Fans” such a prominent (if laughable) metric!

To recap, the viral reach of a page or a post is the number of unique people who were exposed to content as a result of another user generating a story (“talking about” the page or post – liking, sharing, commenting, etc.). This differs from organic reach, which is the number of unique people who visited the page or saw an item in their news feed or ticker as a direct result of the page posting the content.

Here are a couple of dirty little clarifications and secrets about virality, though:

• The most common type of viral reach is from someone liking your page – despite Facebook’s insinuations that getting people to like and comment on your page posts will tap into that ginormous “friends of fans” number…those user actions tend to go nowhere. When someone likes your page, though, that generates a story that has a meaningful viral reach (unfortunately, that is a one-time viral exposure — that same user may comment on 10 page posts over the next week and the viral reach generated from those actions will be virtually nil).
• A page’s virality is dramatically impacted by paid media – If a Facebook Ad for the page is run and a user is exposed to the ad, then that exposure counts as 1 person towards the page’s Paid Reach. If the person clicks the Like button, Facebook will record that as a Like Source of “ads” (why they don’t have that data field name capitalized bothers my OCD, FWIW). But, a good chunk of their friends are going to get an item in their ticker that the person liked the page. All of those friends being exposed get counted as viral reach and impressions.
• Oh…yeah…and Facebook Questions – Facebook Questions are the single type of Facebook page post that appear to drive meaningful viral reach (presumably, because the Ask friends action is more valued by Facebook than other actions such as standard likes, comments, and shares). Questions are good for that! Unfortunately, we’ve seen several cases over the last month across different pages where the Organic Reach of Facebook Questions was reported as dramatically lower than the typical reach for a status update on the page. It’s unclear whether those lower numbers reflect reality or whether they are simply a Facebook Insights glitch

All this is to say that viral reach is messy (…and don’t take what Facebook espouses at face value).

In my last post in this unofficial series, I’ll provide a list of the KPIs we’ve been gravitating towards with our clients and why.

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Well, a few months have passed, Facebook has quietly rolled out some changes to Facebook Insights, and we’ve gotten a chance to actually dive into some of these metrics. This post and the next two are the result of some digging that Mike Amer and I have done on behalf of Resource Interactive and our clients.

Note: This is minimally a post about the web-based Facebook Insights interface. Rather, it is focused on the slightly deeper data that is available behind that interface, which is available by exporting page-level and post-level data or through the Facebook API.

Understanding the Basics – Reach, Talking About, Engaged Users, Consumers

I get a little depressed when I think about the number of times I have read and re-read the same one-line Facebook Insights definitions for various metrics, which have the illusion of being crystal clear on an initial reading, and then get increasingly confusing with each subsequent cycle of trying to actually interpret the data.

I continue to think that the best way to understand the main new metrics is via a Venn diagram. But, the page-level Venn diagram has evolved a bit since my initial post, as Facebook quietly added a page-level Engaged Users metric, which is the union of People Talking About and Consumers. I also think that Facebook changed the definition of Consumers to include clicks that generated a story, but I haven’t tracked down old printouts to fully confirm.

Below is an updated Venn diagram for page-level Facebook metrics.

And, below is an (unchanged) Venn diagram for post-level metrics:

What About Paid/Organic/Viral (especially Viral!)?

At both the page level and the post level, Facebook breaks down a number of metrics by “paid,” “organic,” and “viral” Here’s how I’ve been describing these when it comes to page-level reach:

• Organic – unique people who visited the page or saw an item published by the page in their news feed or ticker
• Viral – unique people who were exposed to content as a result of another user generating a story (“talking about” the page – liking the page, sharing a post, etc.)
• Paid – unique people who saw a Sponsored Story or Ad pointing to the page

A single user can be reached by multiple ways in a given time period (e.g., they saw a post from the page that they’re a fan of in their news feed – organic – and then saw that a friend of theirs responded to a question on the page in their ticker – viral – and then was exposed to a Facebook Ad – paid), so, when it comes to reach, the sum of organic reach plus viral reach plus paid reach is greater than the total reach. Reach measures are always de-duped to be a count of unique users.

When it comes to impressions, though, there is no de-duping, so the sum of the different types of impressions equals the total impressions.

In my next post, I’ll dig into “virality” a little deeper (it turns out to be a bit of a bugaboo metric, but it’s also one that turns out to reveal some sneaky little unpleasantries about Facebook’s EdgeRank algorithm).

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On Friday, November 19, 2011, the good folk over at Web Analytics Demystified experimented with a new format for a digital analytics conference, dubbed #ACCELERATE. The key features of the event:

• It was entirely free to attendees (it was sponsored by Tealeaf, OpinionLab, and Ensighten)
• It lasted a single day
• It had two distinct presentation formats — a 20-minute format and a 5-minute format

The 20-minute presentations were  in a “10 Tips in 20 Minutes” format on topics that the organizers selected and then recruited speakers to present. The 5-minute presentations were left entirely up to the presenter when it came to topic selection, but they were encouraged to bring a “Big Idea” and make it “FUN.”

I’ve actually found myself doing more reflection on the conference structure, format, and details than I’ve found myself mulling over the content itself. I’d find that troubling if it weren’t for the fact that I picked up a solid set of intriguing and re-usable nuggets from the content. And, I’ve seen a few blog posts already that do a great job of recapping the event:

• Michele Hinojosa’s Top 10 Takeaways plays with the “list of 10″ format of the event by listing three different sets of 10 takeaways (she left off her own session which provided one of the enduring images for me when she plotted the four different “types” of digital analytics jobs — industry, vendor, agency, consultant — on a 2×2 grid that illustrated how the experiences differ; it’s a handy graphical view of the career development guide she spearheaded for the WAA earlier this year)
• Corry Prohens’s review of the event recaps the content session by session (but, of course, left out his own excellent session on how to go about recruiting and hiring the right digital analyst for the job).
• Gabriele Endress recapped the event as well, including a “top 5 learnings” that are spot-on when it comes to the key realities of the dynamic world of digital analytics

I really don’t have much to add to those summaries. The content was great, and I’ve walked away with an array of actions/requests/hopes:

• I’ve secured a copy of June Dershowitz’s presentation and the blog post that inspired it (top geek humor from the event: “?q=<3″)
• I’ve prodded Michele to elaborate on her 2×2 grid
• I’ve been mulling over the vendor-user relationship as described by Ben Gaines (while I have been critical of technology platforms, I also think most vendors with whom I’ve worked closely would put me at least marginally above average on the collaboration/partnership front)
• I’ve re-cemented Justin Kistner in my brain as my go-to resource for all things Facebook
• I’m looking forward to Chicago and fervently hoping that Ken Pendergast (or someone) takes another run at making the case for one of the enterprise web analytics vendors to offer a freemium option (I’ve heard that that’s been bandied about over the years at Adobiture, but it’s never been something they’ve been able to effectively justify)

That’s all of the stuff I’m not going to cover in this post. Instead, I’m going to cover more of a meta analysis of the event — a range of factors that made the event stand out and positioned it for on-going evolution and excellence.

Social Media Integration

Social media was heavily incorporated into the event:

• Twitter-friendliness Part 1 — the event’s name itself — #ACCELERATE — was a ready-made Twitter hashtag. That was clever, as it meant that all Twitter references to the event automatically used Twitter conventions that made the content easy to find, follow, and amplify.
• Twitter-friendliness Part 2 – throughout the day, Eric Peterson encouraged attendees to use both #ACCELERATE and #measure as they tweeted, and there were incentives for participants to tweet (with quality tweets) both before and during the event (with winners selected using Twitalyzer and TweetReach). This had the effect of #ACCELERATE dominating the #measure world for the day (at one point, TweetReach reported that over 70% of all #measure tweets for the day also included #ACCELERATE in the tweets). That meant that no one who is at least nominally following the #measure hashtag could fail to be aware of the event and aware of the fact that it was a very “socially active” conference.
• Twitter-maybe-not-so-friendliness Qualifier — the slightly unfortunate side effect of the “10 tips” presentation format, combined with the tweet encouragement, was that it was really easy to simply tweet the title of each “tip,” which often really weren’t all that useful without listening and re-articulating the presenter’s explanation of the tip. A tweet I saw from a non-attendee asked a good question on that front:

“…most of the #measure tweets today were about #ACCELERATE… but was it always relevant?”

• Post-event buzz bounty – Eric tacked on an incentive for conference attendees to write about (either publicly or privately in an email) their experiences at the event, with the Web Analytics Demystified team being the judges of the “best” write-up. I suspect that will result in a higher number of blog posts than would otherwise have occurred.

Overall, it was a big win on the Twitter front — I haven’t been to a conference that so actively leveraged the platform both for pre-event buzz generation and during-event content sharing (and further buzz generation). See the last section of Michele Hinojosa’s post for more detail on the Twitter activity.

Presentations Functioning on Two Levels

When it came to the presentation structure, the organizers bent over backwards to set the speakers up for success. In his recap of the event, Corry Prohens credited Craig Burgess with the following observation:

“The conference was also a study on presentation styles and techniques. How often do you get to see 26 presentations in a day? It is a rare opportunity to spot trends and take note of what works. In a field where we all have to present what we know (to clients, stakeholders, etc.) this was a big value-add to the digital measurement insights.”

This was an excellent point. Any conference is going to include sessions that stand out as being fantastic, as well as a few sessions that fall flat. One notable exception (qualifying full disclosure: it’s a conference I’ve never attended): TED.  Whether Eric and company consciously drew inspiration from TED or not, I don’t know, but there are two taglines on the TED home page that could easily be applied to the aspirations for #ACCELERATE:

“Ideas worth spreading”

“Riveting talks by remarkable people, free to the world”

By packing so many sessions into a single day and enforcing brevity (out of necessity), #ACCELERATE had a great pace and kept the attendees engaged for the entire event. Presenters were pushed to bring their “A” game to their sessions, both by repeated reminder-admonitions from Eric, as well as by the inclusion of audience-awarded $500 Best Buy gift cards for the top session of each format.

The presentations were set up to effectively convey useful and engaging content. At the same time, the presentations were set up to give the presenters a set of liberating constraints — establishing distinct guardrails for the content that then empowered the presenters to really focus in on the content and the way they communicated it. This benefited the presenters, certainly, by helping them hone the craft of presenting (that was my experience, at least), but it also benefited the audience by exposing them to a large number of presenters in a concentrated period. I hope everyone took away a few useful nuggets that they can incorporate into their own future presentations (internally or at conferences).

I haven’t attended a single conference in the last 18 months where one of the sub-themes of the conference wasn’t, “As analysts, we’ve got to get better at telling stories rather than simply presenting data.” There is real value in a conference that is designed to help analysts develop their storytelling chops.

Audience Participation

Having the audience directly vote for the winning presentation was another innovation from the event. While it is not at all unheard of to have audience-based voting on presentations, the fact that #ACCELERATE put this at the forefront was something new for digital analytics conferences, as far as I’m aware.

OpinionLab’s DialogCentral platform was leveraged to allow real-time voting and feedback on each session as it occurred. I saw a demo of DialogCentral over a year ago, found it intriguing, and then could never remember what it was called or what ever happened to it, so it was good to see it put into action. Any audience member who had a smartphone could quickly navigate to a mobile-optimized site and vote the presentation on a 5-point scale, leave an open-ended comment, and leave contact info if desired.

There were some glitches on that front, in that there were some participants who did not have smartphones (well, 2 or 3), and at least one attendee reported that the system did not work on her Blackberry. Overall, the voting occurred in smaller numbers than I think the organizers hoped, but it was a great idea and it worked perfectly adequately for a first-time attempt.

And…It Was Free

It’s easy to simply rattle off that “free is better” and leave it at that.  As a first-time event, I’m sure the fact that the event was fully sponsor-supported helped make it fill up quickly. The challenge with having a free event is that the registrants have no real skin in the game — it’s easy to sign up first and then figure out if you can actually attend. If you can’t, well, no worries, because it’s no money out of your pocket! Having co-organized Web Analytics Wednesdays in Columbus — also free events — for several years now, I’ve lived with this challenge firsthand. Trying to accurately predict the no-show rate is an art unto itself, which introduces a range of logistical headaches.

At the other extreme from “free,” the major established digital analytics conferences all have hefty price tags, which makes them cost-prohibitive for many potential attendees who are operating in organizations that have extremely limited training and conference budgets (not to mention the personal budgets for analysts who are in between jobs and could really benefit from the networking opportunities at conferences). That, I suspect, leads to misaligned speaker incentives — members of the industry desperately angling for speaking slots so they can reduce the cost of the overall conference attendance rather than because they have something unique and worthwhile to share.

I could totally see #ACCELERATE evolving to have a nominal registration fee — something like $100 would ensure there was a real commitment required by registrants, but it would also make it totally feasible for someone to attend without corporate backing (make it $25 for students, and, heck, provide bartered alternatives where people can blog about the event or get referral credits).

Overall, free is good, and that made the event right-sized — ~300 people was enough to keep a single track, provide plenty of opportunity for worthwhile networking, while also keeping the setting relatively intimate.

I’m looking forward to Chicago!Similar Posts:

• Gilligan Meets Super #ACCELERATE — Recreated

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Fairly regularly, I’m drawn into work where the team has unrealistic expectations of the degree to which I can accurately quantify the impact of their initiatives on their top (or bottom) line. I’ve come at these discussions from a variety of angles:

• Acknowledging why expectations are inflated — what I’ve termed The Frustration Gap
• Articulating the myriad ways that media mix modeling (MMM) has started to crumble
• Developing a handy analogy based on the Mississippi River to explain the complexity of today’s measurement reality
• Putting forth a framework for social media measurement that makes the distinction between measuring the performance of individual channels and measuring the overall brand outcomes that result from these channels working in concert

This post is largely an evolution of the last link above. It’s something I’ve been exploring over the past six months, and which was strongly reinforced when I read John Lovett’s recent book. As I’ve been doing measurement planning (measurement strategy? marketing optimization planning?) with clients, it’s turned out to be quite useful when I have the opportunity to apply it.

Initially, I referred to this approach as developing a “logical model” (that’s even what I called it towards the end of my second post that referenced John’s book), but that was a bit bothersome, since “logical model” has a very specific meaning in the world of database design. Then, a couple of months ago, I stumbled on an old Harvard Business Review paper about using non-financial measures for performance measurement, and that paper introduced the same concept, but referred to it as a “causal model.” I like it!

How It Works

The concept is straightforward, it’s not particularly time-consuming, it’s a great exercise for ensuring everyone involved is aligned on why a particular initiative is being kicked off, it sets up meaningful optimization work as individual tactics and campaigns are implemented, and it positions you to be able to demonstrate a link (correlation) between marketing activities and business results.

This approach acknowledges that there is no existing master model that shows exactly how a brand’s target consumers interact and respond to brand activity. The process starts with more “art” than “science” — knowledge of the brand’s target consumers and their behaviors, knowledge of emerging channels and where they’re most suited (e.g., a QR code on a billboard on a busy highway…not typically a good match), and a hefty dose of strategic thought.

The exact structure of this sort of model varies widely from situation from situation, but I like to have my measurable objectives — what we think we’re going achieve through the initiative or program that we believe has underlying business value — listed on the left side of the page, and then build linkages from that to a more definitive business outcome on the right:

It should fit on a single page, and it requires input from multiple stakeholders. Ultimately, it can be a simple illustration of “why we’re doing this” for anyone to review and critique. If there are some pretty big leaps required, or if there are numerous steps along the way to get to tangible business value, then it begs the question: “Is this really worth doing?” It’s an easy litmus test as to whether an initiative makes sense.

What I’ve found is that this exercise can actually alter the original objectives in the planning stage, which is a much better time and place to alter them than once execution is well under way!

Once the model is agreed to, then you can focus on measuring and optimizing to the outputs from the base objectives — using KPIs that are appropriate for both the objective and the “next step” in the causal model.

And, over time, the performance of those KPIs can be correlated with the downstream components of the causal model to validate (and adjust) the model itself.

This all gets back to the key that measurement and analytics is a combination of art and science. Initially, it’s more art than science — the science is used to refine, validate, and inform the art.

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | One comment | Add to del.icio.us
Post tags: causal model

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Basically…We’re Moving in a Good Direction!

At the core of the release is a shift away from “Likes” and “Impressions” and more to “exposed and engaged people.” There are now a slew of metrics available at both the page level and the individual post level that are “unique people” counts. That…is very fine indeed! It’s progress!

Visually Explaining the New Metrics

As I sifted through the new Facebook Page Insights product guide (kudos to Facebook for upping the quality of their documentation over the past year!) with some co-workers, it occurred to me that a visual representation of some of the new terms might be useful. I settled on a Venn diagram format, with one diagram for the main page-level metrics and one for the main post-level metrics.

Starting with page-level metrics:

Defining the different metrics — heavily cribbed from the Facebook documentation:

• Page Likes – The number of unique people who have liked the page; this metric is publicly available (and always has been) on any brand’s Facebook page.
• Total Reach – The number of unique people who have seen any content associated with a brand’s page. They don’t have to like the page for this, as they can see content from the page show up in their ticker or feed because one of their friends “talked about it” (see below).
• People Talking About This – The number of unique people who have created a story about a page. Creating a story includes any action that generates a News Feed or Ticker post (i.e. shares, comments, Likes, answered questions, tagged the page in a post/photo/video). This number is publicly available (it’s the “unique people who have talked about this page in the last 7 days”) on any brand’s Facebook page.
• Consumers – The number of unique people who clicked on any of your content without generating a story.

A couple of things to note here that are a little odd (and likely to be largely inconsequential), but which are based on a strict reading of the Facebook documentation:

• A person can be counted in the Total Reach metric without being counted in the Page Likes metric (this one isn’t actually odd — it’s just important to recognize)
• A person can be counted as Talking About This without being included in the Reach metric. As I understand it, if I tag a page in a status update or photo, I will be counted as “talking about” the page, and I can do that without being a fan of the page and without having been reached by any of the page’s content. In practice, this is probably pretty rare (or rare enough that it’s noise).
• Consumers can also be counted as People Talking About This (the documentation is a little murky on this, but I’ve read it a dozen times: “The number of people who clicked on any of your content without generating a story.” Someone could certainly click on content — view a photo, say — and then move on about their business, which would absolutely make them a Consumer who did not Talk About the page. But, a person could also click on a photo and view it…and then like it (or share it, or comment on the page, etc.), in which case it appears they would be both a Consumer and a Person Talking About This.
• A person cannot be Consumer without also being Reached…but they can be a Consumer without being a Page Like.

Okay, so that’s page-level metrics. Let’s look at a similar diagram for post-level metrics:

It’s a little simpler, because there isn’t the “overall Likes” concept (well…there is…but that’s just a subset of Talking About, so it’s conceptually a very, very different animal than the Page Likes metric).

Let’s run through the definitions:

• Reach – The number of unique people who have seen the post
• Talking About – The number of unique people who have created a story about the post by sharing, commenting, or liking it; this is publicly available for any post, as Facebook now shows total comments, total likes, and total shares for each post, and Talking About is simply the sum of those three numbers
• Engaged Users – The number of unique people who clicked on anything in the post, regardless of whether it was a story-generating click

And, there is a separate metric called Virality which is a simple combination of two of the metrics above:

That’s not a bad metric at all, as it’s a measure of, for all the people who were exposed to the post, what percent of them actively engaged with it to the point that their interaction “generated a story.”

The Reach and Talking About metrics are direct parallels of each other between the page-level metrics and the post-level metrics. However (again, based on a close reading of the limited documentation), Consumers (page-level) and Engaged Users (post-level) are not analogous. At the post-level, Talking About is a subset of Engaged Users. It would have made sense, in my mind, if, at the page-level Talking About was a pure subset of Consumers…but that does not appear to be the case.

KPIs That I Think Will Likely “Matter” for a Brand

There have been several posts that have jumped on the new metrics and proposed that we can now measure “engagement” by dividing People Talking About by Page Likes. The nice thing about that is you can go to all of your competitors’ pages and get a snapshot of that metric, so it’s handy to benchmark against. I don’t think that’s a sufficiently good reason to recommend as an approach (but I’ll get back to it — stick with me to the end of this post!).

Below are what I think are some metrics that should be seriously considered (this is coming out of some internal discussion at my day job, but it isn’t by any means a full, company-approved recommendation at this point).

We’ll start with the easy one:

This is a metric that is directly available from Facebook Insights. It’s a drastic improvement over the old Active Users metric, but, essentially, that’s what it’s replacing. If you want to know how many unique people are receiving any sort of message spawned from your Facebook page, Total Reach is a pretty good crack at it. Oh, and, if you look on page 176 of John Lovett’s Social Media Metrics Secrets book…you’ll see Reach is one of his recommended KPIs for an objective of “gaining exposure” (I don’t quite follow his pseudo-formula for Reach, but maybe he’ll explain it to me one of these days and tell me if I’m putting erroneous words in his mouth by seeing the new Facebook measure as being a good match for his recommended Reach KPI).

Another possible social media objective that John proposes is “fostering dialogue,” and one of his recommended KPIs for that is “Audience Engagement.” Adhering pretty closely to his formula there, we can now get at that measure for a Facebook page:

Now, I’m calling it Page Virality because, if you look up earlier in this post, you’ll see that Facebook has already defined a post-level metric called Virality that is this exact formula using the post-level metrics. The two are tightly, tightly related. If you increase your post Virality starting tomorrow by publishing more “engage-able” posts (posts that people who see it are more like to like, comment, or share), then your Page Virality will increase.

There’s a subtle (but important this time) reason for using Total Reach in the denominator rather than Page Likes. If you have a huge fan base, but you’ve done a poor job of engaging with those fans in the past, your EdgeRank is likely going to be pretty low on new posts in the near term, which means your Reach-to-Likes ratio is going to be low (keep reading…we’ll get to that). To measure the engage-ability of a post, you should only count against the number of people who saw the post (which is why Facebook got the Virality measure right), and the same holds true for the page.

Key Point: Page Virality can be impacted in the short-term; it’s a “speedboat measure” in that it is highly responsive to actions a brand takes with the content they publish

This is all a setup for another measure that I think is likely important (but which doesn’t have a reference in John’s book — it’s a pretty Facebook-centric measure, though, so I’m going to tell myself that’s okay):

I’m not in love with the name for this (feel free to recommend alternatives!). This metric is a measure (or a very, very close approximation — see the messy Venn diagram at the start of this post) of what percent of your “Facebook house list” (the people who like your page) are actually receiving messages from you when you post a status update. If this number is low, you’ve probably been doing a lousy job of providing engaging content in the past, and your EdgeRank is low for new posts.

Key Point: Reach Penetration will change more sluggishly than Page Virality; it’s an “aircraft carrier measure” in that it requires a series of more engaging posts to meaningfully impact it

(I should probably admit here that this is all in theory. It’s going to take some time to really see if things play out this way).

Those are the core metrics I like when it comes to gaining exposure and fostering dialogue. But, there’s one other slick little nuance…

Talking About / Page Likes

Remember Talking About / Page Likes? That’s the metric that is, effectively, publicly available (as a point in time) for any Facebook page. That makes it appealing. Well, two of the metrics I proposed above are, really, just deconstructing that metric:

This is tangentially reminiscent of doing a DuPont Analysis when breaking down a company’s ROE. In theory, two pages could have identical “Talking About / Page Likes” values…with two very fundamentally different drivers going on behind the scenes. One page could be reaching only a small percentage of its total fans (due to poor historical engagement), but has recently started publishing much more engaging content. The other page could have historically engaged pretty well (leading to higher reach penetration), but, of late, has slacked off (low page virality). Cool, huh?

What do you think? Off my rocker, or well-reasoned (if verbose)?Similar Posts:

• Have You Picked Up a Copy of “Social Media Metrics Secrets” Yet?
• “Demystifying” the Formula for Social Media ROI (Spoiler: There Isn’t One)

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 10 comments | Add to del.icio.us
Post tags: John Lovett, Reach

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Nevertheless, the Most Basic of the Basics

A QR code contains data in a black-and-white pixelated pattern. That’s all there is to it. It can store lots of different types of data (only a finite amount, of course), but the most common data for that pattern to store is a URL. For instance, the QR code below stores the URL for this blog post:

Please, DON’T Do What I Just Did!

Here’s the key point to this whole post: the example above is a perfect example of how NOT to generate a QR code.

Two reasons:

• It will not be possible to track the number of scans of the QR code
• The QR code is needlessly complex, which requires a larger, more involved QR code

With the QR code above, the QR code reader on a person’s phone reads the underlying URL and routes the user to the target address:

The problem here is that, if you’re using QR codes in multiple places — printed circulars, product packaging, in-store displays, etc. — and they’re sending the user to the same destination URL, you won’t be able to distinguish which of the different physical placements is generating which traffic to that destination URL.

That’s a problem, because, inevitably, you’ll want to know whether your target users are even scanning the codes and, if so, which codes they’re scanning. It would be one thing if QR codes were inherently attractive and added to the aesthetics of analog collateral. But, like their barcode ancestors, they tend to lack visual appeal. If they’re not adding value and not being used, it’s best that they be removed!

Why, Yes, There IS a Better Way. I’m Glad You Asked.

The QR code below sends the user to the exact same destination (this post):

Notice anything different? For starters, the code itself is much, much smaller than the first example above. That’s nice — it takes up less room wherever it’s printed! Designers will hug you (well, they won’t exactly hug you — they’ll still blanch at your requirement to drop this pixelated box into an otherwise attractively designed piece of printed material…but they’ll gnash their teeth moderately less than if they were required to use the much larger QR code from above).

The trick? Well, this new QR code doesn’t include the full URL for this page. Rather it has a much simpler, much shorter, URL encoded in its pixels:

http://goo.gl/H104m.qr

It makes sense, doesn’t it, that a shorter URL like this one will require fewer black and white pixels to be represented in a QR code format? This URL, you see, was generated using http://goo.gl — a URL shortener. You can also generate QR codes using http://bit.ly. Both are free services and both have a reputation of high availability.

Using some flavor of URL shortener is one of those things consultants and tradesfolk refer to as a “best practice” for QR code generation. What’s going on is that the process relies on an intermediate server-side redirect (of which goo.gl and bit.ly are both examples) to route the user to the final destination URL. This alters the actual user flow slightly so that it looks something like the diagram below:

That adds a little bit of complexity to the process, and, depending on the user’s QR code reader and settings therein, he/she may actually see the intermediate URL before getting routed to the final destination. That’s really not the end of the world, as it’s a fairly innocuous step with a dramatic upside. (Technically, this approach introduces an additional potential failure point into the overall process, but that plays out as more of a theoretical concern than a practical one.)

Why Is This Marginally Convoluted Approach Better?

By introducing the shortened URL, you get two direct benefits:

• A smaller, cleaner QR code (we covered that already)
• The ability to count the number of scans of each unique QR code

This second one is the biggie. To be clear, this isn’t going to distinguish between each individual printout of the same underlying QR code, but it will enable you to, for instance, identify scans of a code that is printed on a particular batch of direct mail from scans that are printed in a newspaper circular.

How is it doing that, you ask? Well, exactly the same way that URL shorteners like goo.gl and bit.ly provide data on how many times URLs created using them were scanned: when the “URL Shortener Server” gets a request for the shortened URL, it not only redirects the user to the full destination URL, but it increments a count of how many times the URL was “clicked” (and, in the case of a QR code, “click” = “scanned”) in an internal database. You can then access that data using the URL shortener / QR code generator’s reporting system.

But Wait! There’s MORE!

Take another look at the full URL that the shortened URL (embedded in the QR code) is redirecting to:

http://www.gilliganondata.com/index.php/2011/10/12/qr-codes-how-they-work-at-least-for-analytics?utm_source=gilliganondata&utm_medium=qr_code&utm_campaign=oct_2011_blog

Notice how it has Google Analytics campaign tracking parameters tacked onto the end of it? That’s a second recommended best practice for QR codes that send the user to web sites that have campaign tracking capabilities. This is just like setting up a banner ad or some other form of off-site promotion or advertising: you control the URL, so you should include campaign tracking parameters on it! This will enable you to look at post-scan activity — did users who scanned the QR code from the product packaging convert at a higher rate on-site than users who scanned the in-store display QR code? You get the idea.

A Final Note on This — Where bit.ly and goo.gl Come Up Short

The upsides to goo.gl and bit.ly QR code generation is that they’re free and have decent click/scan analytics. The downside is that, once a short URL is generated, the target URL can’t be edited (they have their reasons).

Paid services such as the service offered by 3GVision i-nigma both offer solid analytics and allow QR codes to be edited after the short URLs (which the QR codes then represent) are created. This makes a lot of sense, because a printed QR code may stay in-market for a sustained period of time, while the digital content that supports the placement of that code may need to be updated. Or, say that someone creates a QR code and uses a target URL that is devoid of campaign tracking parameters — with a service like 3GVision’s, you can add the tracking parameters after the QR code has been generated and even after it has gone to print (any resemblance to actual situations where this has occurred is purely coincidental! …or so the blogger innocently claimed…). You can’t go backwards in time and add campaign tracking for scans that have already occurred, but you can at least “fix” the tracking going forward.

As is my modus operandi, this has been a pretty straightforward concept with a couple of tips and best practices…and I’ve turned it into a rather verbose and hyper-descriptive post. <sigh> I hope you found it informative.

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 9 comments | Add to del.icio.us
Post tags: 3GVision, bit.ly, goo.gl, i-nigma, QR codes

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One of the more directly usable sections of the book is in Chapter 5, where Lovett lays out pseudo formulas for KPIs for various possible (probable) social media business objectives. This post started out to be about my experiences drilling down into some of those formulas…but then the content took a turn, and one of Lovett’s partners at Web Analytics Demystified wrote a provocative blog post…so I’ll save the formula exploration for a subsequent post.

Instead…Social Media ROI

Lovett explicitly notes in his book that there is no secret formula for social media ROI. In my mind, there never will be — just as there will never be unicorns, world peace, or delicious chocolate ice cream that is as healthy as a sprig of raw broccoli, no matter how much little girls and boys, rationale adults, or my waistline wish for them.

Yes, the breadth of social media data available is getting better by the day, but, at best, it’s barely keeping pace with the constant changes in consumer behavior and social media platforms. It’s not really gaining ground.

What Lovett proposes, instead of a universally standard social media ROI calculation, is that marketers be very clear as to what their business objectives are – a level down from “increase revenue,” “lower costs,”and “increase customer satisfaction” – and then work to measure against those business objectives.

The way I’ve described this basic approach over the past few years is using the phrase “logical model,” – as in, “You need to build a logical link from the activity you’re doing all the way to ultimate business benefit, even if you’re not able to track those links all the way along that chain. Then…measure progress on the activity.”

Unfortunately, “logical model” is a tricky term, as it already has a very specific meaning in the world of database design. But, if you squint and tilt you’re head just a bit, that’s okay. Just as a database logical model is a representation of how the data is linked and interrelated from a business perspective (as opposed to the “physical model,” which is how the data actually gets structured under the hood), building a logical model of how you expect your brand’s digital/social activities to ladder up to meaningful business outcomes is a perfectly valid  way to set up effective performance measurement in a messy, messy digital marketing world.

No Wonder These Guys Work Together

Right along the lines of Lovett’s approach comes one of the other partners at Web Analytics Demystified with, in my mind, highly complementary thinking. Eric Peterson’s post about The Myth of the “Data-Driven Business” postulates that there are pitfalls a-looming if the digital analytics industry continues to espouse “being totally data-driven” as the penultimate goal. He notes:

…I simply have not seen nearly enough evidence that eschewing the type of business acumen, experience, and awareness that is the very heart-and-soul of every successful business in favor of a “by the numbers” approach creates the type of result that the “data-driven” school seems to be evangelizing for.

What I do see in our best clients and those rare, transcendent organizations that truly understand the relationship between people, process, and technology — and are able to leverage that knowledge to inform their overarching business strategy — is a very healthy blend of data and business knowledge, each applied judiciously based on the challenge at hand. Smart business leaders leveraging insights and recommendations made by a trusted analytics organization — not automatons pulling levers based on a hit count, p-value, or conversion rate.

I agree 100% with his post, and he effectively counters the dissenting commenters (partial dissent, generally – no one has chimed in yet fully disagreeing with him). Peterson himself questions whether he is simply making a mountain out of a semantic molehill. He’s not. We’ve painted ourselves into corners semantically before (“web analyst” is too confining a label, anyone…?). The sooner we try to get out of this one, the better — it’s over-promising / over-selling / over-simplifying the realities of what data can do and what it can’t.

Which Gets Back to “Is It Easy?”

Both Lovett’s and Peterson’s ideas ultimately go back to the need for effective analysts to have a healthy blend of data-crunching skills and business acumen. And…storytelling! Let’s not forget that! It means we will have to be communicators and educators — figuring out the sound bites that get at the larger truths about the most effective ways to approach digital and social media measurement and analysis. Here’s my quick list of regularly (in the past…or going forward!) phrases:

• There is no silver bullet for calculating social media ROI — the increasing fragmentation of the consumer experience and the increasing proliferation of communication channels makes it so
• We’re talking about measuring people and their behavior and attitudes — not a manufacturing process; people are much, much messier than widgets on a production line in a controlled environment
• While it’s certainly advisable to use data in business, it’s more about using that data to be “data-informed” rather than aiming to be “data-driven” — experience and smart thinking count!
• Rather than looking to link each marketing activity all the way to the bottom line, focus on working through a logical model that fits each activity into the larger business context, and then find the measurement and analysis points that balance “nearness to the activity” with “nearness to the ultimate business outcome.”
• Measurement and analytics really is a mix of art and science, and whether more “art” is required or more “science” is required varies based on the specific analytics problem you’re trying to solve

There’s my list — cobbled from my own experience and from the words of others!Similar Posts:

• Have You Picked Up a Copy of “Social Media Metrics Secrets” Yet?
• Complex Processes and Analyses Therein
• The New Facebook Insights — One More Analyst’s Take

© Tim for Gilligan on Data by Tim Wilson, 2011. | Permalink | 4 comments | Add to del.icio.us
Post tags: Eric Peterson, John Lovett

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