Alignment

Properly conducted measurement adds value long before a single data point is captured. The process of identifying KPIs and targets is a fantastic tool for identifying when the appearance of alignment among the stakeholders hides an actual misalignment beneath the surface. “We are all in agreement that we should be investing in social media,” may be a true statement, but it lacks the specificity and clarity to ensure that the “all” who are in agreement are truly on the same page as to the goals and objectives for that investment. Collaboratively establishing KPIs and targets may require some uncomfortable and difficult discussions, but it’s a worthwhile exercise, because it forces the stakeholders to articulate and agree on quantifiable measures of success. For any of our client engagements, we spend time up front really nailing down what success looks like from a hard data perspective for this very reason. As a team begins to execute an initiative, being able to hold up a concise set of measures and targets helps everyone, regardless of their role, focus their efforts. And, of course, Alignment is a foundation for Performance Measurement.

Performance Measurement

The value of performance measurement is twofold:

• During the execution of an initiative, it clearly identifies whether the initiative is delivering the intended results or not. It separates the metrics that matter from the metrics that do not (or the metrics that may be needed for deeper analysis, but which are not direct measures of performance). It signifies both when changes must be made to fix a problem, and it complements Optimization efforts by being the judge as to whether a change is delivering improved results.
• Performance Measurement also quantifies the results and the degree to which an initiative added value to the business. It is a key tool in driving Internal Learning by answering the questions: “Did this work? Should we do something like this again? How well were we able to project the final results before we started the work?”

Performance Measurement is a foundational component of a solid analytics process, but it’s Optimization and Learning that really start to deliver incremental business value.

Optimization

Optimization is all about continuous improvement (when things are going well) and addressing identified issues (when KPIs are not hitting their targets). Obviously, it is linked to Performance Measurement, as described above, but it’s an analytics value area unto itself. Optimization includes A/B and multivariate testing, certainly, but it also includes straight-up analysis of historical data. In the case of social media, where A/B testing is often not possible and historical data may not be sufficiently available, optimization can be driven by focused experimentation. This is a broad area indeed! But, while reporting squirrels can operate with at least some success when it comes to Performance Measurement, they will fail miserably when it comes to delivering Optimization value, as this is an area that requires curiousity, creativity, and rigor rather than rote report repetition. Optimization is a “during the on-going execution of the initiative” value area, which is quite different (but, again, related) to Internal Learning.

Learning

While Optimization is focused on tuning the current process, Internal Learning is about identifying truths (which may change over time), best practices, and, “For the love of Pete, let’s not make the mistake of doing that again!” tactics. It pulls together the value from all three of the other analytics value areas in a more deliberative, forward-looking fashion. This is why it sits at the nexxus of the other three areas in the diagram at the beginning of this post. While, on the one hand, Learning seems like a, “No, duh!” thing to do, it actually can be challenging to do effectively:

• Every initiative is different, so it can be tricky to tease out information that can be applied going forward from information that would only be useful if Doc Brown appeared with his Delorean
• Capturing this sort of information is, ideally, managed through some sort of formal knowledge management process or program, and such programs are quite rare (consultancies excluded)
• Even with a beautifully executed Performance Management process that demonstrates that an initiative had suboptimal results, it is still very tempting to start a subsequent initiative based on the skeleton of a previous one. Meaning, it can be very difficult to break the, “that’s how we’ve always done it” barrier to change (remember how long it took to get us to stop putting insanely long registration forms on our sites?)

Despite these challenges, it is absolutely worth finding ways to ensure that ongoing learning is part of the analytics program:

• As part of the Performance Measurement post mortem for a project, formally ask (and document), what aspects, specifically, of the initiative’s results contain broader truths that can be carried forward.
• As part of the Alignment exercise for any new initiative, consciously ask, “What have we done in the past that is relevant, and what did we learn that should be applied here?” (Ideally, this occurs simply by tapping into an exquisite knowledge management platform, but, in the real world, it requires reviewing the results of past projects and even reaching out and talking to people who were involved with those projects)
• When Optimization work is successfully performed, do more than simply make the appropriate change for the current initiative — capture what change was made and why in a format that can be easily referenced in the future

This is a tough area that is often assumed to be something that just automatically occurs. To a certain extent, it does, but only at an individual level: I’m going to learn from every project I work on, and I will apply that learning to subsequent projects that I work on. But, the experience of “I” has no value to the guy who sits 10′ away if he is currently working on a project where my past experiences could be of use if he doesn’t: 1) know I’ve had those experiences, or 2) have a centralized mechanism or process for leveraging that knowledge.

What Else?

What do you say when someone asks you, “How does analytics add value?” Do you focus on one or more of the areas above, or do you approach the question from an entirely different perspective? I’d love to hear!

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | No comment | Add to del.icio.us
Post tags:

Feed enhanced by Better Feed from Ozh

Some of these tips overlap with the FAQ posted on the WAW site, and I’ve also created a one-page Excel checklist that covers the various details that go into our events to supplement this post.

And now, onto the tips!

Tip No. 1: Start Small

In Columbus, we now have a WAW almost every month, and we have between 40 and 60 attendees at each on . It took us several years to get to that level of consistent turnout, and that, in my mind, was a good thing. The core group that met over the first year or so got to know each other really well, as there were only 8-15 us at each event, and we could actually have group discussions in which everyone participated. Those early participants are still regularly attendees. People came consistently because they enjoyed the people, and they were patient with logistical hiccups and not-so-great venues. They provided feedback and made suggestions that helped us refine the what, the how, and the where of future events.

The other benefit of starting small is that you don’t have to worry about paying for the event – the Web Analytics Wednesday Global Sponsors are insanely easy to tap into to cover the cost (more on that in Tip No. 9).

Tip No. 2: Location, Location, Location

Location matters. In Columbus, this was something that took us over a year to really nail down, and I wasn’t much help, as I had only recently moved to the area. Some things to look for in a venue:

• Centrally located – most cities have some degree of sprawl, so there is no location that is perfect for everyone; but, what we’ve found is that, the closer we can get the venue to the main business district, the better
• Separate meeting room – lots of restaurants have rooms that can be reserved for private parties; sometimes, they require a separate fee, but sometimes they just require a minimum total spend. All things are negotiable – you’re bringing business to them on a Wednesday night, so they are generally flexible.
• Low-to-moderate noise level – if the venue has a separate room, this is less of an issue; if it doesn’t, the noise level is key. WAWs are, first and foremost, about people meeting and talking to other people, and no one wants to be hoarse on Thursday morning. Live music and happenin’ bar scenes are cool…but they don’t make for great WAWs
• Presentation-friendly – at a minimum, having a room that has a layout that is conducive to a projector and screen is important if there will be any presenting (see Tip No. 7); some venues have screens, and some actually have projectors. But, if the room layout isn’t such that it will support a projector and screen, then make sure you’ve thought through how visual information will be shared in the absence (tip: large companies typically have projectors that employees can check out for meetings – we regularly tap into attendees who work at such companies to actually provide the projectors). Handouts work, too.

Nailing down a single good location is hard enough, but we actually now have 2-3 good locations. This allows us to mix things up so that the event doesn’t start to seem like it has fallen into a rut. And, it gives us options – if one venue is booked for the preferred WAW date, another one is likely to be open.

Tip No. 3: Be Consistent

The cadence of WAWs seems to matter. We aim for an event once per month and know that, occasionally, we won’t manage to have one. Having the events on a regular schedule adds credibility to the event overall (which helps with sponsors and attendees alike), and it really helps convert “networking acquaintances” into “professional friends.”

There is definitely a commitment required in order to follow this tip. From the get-go in Columbus, we had multiple co-organizers, and that group of organizers has grown. We split up the effort — one secured a venue each month, one person handled the emails to past attendees, another person handled finding new ways to promote the event — and have built a pretty solid and repeatable process.

It’s difficult to build momentum without a consistent and recurring schedule, so getting organized and making it a group effort is key (see Tip No. 10).

Tip No. 4: Build a WAW Database

From our first event onward, I started entering the name and email address of each person who registered for a Columbus WAW into a Google Spreadsheet (I now use ExactTarget for this). This requires a little bit of sleuthing, as the WAW registration form only collects an email address. But, 9 times out of 10, it’s pretty easy to figure out the person’s name (the internet being scary that way and all…) and company. This is a bit tedious, but it’s worth it, as it gives us an ever-growing “house list” to whom we can promote upcoming events.

We now have a sign-in sheet at every event to collect the name and email address of each attendee. To reduce the level of data entry and handwriting-deciphering required, I pre-print a list of all registrants for the sign-in sheet and just ask people to check a box next to their name to indicate they’ve arrived. That sheet has blank rows for people who registered late or didn’t register to write in their information.

Tip No. 5: Invite and Remind

Obviously, it’s not enough to just build and maintain a house list if it doesn’t get used. For every WAW, each person on that list gets sent at least two emails (but no more than three):

• Notification / invitation – a couple of weeks out, we send an email to the entire list letting them know of the upcoming event
• Second invitation – for anyone who has not registered a week out, we send a second invitation; the content is very similar to the first one, but we generally mix up the subject line and the body copy a bit
• Reminder – for anyone who has registered, we send a reminder email 2-3 days before the event

We try to consistently hit some key information with each email:

• The date and location for the event
• Information as to the topic that will be presented (if we have a presentation)
• A reminder that the event is free
• A link to the event registration page on the WAW site

We’ve even done some A/B testing on the subject lines, but, with a list that is only several hundred people, that’s more because it’s a good way to experiment with the process for A/B testing in ExactTarget than because we’ve been able to learn anything of note about effective subject lines for WAW emails.

And, while we haven’t always been 100% CAN-SPAM compliant, we’ve always been clear in all communications as to how the recipient could opt out of future emails, and we honor any opt out requests we receive.

Tip No. 6: Multi-Channel Promotion

In addition to email, we consistently push out notifications through as many channels as possible:

• Through the Facebook group we created for our WAW
• Through the LinkedIn group we created for our WAW
• Through Twitter (using the #measure hashtag)
• Through other local channels (in our case, there is a very active tech-oriented community on meetup.com)

We don’t actively maintain any of these channels for any purpose other than notifications of upcoming events. That may not be a social media best practice, but it works, in that participants can opt in to non-email communication through whatever channel they prefer.

One thing we did learn was that we shouldn’t just sit down on one night and send out the email and simultaneously update every social media channel. This just meant that users who were connected through multiple means got spammed with the same information all at one point in time, which reduced its effectiveness (and was a little annoying). We now spread out the updates over the course of several days.

Tip No. 7: Limited Formal Presentations / Plenty of Time for Networking

We tell our presenters to aim for 15-20 minutes and to avoid presentations that are simply sales pitches for their companies. With brief presentations on relevant topics (sometimes the sponsor presents, sometimes it’s simply one of the organizers or an attendee who has volunteered a topic), we tend to spend another 15-30 minutes in Q&A and discussion. The feedback we’ve consistently gotten is that attendees enjoy both the networking and having some formally presented content. So, we strive to keep a balance between the two. Two keys to that:

• Very clear (polite, but firm) communication to the presenters ahead of time as to expectations regarding presentation length
• Having one of the organizers prepared to manage the clock — be it signaling the presenter to wrap up or announcing “let’s do one more question” if things run long and the crowd starts to squirm (some day, I’ll live down cutting off Chris Grant after she traveled all the way down from Michigan for our WAW…)

The schedule we’ve followed for the past few years is:

• 6:30 – 7:00 — sign-in and networking
• 7:00 – 7:10-ish — find seats, welcome and announcements
• 7:10 – 7:45-ish — presentation and Q&A
• 7:45-ish – 8:30/9:00 — more networking

I’ve got the word “networking” in the title of this tip and a couple of times in the listed schedule above, but, honestly, “hanging out” is probably a better description. Like-minded people with food and beer… it’s fun!

Tip No. 8: Encourage Tweeting

We encourage tweeting at our WAWs for all of the same reasons tweeting is encouraged at conferences:

• It publicizes the event and content out to the followers of the attendees
• It fosters networking as people engage with each other during the presentation
• It provides a nice way to have crowdsourced “notes” from the presentation

To promote tweeting, we have started printing out little cards that we put at all of the tables that include:

• The Twitter usernames of the presenter(s)
• The hashtag for the event (we use #cbuswaw)
• The logos of our sponsors (nothing should get printed or emailed that doesn’t include a thank you to the sponsors)

Even if there are only a small number of attendees, and even if there is no formal presentation, tweets can help spread the word.

Tip No. 9: Free Drinks (and Food, if Possible)

We’re reaching the end of this list, but that doesn’t mean these tips are any less important! Free drinks are a must! While no one attends a WAW simply because they are burdened with an empty bank account and a drinking problem, by offering booze, the overall vibe and purpose gets communicated as a “fun event” more than a “professional obligation.”

Providing free drinks can get expensive…but it’s worth the effort to make sure it happens. Sub-tips on that front:

• If you’re just getting started, and it’s a small event, tap into the Web Analytics Wednesday Global Sponsors. That’s what their sponsorship is there for!
• Use drink tickets to manage the total outlay. I have yet to host an event at a bar or restaurant that doesn’t have drink tickets on hand for our use, and, by handing out 1-2 tickets (we usually do 2), you can ensure that your sponsors aren’t inadvertently funding a fraternity party
• Seek out sponsors — the smaller the event, the smaller the ask; the larger the event, the more worthwhile it is for the sponsor. Use your and other attendees connections to the analytics vendors and services they use. Many of them have marketing funds available, and it’s a great way for them to make connections with prospective customers in their territory.

We almost always provide food at our events as well. To manage costs on that front, we typically go with a “heavy appetizer buffet” rather than a full-on meal. We typically order food to cover 15-20% fewer people than we actually expect to attend. Otherwise, we wind up with crazy amounts of leftovers

Tip No. 10: Ask for Help

As I put together the checklist to accompany this post, and as I wrote the post itself, I realized how many moving parts there are in our process. No single event will ever be perfect, and it doesn’t have to be. But, the more details that get consistently covered, the more likely the WAWs are to flourish and grow. The best way to cover those details is through organization and teamwork: ask for volunteers to help with future events at each of your events; pay attention to who seems to be most engaged and has useful ideas and suggestions for future events. Recruit!

What’s Missing?

The downloadable checklist is intended as a companion to these tips, and it’s organized based on the different aspects of managing a WAW. I hope you find it useful.

What else have you seen — either when organizing or attending a WAW — that works particularly well? I’d love to get some comments that give us some ideas for continuing to improve our events!Similar Posts:

• Columbus Web Analytics Wednesday — Feedback Analysis
• Old School Online Community Leads to a Dozen Data Geeks and Drinks
• Columbus Web Analytics Wednesday: A Speedy April
• A Record-Setting Web Analytics Wednesday in Columbus
• Columbus Web Analytics Wednesday Recap: Don’t “Antisappoint” Visitors

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | 3 comments | Add to del.icio.us
Post tags: WAW Columbus, web analytics wednesday

Feed enhanced by Better Feed from Ozh

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.”

Lately, though, it seems that the emphasis on “stories” has shifted to a more fundamental focus on “communication.” As evidence, I present the following:

A 4-Part Blog Series

Michele Kiss published a 4-part blog series over the course of last week titled “The Most Undervalued Analytics Tool: Communication.” The series covered communication within your analytics team, communication across departments, communication with executives and stakeholders, and communication with partners. Whether intentionally or not, the series highlighted how varied and intricate the many facets of “communication” really are (and she makes some excellent tips for addressing those different facets!).

A Data Scientist’s “Day to Day” Advice

Christopher Berry, VP of Marketing Science at Syncapse, also published a post last week that touched on the importance of communication. Paraphrasing (a bit), he advised:

• Recognize that you’re going to have to repeat yourself — not because the people your communicating with are stupid, but because they’re not as wired to the world of data as you are
• Communicate to both the visual and auditory senses — different people learn better through different channels (and neuroscience has shown that ideas stick better when they’re received through multiple sensory registers)
• Use bullet points (be concise)

Christopher is one of those guys who could talk about the intricacies of shoe leather and have an audience spellbound…so his credibility on the communication front comes more from the fact that he’s a great communicator than from his position as a top brain in the world of data scientistry.

Repetition at ACCELERATE

During last Wednesday’s ACCELERATE conference in Chicago, I tweeted the following:

The tweet was mid-afternoon, and it was after a run of sessions — all very good — where the presenters directly spoke to the importance of communication when it come to a range of analytics responsibilities and challenges.

A Chat with Jim Sterne

At the Web Analytics Wednesday that followed the conference, I got my first chance (ever!) to have more than a 2-sentence conversation with Jim Sterne (I’m pretty sure the smile on his face all day was the smile of a man who was attending a conference as a mere attendee than as a host and organizer, and the plethora of attendant stresses of that role!).

During that discussion, Jim asked me the question, “What is it that you are doing now that is moving towards [where you want to be with your career].” We’ll leave the details of the bracketed part of my quote aside and focus on my answer, which I’d never really thought of in such explicit terms. My answer was that, being a digital analyst at an agency that was built over the course of 3 decades on a foundation of great design work and outstanding consumer research (as in: NOT on measurement and analytics), I have to keep honing my communication skills. In many, many ways I have a conversation every day where I am trying to communicate the same basics about digital analytics that I’ve been communicating for the past decade in different environments. But, I’m not just repeating myself. If I look back over my 2.5 years at the agency, I’ve added a new “tool” to my analytics communication toolbox every 2-3 months, be it a new diagram, a new analogy, a new picture, or a new anecdote. I’ve been working really hard (albeit not explicitly or even consciously) to become the most effective communicator I can be on the subject of digital analytics. Not every new tool sticks, and I try to discard them readily when I realize they’re not resonating.

It’s a work in progress. Are you consciously working on how you communicate as an analyst? What’s your best tip?

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | 6 comments | Add to del.icio.us
Post tags:

Feed enhanced by Better Feed from Ozh

• Paul Phillips of Causata spoke at eMetrics in San Francisco, and his talk was about leveraging data from customer touchpoints across multiple channels to provide better customer relationship management
• I’ve been re-reading John Lovett’s Social Media Metrics Secrets book as part of an internal book group at Resource Interactive
• We’ve had clients approaching us with some new and unique questions related to their social media efforts

What’s become clear is that “social media analytics” is a broad and deep topic, and discussions quickly run amok when there isn’t some clarity as to which aspect of social media analytics is being explored.

As I see it, there are four broad buckets of ways that social media data can be put to use by companies:

No company that is remotely serious about social media in 2012 can afford to ignore the top two boxes. The bottom two are much more complex and, therefore, require a substantial investment, both in people and technology.

Now, I could stop here and actually have a succinct post. But, why break a near-perfect (or consistently imperfect) streak? Let’s take a slightly deeper look at each bucket.

Operational Execution

(I almost labeled this bucket “Community Management,” but the variety of viewpoints in the industry on the scope of that role convinced me to leave that can of worms happily sealed for the purposes of this post.)

Social media requires a much more constant intake and rapid response/action based on data than web sites typically do. Having the appropriate tools, processes, and people in place to respond to conversations with appropriate (minimal) latency is key.

Key challenges to effectively managing this aspect of social media data include: determining a reasonable scope, being realistic about the available on-going people who will manage the process, and, to a lesser extent, selecting the appropriate set of tools. Tool selection is challenging because this is the area where the majority of social media platforms are choosing to play — from online listening platforms like Radian6, Sysomos, Alterian, and Syncapse; to “social relationship management” platforms like Vitrue, Buddy Media, Wildfire, (Adobe) Context Optional, and Shoutlet; and even to the low-cost platforms such as Hootsuite and TweetDeck. These platforms have a range of capabilities, and their pricing models vary dramatically.

Performance Measurement

Ahhh, performance measurement. When it comes to social media, it definitely falls in the “simple, but not easy” bucket. And, it’s an area where marketers are perpetually dissatisfied when they discover that there is no “value of a fan” formula, nor is there “the ROI of a tweet.” But, any marketer who has the patience to step back and consider where social media plays in his/her business can absolutely do effect performance measurement and report on meaningful business results!

Chapters 4 and 5 of John Lovett’s book, Social Media Metrics Secrets, get to the heart of social media performance measurement by laying out possible social media objectives and appropriate KPIs therein. High on my list is to make it through Olivier Blanchard’s Social Media ROI: Managing and Measuring Social Media Efforts in Your Organization, as I’m confident that his book is equally full of usable gems when it comes to quantifying the business value delivered from social media initiatives.

When it comes to technologies for social media performance measurement, we generally find ourselves stuck trying to make use of the Operational Execution platforms. They all tout their “powerful analytics,” but their product roadmaps have typically been driven more by “listenting” and “publishing” features than they have been driven by “metrics” capabilities. With Google’s recent announcement ofGoogle Analytics Social Reports, and with Adobe’s recent announcement of Adobe Social, this may be starting to change.

(Social-Enhanced) CRM

Leveraging social media data to improve customer relationship management is something that there has been lots of talk about…but that very few companies have successfully implemented. At its most intriguing, this means companies identifying — through explicit user permission or through mining the social web — which Twitter users, Facebook fans, Pinterest users, Google+ users, and so on can be linked to their internal systems. Then, by listening to the public conversations of those users and combining that information with internally-captured transactional data (online purchases, in-store purchases, loyalty program membership, email clickthroughs, etc.), getting a much more comprehensive view of their customers and prospects. That “more comprehensive view,” in theory, can be used to build much more robust predictive models that can let the brand know how, when, and with what content to engage individual customers to maximize the value of that relationship for the brand.

The challenges are twofold:

• Consumer privacy concerns — even if a brand doesn’t do anything illegal, consumers and the press have a tendency to get alarmed when they realize how non-anonymous their relationship with the brand is (as Target learned…and they weren’t even using social media data!)
• Complexity and cost — there is a grave tendency for marketers to confuse “freely available data” with “data that costs very little to gather and put to good use.” Companies’ customer data is data they have collected through controllable interactions with consumers — through a form they filled out on the web, through a credit card being run as part of a purchase, through a call into the service center, etc. Data that is pulled from social media platforms is at the whim of the platforms and the whim of the consumer who set up the account. No company (except Twitter) can go out to a Twitter account and, in an automated fashion, bring back the user’s email address, real name, gender, or even country of residence. It takes much more sophisticated data crawling, combined with probabilistic matching engines, to get this data.

Despite these challenges, this is an exciting opportunity for brands. And, the technology platforms are starting to emerge, with the three that spring the most quickly to my mind being Causata, iJento, and Quantivo.

Trend / Opportunity Prediction

This is another area that is really tough to pull off, but it’s an area that, admittedly, has great potential. It’s a “Big Data” play if ever there was one — along the lines of how the Department of Homeland Security supposedly harnesses the data in millions of communications streams to identify terrorist hot spots. It’s sifting through a haystack and not knowing whether your’re looking for a needle, a twig, a small piece of wire, or a paperclip, but knowing that, if you find any of them, you’ll be able to put it to good use.

The wistfully optimistic marketing strategist describes this area something like this: “I want to pick up on patterns and trends in the psychographic and attitudinal profile of my target consumers that emerge in a way that I can reasonably shift my activities. I want an ‘alert’ that tells me, ‘There’s something of interest here!’”

It’s a damn vague dream…but that doesn’t mean it’s unrealistic. It’s a multi-faceted challenge, though, because it requires the convergence of some rather sticky wickets:

• Identifying conversations that are occurring amongst people who meet the profile of a brand’s target consumers (demographic, psychographic, or otherwise) — yet, social media profiles don’t come with a publicly available list of the user’s attitudes, beliefs, purchasing behavior, age, family income, educational level, etc.
• Identifying topics within those conversations that might be relevant for the brand — we’re talking well beyond “they’re talking about what the brand sells” and are looking for content with a much, much fuzzier topical definition
• Identifying a change in these topics — generally, what marketers want most is to pick up on an emerging trend rather than simply a long-held truism

To pull this off will require a significant investment in technology and infrastructure, a significant investment in a team of people with specialized skills, and a significant amount of patience. I chuckle every time I hear an anecdote about how a brand managed to pick up on some unexpected opportunity in real time and then quickly respond…without a recognition that the brand was spending an awful lot of time listening in real-time and picking up nothing of note!

This area, I think, is what a lot of the current buzz around Big Data is focused on. I’m hoping there are enough companies investing in trying to pull it off that we get there in the next few years, because it will be pretty damn cool. Maybe IBM can set Watson up with a Digital Marketing Optimization Suite login and see what he can do!

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | No comment | Add to del.icio.us
Post tags: Big Data, Causata, iJento, Quantivo, Watson

Feed enhanced by Better Feed from Ozh

I’ve had several unrelated experiences with different clients and internal teams of late that have led me to try to capture, in a similar fashion, the three-legged stool of an online analytics program. Just as others have started tacking on additional components to Stéphane’s three skillsets, I’m sure my three-legged stool will quickly become a traditional chair…then some sort of six-legged oddity. But, I’d be thrilled if I could consistently communicate the basics to my non-analyst co-workers and clients:

I hold to a pretty strict distinction between “measurement and reporting” and “analysis,” and I firmly believe there is value in “reporting,” as long as that reporting is appropriately set up and applied.

Just as I believe that reporting should generally occur either as a one-time event (campaign wrap-up, for instance) or at regular intervals, I firmly believe that testing and analysis should not be forced into a recurring schedule. It’s fine (desirable) to be always conducting analysis, but the world of “present the results of your analysis — and your insights and recommendations therein — once/month on the first Wednesday of the month” is utterly asinine. Yet…it’s a mindset with which a depressing majority of companies operate.

Reporting Done Poorly…Which Is an Unfortunately Ubiquitous Habit

I’ve been client side. I’ve been agency side. I’ve done a decent amount of reading on human nature as it relates to organizational change. My sad conclusion:

The business world has conditioned itself to confuse “cumbersome decks of data” with “reporting done well.”

It happens again and again. And again. And…again! It goes like this:

1. Someone asks for some data in a report
2. Someone else pulls the data
3. The data raises some additional questions, so the first person asks for more data.
4. The analyst pulls more data
5. The initial requestor finds this data useful, so he/she requests that the same data be pulled on a recurring schedule
6. The analyst starts pulling and compiling the data on a regular schedule
7. The requestor starts sharing the report with colleagues. The colleagues see that the report certainly should be useful, but they’re not quite sure that it’s telling them anything they can act on. They assume that it’s because there is not enough data, so they ask the analyst to add in yet more data to the report
8. The report begins to grow.
9. The recipients now have a very large report to flip through, and, frankly, they don’t have time month in and month out to go through it. They assume their colleagues are, though, so they keep their mouths shut so as to not advertise that the report isn’t actually helping them make decisions. Occasionally, they leaf through it until they see something that spikes or dips, and they casually comment on it. It shows that they’re reading the report!
10. No one tells the analyst that the report has grown too cumbersome, because they all assume that the report must be driving action somewhere. After all, it takes two weeks of every month to produce, and no one else is speaking up that it is too much to manage or act on!
11. The analyst (now a team of analysts) and the recipients gradually move on to other jobs at other companies. At this point, they’re conditioned that part of their job is to produce or receive cumbersome piles of data on a regular basis. Over time, it actually seems odd to not be receiving a large report. So, if someone steps up and asks the naked emperor question: “How are you using this report to actually make decisions and drive the business?”…well…that’s a threatening question indeed!

In the services industry, there is the concept of a “facilitated good.” If you’re selling brainpower and thought, the theory goes, and you’re billing out smart people at a hefty rate, then you damn well better leave behind a thick binder of something to demonstrate that all of that knowledge and consultation was more than mere ephemera!

And, on the client side, if the last 6 consultancies and agencies that you worked with all diligently delivered 40-slide PowerPoint decks or 80-page reports, then, by golly, you’re going to look askance at the consultant who shows up and aims for actionable concision!

Nonetheless, I will continue my quixotic quest to bring sanity to the world. So, onto the three legs of my analytics stool…

First, Plan (Dammit!!!)

Get a room full of experienced analysts together and ask them where any good analytics program or initiative starts, and you’ll get a unanimous response that it starts: 1) at the beginning of the initiative, and 2) with some form of rigorous planning.

The most critical question to answer during analytics planning is: “How are we going to know if we’re successful?” Of course, you can’t answer that question if you haven’t also answered the question: “What are we trying to accomplish?” Those are the two questions that I wrote about in this Getting to Great KPIs post.

Of course, there are other components of analytics planning:

• Where will the data come from that we’ll use?
• What other metrics — beyond the KPIs — will we need to capture?
• What additional data considerations need to be factored into the effort to ensure that we are positioned for effective analysis and optimization down the road?
• What (if any) special tagging, tracking, or monitoring do we need to put into place (and who/how will that happen)?
• What are the known limitations of the data?
• What are our assumptions about the effort?
• …and more

In my experience both agency-side and client-side, this step regularly gets skipped like it’s a smooth round, rock in the hand of an adolescent male standing on the shore of a lake on a windless day.

An offshoot of the planning is the actual tagging/tracking/monitoring configuration…but I consider that an extension of the planning, as it may or may not be required, depending on the nature of the initiative.

Next, Measure and Report

Yup. Measurement’s important. That’s how you know if you’re performing at, above, or below your KPIs:

Here’s where I start to get into debates, both inside the analytics industry and outside. I strongly believe that it is perfectly acceptable to deliver reports without accompanying insights and analysis. Ideally, reports are automated. If they’re not automated, they’re produced damn quickly and efficiently.

Dashboards — the most popular form of reports — have a pretty simple purpose: provide an at-a-glance view of what has happened since the last update, and ensure that, at a glance, any anomalies jump out. More often than not, there won’t be anomalies, so there is nothing that needs to be analyzed based on the report! That’s okay!

I was discussing this concept with a co-worker recently, and, in response to my claim that reports should simply get delivered with minimal latency and, at best, a note that says, “Hey, I noticed this apparent anomaly that might be important. I’m going to look into it, but if you (recipient) have any ideas as to what might be going on, I’d love to get your thoughts,” she responded:

I think this makes sense, but wouldn’t we provide some analysis as to the “why” on the monthly reports?

I immediately went to the “dashboard in your car” analogy (I know — it breaks down on a lot of fronts, but it works here) with my response:

You don’t look at your fuel gauge when you get in the car every day and ask, “Why is the needle pointing where it is?” You take a quick look, make sure it’s not pegged on empty, and then go about your day.

That’s measurement. It may spawn analysis, but, often, it does not. And that’s to be expected!

Which Brings Us to Testing and Analysis

Analysis requires (or, at least, is much more likely to yield value in an efficient manner) having conducted some solid planning and having KPI-centric measurement in place. But, the timing of analysis shouldn’t be forced into a fixed schedule.

The bottom part of the figure above gets to the crux of the biscuit when it comes to timing: sometimes, the best way to answer a business question is through analyzing historical data. Sometimes, the best way to answer a question is through go-forward testing. Sometimes, it’s a combination of the two (develop a theory based on the historical data, but then test it by making a change in the future and monitoring the results). Sometimes the analysis can be conducted very quickly. Other times, the analysis requires a large chunk of analyst time and may take days or weeks to complete.

Facilitating the collaboration with the various stakeholders, managing the analysis projects (multiple analyses in flight at once — starting and concluding asynchronously based on each effort’s unique nature), can absolutely fall under the purview of the analyst (again referencing Stéphane’s post, this should be an analyst with a strong “head” for business acumen).

In Conclusion…(I promise!)

There is a fundamental flaw in any approach to using data that attempts to bundle scheduled reporting with analysis. It forces efforts to find “actionable insights” in a context where there may very well be none. And, it perpetuates an assumption that it’s simply a matter of pointing an analyst at data and waiting for him/her to find insights and make recommendations.

I’ve certainly run into business users who flee from any effort to engage directly when it comes to analytics. They hide behind their inboxes lobbing notes like, “You’re the analyst. YOU tell me what my business problem is and make recommendations from your analysis!” I’m sure some of these users had one too many (and one is “too many”) interactions with an analyst who wanted to explain the difference between a page view and a visit, or who wanted to collaboratively sift through a 50-page deck of charts and tables. That’s not good, and that analyst should be flogged (unless he/she is less than two years out of college and can claim to have not known any better). But, using data to effectively inform decisions is a collaborative effort. It needs to start early (planning), it needs to have clear, concise performance measurement (KPI-driven dashboards), and it needs to have flexibility to drive the timing and approach of analyses that deliver meaningful results.

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | One comment | Add to del.icio.us
Post tags:

Feed enhanced by Better Feed from Ozh

The challenge, it turns out, is that there are multiple aspects of what sounds like a pretty straightforward assessment:

• What metric(s) actually make for a “successful” post?
• How do you effectively consider time of day and day of week?
• Have you actually posted on a sufficient variety of dates and times to have the data to do a meaningful analysis?

After scraping together some hasty cuts at this, I thought it would be worthwhile to try to knock out something that was easily shareable and reusable. The result is the downloadable spreadsheet at the end of this post.

What It Looks Like

The spreadsheet takes a simple export of post-level data from Facebook Insights (the .csv format) and generates three basic charts.

The first chart simply shows the number of posts in each time slot and each day of week — this answers the question of, “What spots have I not even really tried posting in?”

In this example, there have not been any posts from 9:00 PM to 6:00 AM, only one post between 6:00 AM and 9:00 AM, and only four posts on a Friday. Don’t worry if you don’t like the time windows — we’ll get to that in a bit.

The next two charts are crude heatmaps of a couple of metrics, but they both use the same grid as above, and they use a pretty simple green-to-red spectrum to show which spots performed best/worst relative to the other slots:

(I know, I know: red/green is not a colorblind-friendly palette selection. I’ll keep working on the visualization technique!)

The first of these charts looks at the average total reach of the updates that were posted in each time slot — the number of unique users of Facebook who were exposed to the post:

In the example above, Wednesdays looked to perform pretty well reach-wise, as did Saturday afternoon. If you have Facebook paid media running, these results may get skewed. It’s easy enough to update this chart to use Organic Reach rather than Total Reach, or, you can simply factor an awareness of what was running and when into your assessment of the results. Also, keep in mind that Facebook continues to fiddle with the EdgeRank/GraphRank algorithm, so there are aspects of a post’s reach that are beyond your control.

The next chart shows the average engagement rate of the posts, defined as the number of users who engaged with the post in some way (clicked on a link, posted a comment, liked the post, viewed a photo, etc.) divided by the total reach of the post. This is a pretty solid measure of the content quality — did the post drive the users who saw it to take some action to engage with the content? Now, arguably, the propensity for a user to engage is less impacted by the time of day and day of week, but, who knows?

In this example, Sundays and Thursdays were the days when posts appeared to get more engagement (although be leery of that Sunday, 6:00 PM to 9:00 PM, block — there was only a single post in the data set).

Timeframe Flexibility

Picking a set of timeframes is the most subjective aspect of this whole analysis, and it may be worth iterating through a few times to get to timeframes that are likely to be meaningful for the page given the target consumer. So, I’ve set up the worksheet to make it easy to customize these timeframes. For, instance, below is the same data set used above, but with only four windows of time:

The change look less than 60 seconds to implement (it’s all about VLOOKUPS, pivot tables, and conditional formatting!).

How to Use This for Your Own Page

If you want to try this out for your page(s), simply download the Excel file (this was created using Excel 2007, so it should work fine in both 2007 and 2010) and follow the instructions embedded in the worksheet. You will need to export post-level Facebook Insights data for your page, which may require several iterations (we’ve found that Facebook Insights is prone to hanging up if you try to export more than a couple of months of data at once):

Then, just follow the instructions in the spreadsheet and drop me a note if you run into any issues!

Some Notes on the Shortcomings

This approach isn’t perfect, and, if you have ideas for improving it, please leave a comment and I’ll be happy to iterate on the tool. Specifically:

• This approach measures all updates against the other posts for the same page — there is no external benchmarking. This doesn’t bother me, as I’m a proponent of focusing on driving continuous improvement in your performance by starting where you are. Certainly, this analysis should be complemented by performance measurement that tracks the actual values of these metrics over time.
• The overall visualization could be better. It’s not ideal that you need to jump back and forth between three different visualizations to draw conclusions about what days/times are really “good” or “bad”…including factoring in the sample size. I’ve toyed with making more of a weighted score and then doing the same color grid, but, then, you’d be looking at a true abstraction of the performance, so I didn’t go that route. Suggestions?
• A red–>yellow–>green scale just isn’t good when it comes to supporting: 1) black-and-white printouts, and 2) certain forms of color blindness. A more iconographic approach might make more sense.

Please do weigh in with how you would change this. I’m happy to rev it based on input!

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | 17 comments | Add to del.icio.us
Post tags:

Feed enhanced by Better Feed from Ozh

That got me thinking that a partial solution would be to have a Google Analytics profile that only includes organic search traffic. This isn’t a profile that you would use for cross-session analytics, but it’s one that would allow simplified segmentation, reduced cases of sampling, and, perhaps, a more complete data set.

As it turns out, it was pretty simple to set up, and it seems to do the trick.

Step 1: Make a New Profile

Create a new profile under the same web property that you’re using for your site and name it Organic Search Traffic Only:

There’s nothing magic about this. The key is that this is a profile that uses the same web property ID as the profile where you’re running into sampling issues with your SEO analysis. We’re just going to take that same feed of data coming in as visitors visit your site and carve out the subset of that data that is traffic from organic search referrals.

Step 2: Apply an Organic Search Filter

The next (and final) step is to create a filter and apply it to the profile such that only organic search traffic is included.

In the new profile you just created, select the Filters tab and then click New Filter:

From there:

1. Give the filter a name like “Organic Search Referrals”
2. Select Custom Filter as the Filter Type
3. Set the filter as an Include filter
4. Set the Filter Field to Campaign Medium
5. Set the Filter Pattern to “organic”
6. Save the filter

The screen below shows the filter settings:

Step 3: Sit Back and Let the Data Roll In

The profile is only going to include data from the point you set it up going forward. But, it will accurately reflect (to the extent that any web analytics package can accurately reflect this) new versus returning visitors for all time (well, since you initially implemented Google Analytics), because it’s getting that data from the cookie that already exists on users’ machines.

Initially, I saw some odd data on the unique visitors front, which I can semi-intuitively understand…but not quite explain.

Suffice it to say that, once you have the profile up and running for a week or so, you can select the Non-paid Search Traffic segment in your main profile and compare it to the All Visits segment in your new profile, and the numbers will be virtually identical. But, you can now do SEO analysis with a base set of data that only includes search traffic.

Is that handy?Similar Posts:

• Google Analytics = Strawberry?
• Web Analytics Platforms Are Fundamentally Broken
• A Quick Explanation of Sitecatalyst Events for the Google Analytics Power User
• SEO Tips and Thoughts at Web Analytics Wednesday
• Web Analytics Tools Comparison — Columbus WAW Recap Part 2

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | 2 comments | Add to del.icio.us
Post tags: Google Analytics, SEO

Feed enhanced by Better Feed from Ozh

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 | 2 comments | Add to del.icio.us
Post tags:

Feed enhanced by Better Feed from Ozh

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
Post tags: responsive design

Feed enhanced by Better Feed from Ozh

Tip No. 10: Respect the Audience

This last tip is more of a perspective than a tip.

It’s last because it’s the tip that drives the reason for paying attention to all of the other tips.

It’s last because it’s a tip that is all too often flagrantly ignored.

It’s last because it can be a little scary.

The experience that prompted me to write this series was my participation in the inaugural #ACCELERATE conference in San Francisco last fall. As it turned out, I was the last presenter of the day — one of the 5-minute Super #ACCELERATE presentations.

Here’s one way I could have viewed my presentation:

It’s only 5 minutes, so I should try to do something pretty solid, but, if it falls flat, it’s only a small fraction of the overall conference.

Here’s how I actually viewed the presentation:

 It’s 5 minutes, but it’s 5 minutes in front of of 300 people, so that’s actually 1500 minutes, or 25 hours. If I swag that the fully loaded cost of the members of the audience is, on average, $50/hour, then I need to deliver a $1,250 presentation!

Okay, so it’s a little tough to really make this math work is a 5-minute presentation, but think about a 20-minute presentation ($5,000) or a 30-minute presentation ($7,500) or an hour-long presentation ($15,000). Change the hourly cost however you see fit, but do the mental exercise to consider the opportunity cost of the presentation — the total amount that is being invested by the audience members who could be doing something else rather than listening to you present. That is the amount of value you should fully commit to delivering with your presentation.

Each member of the audience is paying to watch your presentation, regardless of whether they had to pay a monetary fee to sit through it.

They’re paying with a finite and valuable commodity: their time.

Recognize that. Respect that. Do everything you can to make it a worthwhile investment on their part.

Photo by Eric T. Peterson

Similar Posts:

• 10 Presentation Tips: Tip No. 2 — Pay Attention to What Doesn’t Work
• 10 Presentation Tips: Tip No. 3 — NO SLIDEUMENTS (a Picture IS Worth 1,000 Words!)
• 10 Presentation Tips: Tip No. 4 — Go with a Flow
• 10 Presentation Tips: Tip No. 6 — Bring the Energy!
• 10 Presentation Tips: Tip No. 8 — We Have Five Senses. Use TWO!

© Tim for Gilligan on Data by Tim Wilson, 2012. | Permalink | No comment | Add to del.icio.us
Post tags: 10 Presentation Tips

Feed enhanced by Better Feed from Ozh