01.30.08
Posted in Uncategorized at 1:16 am by Maigari
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I know I haven’t written in this blog for quite a long a while. As you can gather from the title, PlusOne Analytics the company is dead…or better yet, d/b/a Stratigent. We are doing business as Stratigent.
This has been in the works for a couple of months now. I won’t get into the ins and outs, but what started as a possible partnership relationship ended with our “consumption” by Stratigent. Strategically, this makes sense for both sides, and Arlyn and I are looking forward for this opportunity for personal and professional growth.
Sadly, these blog posts here must end. I couldn’t even begin to tell you how much I’ve learned these last few months on the job. The learning curve for this industry is just so frickin’ steep….it’s awesome! It’s my personal and humble opinion that the learning curve steepens when working with a well-established consulting company.
If you will be at the Omniture Summit or any conferences, please feel free to drop me a note (Maigari[at]stratigent[dot]com).
Adios,
Maigari
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Posted in Year in Review at 6:59 pm by Maigari
I have seen many 2008 predictions written for web analytics, consumer and business technology, and our consumption of social media and web-based applications. As an entrepreneur, my perspective is a bit different. Predictions have very little practical value. Actually, let me clarify. Predictions that turn out to be correct have great value, but given the probability and expected value, predictions have very little value relative to lessons we’ve learned over the past year as we began this enterprise, PlusOne Analytics.
As Peter Drucker said, “The only thing we know about the future is that it will be different.” It is different in an unknowable and unquantifiable way, but lessons learned allow us to take better advantage of the future.
Why Predictions are Not as Valuable as We May Think
The first to jump on a trend hardly ever profits the most from it. Did Google invent the search engine? Did Microsoft invent the personal computer? Did Toyota invent the car? Rather, taking advantage of what happens in the future is not a direct function of the predicting the future. Companies profit more by understanding lessons learned while atop the shoulders of the giants who have come before.
My college roommate’s dad started a chip company based on his Ph.D dissertation, sold the company to AMD, and become a venture capitalist. I remember my roommate telling me endless stories about the VC industry. One particular story was that venture capitalists felt far more comfortable giving money to those who had failed in prior ventures than those who were beginning their first. Why? Because of lessons learned.
The future, we always hope, will be better than the present, but for practical purposes, a look backward is always more beneficial because the lessons learned/not learned affect how we respond to the challenges that lie ahead.
Without further adieu, here is the top lesson we’ve learned as a company:
Simplicity.
One word that encompasses everything. What I’ve found is that there is a life cycle for ideas and concepts.
We start off with simple ideas, concepts, and precepts and begin to expand and expound on them. Ideas fly back and forth. We become enamored with our creativity and then find that we’ve created something astronomical in size and complexity. It’s a beautiful concoction, but what do we really have?
Next, we begin to slice the superfluous from the idea little by little. Committees are created and meetings held all in an attempt to reduce it to it’s most actionable and simple level.
This long process usually ends back at the beginning. The same precepts and concepts that were the initial building blocks become the final foundation.
I won’t go into my feelings explaining why the process above may seem unnecessary, but is the essence of innovation. What we learn through the process adds to our knowledge base, and fuels future innovation.
But in the conclusion above, we realize that through all of the changes in this dynamic, technology-focused era, we must be focused even more completely on the basic principles from which everything flows. Failure to do this, means that companies will lose sight of up, down, left, and right of their industry and business. It will be like flying an airplane through a night-time thunderstorm without understanding the instrument panel.
I have chosen to focus on three things areas in which I have some experience and that also apply to the web analytics industry: Marketing, Web Analytics, and Life.
Marketing Principles
In this day and age, we are inundated with a seemingly limitless number of social media options. What is a business to do? New media crop up that carry your message straight from your company’s mouth to the wallet of the prospect. How does one take advantage of this dizzying array of methods and channels available to optimize marketing?
What are these marketing these marketing principles? I have *borrowed* Jeffrey Beale’s five principles:
1) First, define who your customers are in great detail.
2) Second, find out what it is that your target markets want or need, and then provide a product and/or service to fulfill those wants/needs.
3) Third, develop a message that speaks directly to their needs. Offer clear benefits, not features and fluff.
4) Fourth, position yourself in the marketplace where your target markets already go.
5) And fifth, make your products and services easily accessible to your target.
When marketing goes wrong, is when we deviate from these basic principles. The same can be said for web analytics.
Web Analytics Principles
As the practice of web analytics becomes more sophisticated with the advent of Web Analytics 2.0, Web Analytics 3.0, etc., there has never been a greater need to tie every opportunity back to the basic principles.
Within the practice of web analytics, practitioners’ heads are spinning because their web analytics tools aren’t enough. They need to tie their CRMs, CEMs, VOCs, and all other systems together. With so many options, people are not sure how to optimize and take advantage of their web analytics initiatives. Everything you do must be contained within these 8 steps that Judah Phillips outlines:
1. Determine your business objectives. Like everything in web analytics, you can’t optimize what you haven’t defined as a goal. A business objective driving segmentation might be to “increase conversion rate (over historical numbers)” or “to improve frequency” by offering something valuable to that segment.
2. Define segments. Basic dimensions for segmentation in web analytics include: new visitors, repeat visitors, geography, time, referrer, keyword, and campaign type. Many more dimensions and attributes can be used for segmentation too.
3. Identify expected segment behavior. By reconciling goals, historic performance data, and VOC research, you should be able to identify the expected behavior of the segment. If your business objective is to “increase conversion rate,” your expected segment behavior might be to “complete the form” or “click on a link.”
4. Measure current segment behavior. Sounds easy, right, but it will take system configuration and the right tool. Pages may need to be (re)instrumented, tracking codes may need to be applied, query string parameters may need to be parsed, and in the worse case dimensions you want to segment or the metrics you may want to measure against may not be available in your web analytics tool. For example, how would you use your tool identify the “conversion rate” for a segment of repeat visitors from newsletter X who came from Tokyo and previously downloaded a whitepaper?
5. Create “optimization hypotheses.” Once you’ve measured current behavior, create a hypothesis or hypotheses to test in order to optimize the behavior. You may want to perform controlled experimentation whether a simple AB test (i.e. champion/challenger), multivariate test, or experience test. For example, I may have detected that repeat visitors from Newsletter X responded better to Y offer after being exposed to a certain element than those visitors in the same segment who did were not exposed. That element could have been a content theme, offer, call to action, creative, and so on. Thus, I might create a hypothesis to test that combines elements of the user experience that I feel are key to persuading the behavior and thus fulfilling the business objective.
6. Optimize content, offerings, user experience, and other site elements. Based on your hypothesis, make subsequent changes to the elements that you think will drive the desired segment behavior. For example, you may split traffic to two landing pages each with a completely different offer, creative, and call to action. Or you may choose to switch out specific elements on one landing page (such as an image or call to action) using multivariate methods just to get Visitor X to “complete that form” or “click that link” to improve your “conversion rate.”
7. Analyze segment behavior against hypothesis. How did the segment perform against expected behavior and business objectives based on testing your hypotheses? Tools that provide drill-down/drill-up and cross-dimensional capability allow to analyze segments and answer such questions. The tools I’m talking about are advanced and powerful, such as Unica NetInsight, Visual Sciences Visual Site, Omniture Discover, and WebTrends Marketing Warehouse. Capabilities for segmentation analytics vary by tool, so make sure to dig deep into the offerings because not all tools with let you correlate metrics like “conversion rate” with dimensions like “keyword,” let alone build complex multi-dimensional segments. In fact, some free web analytics don’t allow you to segment data at all (just filter it)!
8. Go with what works. Web analytics segmentation analysis will let you know what appeals to and works for a segment. Success with web analytics segmentation means that you met your business goals and improved key performance of that segment. Rinse, lather, and repeat the segmentation analysis and optimization process so your campaign outperforms and margins soar!
These are the basics that help focus your organization’s attention as you spend much of your time running around like a chicken with the head cut off.
Life Principles
It seems like yesterday that I was graduating from high school with my life ahead. 10 years later, it has been one big whirlwind. One of the major lessons I’ve learned is that we tend to make things far more complex before they are reduced back to simplicity. It’s the simple platitudes that resonate because in simplicity there is truth. That is why I continue to go back to one of my favorite books of all-time, “All I Really Need to Know I Learned in Kindergarten.” Here are Robert Fulghum’s basic precepts below:
All I really need to know about how to live and what to do and how to be I learned in kindergarten. Wisdom was not at the top of the graduate school mountain, but there in the sand pile at school.
These are the things I learned:
• Share everything.
• Play fair.
• Don’t hit people.
• Put things back where you found them.
• Clean up your own mess.
• Don’t take things that aren’t yours.
• Say you’re sorry when you hurt somebody.
• Wash your hands before you eat.
• Flush.
• Warm cookies and cold milk are good for you.
• Live a balanced life - learn some and think some and draw and paint and sing and dance and play and work every day some.
• Take a nap every afternoon.
• When you go out in the world, watch out for traffic, hold hands and stick together.
• Be aware of wonder. Remember the little seed in the Styrofoam cup: the roots go down and the plant goes up and nobody really knows how or why, but we are all like that.
• Goldfish and hamsters and white mice and even the little seed in the Styrofoam cup - they all die. So do we.
• And then remember the Dick-and-Jane books and the first word you learned - the biggest word of all - LOOK.
Everything you need to know is in there somewhere. The Golden Rule and love and basic sanitation. Ecology and politics and equality and sane living.
Take any one of those items and extrapolate it into sophisticated adult terms and apply it to your family life or your work or government or your world and it holds true and clear and firm. Think what a better world it would be if we all - the whole world - had cookies and milk at about 3 o’clock in the afternoon and then lay down with our blankies for a nap. Or if all governments had as a basic policy to always put things back where they found them and to clean up their own mess.
And it is still true, no matter how old you are, when you go out in the world, it is best to hold hands and stick together.
Do these things, and no matter what predictions do or do not come true in 2008, a year from now, you will find yourself looking back with pride at your professional and personal accomplishments.
Happy Holidays from PlusOne Analytics,
Maigari
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12.18.07
Posted in web analytics, web analytics tools, google analytics at 5:32 pm by Maigari
Cigarettes to alcohol. Alcohol to Marijuana. Marijuana to harder drugs. We all know the lessons many of us were taught in elementary school. “Just say no.” “Take a drag and kiss your life goodbye.”
Why am I Writing About This?
During the Q&A portion of Semphonic’s webinar last week, a question came up regarding the effect that Google Analytics has had on enterprise-level platforms.
Gary and the crew all seemed to agree that Google Analytics has had a very beneficial effect for the enterprise vendors like Coremetrics, Omniture, Webtrends, & Visual Sciences. This is something that I completely agree with. Remember, this is backward-looking and in no way makes any predictions about the future as Google continues to add more much needed functionality to Google Analytics.
How is Google Analytics a Gateway Drug to Web Analytics?
You smoke a cigarette, have your first beer, and voila…the harder drugs just don’t seem as bad (in theory of course). Google Analytics has served the exact same purpose for the industry. It has expanded the educational base for organizations who use it as a tool to dip their toes into the web analytics pool. After time spent using Google Analytics, clients are already sold on the value proposition of web analytics. This effect cannot be underestimated.
Is it easy to lead someone to a waterhole AND force them to drink? Isn’t it much better for that same person to show up already parched and looking for any liquid refreshment? The former analogy was the state of the web analytics world BG (Before Google). AG (After Google) sales cycles are shorter and life is in some ways much, much easier. An educated consumer is always a better client — unless of course you are a snake-oil salesman.
As clients gain a greater understanding of their web analytics needs and/or their online and offline presence becomes larger and more complex, there is need for a more robust tool. As they seek to integrate their web analytics data with voice of the customer (VOC), customer relationship management (CRM), and customer experience management (CEM) platforms, many choose to migrate to a new tool.
This web analytics vendor life cycle directly reflects the relationship between customers and their level of web analytics education. Not in every case is the “educated” consumer an enterprise-level application client, but it is definitely the trend.
After organizations have moved along the educational path and reach level 35 out of 100 on Avinash’s scale of black belt web analytics analysts, many are ready to graduate to other more sophisticated tools.
In two informative posts located here and here, Judah Phillips covers the issue of “Web Analytics - How Do I Know I’ve Outgrown Mine?”
“Web analytics tools can be outgrown by companies, just like pants can be outgrown by people. Over time, an analytics tool may no longer fit organizational needs or be well suited to deliver on complex organizational requirements for site optimization and multichannel integration (among other things). “
According to Judah, there are five major reasons that companies choose to upgrade their web analytics tools:
• Inadequate segmentation
• Poor visualization
• No custom reporting
• Limited Integration
• Cost
It is hard to justify paying a licensing fee for a web analytics tool of $70,000 a year until your organization has felt financial pain behind any and all of these five reasons. Google Analytics pushes organizations further up the pain curve much faster than what was possible a few years ago. Omniture, Webtrends, VS, Indextools, etc. are all there waiting capture unsatisfied GA clients.
Easy formula…
GA=Gateway Drug
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12.12.07
Posted in web analytics at 4:17 am by Maigari
I felt it was time to answer the “now what” question again. I’ve done it once before regarding the moment after a company buys an enterprise-level application and is confronted with the question, “now what?” What do I need to think about when it comes to implementing the darn thing?
This post is not for experienced web analysts who are dealing with Web Analytics 2.0 issues. This post is not even for someone who has spent 6 months or more in the industry (but we can always use a bit of a tune-up). This post is for a special class of aspiring “analysts” we have run into recently. The “I purchased a Ferrari, but I can’t drive a stick” class of analysts.
There are some organizations who purchase a enterprise level web analytics solution having been sufficiently wowed by the demo. The sales rep regaled them with cool graphs and promises that their online issues would be solved if only they purchased their tool.
This is never the case. Man has chased the magical elixir forever. Ponce de Leon’s fountain of youth, Hitler’s Lebensraum, and Gollum’s “precious” ring have all been the focal point of a utopian vision. Unfortunately, with web analytics, there is no such panacea.
Today I wanted to tackle the post-implementation predicament where many of companies find themselves entangled. Now what? Once the implementation ends, data is reporting accurately, the organizational business user(s) face a dilemma. What next?
We have run into this issue so often with clients that I wanted to write down a quick methodology that a beginning analyst can use to analyze online data. This isn’t meant as an exhaustive study into what an analyst should and shouldn’t do, but it is meant as a guide to get started.
What I’ve noticed in my career is that the hardest thing for even very intelligent people to do is take the first step and begin the journey. Once they begin the journey, get out of the way and watch them do great things. Inertia is our greatest enemy. Newton had it right. “An object at rest tends to stay at rest, and an object in motion tends to stay in motion.” It is our job, to makes sure the analyst(s) moves along the correct path of analysis.
How does one move from just “reporting” to “analysis”? How does the analyst keep from just “reading” the numbers? Anyone can bookmark and send reports on to management. But crafting a narrative based on the data is a much rarer skill-set.
Analysis is much more than reporting. It is not a skill organizations acquire immediately. You don’t go from white belt to black belt at the snap of your fingers, but you must start somewhere.
Where Do You Start?
Define KPIs
1. First, you need to Define KPIs. In the same way that it is not wise to begin a journey from one end of the Sahara desert to another without a roadmap, you cannot begin an analytical journey without first creating a roadmap. KPIs are that roadmap.
These Key Performance Indicators (KPIs) are the most important measurements on your site. A clearer definition is that these metrics clearly illuminate the money trail on your site. Just remember Cuba Gooding, Jr., from “Jerry Maguire.” Ask yourself, “Where is the money?”
Follow the money trail. Every site has a money trail. And along the way, people perform certain actions, “success events.” These success events contribute to the bottom line of your company. What are these? Signing up for your newsletter, reviewing your product, buying a product/service, site visitor engagement measures etc.
To read more on this step, head over to Avinash’s blog. He wrote a great post about metrics today.
Reporting
2. The 2nd step is to establish the Requisite Reporting around the KPIs. Here is a point that Jason Burby and Shane Atchison covered very clearly in Actionable Web Analytics. This step is one that causes migraines for beginning practitioners. The trouble they have is that they sometimes look at this one-dimensionally. Reporting is only done on these success events rather than what occurs concentrically and tangentially around these success events. One dimensional thinking is, “Did newsletter subscriptions go up or go down last month? How does that compare month-over-month? How does that compare month-over-year?”
It is important expand your reporting concentrically beyond just your KPIs. What parts of your marketing campaigns (both online and offline) effect the KPIs? Which search engine’s visitors performed more success events proportionally than the others? Do certain keywords convert more than others? This is the ability to measure and analyze the bond between your success events and the variable factors in your control (campaigns).
I believe in the equality of all steps. No step is more “equal” than the others, but I think (as do many others who I respect in this industry) that you are not truly practicing web analytics until you are Making Changes Based Upon the Analysis. This is called testing.
Testing
3. Any type of testing is recommended. A/B, A/B/C, MVT…any acronym works as long as it is carried out. For those who are petrified by the idea of testing, Google Website Optimizer provides the easiest, and least scary, introduction into this world. Changing copy tone, calls-to-action, imaging, image and copy placement allows your analytics tool to “speak” to you.
I like to think of this as a relationship between the analyst and the tool. There is constant communication, mostly non-verbal until the durn thing doesn’t work, between the two. The problem most analysts have is an inability to listen to what the analytical tool “says.” They hear but they do not listen.
Skipping this step is analogous to taking the pile of money you currently spend on your yearly license for the tool and its implementation and burn it in a big bonfire (I know, a jaded reference). This is not an option. Try telling your boss that you’d like to pay someone $50,000 a year to sit on their butt and do absolutely nothing. Would that fly? Nope! Never! But that is what many organizations do when they pay a lot of money for a tool that they use to create fancy reports without taking action on anything that they see.
What’s next?
Rinse, Repeat…
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12.03.07
Posted in Facebook, privacy at 4:22 am by Maigari
I know I have been harping on Facebook (here and here) from the beginning of the release of their Beacon platform discussing what I think was a huge mistake regarding their use/misuse of user information.
Mark Zuckerberg and the rest of the Facebook executives firmly believed that they had revolutionized the online advertising space. Beacon was meant to move advertisers beyond search, i.e. Google. Facebook miscalculated. They thought that the furor would be short-lived. Just as the general public threw a hissy fit and then accepted the then controversial introduction of Newsfeeds, the Facebook team believed this user acceptance model would follow with Beacon.
As it has been reported elsewhere, Facebook decided to acquiesce to public pressure and make changes to its Beacon platform to provide greater user protection–making it user opt-in. It had gotten the point that Coke and other advertisers were pulling their Beacon campaigns due to this pressure.
This notion of privacy has gargantuan implications for the web analytics industry. The public outcry that followed Facebook’s faux pas could have had and still may have serious implications, increasing the likelihood of governmental intervention that could provide far more restrictive rules and guidelines going forward.
I have been reading about this news for a couple of days and wanted to sum up the sentiment around the blogosphere.
Here is what Dan Blank had to say:
This snowy Monday morning has several folks thinking about the incredible backlash that Facebook is experiencing due to its “Beacon” service, which is an attempt to monetize the site:
• Scott Karp:
“Facebook Beacon, currently in the process of going down in flames, is a classic case of overreaching… Facebook overreached because it’s acting like a traditional media company with monopoly control of its channel.”
• Fred Wilson:
“It’s always like this, the euphoria is always followed by the backlash. Facebook has had a great year, the evolution from a service for students to a service for everyone, the opening of the platform, and the $15bn valuation from Microsoft. It’s hard to beat the year they’ve had. But now the backlash is here. It’s largely related to their efforts to monetize their service via behavioral targeting…”
• Robert Scoble:
“Personally I’m having lots of trouble with Facebook but Facebook doesn’t care about people who have more than 5,000 friends unless they can figure out a way to monetize us. Everytime I look at Facebook I am reminded of how little Facebook cares about me. So, I care less and less about Facebook every day.”
Here are a few more posts over the last couple days about this issue:
Facebook Beacon: A Cautionary Tale About New Media Monopolies
Facebook Beacon to Become Opt-in
Facebook Flips
Facebook Signals Changes with Beacon
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11.29.07
Posted in web analytics at 5:55 am by Maigari
Sebastian over at webanalyticsbook.com believes that the web analytics industry hasn’t fully arrived yet. Do I agree? You bet! I’ve even written about this subject before.
I love where the web analytics industry is heading right now. Many practitioners and consultants are pushing the envelope with more creative solutions and analyses for clients, but what we at PlusOne Analytics have found is that clients are increasingly frustrated by the most elementary of web analytics practices, Web Analytics 1.0: Implementing the tool properly, defining KPIs, moving beyond reporting to actual analysis, etc.
Clients are frustrated because they have read what other organizations are doing with their web analytics tools. They understand what web analytics can mean for their organization, but for whatever reason, they aren’t able to take full advantage of it. Gurus speak promisingly about a land flowing with milk and honey (Testing and Behavior Targeting), but many in the trenches are left wandering around in their own web analytics desert. Many organizations find that reaching that level of sophistication as much a fantasy as Neverland. Here are a few statistics that Sebastian shares from Marketingsherpa to back up his thesis:
48% Can’t do any a/b testing
44% Can’t measure LP test results
40% Only test at launch & leave forever
16% Don’t share test results w/ agency
If you are one of these organizations, don’t feel discouraged because there are many other companies who are in the same boat working to bail themselves out of this predicament.
If you find your organization in this situation what do you do?
You will need to conduct an audit of your existing web analytics solution. What should the audit include?
1) Gathering and updating business requirements from business stakeholders (creating a questionnaire will help get the creative juices flowing)
2) Evaluate potential value of integrations between any current systems (CRM, email, lead management, etc.) and your web analytics solution
3) Create a detailed testing plan and process, but remember, do not bite off more than you can chew because you can always delve deeper later
4) Identify gaps between the original web analytics solution design and the actual code on the site to determine tagging errors
5) Identify gaps between the original solution design and the new business requirements
6) Create business process changes that help avoid future tagging errors streamline the flow of actionable data to stakeholders, while establishing an analytical system that always ends in an action
7) Once finished, deliver a scorecard analysis of recommendations based on strategic value vs. the cost (labor, opportunity cost, time, etc.) to implement
We have found that clients who go through this process become reinvigorated and because they have a clear roadmap to an improved bottom line, they become more motivated to work with their analytics’ tool. To bastardize and paraphrase Newton’s 1st law of motion, “An object in motion, tends to stay in motion.”
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11.21.07
Posted in Uncategorized at 5:05 am by Maigari
To show that I am not a complete web analytics geek and that I have a life outside of the industry, I wanted to compile another list of 10 things I’m thankful for.
1. My family who have been here with me every step of the way, loving me unconditionally no matter how crazy I make them.
2. Devin Hester
3. Will Ferrell
4. Fall in Washington DC
5. Celebrity blogs
6. Napa Valley
7. Georgetown Basketball
8. Friends who allow me my eccentricities while still poking fun of me for them
9. International Stocks
10. Soulja Boy
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Posted in Uncategorized at 4:30 am by Maigari
In honor of Thanksgiving, I wanted to quickly memorialize 10 things about this industry that I’m grateful for this year. It has been an incredible year for PlusOne Analytics, going 0-60 in such a short time. The learning curve has been steep, but well worth it.
1. Web Analytics Association for being a tremendous resource that is doing much more than just educating web analytics professionals, but also sees itself as an influential body blazing a trail by creating standards and creating university curriculum
2. Web Analytics Forum, the modern-day web analytics salon.
3. Omniture, the rhino-turned hippo in the room.
4. Omniture Discover™ on-the-fly segmentation. You get what you pay for.
5. eMetrics, ‘nuff said.
6. Other, better web analytics blogs (Controversial list)
7. Web Analytics Gurus (No names for fear of retribution from those not on the list)
8. Insightful, informative web analytics books
9. Friends in the industry I’ve made outside of the traditional networking settings who have reminded me that it is okay to be a bit dorky: Brian McGee, Jesse Gross, Manoj Jasra, Tom Miller, et al.
10. The practitioners, consultants, and vendors who I talk to every day who do the heavy lifting for clients and companies who are constantly asking and answering questions that move everyone in their organizations one step closer to the light at the end of the tunnel.
To those in the States, Happy Thanksgiving!
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11.20.07
Posted in Facebook, privacy at 3:27 pm by Maigari
At first, so many in the online space were in awe of Facebook’s Social Ads. I’ll readily admit I was one of them. I wrote a post earlier about Microsoft’s attempt to relegate search to irrelevancy by devaluing it’s participation in conversion. Social Ads may not have become a killer app, but on the surface it was an app that would do quite a bit of “killing”.
With Google reaching a new apogee in power, influence, and hubris every single day, people started looking for a Google-slayer. Jimbo Wales, the founder of Wikipedia was one of the first to announce that he would try a suicidal full-frontal assault with Search Wikia, a user generated search engine. We still await the fruits of his latest labor of love.
Many have stepped up to the plate and struck out embarrassingly, some ceremoniously and others not so…until Facebook. When Facebook announced the establishment of Social Ads, the Google-slayer/Google-slasher seemed to have arrived.
Now that the original excitement and furor have died down, many are beginning to examine SocialAds in a bit more depth. What they’ve found scares them. The New York Times “Bits” blog and CNET actually question the legality of SocialAds. Just perform a quick search on “Facebook Privacy” and look at the number of articles that come up warning about Facebook’s imperialistic reach. What raises red flags for me is the people who have voiced privacy concerns are industry insiders rather than privacy watchdogs. Even those who would benefit monetarily from this new paradigm have taken a step back and declare that Social Ads, in its current iteration, takes things a bit too far.
I will quote from this post by Josh Porter. Because he is a web and social media designer he sees a level of genius, albeit evil genius, in the way the Facebook has designed the opt-out mechanism.
“You then log into your Facebook account, and it says that “Blockbuster is sending a story to your account”. You have the option to say no to this, but it is not apparent at all. In fact, Facebook gives you the option “Don’t show me this again”, which seems to suggest that they agree this message is annoying. They have designed this screen for you to focus on the pain of having to read a silly message and dismiss it. But what isn’t very clear is that when you do so you’re also giving implicit instruction that all services can send information to your news feed in the future. This is a HUGE deal to Facebook…this is how they’re going to make money.”
As I wrote about earlier, Facebook will have to answer the privacy question sooner or later.
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